Papers by Conference Session

Click the title of a conference paper to get the PDF.

Journal track papers can be found at the special issue websites:

Active, semi-sup. learning (1)190SemiITE: 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)442Exploring 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)891SMFM4L: 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)926Consistent 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)952Multi-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)1128Near 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)375GraphMixup: 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)481Multi-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)531A 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)1054A 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)1177Deep 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
Active, semi-sup. learning (3)J29Stream-Based Active Learning for Sliding Windows Under Verification Latency
Tuan Minh Pham 
Active, semi-sup. learning (3)J37Semi-supervised Latent Block Model with pairwise constraints
Paul Riverain 
Anomaly detection146Anomaly Detection via Few-shot Learning on Normality
Shin Ando (Tokyo University of Science)*; Ayaka Yamamoto (Tokyo University of Science)
Anomaly detection158ARES: 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 detection245Detecting Anomalies with Autoencoders on Data Streams
Lucas Cazzonelli (FZI Research Center for Information Technology)*; Cedric Kulbach (FZI Research Center for Information Technology)
Anomaly detection578R2-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 detection927Hop-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 detection1334UrbanAnom: An Approach to Predict Urban Anomaly from Multi-Stream Data
Bhumika . (IIT Jodhpur)*; Debasis Das (Indian Institute of technology(IIT) Jodhpur)
Applications (1)712Grasping 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)802GALG: 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)904Waypoint 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)1022Is 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)1210Physically Invertible System Identification for Monitoring System Edges with Unobservability
Jingyi Yuan (Arizona State University)*; Yang Weng (Arizona State University)
Applications (1)J49A User-Guided Bayesian Framework for Ensemble Feature Selection in Life Science Applications (UBayFS)
Stefan Schrunner 
Applications (2)J11SOKNL: 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)J15Provable randomized rounding for minimum-similarity diversification
Bruno Ordozgoiti 
Applications (2)J22Counterfactual 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)J46BT-Unet: A self-supervised learning framework for biomedical image segmentation using Barlow Twins with U-Net models
Narinder Singh Punn 
Applications (2)J9Transfer 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)229Meta 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)407Recognizing 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)755Placing (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)828Automatic 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)1000John 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)1025Looking 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)212FFBDNet: 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)408Recognizing 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)586Towards 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)829EpiGNN: 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)944Detection 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)954MepoGNN: 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)J18Synwalk – Community Detection via Random Walk Modelling
Christian Toth, Denis Helic, Bernhard C. Geiger
Apps – Transportation583Route 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 – Transportation800BusWTE: 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 – Transportation923A Bayesian Markov Model for Station-Level Origin-Destination Matrix Reconstruction
Victor Amblard (CITiO); Amir Dib (CITIO); Noëlie Cherrier (CITiO)*; Guillaume Barthe (CITiO)
Apps – Transportation1093PathOracle: 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 – Transportation1224Attention, 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 – Transportation1422Can we Learn from Outliers? Unsupervised Optimization of Intelligent Vehicle Traffic Management Systems
Tom A Mertens (Tu/e); Marwan Hassani (TU Eindhoven)*
Bandits & Online Lrn643On the complexity of All $\epsilon$-Best Arms Identification
Aymen Al Marjani (ENS Lyon)*; Tomáš Kocák (University of Potsdam); Aurélien Garivier (ENS Lyon)
Bandits & Online Lrn671Hypothesis Transfer in Bandits by Weighted Models
Steven Bilaj (University of Tübingen)*; Sofien Dhouib (University of Tübingen); Setareh Maghsudi (University of Tübingen)
Bandits & Online Lrn1242Multi-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 Lrn1280Hierarchical Unimodal Bandits
TIANCHI ZHAO (The University of Arizona)*; Chicheng Zhang (University of Arizona); Ming Li (University of Arizona)
Bandits & Online Lrn1308Improved Regret Bounds for Online Kernel Selection under Bandit Feedback
Junfan Li (Tianjin University); Shizhong Liao (Tianjin University)*
Bandits & Online Lrn1383Online learning of convex sets on graphs
Maximilian Thiessen (TU Wien)*; Thomas Gärtner (TU Wien)
Classification (1)1317LCDB 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)J3MultiRocket: Multiple pooling operators and transformations for fast and effective time series classification
