Click the title of a conference paper to get the PDF.
Journal track papers can be found at the special issue websites:
DAMI: https://link.springer.com/journal/10618/topicalCollection/AC_13980d06adfa02e8775bc2918215db3d/page/1
MLJ: https://link.springer.com/journal/10994/topicalCollection/AC_55dcd9c0be16cbe704ad776956f7aafb/page/1
| Session | ID | Paper |
|---|---|---|
| Active, semi-sup. learning (1) | 190 | SemiITE: Semi-supervised Individual Treatment Effect Estimation via Disagreement-Based Co-training Qiang Huang (Jilin University)*; Jing Ma (University of Virginia); Jundong Li (University of Virginia); Huiyan Sun (Jilin University); Yi Chang (Jilin University) |
| Active, semi-sup. learning (1) | 442 | Exploring Latent Sparse Graph for Large-Scale Semi-supervised Learning Zitong Wang (Columbia University in the City of New York); Li Wang (University of Texas at Arlington)*; Raymond Chan (City University of Hong Kong); Tieyong Zeng (The Chinese University of Hong Kong) |
| Active, semi-sup. learning (1) | 891 | SMFM4L: Multi-typed Objects Multi-view Multi-instance Multi-label Learning based on Selective Matrix Factorization Yuanlin Yang (Southwest University)*; Guangyang Han (Southwest University); Runmin Wang (Southwest University); weiwei sao (Southwest university); Baiyan Hua (Southwest University); Yuanlin Yang (Southwest University) |
| Active, semi-sup. learning (1) | 926 | Consistent and Tractable Algorithm for Markov Network learning Vojtech Franc (Center for Machine Perception)*; Daniel Prusa (Czech Technical University in Prague); Andrii Yermakov (Czech Technical University in Prague) |
| Active, semi-sup. learning (1) | 952 | Multi-Task Adversarial Learning for Semi-Supervised Trajectory-User Linking Sen Zhang (Nanjing University of Aeronautics and Astronautics); Senzhang Wang (Central South University)*; Shigeng Zhang (Central South University); Hao Miao (Aalborg University); Xiang Wang (National University of Defense Technology); Junxing Zhu (National University of Defense Technology) |
| Active, semi-sup. learning (1) | 1128 | Near out-of-distribution detection for low-resolution radar micro-Doppler signatures Martin Bauw (MINES ParisTech)*; Santiago Velasco-Forero (MINES ParisTech); Jesus Angulo (Mines Paris Tech); Claude Adnet (Thales LAS France); Olivier Airiau (Thales LAS France) |
| Active, semi-sup. learning (2) | 375 | GraphMixup: Improving Class-Imbalanced Node Classification by Reinforcement Mixup and Self-supervised Context Prediction Lirong Wu (Westlake University)*; Jun Xia (Westlake University and Zhejiang University); Zhangyang Gao (westlake university); Haitao Lin (westlake university); Cheng Tan (Westlake University); Stan Z. Li (Westlake University) |
| Active, semi-sup. learning (2) | 481 | Multi-domain Active Learning for Semi-supervised Anomaly Detection Vincent Vercruyssen (KU Leuven)*; Lorenzo Perini (KU Leuven); Wannes Meert (KU Leuven); Jesse Davis (KU Leuven) |
| Active, semi-sup. learning (2) | 531 | A Class-Mixed Data Generation Approach to Out-Of-Distribution Detection Mengyu Wang (Peking University)*; Yijia Shao (Peking University); Haowei Lin (Peking University); Wenpeng Hu (Peking University); Bing Liu (UIC) |
| Active, semi-sup. learning (2) | 1054 | A Stopping Criterion for Transductive Active Learning Daniel Kottke (University of Kassel)*; Christoph Sandrock (University of Kassel); Georg Krempl (Utrecht University); Bernhard Sick (University of Kassel) |
| Active, semi-sup. learning (2) | 1177 | Deep Active Learning for Detection of Mercury’s Bow Shock and Magnetopause Crossings Sahib Julka (University of Passau)*; Nikolas Kirschstein (University of Passau); Michael Granitzer (University of Passau); Alexander Lavrukhin (M.V.Lomonosov Moscow State University); Ute V Amerstorfer (Space Research Institute, Austrian Academy of Sciences) |
| Active, semi-sup. learning (3) | J28 |
Online Active Classification via Margin-based and Feature-based Label Queries TINGTING ZHAI |
| Active, semi-sup. learning (3) | J29 | Stream-Based Active Learning for Sliding Windows Under Verification Latency Tuan Minh Pham |
| Active, semi-sup. learning (3) | J37 | Semi-supervised Latent Block Model with pairwise constraints Paul Riverain |
| Anomaly detection | 146 | Anomaly Detection via Few-shot Learning on Normality Shin Ando (Tokyo University of Science)*; Ayaka Yamamoto (Tokyo University of Science) |
| Anomaly detection | 158 | ARES: Locally Adaptive Reconstruction-based Anomaly Scoring Adam Goodge (National University of Singapore)*; Bryan Hooi (NUS); See Kiong Ng (National University of Singapore); Wee Siong Ng (Institute for Infocomm Research, Singapore) |
| Anomaly detection | 245 | Detecting Anomalies with Autoencoders on Data Streams Lucas Cazzonelli (FZI Research Center for Information Technology)*; Cedric Kulbach (FZI Research Center for Information Technology) |
| Anomaly detection | 578 | R2-AD2: Detecting Anomalies by Analysing the Raw Gradient Jan-Philipp Schulze (Fraunhofer AISEC)*; Philip Sperl (Fraunhofer AISEC); Ana Radutoiu (Technische Universität München); Carla Christin Sagebiel (Fraunhofer AISEC); Konstantin Böttinger (Fraunhofer AISEC) |
| Anomaly detection | 927 | Hop-Count Based Self-Supervised Anomaly Detection on Attributed Networks Tianjin Huang (Eindhoven University of Technology)*; Yulong Pei (TU Eindhoven); Vlado Menkovski (Eindhoven University of Technology); Mykola Pechenizkiy (TU Eindhoven) |
| Anomaly detection | 1334 | UrbanAnom: An Approach to Predict Urban Anomaly from Multi-Stream Data Bhumika . (IIT Jodhpur)*; Debasis Das (Indian Institute of technology(IIT) Jodhpur) |
| Applications (1) | 712 | Grasping Partially Occluded Objects Using Autoencoder-Based Point Cloud Inpainting Alexander Koebler (Siemens AG)*; Ralf Gross (Siemens AG); Florian Buettner (German Cancer Research Center and Frankfurt University); Ingo Thon (Siemens AG) |
| Applications (1) | 802 | GALG: Linking Addresses in Tracking Ecosystem Using Graph Autoencoder with Link Generation Tianyu Cui (Institute of Information Engineering)*; gang xiong (Institute of Information Engineering，Chinese Academy of Sciences); Chang Liu (Institute of Information Engineering, CAS); Junzheng Shi (Institute of Information Engineering，Chinese Academy of Sciences); peipei fu ( Institute of Information Engineering, CAS); Gaopeng Gou (Institute of Information Engineering, CAS) |
| Applications (1) | 904 | Waypoint Generation in Row-based Crops with Deep Learning and Contrastive Clustering Francesco Salvetti (Politecnico di Torino)*; Simone Angarano (Politecnico di Torino); Mauro Martini (Politecnico di Torino); Simone Cerrato (Politecnico di Torino); Marcello Chiaberge (Politecnico di torino) |
| Applications (1) | 1022 | Is this bug severe? A text-cum-graph based model for bug severity prediction Rima Hazra (IIT Kharagpur)*; Arpit Dwivedi (Indian Institute of Technology Kharagpur); Animesh Mukherjee (IIT Kharagpur) |
| Applications (1) | 1210 | Physically Invertible System Identification for Monitoring System Edges with Unobservability Jingyi Yuan (Arizona State University)*; Yang Weng (Arizona State University) |
| Applications (1) | J49 | A User-Guided Bayesian Framework for Ensemble Feature Selection in Life Science Applications (UBayFS) Stefan Schrunner |
| Applications (2) | J11 | SOKNL: A Novel Way of Integrating K-Nearest Neighbours with Adaptive Random Forest Regression for Data Streams Yibin Sun, Bernhard Pfahringer, Heitor Murilo Gomes, Albert Bifet |
| Applications (2) | J15 | Provable randomized rounding for minimum-similarity diversification Bruno Ordozgoiti |
| Applications (2) | J22 | Counterfactual Inference with Latent Variable and its Application in Mental Health Care Guilherme F Marchezini, Anisio M Lacerda, Gisele Lobo Pappa, Wagner Meira Jr, Debora Miranda, Marco A Romano-Silva, Danielle S Costa, Leandro Malloy Diniz |
| Applications (2) | J46 | BT-Unet: A self-supervised learning framework for biomedical image segmentation using Barlow Twins with U-Net models Narinder Singh Punn |
| Applications (2) | J9 | Transfer How Much: A Fine-Grained Measure of the Knowledge Transferability of User Behavior Sequences in Social Network Nuo Li, Bin Guo, Yan Liu, Yasan Ding, En Xu, LinaYao, Zhiwen Yu |
| Applications (3) | 229 | Meta Hierarchical Reinforced Learning to Rank for Recommendation: A Comprehensive Study in MOOCs Yuchen Li (Shanghai Jiao Tong University)*; Haoyi Xiong (Baidu Research); Linghe Kong (Shanghai Jiao Tong University); Rui Zhang (Shanghai Jiao Tong University); Dejing Dou (Baidu); Guihai Chen (Shanghai Jiao Tong University) |
| Applications (3) | 407 | Recognizing Cognitive Load by a Hybrid Spatio-Temporal Causal Model from Multivariate Physiological Data Zirui Yong (Chongqing University); Li Liu (Chongqing University)*; Guoxin Su (University of Wollongong); Xiaohu Li (Chongqing University); Lingyun Sun (Zhejiang University); Zejian Li (Zhejiang University) |
| Applications (3) | 755 | Placing (Historical) Facts on a Timeline: A Classification cum Co-ref Resolution Approach Sayantan Adak (IIT Kharagpur)*; Altaf Ahmad (IIT Kharagpur); Aditya Basu (IIT Kharagpur); Animesh Mukherjee (IIT Kharagpur) |
