Pass-Efficient Randomized SVD with Boosted Accuracy
Xu Feng, Wenjian Yu, and Yuyang Xie
CDPS: Constrained DTW-Preserving Shapelets
Hussein El-Amouri, Thomas Lampert, Pierre Gançarski, and Clément Mallet
Structured Nonlinear Discriminant Analysis
Christopher Bonenberger, Wolfgang Ertel, Markus Schneider, and Friedhelm Schwenker
Latent Space Clustering-Based Active Learning for Node Classification
Juncheng Liu, Yiwei Wang, Bryan Hooi, Renchi Yang, and Xiaokui Xiao
Powershap: A Power-full Shapley Feature Selection Method
Jarne Verhaeghe, Jeroen Van Der Donckt, Femke Ongenae, and Sofie Van Hoecke
Automated Cancer Subtyping via Vector Quantization Mutual Information Maximization
Zheng Chen, Lingwei Zhu, Ziwei Yang, and Takashi Matsubara
FastDEC: Clustering By Fast Dominance Estimation
Geping Yang, Hongzhang Lv, Yiyang Yang, Zhiguo Gong, Xiang Chen, and Zhifeng Hao
SECLEDS: Sequence Clustering in Evolving Data Streams via Multiple Medoids and Medoid Voting
Azqa Nadeem and Sicco Verwer
Knowledge Integration in Deep Clustering
Nguyen-Viet-Dung Nghiem, Christel Vrain, and Thi-Bich-Hanh Dao
ARES: Locally Adaptive Reconstruction-based Anomaly Scoring
Adam Goodge, Bryan Hooi, See Kiong Ng, and Wee Siong Ng
R2-AD2: Detecting Anomalies by Analysing the Raw Gradient
Jan-Philipp Schulze, Philip Sperl, Ana Radutoiu, Carla Sagebiel, and Konstantin Bottinger
Hop-Count Based Self-Supervised Anomaly Detection on Attributed Networks
Tianjin Huang, Yulong Pei, Vlado Menkovski, and Mykola Pechenizkiy
Deep learning based Urban Anomaly Prediction from Spatiotemporal Data
Bhumika and Debasis Das
Detecting Anomalies with Autoencoders on Data Streams
Lucas Cazzonelli and Cedric Kulbach
Interpretations for Lifestyle-related Diseases at Multiple Time Intervals
Yuki Oba, Taro Tezuka, Masaru Sanuki and Yukiko Wagatsuma
Fair and Efficient Alternatives to Shapley-based Attribution Methods
Charles Condevaux, S'ebastien Harispe and St'ephane Mussard
SMACE: A New Method for the Interpretability of Composite Decision Systems
Gianluigi Lopardo, Damien Garreau, Frédéric Precioso, and Greger Ottosson
Knowledge-Driven Interpretation of Convolutional Neural Networks
Riccardo Massidda and Davide Bacciu
Neural Networks with Feature Attribution and Contrastive Explanations
Housam K. B. Babiker, Mi-Young Kim, and Randy Goebel
Explaining Predictions by Characteristic Rules
Amr Alkhatib, Henrik Bostrom, and Michalis Vazirgiannis
Session-based Recommendation along with the Session Style of Explanation
Panagiotis Symeonidis, Lidija Kirjackaja, and Markus Zanker
ProtoMIL: Multiple Instance Learning with Prototypical Parts for Whole-Slide Image Classification
Dawid Rymarczyk, Adam Pardyl, Jarosław Kraus, Aneta Kaczyńska, Marek Skomorowski, and Bartosz Zieliński
VCNet: A self-explaining model for realistic counterfactual generation
Victor Guyomard, Franc coise Fessant, Thomas Guyet, Tassadit Bouadi, and Alexandre Termier
A Recommendation System for CAD Assembly Modeling based on Graph Neural Networks
Carola Gajek, Alexander Schiendorfer, and Wolfgang Reif
AD-AUG: Adversarial Data Augmentation for Counterfactual Recommendation
Yifan Wang, Yifang Qin, Yu Han, Mingyang Yin, Jingren Zhou, Hongxia Yang, and Ming Zhang
Bi-directional Contrastive Distillation for Multi-behavior Recommendation
Yabo Chu, Enneng Yang, Qiang Liu, Yuting Liu, Linying Jiang, and Guibing Guo
Improving Micro-video Recommendation by Controlling Position Bias
Yisong Yu, Beihong Jin, Jiageng Song, Beibei Li, Yiyuan Zheng, and Wei Zhuo