Chang Wei Tan, Angus Dempster, Christoph Bergmeir, Geoffrey I Webb
Classification (1)J52Speeding-up One-vs-All Training for Extreme Classification via Mean-Separating Initialization
Erik Schultheis
Classification (1)J53Cross-Model Consensus of Explanations and Beyond for Image Classification Models: An Empirical Study
Haoyi Xiong
Classification (2)J6Human-in-the-loop Handling of Knowledge Drift
Andrea Bontempelli, Fausto Giunchiglia, Andrea Passerini, Stefano Teso
Classification (2)J26Efficient SVDD Sampling with Approximation Guarantees for the Decision Boundary
Adrian Englhardt, Holger Trittenbach, Daniel Kottke*, Bernhard Sick, Klemens Böhm
Classification (2)J30One-Stage Tree: End-to-End Tree Builder and Pruner
Guanghui Zhu 
Classification (2)J56A flexible class of dependence-aware multi-label loss functions
Marcel Wever 
Clustering & Dim Red. (2)240Nonparametric Bayesian Deep Visualization
Haruya Ishizuka (Bridgestone Corporation)*; Daichi Mochihashi (Institute of Statistical Mathematics)
Clustering & Dim Red. (2)366Wasserstein 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)518SECLEDS: 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)547Automated 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)655Knowledge 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)681FastDEC: 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)348CDPS: 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)849Pass-Efficient Randomized SVD with Boosted Accuracy
Xu Feng (Tsinghua University)*; Wenjian Yu (Tsinghua University); Yuyang Xie (Tsinghua University)
Clustering & Dim. Red. (1)953Structured 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)1038Powershap: 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)1368LSCALE: 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)298A 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)363Rethinking 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)474Submodular 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)792Learnable 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)873SAViR-T: Spatially Attentive Visual Reasoning with Transformers
Pritish Sahu (Rutgers University)*; Kalliopi Basioti (Rutgers University); Vladimir Pavlovic (Rutgers University)
Computer Vision (1)1330No 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)191Charge 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)359A 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)795Understanding Adversarial Robustness of Vision Transformers via Cauchy Problem
Zheng Wang (Exeter University)*; Wenjie Ruan (University of Exeter)
Computer Vision (2)932Supervised 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)J51Wavelet-Packets for Deepfake Image Analysis and Detection
Moritz Wolter 
Conversational Systems445MFDG: 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 Systems860Do 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 Systems1186Customized 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 Systems1338Contextual 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)381Understanding 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)519Class-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)620Foveated 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)1085Avoiding 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)1256Trigger 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)1401PrUE: 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)435DialCSP: 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)J41Stateless Neural Meta-Learning using Second-Order Gradients
Mike Huisman 
Deep Learning (2)J42A Brain-inspired Algorithm for Training Highly Sparse Neural Networks
Zahra Atashgahi 
Deep Learning (2)J47Explainable Online Ensemble of Deep Neural Network Pruning for Time Series Forecasting
Matthias Jakobs 
Deep Learning (3)J34DEFT: Distilling Entangled Factors by Preventing Information Diffusion
Lin Wang 
Deep Learning (3)J36Context-aware Spatio-temporal Event Prediction via Convolutional Hawkes Processes
Maya Okawa 
Deep Learning (3)J43GENs: Generative Encoding Networks
Surojit Saha 
Deep Learning (3)J50Recursive Tree Grammar Autoencoders
Benjamin Paassen 
Financial ML410Distributional 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 ML523Banksformer: 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 ML662Stock 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 ML713A Prescriptive Machine Learning Approach for Assessing Goodwill in the Automotive Domain
Stefan Haas (BMW)*; Eyke Hüllermeier (University of Munich)
Financial ML1012Risk-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 ML1298Uncertainty 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 Models548Scalable 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 Models788STGEN: Deep Continuous-time Spatiotemporal Graph Generation
Chen Ling (Emory University)*; Hengning Cao (Cornell University); Zhao Liang (Emory University)
Generative Models1229Direct 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 Models1293TrafficFlowGAN: 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 MiningJ10INK: knowledge graph embeddings for node classification
Bram Steenwinckel, Gilles Vandewiele, Terencio Agozzino, Michael Weyns, Filip De Turck, Femke Ongenae
Graph MiningJ16Sequential 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 MiningJ20Strengthening ties towards a highly-connected world
Antonis Matakos, Aristides Gionis
Graph MiningJ25VPint: Value propagation-based spatial interpolation
Laurens Arp, Mitra Baratchi, Holger Hoos
Graph MiningJ8EmbAssi: Embedding Assignment Costs for Similarity Search in Large Graph Databases