| Applications (3) | 828 | Automatic Grading of Student Code with Similarity Measurement Dongxia Wang (East China Normal University); En Zhang (East China Normal University); Xuesong Lu (East China Normal University)* |
| Applications (3) | 1000 | John ate 5 apples’ != ‘John ate some apples’: Self-Supervised Paraphrase Quality Detection for Algebraic Word Problems Rishabh Gupta (IIIT Delhi)*; Venktesh V (Indraprastha Institute of Information Technology); Mukesh Mohania (IIIT Delhi); Vikram Goyal (“IIIT Delhi, India”) |
| Applications (3) | 1025 | Looking Beyond the Past: Analyzing the Intrinsic Playing Style of Soccer Teams Jeroen Clijmans (KU Leuven); Maaike Van Roy (KU Leuven)*; Jesse Davis (KU Leuven) |
| Applications (4) | 212 | FFBDNet: Feature Fusion and Bipartite Decision Networks for Recommending Medication Combination Zisen Wang (Institute of Computing Technology, Chinese Academy of Sciences); Ying Liang (Institute of Computing Technology, Chinese Academy of Sciences)*; Zhengjun Liu (Institute of Computing Technology, Chinese Academy of Sciences) |
| Applications (4) | 408 | Recognizing Non-Small Cell Lung Cancer Subtypes by a Constraint-Based Causal Network from CT Images Zhengqiao Deng (Chongqing University); Shuang Qian (Chongqing University); Jing Qi (Tianjin Medical University Cancer Institute and Hospital); Li Liu (Chongqing University)*; Bo Xu (Tianjin Medical University Cancer Institute and Hospital, Chongqing University Cancer Hospital) |
| Applications (4) | 586 | Towards Federated COVID-19 Vaccine Side Effect Prediction Jiaqi Wang (Penn State University)*; Cheng Qian (IQVIA); Suhan Cui (Pennsylvania State University); Lucas Glass (IQVIA); Fenglong Ma (Pennsylvania State University) |
| Applications (4) | 829 | EpiGNN: Exploring Spatial Transmission with Graph Neural Network for Regional Epidemic Forecasting Feng Xie (National University of Defense Technology)*; Zhong Zhang (National University of Defense Technology); Liang Li (National University of Defense Technology); Bin Zhou (National University of Defense Technology); yusong tan (College of Computer, National University of Defense Technology) |
| Applications (4) | 944 | Detection of ADHD based on Eye Movements during Natural Viewing Shuwen Deng (University of Potsdam)*; Paul Prasse (University of Potsdam); David R Reich (Universität Potsdam); Sabine Dziemian (University of Zurich); Maja Stegenwallner-Schütz (University of Potsdam); Daniel Krakowczyk (Universität Potsdam); Silvia Makowski (University of Potsdam); Nicolas Langer (University of Zurich); Tobias Scheffer (University of Potsdam); Lena A. Jäger (University of Potsdam) |
| Applications (4) | 954 | MepoGNN: Metapopulation Epidemic Forecasting with Graph Neural Networks Qi CAO (The University of Tokyo)*; Renhe Jiang (The University of Tokyo); Chuang Yang (The University of Tokyo); Zipei Fan (University of Tokyo); Xuan Song (The University of Tokyo); Ryosuke Shibasaki (University of Tokyo) |
| Applications (4) | J18 | Synwalk – Community Detection via Random Walk Modelling Christian Toth, Denis Helic, Bernhard C. Geiger |
| Apps – Transportation | 583 | Route to Time and Time to Route: Travel Time Estimation from Sparse Trajectories Zhiwen Zhang (The University of Tokyo); Hongjun Wang (Southern University of Science and Technology); Jiyuan Chen (Southern University of Science and Technology); Zipei Fan (University of Tokyo)*; Xuan Song (Southern University of Science and Technology); Ryosuke Shibasaki () |
| Apps – Transportation | 800 | BusWTE: Realtime Bus Waiting Time Estimation of GPS Missing via Multi-Task Learning yuecheng rong (Baidu)*; Jun Liu (Xi’an Jiaotong University); Zhilin Xu (Baidu); Jian Ding (Baidu); Chuanming Zhang (Baidu); Jiaxiang Gao (Baidu) |
| Apps – Transportation | 923 | A Bayesian Markov Model for Station-Level Origin-Destination Matrix Reconstruction Victor Amblard (CITiO); Amir Dib (CITIO); Noëlie Cherrier (CITiO)*; Guillaume Barthe (CITiO) |
| Apps – Transportation | 1093 | PathOracle: A Deep Learning Based Trip Planner for Daily Commuters Md. Tareq Mahmood (Bangladesh University of Engineering and Technology (BUET))*; Mohammed Eunus Ali (Bangladesh University of Engineering and Technology (BUET)); Muhammad Aamir Cheema (Monash University); Syed Md. Mukit Rashid (Bangladesh University of Engineering and Technology (BUET)); Timos Sellis (Athena Research Center) |
| Apps – Transportation | 1224 | Attention, Filling in The Gaps for Generalization in Routing Problems Ahmad Bdeir (University of Hildesheim)*; Jonas K Falkner (University of Hildesheim); Lars Schmidt-Thieme (Universität Hildesheim) |
| Apps – Transportation | 1422 | Can we Learn from Outliers? Unsupervised Optimization of Intelligent Vehicle Traffic Management Systems Tom A Mertens (Tu/e); Marwan Hassani (TU Eindhoven)* |
| Bandits & Online Lrn | 643 | On the complexity of All $\epsilon$-Best Arms Identification Aymen Al Marjani (ENS Lyon)*; Tomáš Kocák (University of Potsdam); Aurélien Garivier (ENS Lyon) |
| Bandits & Online Lrn | 671 | Hypothesis Transfer in Bandits by Weighted Models Steven Bilaj (University of Tübingen)*; Sofien Dhouib (University of Tübingen); Setareh Maghsudi (University of Tübingen) |
| Bandits & Online Lrn | 1242 | Multi-Agent Heterogeneous Stochastic Linear Bandits Avishek Ghosh (University of California, San Diego)*; Abishek Sankararaman (Amazon); Ramchandran Kannan (Department of Electrical Engineering and Computer Science University of California, Berkeley) |
| Bandits & Online Lrn | 1280 | Hierarchical Unimodal Bandits TIANCHI ZHAO (The University of Arizona)*; Chicheng Zhang (University of Arizona); Ming Li (University of Arizona) |
| Bandits & Online Lrn | 1308 | Improved Regret Bounds for Online Kernel Selection under Bandit Feedback Junfan Li (Tianjin University); Shizhong Liao (Tianjin University)* |
| Bandits & Online Lrn | 1383 | Online learning of convex sets on graphs Maximilian Thiessen (TU Wien)*; Thomas Gärtner (TU Wien) |
| Classification (1) | 1317 | LCDB 1.0: An extensive Learning Curves Database for Classification Tasks Felix Mohr (Universidad de La Sabana)*; Tom J Viering (Delft University of Technology, Netherlands); Marco Loog (Delft University of Technology & University of Copenhagen); Jan Van Rijn (Leiden University) |
| Classification (1) | J3 | MultiRocket: Multiple pooling operators and transformations for fast and effective time series classification Chang Wei Tan, Angus Dempster, Christoph Bergmeir, Geoffrey I Webb |
| Classification (1) | J52 | Speeding-up One-vs-All Training for Extreme Classification via Mean-Separating Initialization Erik Schultheis |
| Classification (1) | J53 | Cross-Model Consensus of Explanations and Beyond for Image Classification Models: An Empirical Study Haoyi Xiong |
| Classification (2) | J6 | Human-in-the-loop Handling of Knowledge Drift Andrea Bontempelli, Fausto Giunchiglia, Andrea Passerini, Stefano Teso |
| Classification (2) | J26 | Efficient SVDD Sampling with Approximation Guarantees for the Decision Boundary Adrian Englhardt, Holger Trittenbach, Daniel Kottke*, Bernhard Sick, Klemens Böhm |
| Classification (2) | J30 | One-Stage Tree: End-to-End Tree Builder and Pruner Guanghui Zhu |
| Classification (2) | J56 | A flexible class of dependence-aware multi-label loss functions Marcel Wever |
| Clustering & Dim Red. (2) | 240 | Nonparametric Bayesian Deep Visualization Haruya Ishizuka (Bridgestone Corporation)*; Daichi Mochihashi (Institute of Statistical Mathematics) |
| Clustering & Dim Red. (2) | 366 | Wasserstein t-SNE Fynn S. Bachmann (Universität Hamburg)*; Dmitry Kobak (University of Tübingen); Philipp Hennig (University of Tübingen and MPI for Intelligent Systems, Tübingen) |
| Clustering & Dim Red. (2) | 518 | SECLEDS: Sequence Clustering in Evolving Data Streams via Multiple Medoids and Medoid Voting Azqa Nadeem (Delft University of Technology)*; Sicco Verwer (Delft University of Technology) |
| Clustering & Dim Red. (2) | 547 | Automated Cancer Subtyping via Vector Quantization Mutual Information Maximization Zheng Chen (Osaka univerisity)*; Lingwei Zhu (NAIST); Ziwei Yang (NAIST); Takashi Matsubara (Osaka University) |
| Clustering & Dim Red. (2) | 655 | Knowledge Integration in Deep Clustering Nguyen-Viet-Dung Nghiem (University of Orléans)*; Thi-Bich-Hanh Dao (University of Orleans); Christel Vrain (University of Orleans) |
| Clustering & Dim Red. (2) | 681 | FastDEC: Clustering By Fast Dominance Estimation Geping Yang (Guangdong Universty of Technology); Hongzhang Lv (Guangdong University of Technology); Yiyang Yang (Guangdong Universty of Technology)*; Zhiguo Gong (University of Macau); Xiang Chen (Sun Yat-sen University); Zhifeng Hao (Shantou University) |
| Clustering & Dim. Red. (1) | 348 | CDPS: Constrained DTW-Preserving Shapelets Hussein EL AMOURI (University of Strasbourg)*; Thomas Lampert (University of Strasbourg); Pierre Gançarski (University of Strasbourg); Clement Mallet (“IGN, France”) |
| Clustering & Dim. Red. (1) | 849 | Pass-Efficient Randomized SVD with Boosted Accuracy Xu Feng (Tsinghua University)*; Wenjian Yu (Tsinghua University); Yuyang Xie (Tsinghua University) |