Mitigating Confounding Bias for Recommendation via Counterfactual Inference
Ming He, Xinlei Hu, Changshu Li, Xin Chen, and Jiwen Wang
Recommending Related Products Using Graph Neural Networks in Directed Graphs
Srinivas Virinchi, Anoop Saladi, and Abhirup Mondal
A U-shaped Hierarchical Recommender by Multi-resolution Collaborative Signal Modeling
Peng Yi, Cai, and Li
Basket Booster for Prototype-based Contrastive Learning in Next Basket Recommendation
Ting-Ting Su, Zhen-Yu He, Man-Sheng Chen, and Chang-Dong Wang
Graph Contrastive Learning with Adaptive Augmentation for Recommendation
Mengyuan Jing, Yanmin Zhu, Tianzi Zang, Jiadi Yu, and Feilong Tang
Multi-Interest Extraction Joint with Contrastive Learning for News Recommendation
Shicheng Wang, Shu Guo, Lihong Wang, Tingwen Liu and Hongbo Xu
On the relationship between disentanglement and multi-task learning
L ukasz Maziarka, Aleksandra Nowak, Maciej Wol czyk, and Andrzej Bedychaj
InCo: Intermediate Prototype Contrast for Unsupervised Domain Adaptation
Yuntao Du, Hongtao Luo, Haiyang Yang, Juan Jiang and Chongjun Wang
Fast and Accurate Importance Weighting for Correcting Sample Bias
Antoine de Mathelin, Francois Deheeger, Mathilde Mougeot and Nicolas Vayatis
Overcoming Catastrophic Forgetting via Direction-Constrained Optimization
Yunfei Teng, Anna Choromanska, Murray Campbell, Songtao Lu, Parikshit Ram, and Lior Horesh
Newer is not always better: Rethinking transferability metrics, their peculiarities, stability and performance
Shibal Ibrahim, Natalia Ponomareva, and Rahul Mazumder
Learning to Teach Fairness-aware Deep Multi-task Learning
Arjun Roy and Eirini Ntoutsi
Algorithmic Tools for Understanding the Motif Structure of Networks
Tianyi Chen, Brian Matejek, Michael Mitzenmacher and Charalampos E. Tsourakakis
Anonymity Can Help Minority: A Novel Synthetic Data Over-sampling Strategy on Multi-label Graphs
Yijun Duan, Xin Liu, Adam Jatowt, Hai-tao Yu, Steven Lynden, Kyoung-Sook Kim and Akiyoshi Matono
Understanding the Benefits of Forgetting when Learning on Dynamic Graphs
Julien Tissier and Charlotte Laclau
Summarizing Labeled Multi-Graphs
Dimitris Berberidis, Pierre J. Liang and Leman Akoglu
Inferring Tie Strength in Temporal Networks
Lutz Oettershagen, Athanasios L. Konstantinidis and Giuseppe F. Italiano
Joint Learning of Hierarchical Community Structure and Node Representations: An Unsupervised Approach
Ancy Sarah Tom, Nesreen K. Ahmed and George Karypis
ProcK: Machine Learning for Knowledge-Intensive Processes
Tobias Jacobs, Jingyi Yu, Julia Gastinger and Timo Sztyler
Enhance Temporal Knowledge Graph Completion via Time-aware Attention Graph Convolutional Network
Haohui Wei, Hong Huang, Teng Zhang, Xuanhua Shi and Hai Jin
Start Small, Think Big: On Hyperparameter Optimization for Large-Scale Knowledge Graph Embeddings
Adrian Kochsiek, Fritz Niesel and Rainer Gemulla
Multi-source Inductive Knowledge Graph Transfer
Junheng Hao, Lu-An Tang, Yizhou Sun, Zhengzhang Chen, Haifeng Chen, Junghwan Rhee, Zhichuan Li and Wei Wang
MULTIFORM: Few-Shot Knowledge Graph Completion via Multi-Modal Contexts
Xuan Zhang, Xun Liang, Xiangping Zheng, Bo Wu and Yuhui Guo
RDF Knowledge Base Summarization by Inducing First-order Horn Rules
Ruoyu Wang, Daniel Sun and Raymond Wong
A Heterogeneous Propagation Graph Model for Rumor Detection under the Relationship among Multiple Propagation Subtrees
Guoyi Li, Jingyuan Hu, Yulei Wu, Xiaodan Zhang, Wei Zhou and Honglei Lyu
DeMis: Data-efficient Misinformation Detection using Reinforcement Learning
Kornraphop Kawintiranon and Lisa Singh