Franka Bause, Erich Schubert, Nils Morten Kriege
Graph Nns (1)216Self-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)433GNN 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)542Masked 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)803SEA: 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)1052TopoAttn-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)1287Edge 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)486Learning to solve Minimum Cost Multicuts efficiently using Edge-Weighted Graph Convolutional Neural Networks
Steffen Jung (MPII)*; Margret Keuper (University of Mannheim)
Graph Nns (2)672Transforming PageRank into an Infinite-Depth Graph Neural Network
Andreas Roth (TU Dortmund)*; Thomas Liebig (TU Dortmund)
Graph Nns (2)764Supervised 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)848NE-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)
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)J31Polynomial-Based Graph Convolutional Neural Networks For Graph Classification
Luca Pasa 
Interp. and Explain. (1)477Fair 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)494Calibrate to interpret
Gregory D Scafarto (EURA NOVA)*; Antoine Bonnefoy (EURA NOVA); Nicolas P Posocco (EURA NOVA)
Interp. and Explain. (1)648SMACE: 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)1340Interpretations 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)J39Scrutinizing XAI using linear ground-truth data with suppressor variables
Rick Wilming 
Interp. and Explain. (2)192Session-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)346ProtoMIL: 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)505Knowledge-Driven Interpretation of Convolutional Neural Networks
Riccardo Massidda (Università di Pisa)*; Davide Bacciu (Univeristy of Pisa)
Interp. and Explain. (2)633VCNet: 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)707Explaining 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)867Neural 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 Graphs178Multi-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 Graphs354MULTIFORM: 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 Graphs587Enhance 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 Graphs604Start 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 Graphs666ProcK: 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 Graphs696RDF 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 NAS526 MRF-UNets: Searching UNet with Markov Random Fields
Zifu Wang (KU Leuven)*; Matthew B. Blaschko (KU Leuven)
Meta-Learning NAS759Efficient 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 NAS852Adversarial 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 NAS1120Context 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 NAS1358Discovering wiring patterns influencing neural network performance
Aleksandra I Nowak (Jagiellonian Univeristy)*; Romuald Janik (Jagiellonian University)
Meta-Learning NAS1367Automatic 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 RL506Reinforcement 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 RL869DistSPECTRL: Distributing Specifications in Multi-Agent Reinforcement Learning Systems
Joe K Eappen (Purdue University)*; Suresh Jagannathan (Purdue University)
Multi-Agent RL883Team-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 RL1147Heterogeneity Breaks the Game: Evaluating Cooperation-Competition with Multisets of Agents
Yue Zhao (Northwestern Polytechnical University)*; José Hernández-Orallo (Universitat Politècnica de València)
Multi-Agent RL1297MAVIPER: 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 RL1379Constrained 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 & Graphs731Summarizing Labeled Multi-Graphs
Dimitris Berberidis (Carnegie Mellon University); Pierre Liang (Carnegie Mellon University); Leman Akoglu (CMU)*
Networks & Graphs821Joint 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 & Graphs872Understanding 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 & Graphs1027Anonymity 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 & Graphs1117Inferring Tie Strength in Temporal Networks
Lutz Oettershagen (University of Bonn)*; Athanasios Konstantinidis (Luiss University); Giuseppe F. Italiano (LUISS University)
Networks & Graphs1184Algorithmic Tools for Mining the Motif Structure of Networks
Tianyi Chen (Boston University); Brian Matejek (Harvard University ); Michael Mitzenmacher (Harvard); Charalampos Tsourakakis (Boston University)*
NLP311An 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)
NLP561On 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)
NLP791“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)
NLP988Hyperbolic 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)
NLP1132Vec2Node: 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)
NLP1333AutoMap: 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-Mining249Self-Distilled Pruning of Neural Networks
James T O’ Neill (University of Liverpool)*; Sourav Dutta (Huawei Research Centre); Haytham Assem (Huawei Research)
NLP and Text-Mining440Bi-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-Mining626Contextualized 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-Mining928FairDistillation: Mitigating Stereotyping in Language Models
Pieter Delobelle (KU Leuven)*; Bettina Berendt (KU Leuven)
NLP and Text-Mining1275MultiLayerET: 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-MiningJ14Controlling Hallucinations at Word Level in Data-to-Text Generation
Clément Rebuffel, Marco Roberti, Laure Soulier, Geoffrey Scoutheeten, Rossella Cancelliere, Patrick Gallinari