| Clustering & Dim. Red. (1) | 953 | Structured Nonlinear Discriminant Analysis Christopher M. A. Bonenberger (Institute for Artificial Intelligence, Ravensburg-Weingarten University of Applied Sciences)*; Wolfgang Ertel (Hochschule Ravensburg-Weingarten); Markus Schneider (Hochschule Ravensburg-Weingarten); Prof. Friedhelm Schwenker U of Ulm Germany ANN Pattern Rec. (“Respected member, IPC, ICIEV”) |
| Clustering & Dim. Red. (1) | 1038 | Powershap: A power-full shap feature selection method Jarne Verhaeghe (imec – Ghent University, IDLab)*; Jeroen Van Der Donckt (UGent – imec); Femke Ongenae (imec – Ghent University, IDLab); Sofie Van Hoecke (UGent-imec ) |
| Clustering & Dim. Red. (1) | 1368 | LSCALE: Latent Space Clustering-Based Active Learning for Node Classification Juncheng Liu (National University of Singapore)*; Yiwei WANG (National University of Singapore); Bryan Hooi (National University of Singapore); Renchi Yang (National University of Singapore); Xiaokui Xiao (National University of Singapore) |
| Computer Vision (1) | 298 | A Scaling Law for Synthetic-to-Real Transfer: How Much Is Your Pre-training Effective? Hiroaki Mikami (Preferred Networks, Inc.); Kenji Fukumizu (The Institute of Statistical Mathematics); Shogo Murai (Preferred Networks, Inc.); Shuji Suzuki (Preferred Networks, Inc.); Yuta Kikuchi (Preferred Networks, Inc.); Taiji Suzuki (The University of Tokyo / RIKEN); Shin-ichi Maeda (Preferred Networks, inc.); Kohei Hayashi (Preferred Networks, Inc.)* |
| Computer Vision (1) | 363 | Rethinking the Misalignment Problem in Dense Object Detection Yang Yang (Institute of Information Engineering, Chinese Academy of Sciences)*; Min Li (Institute of Information Engineering, Chinese Academy of Sciences); Bo Meng (School of Optics and Electronics, Beijing Institute of Technology); Zihao Huang (Institute of Information Engineering, Chinese Academy of Sciences); Junxing Ren (Institute of Information Engineering, Chinese Academy of Sciences); degang Sun (Institute of Information Engineering，Chinese Academy of Sciences) |
| Computer Vision (1) | 474 | Submodular Meta Data Compiling for Meta Optimization Fengguang Su (Tianjin University)*; Yu Zhu (Tianjin University); Ou Wu (Tianjin University); Yingjun Deng (Tianjin University ) |
| Computer Vision (1) | 792 | Learnable Masked Tokens for Improved Transferability of Self-Supervised Vision Transformers Hao Hu (KTH Royal Institute of Technology)*; Federico Baldassarre (KTH – Royal Institute of Technology); Hossein Azizpour (KTH (Royal Institute of Technology)) |
| Computer Vision (1) | 873 | SAViR-T: Spatially Attentive Visual Reasoning with Transformers Pritish Sahu (Rutgers University)*; Kalliopi Basioti (Rutgers University); Vladimir Pavlovic (Rutgers University) |
| Computer Vision (1) | 1330 | No More Strided Convolutions or Pooling: A Novel CNN Architecture for Low-Resolution Images and Small Objects Raja Sunkara (Missouri University of Science & Technology); Tony Luo (Department of Computer Science, Missouri University of Science and Technology)* |
| Computer Vision (2) | 191 | Charge Own Job: Saliency Map and Visual Word Encoder for Image-Level Semantic Segmentation Yuhui Guo (Renmin University of China)*; Xun Liang (Renmin University of China); hui tang (Renmin University of China); Xiangping Zheng (Renmin University of China); Bo Wu (Renmin University of China); Xuan Zhang (Renmin University of China) |
| Computer Vision (2) | 359 | A Novel Data Augmentation Technique for Out-of-Distribution Sample Detection using Compounded Corruptions Ramya S Hebbalaguppe (Indian Institute of Technology, Delhi)*; Soumya Suvra Ghosal (University of Wisconsin-Madison); Jatin Prakash (Indian Institute of Technology, Delhi); Harshad Khadilkar (IIT Mumbai); Chetan Arora (Indian Institute of Technology Delhi) |
| Computer Vision (2) | 795 | Understanding Adversarial Robustness of Vision Transformers via Cauchy Problem Zheng Wang (Exeter University)*; Wenjie Ruan (University of Exeter) |
| Computer Vision (2) | 932 | Supervised Contrastive Learning for Few-Shot Action Classification Hongfeng Han (Renmin University of China); Nanyi Fei (Renmin University of China); Zhiwu Lu (Renmin University of China)*; Ji-Rong Wen (Renmin University of China) |
| Computer Vision (2) | J51 | Wavelet-Packets for Deepfake Image Analysis and Detection Moritz Wolter |
| Conversational Systems | 445 | MFDG: a Multi-Factor Dialogue Graph Model for Dialogue Intent Classification Jinhui Pang (Beijing Institute of Technology); Huinan Xu (Beijing Institute Of Technology)*; Shuangyong Song (Jing Dong); Bo Zou (JD AI Research); Xiaodong He (JD AI Research) |
| Conversational Systems | 860 | Do You Know My Emotion? Emotion-Aware Strategy Recognition towards a Persuasive Dialogue System Wei Peng (Institute of Information Engineering, Chinese Academy of Sciences)*; Yue Hu (Institute of Information Engineering,Chinese Academy of Sciences); Luxi Xing (Institute of Information Engineering, Chinese Academy of Sciences); Yuqiang Xie (Institute of Information Engineering, Chinese Academy of Sciences); Yajing Sun (Institute of Information Engineering,Chinese Academy of Sciences) |
| Conversational Systems | 1186 | Customized Conversational Recommender Systems Shuokai Li (Institute of Computing Technology, Chinese Academy of Sciences)*; Yongchun Zhu (Institute of Computing Technology, Chinese Academy of Sciences); Ruobing Xie (WeChat Search Application Department, Tencent); Zhenwei Tang (King Abdullah University of Science and Technology); Zhao Zhang (Institute of Computing Technology, Chinese Academy of Sciences ); Fuzhen Zhuang (Institute of Artificial Intelligence, Beihang University); Qing He (Institute of Computing Technology, Chinese Academy of Sciences); Hui Xiong (the State University of New Jersey) |
| Conversational Systems | 1338 | Contextual Information and Commonsense Based Prompt for Emotion Recognition in Conversation Yi Jingjie (Fudan University); Deqing Yang (Fudan University)*; Siyu Yuan (Fudan University); Cao Kaiyan (School of Data Science in Fudan University); zhang zhiyao (fudan university); Yanghua Xiao (Fudan University) |
| Deep Learning (1) | 381 | Understanding Difficulty-based Sample Weighting with a Universal Difficulty Measure xiaoling zhou (Tianjin University); Ou Wu (Tianjin University)*; Weiyao ZHU (Center for Applied Mathematics, Tianjin University); Liang ZiYang (TianJin University) |
| Deep Learning (1) | 519 | Class-Incremental Learning via Knowledge Amalgamation Marcus de Carvalho (Nanyang Technological University)*; Mahardhika Pratama (University of South Australia); Jie Zhang (Nanyang Technological University); YAJUAN SUN (A*Star SIMTech) |
| Deep Learning (1) | 620 | Foveated Neural Computation Matteo Tiezzi (University of Siena)*; Simone Marullo (University of Siena); Alessandro Betti (Université Côte d'Azur); Enrico Meloni (University of Florence, University of Siena); Lapo Faggi (University of Florence, University of Siena); Marco Gori (University of Siena); Stefano Melacci (University of Siena) |
| Deep Learning (1) | 1085 | Avoiding Forgetting and Allowing Forward Transfer in Continual Learning via Sparse Networks Ghada Sokar (Eindhoven University of Technology (TU/e))*; Decebal Constantin Mocanu (University of Twente); Mykola Pechenizkiy (TU Eindhoven) |
| Deep Learning (1) | 1256 | Trigger Detection for the sPHENIX Experiment via Bipartite Graph Networks with Set Transformer Tingting Xuan (Stony Brook University)*; Giorgian Borca-Tasciuc (Stony Brook University); Yimin Zhu (Stony Brook University); Yu Sun ( Sunrise Technology Inc.); Cameron Dean ( Los Alamos National Laboratory); Zhaozhong Shi ( Los Alamos National Laboratory); Dantong Yu (New Jersey Institute of Technology) |
| Deep Learning (1) | 1401 | PrUE: Distilling Knowledge from Sparse Teacher Networks Shaopu Wang (Institute of Information Engineering, Chinese Academy of Sciences; School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China); Xiaojun CHEN (Institute of Information Engineering, CAS)*; Mengzhen Kou (Institute of Information Engineering, Chinese Academy of Sciences;School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China); Jinqiao Shi (Beijing University of Posts and Telecommunications) |
| Deep Learning (2) | 435 | DialCSP: A Two-stage Attention-based Model for Customer Satisfaction Prediction in E-commerce Customer Service Zhenhe Wu (beihang university)*; Liangqing Wu (JD AI Research); Shuangyong Song (Jing Dong); Jiahao Ji (Beihang University); Bo Zou (JD AI Research); Zhoujun Li (Beihang University); Xiaodong He (Jing Dong) |
| Deep Learning (2) | J41 | Stateless Neural Meta-Learning using Second-Order Gradients Mike Huisman |
| Deep Learning (2) | J42 | A Brain-inspired Algorithm for Training Highly Sparse Neural Networks Zahra Atashgahi |
| Deep Learning (2) | J47 | Explainable Online Ensemble of Deep Neural Network Pruning for Time Series Forecasting Matthias Jakobs |
| Deep Learning (3) | J34 | DEFT: Distilling Entangled Factors by Preventing Information Diffusion Lin Wang |
| Deep Learning (3) | J36 | Context-aware Spatio-temporal Event Prediction via Convolutional Hawkes Processes Maya Okawa |
| Deep Learning (3) | J43 | GENs: Generative Encoding Networks Surojit Saha |
| Deep Learning (3) | J50 | Recursive Tree Grammar Autoencoders Benjamin Paassen |