The Burden of Being a Bridge: Analysing Subjective Well-Being of Twitter Users during the COVID-19 Pandemic
Ninghan Chen, Xihui Chen, Zhiqiang Zhong and Jun Pang
SkipCas: Information Diffusion Prediction Model Based on Skip-gram
Dedong Ren and Yong Liu
Probing Spurious Correlations in Popular Event-Based Rumor Detection Benchmarks
Jiaying Wu and Bryan Hooi
Self-supervised Graph Learning with Segmented Graph Channels
Hang Gao, Jiangmeng Li and Changwen Zheng
TopoAttn-Nets: Topological Attention in Graph Representation Learning
Yuzhou Chen, Elena Sizikova and Yulia R. Gel
SEA: Graph Shell Attention in Graph Neural Networks
Christian M.M. Frey, Yunpu Ma and Matthias Schubert
GNN Transformation Framework for Improving Efficiency and Scalability
Seiji Maekawa, Yuya Sasaki, George Fletcher and Makoto Onizuka
Masked Graph Auto-Encoder Constrained Graph Pooling
Chuang Liu, Yibing Zhan, Xueqi Ma, Dapeng Tao, Bo Du and Wenbin Hu
Supervised Graph Contrastive Learning for Few-shot Node Classification
Zhen Tan, Kaize Ding, Ruocheng Guo and Huan Liu
A Piece-wise Polynomial Filtering Approach for Graph Neural Networks
Vijay Lingam, Manan Sharma, Chanakya Ekbote, Rahul Ragesh, Arun Iyer and Sundararajan Sellamanickam
NE-WNA: A novel network embedding framework without neighborhood aggregation
Jijie Zhang, Yan Yang, Yong Liu and Meng Han
Transforming PageRank into an Infinite-Depth Graph Neural Network
Andreas Roth and Thomas Liebig
Learning to solve Minimum Cost Multicuts efficiently using Edge-Weighted Graph Convolutional Neural Networks
Steffen Jung and Margret Keuper
AutoMap: Automatic Medical Code Mapping for Clinical Prediction Model Deployment
Zhenbang Wu, Cao Xiao, Lucas M. Glass, David M. Liebovitz and Jimeng Sun
Hyperbolic Deep Keyphrase Generation
Zhang Yuxiang, Yang Tianyu, Jiang Tao, Li Xiaoli and Wang Suge
On the current state of reproducibility and reporting of uncertainty for Aspect-based Sentiment Analysis
Elisabeth Lebmeier, Matthias Ass enmacher and Christian Heumann
An Ion Exchange Mechanism Inspired Story Ending Generator for Different Characters
Xinyu Jiang, Qi Zhang, Chongyang Shi, Kaiying Jiang, Liang Hu and Shoujin Wang
Vec2Node: Self-training with Tensor Augmentation for Text Classification with Few Labels
Sara Abdali, Subhabrata Mukherjee and Evangelos E. Papalexakis
“Let's Eat Grandma”: Does Punctuation Matter in Sentence Representation?
Mansooreh Karami, Ahmadreza Mosallanezhad, Michelle V. Mancenido and Huan Liu
Contextualized Graph Embeddings for Adverse Drug Event Detection
Ya Gao, Shaoxiong Ji, Tongxuan Zhang, Prayag Tiwari and Pekka Marttinen
Bi-matching Mechanism to Combat Long-tail Senses of Word Sense Disambiguation
Junwei Zhang, Ruifang He and Fengyu Guo
FairDistillation: Mitigating Stereotyping in Language Models
Pieter Delobelle and Bettina Berendt
Self-Distilled Pruning of Deep Neural Networks
James O' Neill, Sourav Dutta and Haytham Assem
MultiLayerET: A Unified Representation of Entities and Topics using Multilayer Graphs
Jumanah Alshehri, Marija Stanojevic, Parisa Khan, Benjamin Rapp, Eduard Dragut and Zoran Obradovic
MFDG: a Multi-Factor Dialogue Graph Model for Dialogue Intent Classification
Jinhui Pang, Huinan Xu, Shuangyong Song, Bo Zou and Xiaodong He
Contextual Information and Commonsense Based Prompt for Emotion Recognition in Conversation
Jingjie Yi, Deqing Yang, Siyu Yuan, Kaiyan Cao, Zhiyao Zhang and Yanghua Xiao
Do You Know My Emotion? Emotion-Aware Strategy Recognition towards a Persuasive Dialogue System