Optimal transport352Feature-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 transport777Learning 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 transportJ33Optimal Transport for Conditional Domain Matching and Label Shift
Alain Rakotomamonjy 
Optimal transportJ55Hierarchical Optimal Transport for Unsupervised Domain Adaptation
Mourad El Hamri
Optimization168Mixed Integer Linear Programming for Optimizing a Hopfield Network
Bodo Rosenhahn (Leibniz University Hannover)*
Optimization522Rethinking Exponential Averaging of the Fisher
Constantin O Puiu (Oxford)*
Optimization665Penalised FTRL With Time-Varying Constraints
Douglas Leith (Trinity College Dublin)*; George Iosifidis (TU Delft)
OptimizationJ54Optimistic Optimisation of Composite Objective with Exponentiated Update
Weijia Shao
Optimization, Combin.360On 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.543Branch 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.668SaDe: Learning Models that Provably Satisfy Domain Constraints
Kshitij Goyal (KU Leuven)*; Sebastijan Dumancic (TU Delft); Hendrik Blockeel (KU Leuven)
Optimization, Combin.689Learning 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.1098Time 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.1200Learning Optimal Decision Trees Under Memory Constraints
Gael Aglin (UCLouvain)*; Siegfried Nijssen (Université Catholique de Louvain, BE); Pierre Schaus (UC Louvain)
Priv & Fed. Learning317Differentially 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. Learning415Marginal 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. Learning810Beyond 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. Learning948Differentially 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. Learning1061Noise-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. Learning1304Non-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)441Sparse Horseshoe Estimation via Expectation-Maximisation
Shu Yu Tew (Monash University)*; Daniel F Schmidt (Monash University); Enes Makalic (University of Melbourne)
Probab. Inference (1)515Structure-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)831Summarizing 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)880Non-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)368A 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)370Bayesian 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)642Bounding 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)714On Projectivity in Markov Logic Networks
Sagar Malhotra (Fondazione Bruno Kessler)*; Luciano Serafini (Fondazione Bruno Kessler)
Probab. Inference (2)870Optimization of Annealed Importance Sampling Hyperparameters
Shirin Goshtasbpour (ETH Zurich)*; Fernando Perez-Cruz (ETH Zurich)
Probab. Inference (2)1357From 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, Hardware418GNNSampler: 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, Hardware479Immediate 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, Hardware599FASE: 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, Hardware737Block-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, Hardware1083Training 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, Hardware1392Variational Boson Sampling
Shiv Shankar (University of Massachusetts)*; Don Towsley (University of Massachusetts Amherst)
Ranking & Rec Systems650Graph 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 Systems699Multi-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 Systems843Basket 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 Systems972A U-shaped Hierarchical Recommender for Multi-resolution Collaborative Signal Modeling
Peng YI (UNSW); Xiongcai Cai (UNSW Sydney)*; Ziteng Li (UNSW Sydney )
Ranking & Rec Systems1104Recommending Related Products Using Graph Neural Networks in Directed Graphs
Srinivas Virinchi (Amazon )*; Anoop Saladi (amazon); Abhirup Mondal (Amazon)
Ranking & Rec SystemsJ5Ranking with submodular functions on a budget
Guangyi Zhang, Nikolaj Tatti, Aristides Gionis
Rec. Systems (1)423Bi-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)562Mitigating 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)634A 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)780Improving 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)896AD-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)137Oracle-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)292Multi-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)538Batch Reinforcement Learning from Crowds
Guoxi Zhang (Kyoto University)*; Hisashi Kashima (Kyoto University)
Reinforcement Lrn (1)733Coupling 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)1384Reducing 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)1424Improving Actor-Critic Reinforcement Learning via Hamiltonian Monte Carlo Method
Duo Xu (Georgia Institute of Technology)*
Reinforcement Lrn (2)389Imitation Learning with Sinkhorn Distances
Georgios Papagiannis (Imperial College London)*; Yunpeng Li (University of Surrey)
Reinforcement Lrn (2)459Safe 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)612State Representation Learning for Goal-Conditioned Reinforcement Learning
Lorenzo Steccanella (UPF – Artificial Intelligence and Machine learning)*; Anders Jonsson (UPF)
Reinforcement Lrn (2)798Bootstrap State Representation using Style Transfer for Better Generalization in Deep Reinforcement Learning
Md Masudur Rahman (Purdue University)*; Yexiang Xue (Purdue University)