| Financial ML | 410 | Distributional Correlation–Aware Knowledge Distillation for Stock Trading Volume Prediction Lei Li (Peking University)*; Zhiyuan Zhang (Peking University); Ruihan Bao (Mizuho Bank); Keiko Harimoto (Mizuho Bank); Xu Sun (Peking University) |
| Financial ML | 523 | Banksformer: A Deep Generative Model for Synthetic Transaction Sequences Kyle L Nickerson (Memorial University of Newfoundland)*; Terrence Tricco (Memorial University of Newfoundland); Antonina Kolokolova (Memorial University); Farzaneh Shoeleh (Verafin); Charles Robertson (Verafin Inc); John Hawkin (Verafin); Ting Hu (Queen’s University) |
| Financial ML | 662 | Stock Trading Volume Prediction with Dual-Process Meta-Learning Ruibo Chen (Peking University)*; Wei Li (Beijing Language and Culture University); Zhiyuan Zhang (Peking University); Ruihan Bao (Mizuho Bank); Keiko Harimoto (Mizuho Bank); Xu Sun (Peking University) |
| Financial ML | 713 | A Prescriptive Machine Learning Approach for Assessing Goodwill in the Automotive Domain Stefan Haas (BMW)*; Eyke Hüllermeier (University of Munich) |
| Financial ML | 1012 | Risk-Aware Reinforcement Learning for Multi-Period Portfolio Selection David Winkel (LMU Munich)*; Niklas A Strauß (LMU Munich); Matthias Schubert (Ludwig-Maximilians-Universität München); Thomas Seidl (LMU Munich) |
| Financial ML | 1298 | Uncertainty Awareness for Predicting Noisy Stock Price Movements Yun-Hsuan Lien (National Yang Ming Chiao Tung University); Yu-Syuan Lin (National Yang Ming Chiao Tung University); Yu-Shuen Wang (National Yang Ming Chiao Tung University)* |
| Generative Models | 548 | Scalable Adversarial Online Continual Learning Tanmoy Dam (University of New South Wales Canberra); mahardhika pratama (University of South Australia)*; Md Meftahul Ferdaus (A*STAR); Sreenatha Anavatti (The University of New South Wales Australia); Hussein Abbass (University of New South Wales, Australia) |
| Generative Models | 788 | STGEN: Deep Continuous-time Spatiotemporal Graph Generation Chen Ling (Emory University)*; Hengning Cao (Cornell University); Zhao Liang (Emory University) |
| Generative Models | 1229 | Direct Evolutionary Optimization of Variational Autoencoders With Binary Latents Jakob Drefs (Carl von Ossietzky Universität Oldenburg)*; Enrico Guiraud (CERN); Filippos S Panagiotou (Carl von Ossietzky University Oldenburg); Jörg Lücke (Universität Oldenburg) |
| Generative Models | 1293 | TrafficFlowGAN: Physics-informed Flow based Generative Adversarial Network for Uncertainty Quantification Zhaobin Mo (Columbia University); Yongjie Fu (Columbia University); Daran Xu (Columbia University); Xuan Di (Columbia University)* |
| Graph Mining | J10 | INK: knowledge graph embeddings for node classification Bram Steenwinckel, Gilles Vandewiele, Terencio Agozzino, Michael Weyns, Filip De Turck, Femke Ongenae |
| Graph Mining | J16 | Sequential Stratified Regeneration: MCMC for Large State Spaces with an Application to Subgraph Count Estimation Carlos H. C. Teixeira, Mayank Kakodkar, Vinícius Dias, Wagner Meira Jr., Bruno Ribeiro |
| Graph Mining | J20 | Strengthening ties towards a highly-connected world Antonis Matakos, Aristides Gionis |
| Graph Mining | J25 | VPint: Value propagation-based spatial interpolation Laurens Arp, Mitra Baratchi, Holger Hoos |
| Graph Mining | J8 | EmbAssi: Embedding Assignment Costs for Similarity Search in Large Graph Databases Franka Bause, Erich Schubert, Nils Morten Kriege |
| Graph Nns (1) | 216 | Self-Supervised Graph Learning with Segmented Graph Channels Hang Gao (Institute of Software Chinese Academy of Sciences ); Jiangmeng Li (Institute of Software Chinese Academy of Sciences); Changwen Zheng (Institute of Software, Chinese Academy of Sciences)* |
| Graph Nns (1) | 433 | GNN Transformation Framework for Improving Efficiency and Scalability Seiji Maekawa (Osaka University)*; Yuya Sasaki (Osaka University); George Fletcher (Eindhoven University of Technology); Makoto Onizuka (Osaka University) |
| Graph Nns (1) | 542 | Masked Graph Auto-Encoder Constrained Graph Pooling Chuang Liu (Wuhan University)*; Yibing Zhan (JD Explore Academy); Xueqi Ma (Tsinghua University); Dapeng Tao (Yunnan University); Bo Du (Wuhan University); Wenbin Hu (Wuhan University) |
| Graph Nns (1) | 803 | SEA: Graph Shell Attention in Graph Neural Networks Christian M.M. Frey (Christian-Albrechts-University Kiel)*; Yunpu Ma (Ludwig-Maximilians-Universität München); Matthias Schubert (Ludwig-Maximilians-Universität München) |
| Graph Nns (1) | 1052 | TopoAttn-Nets: Topological Attention in Graph Representation Learning Yuzhou Chen (Princeton University); Elena Sizikova (NYU); Yulia R. Gel (The University of Texas at Dallas)* |
| Graph Nns (1) | 1287 | Edge but not Least: Cross-View Graph Pooling Xiaowei Zhou (University of Technology Sydney)*; Jie Yin (The University of Sydney); Ivor Tsang (University of Technology Sydney) |
| Graph Nns (2) | 486 | Learning to solve Minimum Cost Multicuts efficiently using Edge-Weighted Graph Convolutional Neural Networks Steffen Jung (MPII)*; Margret Keuper (University of Mannheim) |
| Graph Nns (2) | 672 | Transforming PageRank into an Infinite-Depth Graph Neural Network Andreas Roth (TU Dortmund)*; Thomas Liebig (TU Dortmund) |
| Graph Nns (2) | 764 | Supervised Graph Contrastive Learning for Few-shot Node Classification Zhen Tan (Arizona State University)*; Kaize Ding (Arizona State University); Ruocheng Guo (City University of Hong Kong); Huan Liu (Arizona State University) |
| Graph Nns (2) | 848 | NE-WNA: A novel network embedding framework without neighborhood aggregation Jijie Zhang (Heilongjiang University); Yan Yang (Heilongjiang University); Yong Liu (Heilongjiang university)*; Meng Han (Zhejiang University) |
| Graph Nns (2) | 973 | A PIECE-WISE POLYNOMIAL FILTERING APPROACH FOR GRAPH NEURAL NETWORKS Vijay Lingam (Microsoft Research India)*; Manan Sharma (Microsoft Research); Chanakya Ekbote (Microsoft); Rahul Ragesh (Microsoft); Arun Iyer (Microsoft Research); Sundararajan Sellamanickam (Microsoft Research) |
| Graph Nns (2) | J31 | Polynomial-Based Graph Convolutional Neural Networks For Graph Classification Luca Pasa |
| Interp. and Explain. (1) | 477 | Fair and Efficient Alternatives to Shapley-based Attribution Charles Condevaux (Université de Nîmes)*; Sébastien HARISPE (IMT Mines Alès); Stéphane Pr. Mussard (CHROME) |
| Interp. and Explain. (1) | 494 | Calibrate to interpret Gregory D Scafarto (EURA NOVA)*; Antoine Bonnefoy (EURA NOVA); Nicolas P Posocco (EURA NOVA) |
| Interp. and Explain. (1) | 648 | SMACE: A New Method for the Interpretability of Composite Decision Systems Gianluigi Lopardo (Université Côte d’Azur)*; Damien Garreau (Université Côte d’Azur); Frederic Precioso (Université Cote d’Azur); Greger Ottosson (IBM) |
| Interp. and Explain. (1) | 1340 | Interpretations of Predictive Models for Lifestyle-related Diseases at Multiple Time Intervals Yuki OBA (University of Tsukuba)*; Taro TEZUKA (University of Tsukuba); Masaru SANUKI (University of Tsukuba); Yukiko WAGATSUMA (University of Tsukuba) |
| Interp. and Explain. (1) | J39 | Scrutinizing XAI using linear ground-truth data with suppressor variables Rick Wilming |
| Interp. and Explain. (2) | 192 | Session-based Recommendation along with the Session Style of Explanation Panagiotis Symeonidis (University of the Aegean)*; Lidija Kirjackaja (Vilnius Gediminas Technical University); Markus Zanker () |
| Interp. and Explain. (2) | 346 | ProtoMIL: Multiple Instance Learning with Prototypical Parts for Whole-Slide Image Classification Dawid Damian Rymarczyk (Jagiellonian University)*; Aneta Kaczyńska (Jagiellonian University); Jarosław Kaus (Jagiellonian University); Adam Pardyl (Jagiellonian University); Marek Skomorowski (Jagiellonian University); Bartosz Zieliński (Jagiellonian University) |
| Interp. and Explain. (2) | 505 | Knowledge-Driven Interpretation of Convolutional Neural Networks Riccardo Massidda (Università di Pisa)*; Davide Bacciu (Univeristy of Pisa) |
| Interp. and Explain. (2) | 633 | VCNet: A self-explaining model for realistic counterfactual generation Victor V Guyomard (Orange)*; Françoise FF Fessant (Orange); Thomas Guyet (Inria, Centre de Lyon); Alexandre Termier (Inria); Tassadit Bouadi (Universite de Rennes 1) |
| Interp. and Explain. (2) | 707 | Explaining Predictions by Characteristic Rules Amr Alkhatib (KTH Royal Institute of Technology)*; Henrik Bostrom (KTH Royal Institute of Technology); Michalis Vazirgiannis (KTH Royal Institute of Technology) |
| Interp. and Explain. (2) | 867 | Neural Networks with Feature Attribution and Contrastive Explanations Housam Babiker (Department of Computing Science, University of Alberta)*; Mi-Young Kim (University of Alberta); Randy Goebel (University of Alberta) |
| Knowledge Graphs | 178 | Multi-source Inductive Knowledge Graph Transfer Junheng Hao (UCLA)*; Lu-An Tang (NEC Labs America); Yizhou Sun (UCLA); Zhengzhang Chen (NEC Laboratories America, Inc.); Haifeng Chen (NEC Labs); Junghwan Rhee (University of Central Oklahoma); Zhichun Li (Stellar Cyber); Wei Wang (UCLA) |
| Knowledge Graphs | 354 | MULTIFORM: Few-Shot Knowledge Graph Completion via Multi-Modal Contexts Xuan Zhang (Renmin University of China)*; Xun Liang (Renmin University of China); Xiangping Zheng (Renmin University of China); Bo Wu (Renmin University of China); Yuhui Guo (Renmin University of China) |
| Knowledge Graphs | 587 | Enhance Temporal Knowledge Graph Completion via Time-aware Attention Graph Convolutional Network Haohui Wei (Huazhong University of Science and Technology); Hong Huang (Huazhong University of Science and Technology)*; Teng Zhang (Huazhong University of Science and Technology); Xuanhua Shi (Huazhong University of Science and Technology); Hai Jin (Huazhong University of Science and Technology) |
| Knowledge Graphs | 604 | Start Small, Think Big: On Hyperparameter Optimization for Large-Scale Knowledge Graph Embeddings Adrian Kochsiek (University of Mannheim)*; Fritz Niesel (University Mannheim); Rainer Gemulla (Universität Mannheim) |
| Knowledge Graphs | 666 | ProcK: Machine Learning for Knowledge-Intensive Processes Tobias Jacobs (NEC Laboratories Europe GmbH)*; Jingyi Yu (RWTH Aachen University); Julia Gastinger (NEC Laboratories Europe GmbH); Timo Sztyler (NEC Laboratories Europe GmbH) |
| Knowledge Graphs | 696 | RDF Knowledge Base Summarization by Inducing First-order Horn Rules Ruoyu Wang (Shanghai Jiao Tong University)*; Daniel Sun (UNSW); Raymond K Wong (University of New South Wales) |
| Meta-Learning NAS | 526 | MRF-UNets: Searching UNet with Markov Random Fields Zifu Wang (KU Leuven)*; Matthew B. Blaschko (KU Leuven) |
| Meta-Learning NAS | 759 | Efficient Automated Deep Learning for Time Series Forecasting Difan Deng ( Leibniz Universität Hannover)*; Florian M Karl (Fraunhofer-Institut für Integrierte Schaltungen IIS); Frank Hutter (University of Freiburg); Bernd Bischl (LMU Munich); Marius Lindauer (Leibniz University Hannover) |
| Meta-Learning NAS | 852 | Adversarial Projections to Tackle Support-Query Shifts in Few-Shot Meta-Learning. Aroof Aimen (Indian Institute of Techology, Ropar)*; Bharat Ladrecha (Indian Institute of Technology Ropar); Narayanan C Krishnan (IIT Palakkad) |
| Meta-Learning NAS | 1120 | Context Abstraction to Improve Decentralized Machine Learning in Structured Sensing Environments Massinissa Hamidi (Laboratoire LIPN-UMR CNRS 7030, PRES Sorbonne Paris Cité)*; Aomar Osmani (Laboratoire LIPN-UMR CNRS 7030, PRES Sorbonne Paris Cité) |
| Meta-Learning NAS | 1358 | Discovering wiring patterns influencing neural network performance Aleksandra I Nowak (Jagiellonian Univeristy)*; Romuald Janik (Jagiellonian University) |
| Meta-Learning NAS | 1367 | Automatic Feature Engineering through Monte CarloTree Search Yiran Huang (Karlsruhe Institute of Technology)*; Yexu Zhou (KIT); Michael Hefenbrock (TECO); Till Riedel (KIT); Likun Fang (KIT); Michael Beigl (?) |
| Multi-Agent RL | 506 | Reinforcement Learning for Multi-Agent Stochastic Resource Collection Niklas A Strauß (LMU Munich)*; David Winkel (LMU Munich); Max Berrendorf (Ludwig-Maximilians-Universität München); Matthias Schubert (Ludwig-Maximilians-Universität München) |
| Multi-Agent RL | 869 | DistSPECTRL: Distributing Specifications in Multi-Agent Reinforcement Learning Systems Joe K Eappen (Purdue University)*; Suresh Jagannathan (Purdue University) |
| Multi-Agent RL | 883 | Team-Imitate-Synchronize for Multi-Agent Collaboration Ronen Brafman (BGU)*; Guy Shani (Ben-Gurion University); Eliran Abdu (Ben-Gurion University); Nitsan Soffair (Ben-Gurion University) |
| Multi-Agent RL | 1147 | Heterogeneity Breaks the Game: Evaluating Cooperation-Competition with Multisets of Agents Yue Zhao (Northwestern Polytechnical University)*; José Hernández-Orallo (Universitat Politècnica de València) |
| Multi-Agent RL | 1297 | MAVIPER: Learning Decision Tree Policies for Interpretable Multi-Agent Reinforcement Learning Stephanie Milani (Carnegie Mellon University)*; Zhicheng Zhang (Shanghai Jiao Tong University); Nicholay Topin (Carnegie Mellon University); Zheyuan Ryan Shi (Carnegie Mellon University); Charles Kamhoua (Army Research Lab); Evangelos Papalexakis (UC Riverside); Fei Fang (Carnegie Mellon University) |
| Multi-Agent RL | 1379 | Constrained Multiagent Reinforcement Learning for Large Agent Population Jiajing LING (Singapore Management University)*; Arambam James Singh (National University of Singapore); Duc Thien Nguyen (Singapore Management University); Akshat Kumar (Singapore Management University) |
| Networks & Graphs | 731 | Summarizing Labeled Multi-Graphs Dimitris Berberidis (Carnegie Mellon University); Pierre Liang (Carnegie Mellon University); Leman Akoglu (CMU)* |
| Networks & Graphs | 821 | Joint Learning of Hierarchical Community Structure and Node Representations: An Unsupervised Approach Ancy Tom (University of Minnesota, Twin Cities)*; Nesreen Ahmed (Intel Labs); George Karypis (University of Minnesota, Twin Cities) |
| Networks & Graphs | 872 | Understanding the Benefits of Forgetting when Learning on Dynamic Graphs Charlotte Laclau (Laboratory Hubert Curien, Univ. St-Etienne)*; Julien Tissier (Laboratory Hubert Curien, Univ. St-Etienne) |
| Networks & Graphs | 1027 | Anonymity Can Help Minority: A Novel Synthetic Data Over-sampling Strategy on Multi-label Graphs YIJUN DUAN (NAIST)*; Xin Liu (National Institute of Advanced Industrial Science and Technology (AIST)); Adam Jatowt (Kyoto University); Hai-Tao Yu (University of Tsukuba); Steven Lynden (National Institute of Advanced Industrial Science and Technology (AIST)); Kyoung-Sook Kim (Artificial Intelligence Research Center); Akiyoshi Matono (AIST) |
| Networks & Graphs | 1117 | Inferring Tie Strength in Temporal Networks Lutz Oettershagen (University of Bonn)*; Athanasios Konstantinidis (Luiss University); Giuseppe F. Italiano (LUISS University) |
| Networks & Graphs | 1184 | Algorithmic Tools for Mining the Motif Structure of Networks Tianyi Chen (Boston University); Brian Matejek (Harvard University ); Michael Mitzenmacher (Harvard); Charalampos Tsourakakis (Boston University)* |
| NLP | 311 | An Ion Exchange Mechanism Inspired Story Ending Generator for Different Characters Xinyu Jiang (Beijing Institute of Technology); Qi Zhang (University of Technology Sydney); Chongyang Shi (Beijing Institute of Technology)*; Kaiying Jiang (University of Science and Technology Beijing); Liang Hu (Tongji University); Shoujin Wang (Macquarie University) |
| NLP | 561 | On the current state of reproducibility and reporting of uncertainty for Aspect-based Sentiment Analysis Elisabeth Lebmeier (Ludwig-Maxmilians-Universität München); Matthias Aßenmacher (Ludwig-Maxmilians-Universität München)*; Christian Heumann (Ludwig-Maxmilians-Universität München) |
| NLP | 791 | “Let’s Eat Grandma”: Does Punctuation Matter in Sentence Representation? Mansooreh Karami (Arizona State University)*; Ahmadreza Mosallanezhad (Arizona State University); Michelle Mancenido (Arizona State University); Huan Liu (Arizona State University) |
| NLP | 988 | Hyperbolic Deep Keyphrase Generation yuxiang zhang (Civil Aviation University of China)*; Tianyu Yang (Civil Aviation University of China); Tao Jiang (Civil Aviation University of China); Xiaoli Li (Institute for Infocomm Research , A*STAR, Singapore/Nanyang Technological University); Suge Wang (Shanxi University) |
| NLP | 1132 | Vec2Node: Self-training with Tensor Augmentation for Text Classification with Few Labels Sara Abdali (University of California, Riverside )*; Subhabrata Mukherjee (Microsoft Research); Evangelos Papalexakis (UC Riverside) |
| NLP | 1333 | AutoMap: Automatic Medical Code Mapping for Clinical Prediction Model Deployment Zhenbang Wu (UIUC)*; Cao Xiao (Amplitude); Lucas M Glass (Temple University); David M Liebovitz (Northwestern University); Jimeng Sun (UIUC) |
| NLP and Text-Mining | 249 | Self-Distilled Pruning of Neural Networks James T O’ Neill (University of Liverpool)*; Sourav Dutta (Huawei Research Centre); Haytham Assem (Huawei Research) |
| NLP and Text-Mining | 440 | Bi-matching Mechanism to Combat Long-tail Senses of Word Sense Disambiguation Junwei Zhang (Tianjin University)*; Ruifang He (Tianjin University); Fengyu Guo (Tianjin Normal University) |
| NLP and Text-Mining | 626 | Contextualized Graph Embeddings for Adverse Drug Event Detection Ya Gao (Aalto University); Shaoxiong Ji (Aalto Universtiy)*; Tongxuan Zhang (Tianjin Normal University); Prayag Tiwari (Aalto University, Finland); Pekka Marttinen (Aalto University) |
| NLP and Text-Mining | 928 | FairDistillation: Mitigating Stereotyping in Language Models Pieter Delobelle (KU Leuven)*; Bettina Berendt (KU Leuven) |
| NLP and Text-Mining | 1275 | MultiLayerET: A Unified Representation of Entities and Topics Using Multilayer Graphs Jumanah Alshehri (Temple Univesrity)*; Zoran Obradovic (Temple University); Eduard Dragut (Temple Univ.); Marija Stanojevic (Temple University); Parisa Khan (Temple University); Benjamin Rapp (Temple University) |
| NLP and Text-Mining | J14 | Controlling Hallucinations at Word Level in Data-to-Text Generation Clément Rebuffel, Marco Roberti, Laure Soulier, Geoffrey Scoutheeten, Rossella Cancelliere, Patrick Gallinari |
| Optimal transport | 352 | Feature-Robust Optimal Transport for High-Dimensional Data Mathis Petrovich (ENS-PARIS-SACLAY); Chao Liang (Zhejiang University); Ryoma Sato (Kyoto University); Yanbin Liu (The University of Western Australia); Yao-Hung Tsai (Carnegie Mellon University); Linchao Zhu (University of Technology, Sydney); Yi Yang (UTS); Ruslan Salakhutdinov (Carnegie Mellon University); Makoto Yamada (RIKEN AIP / Kyoto University)* |