Reinforcement Lrn (2)1251Model 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)157FROB: 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)176Fooling 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)1041Hypothesis 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)1130PRoA: 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)261Securing 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)406Adversarial 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)645MEAD: 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)673Calibrating 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)793Defending 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)938Resisting 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)955On the Prediction Instability of Graph Neural Networks
Max Klabunde (University of Passau)*; Florian Lemmerich (University of Passau)
Robust & Adversarial ML (2)1068Adversarially Robust Decision Tree Relabeling
Daniël Vos (Delft University of Technology)*; Sicco Verwer (Delft University of Technology)
Robust & Adversarial ML (2)J27Quick and Robust Feature Selection: the Strength of Energy-efficient Sparse Training for Autoencoders
Zahra Atashgahi 
Robust & Adversarial ML (2)J38Robustness Verification of ReLU Networks via Quadratic Programming
Aleksei Kuvshinov 
Robust & Adversarial ML (2)J44Speeding Up Neural Network Robustness Verification via Algorithm Configuration and an Optimised Mixed Integer Linear Programming Solver Portfolio
Matthias König 
Robust & Adversarial ML (2)J48Aliasing and adversarial robust generalization of CNNs
Julia Grabinski 
Sequence MiningJ1Mining 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 MiningJ13An Efficient Procedure for Mining Egocentric Temporal Motifs
Antonio Longa, Giulia Cencetti, Bruno Lepri, Andrea Passerini
Sequence MiningJ7SPEck: Mining Statistically-significant Sequential Patterns Efficiently with Exact Sampling
Steedman Jenkins, Stefan Walzer-Goldfeld, Matteo Riondato
Social Network Analysis272The 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 Analysis394DeMis: Data-efficient Misinformation Detection using Reinforcement Learning
Kornraphop Kawintiranon (Georgetown University)*; Lisa Singh (Georgetown University)
Social Network Analysis448A 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 Analysis661Probing Spurious Correlations in Popular Event-Based Rumor Detection Benchmarks
Jiaying Wu (National University of Singapore)*; Bryan Hooi (National University of Singapore)
Social Network Analysis1028SkipCas: Information Diffusion Prediction Model Based on Skip-gram
Dedong Ren (Heilongjiang University); Yong Liu (Heilongjiang university)*
Social Network Analysis1399Exploring 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. LearningJ40Nested Aggregation of Experts using Inducing Points for Approximated Gaussian Process Regression
Ayano Nakai-Kasai 
Sup. Stat. LearningJ45Learning with risks based on M-location
Matthew Holland 
Sup. Stat. Learning911Improving 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 Leanring215Factorized 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 Leanring347Random Similarity Forest
Maciej Piernik (Poznan University of Technology)*; Dariusz Brzezinski (Poznan University of Technology); Pawel Zawadzki (Adam Mickiewicz University)
Supervised Leanring426Spectral 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 Leanring516Ordinal Quantification through Regularization
Mirko Bunse (TU Dortmund University)*; Alejandro Moreo (ISTI-CNR); Fabrizio Sebastiani (Consiglio Nazionale delle Ricerche); Martin Senz (TU Dortmund University)
Supervised Leanring1018Truly Unordered Probabilistic Rule Sets for Multi-class Classification
Lincen Yang (Leiden University)*; Matthijs van Leeuwen (Leiden University)
Supervised LeanringJ4Dynamic 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
Sustainability175Go green: A decision-tree framework to select optimal box-sizes for product shipments
Karthik S Gurumoorthy (Amazon)*; Abhiraj Hinge (Amazon)
Sustainability310Bayesian 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)
Sustainability1074An Improved Yaw Control Algorithm for Wind Turbines via Reinforcement Learning
Alban Puech (Ecole Polytechnique )*; Jesse Read (Ecole Polytechnique)
Sustainability1189CGPM: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)
Sustainability1289Cubism: 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 Series393TS-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 Series609Learning 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 Series704Few-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 Series943Yformer: 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 Series1106Finding Local Groupings of Time Series
Zed Lee (Stockholm University)*; Marco Trincavelli (H&M Group); Panagiotis Papapetrou (Stockholm University)
Time Series1180Online Adaptive Multivariate Time Series Forecasting
Amal Saadallah (TU Dortmund)*; Hanna Mykula (TU Dortmund); Katharina J. Morik (TU Dortmund)
Transfer and Multitask816InCo: 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 Multitask916Fast 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 Multitask1171Newer 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 Multitask1255Learning to Teach Fairness-aware Deep Multi-Task Learning
Arjun Roy (L3S Research Center)*; Eirini Ntoutsi (Freie Universität Berlin)
Transfer and Multitask1288Overcoming 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 Multitask1371On the relationship between disentanglement and multi-task learning
Łukasz Maziarka (Jagiellonian University)*; Aleksandra Nowak (Jagiellonian University); Maciej Wołczyk (Jagiellonian University); Andrzej Bedychaj (Jagiellonian University)