| Optimal transport | 777 | Learning optimal transport between two empirical distributions with normalizing flows Florentin Coeurdoux (University of Toulouse)*; Nicolas Dobigeon (University of Toulouse); Pierre Chainais (Centrale Lille / CRIStAL CNRS UMR 9189) |
| Optimal transport | J33 | Optimal Transport for Conditional Domain Matching and Label Shift Alain Rakotomamonjy |
| Optimal transport | J55 | Hierarchical Optimal Transport for Unsupervised Domain Adaptation Mourad El Hamri |
| Optimization | 168 | Mixed Integer Linear Programming for Optimizing a Hopfield Network Bodo Rosenhahn (Leibniz University Hannover)* |
| Optimization | 522 | Rethinking Exponential Averaging of the Fisher Constantin O Puiu (Oxford)* |
| Optimization | 665 | Penalised FTRL With Time-Varying Constraints Douglas Leith (Trinity College Dublin)*; George Iosifidis (TU Delft) |
| Optimization | J54 | Optimistic Optimisation of Composite Objective with Exponentiated Update Weijia Shao |
| Optimization, Combin. | 360 | On the Generalization of Neural Combinatorial Optimization Heuristics Sahil Manchanda (IIT Delhi)*; Sofia Michel (Naverlabs Europe); Darko Drakulic (Naverlabs Europe); Jean-Marc Andreoli (Naverlabs Europe) |
| Optimization, Combin. | 543 | Branch Ranking for Efficient Mixed-Integer Programming via Offline Ranking-based Policy Learning Zeren Huang (Shanghai Jiao Tong University)*; Wenhao Chen (SJTU); Weinan Zhang (Shanghai Jiao Tong University); Chuhan Shi (Shanghai Jiao Tong University); Furui Liu (Huawei Noah’s Ark Lab); Hui-Ling Zoe Zhen (Huawei); Mingxuan Yuan (Huawei); Jianye Hao (Tianjin University); Yong Yu (Shanghai Jiao Tong University); Jun Wang (UCL) |
| Optimization, Combin. | 668 | SaDe: Learning Models that Provably Satisfy Domain Constraints Kshitij Goyal (KU Leuven)*; Sebastijan Dumancic (TU Delft); Hendrik Blockeel (KU Leuven) |
| Optimization, Combin. | 689 | Learning to Control Local Search for Combinatorial Optimization Jonas K Falkner (University of Hildesheim)*; Daniela Thyssens (University of Hildesheim); Ahmad Bdeir (University of Hildesheim); Lars Schmidt-Thieme (University of Hildesheim) |
| Optimization, Combin. | 1098 | Time constrained DL8.5 using Limited Discrepancy Search Harold Kiossou (UCLouvain)*; Pierre Schaus (UC Louvain); Siegfried Nijssen (Université Catholique de Louvain, BE); Vinasetan Ratheil HOUNDJI (University of Abomey-Calavi) |
| Optimization, Combin. | 1200 | Learning Optimal Decision Trees Under Memory Constraints Gael Aglin (UCLouvain)*; Siegfried Nijssen (Université Catholique de Louvain, BE); Pierre Schaus (UC Louvain) |
| Priv & Fed. Learning | 317 | Differentially Private Bayesian Neural Networks on Accuracy, Privacy and Reliability Qiyiwen Zhang (University of Pennsylvania – Philadelphia, PA)*; Zhiqi Bu (University of Pennsylvania); Kan Chen (University of Pennsylvania); Qi Long (University of Pennsylvania) |
| Priv & Fed. Learning | 415 | Marginal Release under Multi-Party Personalized Differential Privacy Peng Tang (Shandong University)*; Rui Chen (Harbin Engineering University); Chongshi Jin (Shangdong University); Gaoyuan Liu (Shandong University); Shanqing GUO (Shandong University) |
| Priv & Fed. Learning | 810 | Beyond Random Selection: A Perspective from Model Inversion in Personalized Federated Learning Zichen Ma (The Chinese University of Hong Kong, Shenzhen)*; Yu Lu (The Chinese University of Hong Kong, Shenzhen); Wenye Li (The Chinese University of Hong Kong, Shenzhen); Shuguang Cui (The Chinese University of Hong Kong, Shenzhen) |
| Priv & Fed. Learning | 948 | Differentially Private Federated Combinatorial Bandits with Constraints Sambhav Solanki (Machine Learning Lab, International Institute of Information Technology Hyderabad )*; Samhita Kanaparthy (Machine Learning Lab, International Institute of Information Technology Hyderabad); Sankarshan Damle (Machine Learning Lab, International Institute of Information Technology, Hyderabad); Sujit Gujar (Machine Learning Laboratory, International Institute of Information Technology, Hyderabad) |
| Priv & Fed. Learning | 1061 | Noise-efficient Learning of Differentially Private Partitioning Machine Ensembles Zhanliang Huang (University of Birmingham)*; Yunwen Lei (University of Birmingham); Ata Kaban (University of Birmingham) |
| Priv & Fed. Learning | 1304 | Non-IID Distributed Learning with Optimal Mixture Weights Bojian Wei (Institute of Information Engineering, Chinese Academy of Sciences); Jian Li (Institute of Information Engineering, CAS)*; Yong Liu (Renmin University of China ); Weiping Wang (Institute of Information Engineering, CAS, China) |
| Probab. Inference (1) | 441 | Sparse Horseshoe Estimation via Expectation-Maximisation Shu Yu Tew (Monash University)*; Daniel F Schmidt (Monash University); Enes Makalic (University of Melbourne) |
| Probab. Inference (1) | 515 | Structure-preserving Gaussian Process Dynamics Katharina Ensinger (Bosch Center for Artificial Intelligence)*; Friedrich Solowjow (RWTH Aachen University); Sebastian Ziesche (Bosch Center for Artificial Intelligence); Michael Tiemann (Bosch Center for AI); Sebastian Trimpe (RWTH Aachen University) |
| Probab. Inference (1) | 831 | Summarizing Data Structures with Gaussian Process and Robust Neighborhood Preservation Koshi Watanabe (Hokkaido University)*; Keisuke Maeda (Hokkaido University); Takahiro Ogawa (Hokkaido University); Miki Haseyama (Hokkaido University) |
| Probab. Inference (1) | 880 | Non-Parameteric Bayesian Approach for Uplift Discretization and Feature Selection Mina RAFLA (Orange Labs)*; nicolas Voisine (Orange); Bruno Cremilleux (Université de Caen Normandie); Marc Boulle (Orange Labs) |
| Probab. Inference (2) | 368 | A Pre-Screening Approach for Faster Bayesian Network Structure Learning Thibaud Rahier (Criteo AI Lab)*; Sylvain Marie (Schneider Electric); Florence B.P. Forbes (Inria) |
| Probab. Inference (2) | 370 | Bayesian Nonparametrics for Sparse Dynamic Networks Cian V Naik (University of Oxford)*; Francois Caron (Oxford); Judith Rousseau (University of Oxford); Yee Whye Teh (University of Oxford); Konstantina Palla (Microsoft Research UK) |
| Probab. Inference (2) | 642 | Bounding the Family-Wise Error Rate in Local Causal Discovery using Rademacher Averages Dario Simionato (University of Padua); Fabio Vandin (University of Padova)* |
| Probab. Inference (2) | 714 | On Projectivity in Markov Logic Networks Sagar Malhotra (Fondazione Bruno Kessler)*; Luciano Serafini (Fondazione Bruno Kessler) |
| Probab. Inference (2) | 870 | Optimization of Annealed Importance Sampling Hyperparameters Shirin Goshtasbpour (ETH Zurich)*; Fernando Perez-Cruz (ETH Zurich) |
| Probab. Inference (2) | 1357 | From graphs to DAGs: a low-complexity model and a scalable algorithm Shuyu Dong (LISN – INRIA, Université Paris-Saclay)*; Michele Sebag (CNRS, Université Paris-Saclay) |
| Quantum, Hardware | 418 | GNNSampler: Bridging the Gap between Sampling Algorithms of GNN and Hardware Xin Liu (Institute of Computing Technology Chinese Academy of Sciences)*; Mingyu Yan (Institute of Computing Technology Chinese Academy of Sciences); shuhan song (University of Chinese Academy of Sciences); zhengyang Lv (Institute of Computing Technology Chinese Academy of Sciences); wenming Li (Institute of Computing Technology Chinese Academy of Sciences); Guangyu Sun (Peking University); Xiaochun Ye (Institute of Computing Technology Chinese Academy of Sciences); Dongrui Fan (ICT, Chinese Academy of Sciences) |
| Quantum, Hardware | 479 | Immediate Split Trees: Immediate Encoding of Floating Point Split Values in Random Forests Christian Hakert (TU Dortmund)*; Kuan-Hsun Chen (University of Twente); Jian-Jia Chen (TU Dortmund) |
| Quantum, Hardware | 599 | FASE: A Fast, Accurate and Seamless Emulator for Custom Numerical Formats John H Osorio Ríos (Barcelona Supercomputing Center)*; Adrià Armejach (Barcelona Supercomputing Center); Eric Petit (Intel); Greg Henry (Intel); Marc Casas Guix (Barcelona Supercomputing Center) |
| Quantum, Hardware | 737 | Block-Level Surrogate Models for Latency Estimation in Hardware-Aware Neural Architecture Search Kurt H. W. Stolle (Eindhoven University of Technology ); Sebastian Vogel (NXP Semiconductors); Fons van der Sommen (Dept. Electrical Engineering, Eindhoven University of Technology, Eindhoven, NL); Willem P Sanberg (NXP Semiconductors)* |
| Quantum, Hardware | 1083 | Training Parameterized Quantum Circuits with Triplet Loss Christof Wendenius (Karlsruhe Institute of Technology); Eileen Kuehn (Karlsruhe Institute of Technology)*; Achim Streit (Karlsruhe Institute for Technology) |
| Quantum, Hardware | 1392 | Variational Boson Sampling Shiv Shankar (University of Massachusetts)*; Don Towsley (University of Massachusetts Amherst) |
| Ranking & Rec Systems | 650 | Graph Contrastive Learning with Adaptive Augmentation for Recommendation Mengyuan Jing (Shanghai Jiao Tong University)*; Yanmin Zhu (Shanghai Jiao Tong University); Tianzi Zang (Shanghai Jiao Tong University); Jiadi Yu (Shanghai Jiao Tong University); Feilong Tang (Shanghai Jiao Tong University) |
| Ranking & Rec Systems | 699 | Multi-Interest Extraction Joint with Contrastive Learning for News Recommendation ShiCheng Wang (Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China)*; Shu Guo (National Computer Network Emergency Response Technical Team & Coordination Center of China); lihong wang (CNCERT); Tingwen Liu (Institute of Information Engineering, CAS); Hongbo Xu (Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China) |
| Ranking & Rec Systems | 843 | Basket Booster for Prototype-based Contrastive Learning in Next Basket Recommendation Su Ting-Ting (Sun Yat-Sen University); Zhen-Yu He (Sun Yat-sen University); Man-Sheng Chen (Sun Yat-sen University); Chang-Dong Wang (Sun Yat-sen University)* |
| Ranking & Rec Systems | 972 | A U-shaped Hierarchical Recommender for Multi-resolution Collaborative Signal Modeling Peng YI (UNSW); Xiongcai Cai (UNSW Sydney)*; Ziteng Li (UNSW Sydney ) |
| Ranking & Rec Systems | 1104 | Recommending Related Products Using Graph Neural Networks in Directed Graphs Srinivas Virinchi (Amazon )*; Anoop Saladi (amazon); Abhirup Mondal (Amazon) |
| Ranking & Rec Systems | J5 | Ranking with submodular functions on a budget Guangyi Zhang, Nikolaj Tatti, Aristides Gionis |
| Rec. Systems (1) | 423 | Bi-directional Contrastive Distillation for Multi-behavior Recommendation Yabo Chu (Northeastern University)*; Enneng Yang (Northeastern University); Qiang Liu (Institute of Automation, Chinese Academy of Sciences); Yuting Liu (Northeastern University); Linying Jiang (Software College, Northeastern University); Guibing Guo (Northeastern University) |
| Rec. Systems (1) | 562 | Mitigating Confounding Bias for Recommendation via Counterfactual Inference Ming He (Beijing University of Technology)*; Xinlei Hu (Beijing University Of Technology); Changshu Li (Beijing University Of Technology); Xin Chen (Beijing University Of Technology); Jiwen Wang ( Beijing University of Technology) |
| Rec. Systems (1) | 634 | A Recommendation System for CAD Assembly Modeling based on Graph Neural Networks Carola Gajek (University of Augsburg)*; Alexander Schiendorfer (Technische Hochschule Ingolstadt); Wolfgang Reif (University of Augsburg) |
| Rec. Systems (1) | 780 | Improving Long-Term Metrics in Recommendation Systems using Short-Horizon Reinforcement Learning Bogdan Mazoure (MILA,McGill University)*; Paul Mineiro (Microsoft); Pavithra Srinath (Microsoft Research); Reza Sharifi Sedeh (Microsoft); Doina Precup (McGill University); Adith Swaminathan (Microsoft Research) |
| Rec. Systems (1) | 896 | AD-AUG: Adversarial Data Augmentation for Counterfactual Recommendation Yifan Wang (Peking University)*; Yifang Qin (Peking University); Yu Han (Alibaba Group); Mingyang Yin (Alibaba Group); Jingren Zhou (Alibaba Group); Hongxia Yang (Alibaba Group); Ming Zhang (Peking University) |
| Reinforcement Lrn (1) | 137 | Oracle-SAGE: planning ahead in graph-based deep reinforcement learning Andrew Chester (RMIT University)*; Michael Dann (RMIT University); Fabio Zambetta (RMIT University); John Thangarajah (RMIT University) |
| Reinforcement Lrn (1) | 292 | Multi-Objective Actor-Critics for Real-Time Bidding Haolin Zhou (Shanghai Jiaotong University); Chaoqi Yang (University of Illinois at Urbana-Champaign); Xiaofeng Gao (Shanghai Jiaotong University)*; Qiong Chen (Tencent); Gongshen Liu (Shanghai Jiao Tong University); Guihai Chen (Shanghai Jiao Tong University) |
| Reinforcement Lrn (1) | 538 | Batch Reinforcement Learning from Crowds Guoxi Zhang (Kyoto University)*; Hisashi Kashima (Kyoto University) |
| Reinforcement Lrn (1) | 733 | Coupling User Preference with External Rewards to Enable Driver-centered and Resource-aware EV Charging Recommendation Chengyin Li (Wayne State University); Zheng Dong (Wayne State University); Nathan Fisher (Wayne State University); Dongxiao Zhu (Wayne State Unversity, USA)* |
| Reinforcement Lrn (1) | 1384 | Reducing the Planning Horizon through Reinforcement Learning Logan Dunbar (University of Leeds)*; Benjamin Rosman (University of the Witwatersrand); Anthony G Cohn (University of Leeds); Matteo Leonetti (King’s College London) |
| Reinforcement Lrn (1) | 1424 | Improving Actor-Critic Reinforcement Learning via Hamiltonian Monte Carlo Method Duo Xu (Georgia Institute of Technology)* |
| Reinforcement Lrn (2) | 389 | Imitation Learning with Sinkhorn Distances Georgios Papagiannis (Imperial College London)*; Yunpeng Li (University of Surrey) |
| Reinforcement Lrn (2) | 459 | Safe Exploration Method for Reinforcement Learning under Existence of Disturbance Yoshihiro Okawa (Fujitsu Limited)*; Tomotake Sasaki (Fujitsu Limited); Hitoshi Yanami (Fujitsu Limited); Toru Namerikawa (Keio University) |
| Reinforcement Lrn (2) | 612 | State Representation Learning for Goal-Conditioned Reinforcement Learning Lorenzo Steccanella (UPF – Artificial Intelligence and Machine learning)*; Anders Jonsson (UPF) |
| Reinforcement Lrn (2) | 798 | Bootstrap State Representation using Style Transfer for Better Generalization in Deep Reinforcement Learning Md Masudur Rahman (Purdue University)*; Yexiang Xue (Purdue University) |
| Reinforcement Lrn (2) | 1251 | Model Selection in Reinforcement Learning with General Function Approximations Avishek Ghosh (University of California, San Diego)*; Sayak Ray Chowdhury (Indian Institute of Science) |
| Robust & Adv. ML (1) | 157 | FROB: Few-shot ROBust Model for Joint Classification and Out-of-Distribution Detection Nikolaos Dionelis (The University of Edinburgh)*; Sotirios Tsaftaris (The University of Edinburgh); Mehrdad Yaghoobi (The University of Edinburgh) |
| Robust & Adv. ML (1) | 176 | Fooling Partial Dependence via Data Poisoning Hubert Baniecki (Warsaw University of Technology)*; Wojciech Marek Kretowicz (Warsaw University of Technology); Przemyslaw Biecek (Warsaw University of Technology) |
| Robust & Adv. ML (1) | 1041 | Hypothesis Testing for Class-Conditional Label Noise Rafael Poyiadzis (University of Bristol)*; Weisong Yang (University of Bristol); Niall Twomey (University of Bristol); Raul Santos Rodriguez (University of Bristol) |
| Robust & Adv. ML (1) | 1130 | PRoA: A Probabilistic Robustness Assessment against Functional Perturbations Tianle Zhang (University of Exeter); Wenjie Ruan (University of Exeter)*; Jonathan Fieldsend (University Of Exeter) |
| Robust & Adv. ML (3) | 261 | Securing Cyber-Physical Systems: Physics-Enhanced Adversarial Learning for Autonomous Platoons Guoxin Sun (The University of Melbourne)*; Tansu Alpcan (University of Melbourne); Benjamin Rubinstein (Melbourne); Seyit Camtepe (CSIRO Data61) |
| Robust & Adv. ML (3) | 406 | Adversarial Mask: Real-World Universal Adversarial Attack on Face Recognition Models Alon Zolfi (Ben-Gurion University of the Negev)*; Shai Avidan (TAU Eng.); Yuval Elovici (Ben-Gurion University of the Negev); Asaf Shabtai (Ben-Gurion University of the Negev) |
| Robust & Adv. ML (3) | 645 | MEAD: A Multi-Armed Approach for Evaluation of Adversarial Examples Detectors Federica Granese (INRIA – LIX – Sapienza University of Rome)*; Marine Picot (CentraleSupélec – CNRS – L2S); Marco Romanelli (CentraleSupélec – CNRS – L2S); Francisco Messina (University of Buenos Aires); Pablo Piantanida ( CNRS Université Paris-Saclay ) |
| Robust & Adv. ML (3) | 673 | Calibrating Distance Metrics Under Uncertainty Wenye Li (The Chinese University of Hong Kong, Shenzhen)*; Fangchen Yu (The Chinese University of Hong Kong, Shenzhen) |
| Robust & Adv. ML (3) | 793 | Defending Observation Attacks in Deep Reinforcement Learning via Detection and Denoising Zikang Xiong (Purdue University )*; Joe K Eappen (Purdue University); He Zhu (Rutgers University); Suresh Jagannathan (Purdue University) |
| Robust & Adv. ML (3) | 938 | Resisting Graph Adversarial Attack via Cooperative Homophilous Augmentation Zhihao Zhu (University of Science and Technology of China)*; Chenwang Wu (University of Science and Technology of China); Min Zhou (Huawei Technologies co. ltd); Hao Liao (Shenzhen University); Defu Lian (University of Science and Technology of China); Enhong Chen (University of Science and Technology of China) |
| Robust & Adversarial ML (2) | 955 | On the Prediction Instability of Graph Neural Networks Max Klabunde (University of Passau)*; Florian Lemmerich (University of Passau) |
| Robust & Adversarial ML (2) | 1068 | Adversarially Robust Decision Tree Relabeling Daniël Vos (Delft University of Technology)*; Sicco Verwer (Delft University of Technology) |
| Robust & Adversarial ML (2) | J27 | Quick and Robust Feature Selection: the Strength of Energy-efficient Sparse Training for Autoencoders Zahra Atashgahi |
| Robust & Adversarial ML (2) | J38 | Robustness Verification of ReLU Networks via Quadratic Programming Aleksei Kuvshinov |
| Robust & Adversarial ML (2) | J44 | Speeding Up Neural Network Robustness Verification via Algorithm Configuration and an Optimised Mixed Integer Linear Programming Solver Portfolio Matthias König |
| Robust & Adversarial ML (2) | J48 | Aliasing and adversarial robust generalization of CNNs Julia Grabinski |
| Sequence Mining | J1 | Mining Sequences with Exceptional Transition Behaviour of Varying Order using Quality Measures based on Information-Theoretic Scoring Functions Rianne Margaretha Schouten, Marcos L.P. Bueno, Wouter Duivesteijn, Mykola Pechenikziy |
| Sequence Mining | J13 | An Efficient Procedure for Mining Egocentric Temporal Motifs Antonio Longa, Giulia Cencetti, Bruno Lepri, Andrea Passerini |
| Sequence Mining | J7 | SPEck: Mining Statistically-significant Sequential Patterns Efficiently with Exact Sampling Steedman Jenkins, Stefan Walzer-Goldfeld, Matteo Riondato |
| Social Network Analysis | 272 | The Burden of Being a Bridge: Analysing Subjective Well-Being of Twitter Users during the COVID-19 Pandemic Ninghan Chen (The University of Luxembourg); Xihui Chen (University of Luxembourg)*; Zhiqiang ZHONG (University of Luxembourg); Jun Pang (University of Luxembourg) |
| Social Network Analysis | 394 | DeMis: Data-efficient Misinformation Detection using Reinforcement Learning Kornraphop Kawintiranon (Georgetown University)*; Lisa Singh (Georgetown University) |
| Social Network Analysis | 448 | A Heterogeneous Propagation Graph Model for Rumor Detection under the Relationship among Multiple Propagation Subtrees Guoyi Li (Institute of Information Engineering, Chinese Academy of Sciences)*; Jingyuan Hu (Institute of Information Engineering, Chinese Academy of Sciences); Yulei Wu (University Of Exeter); Xiaodan Zhang (Institute of Information Engineering, Chinese Academy of Sciences); Wei Zhou (Institute of Information Engineering, School of Cyber Security, University of Chinese Academy of Sciences); Honglei Lyu (Institute of Information Engineering, Chinese Academy of Sciences) |
| Social Network Analysis | 661 | Probing Spurious Correlations in Popular Event-Based Rumor Detection Benchmarks Jiaying Wu (National University of Singapore)*; Bryan Hooi (National University of Singapore) |
| Social Network Analysis | 1028 | SkipCas: Information Diffusion Prediction Model Based on Skip-gram Dedong Ren (Heilongjiang University); Yong Liu (Heilongjiang university)* |
| Social Network Analysis | 1399 | Exploring Graph-aware Multi-View Fusion for Rumor Detection on Social Media Yang Wu (Institute of Information Engineering, Chinese Academy of Sciences)*; Jing Yang (Institute of Information Engineering, Chinese Academy of Sciences); Xiaojun Zhou (School of Cyber Security, University of Chinese Academy of Sciences;State Key Laboratory of Information Security, Institute of Information Engineering,Chinese Academy of Sciences); Liming Wang (State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences); Zhen Xu (State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences) |
| Sup. Stat. Learning | J40 | Nested Aggregation of Experts using Inducing Points for Approximated Gaussian Process Regression Ayano Nakai-Kasai |
| Sup. Stat. Learning | J45 | Learning with risks based on M-location Matthew Holland |
| Sup. Stat. Learning | 911 | Improving Micro-video Recommendation by Controlling Position Bias Yisong Yu (Institute of Software Chinese Academy of Sciences); Beihong Jin (Institute of Software, Chinese Academy of Sciences)*; Jiageng Song (Institute of Software Chinese Academy of Sciences); Beibei Li (Institute of Software Chinese Academy of Sciences); Yiyuan Zheng (Institute of Software Chinese Academy of Sciences); Wei Zhuo (MX Media Co., Ltd) |
| Supervised Leanring | 215 | Factorized Structured Regression for Large-Scale Varying Coefficient Models David Ruegamer (LMU Munich)*; Andreas Bender (LMU Munich); Simon Wiegrebe (LMU Munich); Daniel Racek (LMU Munich); Bernd Bischl (LMU Munich); Christian L. Müller (Center for Computational Mathematics, Flatiron Institute); Clemens Stachl (University St. Gallen) |
| Supervised Leanring | 347 | Random Similarity Forest Maciej Piernik (Poznan University of Technology)*; Dariusz Brzezinski (Poznan University of Technology); Pawel Zawadzki (Adam Mickiewicz University) |
| Supervised Leanring | 426 | Spectral Ranking with Covariates Siu Lun Chau (Department of Statistics, University of Oxford)*; Mihai Cucuringu (University of Oxford and The Alan Turing Institute); Dino Sejdinovic (University of Oxford) |
| Supervised Leanring | 516 | Ordinal Quantification through Regularization Mirko Bunse (TU Dortmund University)*; Alejandro Moreo (ISTI-CNR); Fabrizio Sebastiani (Consiglio Nazionale delle Ricerche); Martin Senz (TU Dortmund University) |
| Supervised Leanring | 1018 | Truly Unordered Probabilistic Rule Sets for Multi-class Classification Lincen Yang (Leiden University)*; Matthijs van Leeuwen (Leiden University) |
| Supervised Leanring | J4 | Dynamic Self-paced Sampling Ensemble for Highly Imbalanced and Class-overlapped Data Classification Fang Zhou, Suting Gao, Lyu Ni, Martin Pavlovski, Qiwen Dong, Zoran Obradovic, Weining Qian |
| Sustainability | 175 | Go green: A decision-tree framework to select optimal box-sizes for product shipments Karthik S Gurumoorthy (Amazon)*; Abhiraj Hinge (Amazon) |
| Sustainability | 310 | Bayesian Multi-Head Convolutional Neural Networks with Bahdanau Attention for Forecasting Daily Precipitation in Climate Change Monitoring Firas Gerges (New Jersey Institute of Technology)*; Michel C. Boufadel (New Jersey Institute of Technology); Elie Bou-Zeid (Princeton University); Ankit Darekar (New Jersey Institute of Technology); Hani Nassif (Rutgers University – New Brunswick); Dr. Jason T. L. Wang (New Jersey Institute of Technology) |
| Sustainability | 1074 | An Improved Yaw Control Algorithm for Wind Turbines via Reinforcement Learning Alban Puech (Ecole Polytechnique )*; Jesse Read (Ecole Polytechnique) |
| Sustainability | 1189 | CGPM：Poverty Mapping Framework based on Multi-Modal Geographic Knowledge Integration and Macroscopic Social Network Mining Geng Zhao (JINAN University)*; Ziqing Gao (Xi’an Jiaotong University); ChiHsu Tsai (Jinan University); Jiamin Lu (Jinan University) |
| Sustainability | 1289 | Cubism: Co-Balanced Mixup for Unsupervised Volcano-Seismic Knowledge Transfer Mahsa Keramati (Simon Fraser University)*; Mohammad Tayebi (Simon Fraser University); Zahra Zohrevand (Simon Fraser University); Uwe Glässer (SFU); Juan Anzieta (Simon Fraser University); Glyn Williams-Jones (Simon Fraser University) |
| Time Series | 393 | TS-MIoU: A Time Series Similarity Metric Without Mapping Azim Ahmadzadeh (Georgia State University)*; Yang Chen (Georgia State University); Krishna Rukmini Puthucode (Georgia State University); Ruizhe Ma (University of Massachusetts Lowell); Rafal Angryk (GEORGIA STATE UNIVERSITY) |
| Time Series | 609 | Learning Perceptual Position-aware Shapelets for Time series Classification Xuan-May Le (JAIST)*; Minh-Tuan Tran (KAIST); Nam Huynh (Japan Advanced Institute of Science and Technology, Japan) |
| Time Series | 704 | Few-Shot Forecasting of Time-Series with Heterogeneous Channels Lukas Brinkmeyer (Universität Hildesheim); Rafael Rego Drumond (Universität Hildesheim)*; Johannes Burchert (Universität Hildesheim); Lars Schmidt-Thieme (University of Hildesheim) |
| Time Series | 943 | Yformer: U-Net Inspired Transformer Architecture for Far Horizon Time Series Forecasting Kiran Madhusudhanan (University of Hildesheim)*; Johannes Burchert (University of Hildesheim); Nghia Duong-Trung (Technische Universität Berlin); Stefan Born (Technische Universität Berlin); Lars Schmidt-Thieme (University of Hildesheim) |
| Time Series | 1106 | Finding Local Groupings of Time Series Zed Lee (Stockholm University)*; Marco Trincavelli (H&M Group); Panagiotis Papapetrou (Stockholm University) |
| Time Series | 1180 | Online Adaptive Multivariate Time Series Forecasting Amal Saadallah (TU Dortmund)*; Hanna Mykula (TU Dortmund); Katharina J. Morik (TU Dortmund) |
| Transfer and Multitask | 816 | InCo: Intermediate Prototype Contrast for Unsupervised Domain Adaptation Yuntao Du (Nanjing University)*; Hongtao Luo (Nanjing University); Haiyang Yang (Nanjing University); juan jiang (Nanjing University); Chongjun Wang (Nanjing University) |
| Transfer and Multitask | 916 | Fast and Accurate Importance Weighting for Correcting Sample Bias Antoine de Mathelin (ENS Paris-Saclay)*; François Deheeger (Michelin); Mathilde J MOUGEOT (ENS Paris Saclay); Nicolas Vayatis (ENS Paris Saclay) |
| Transfer and Multitask | 1171 | Newer is not always better: Rethinking transferability metrics, their peculiarities, stability and performance Shibal Ibrahim (Massachusetts Institute of Technology)*; Natalia Ponomareva (Google Research); Rahul Mazumder (Massachusetts Institute of Technology) |
| Transfer and Multitask | 1255 | Learning to Teach Fairness-aware Deep Multi-Task Learning Arjun Roy (L3S Research Center)*; Eirini Ntoutsi (Freie Universität Berlin) |
| Transfer and Multitask | 1288 | Overcoming Catastrophic Forgetting via Direction-Constrained Optimization Yunfei Teng (New York University)*; Anna Choromanska (NYU); Murray Campbell (IBM Research); Songtao Lu (IBM Thomas J. Watson Research Center); Parikshit Ram (IBM Research AI); Lior Horesh (IBM Research) |
| Transfer and Multitask | 1371 | On the relationship between disentanglement and multi-task learning Łukasz Maziarka (Jagiellonian University)*; Aleksandra Nowak (Jagiellonian University); Maciej Wołczyk (Jagiellonian University); Andrzej Bedychaj (Jagiellonian University) |