Wei Peng, Yue Hu, Luxi Xing, Yuqiang Xie and Yajing Sun
Customized Conversational Recommender Systems
Shuokai Li, Yongchun Zhu, Ruobing Xie, Zhenwei Tang, Zhao Zhang, Fuzhen Zhuang, Qing He and Hui Xiong
DialCSP: A Two-stage Attention-based Model for Customer Satisfaction Prediction in E-commerce Customer Service
Zhenhe Wu, Liangqing Wu, Shuangyong Song, Jiahao Ji, Bo Zou, Zhoujun Li, and Xiaodong He
Foveated Neural Computation
Matteo Tiezzi, Simone Marullo, Alessandro Betti, Enrico Meloni, Lapo Faggi, Marco Gori, and Stefano Melacci
Class-Incremental Learning via Knowledge Amalgamation
Marcus de Carvalho, Mahardhika Pratama, Jie Zhang, and Yajuan Sun
Trigger Detection for the sPHENIX Experiment via Bipartite Graph Networks with Set Transformer
Tingting Xuan, Giorgian Borca-Tasciuc, Yimin Zhu, Yu Sun, Cameron Dean, Zhaozhong Shi, and Dantong Yu
Understanding Difficulty-based Sample Weighting with a Universal Difficulty Measure
Xiaoling Zhou, Ou Wu, Weiyao Zhu, and Ziyang Liang
Avoiding Forgetting and Allowing Forward Transfer in Continual Learning via Sparse Networks
Ghada Sokar, Decebal Constantin Mocanu, and Mykola Pechenizkiy
PrUE: Distilling Knowledge from Sparse Teacher Networks
Shaopu Wang, Xiaojun Chen, Mengzhen Kou, and Jinqiao Shi
Fooling Partial Dependence via Data Poisoning
Hubert Baniecki, Wojciech Kretowicz, and Przemyslaw Biecek
FROB: Few-shot ROBust Model for Joint Classification and Out-of-Distribution Detection
Nikolaos Dionelis, Sotirios A. Tsaftaris, and Mehrdad Yaghoobi
PRoA: A Probabilistic Robustness Assessment against Functional Perturbations
Tianle Zhang, Wenjie Ruan, and Jonathan E. Fieldsend
Hypothesis Testing for Class-Conditional Label Noise
Rafael Poyiadzi, Weisong Yang, and Niall Twomey
On the Prediction Instability of Graph Neural Networks
Max Klabunde and Florian Lemmerich
Defending Observation Attacks in Deep Reinforcement Learning via Detection and Denoising
Zikang Xiong, Joe Eappen, He Zhu, and Suresh Jagannathan
Resisting Graph Adversarial Attack via Cooperative Homophilous Augmentation
Zhihao Zhu, Chenwang Wu, Min Zhou, Hao Liao, Defu Lian, and Enhong Chen
Securing Cyber-Physical Systems: Physics-Enhanced Adversarial Learning for Autonomous Platoons
Guoxin Sun, Tansu Alpcan, Benjamin I. P. Rubinstein, and Seyit Camtepe
MEAD
Federica Granese, Marine Picot, Marco Romanelli, Francisco Messina, and Pablo Piantanida
Adversarial Mask: Real-World Universal Adversarial Attack on Face Recognition Models
Alon Zolfi, Shai Avidan, Yuval Elovici, and Asaf Shabtai
TrafficFlowGAN: Physics-informed Flow based Generative Adversarial Network for Uncertainty Quantification
Zhaobin Mo, Yongjie Fu, Daran Xu, and Xuan Di
STGEN: Deep Continuous-time Spatiotemporal Graph Generation
Chen Ling, Hengning Cao, and Liang Zhao
Direct Evolutionary Optimization of Variational Autoencoders With Binary Latents
Jakob Drefs, Enrico Guiraud, Filippos Panagiotou, and Jo
Scalable Adversarial Online Continual Learning
Tanmoy Dam, Mahardhika Pratama, MD Meftahul Ferdaus, Sreenatha Anavatti, and Hussein Abbas
Fine-Grained Bidirectional Attention-Based Generative Networks for Image-Text Matching
Zhixin Li, Zhu, Jiahui Wei, and Yufei Zeng
Learnable Masked Tokens for Improved Transferability of Self-Supervised Vision Transformers
Hao Hu, Federico Baldassarre, and Hossein Azizpour
Rethinking the Misalignment Problem in Dense Object Detection
Yang Yang, Min Li, Bo Meng, Zihao Huang, Junxing Ren, and Degang Sun
No More Strided Convolutions or Pooling: A New CNN Building Block for Low-Resolution Images and Small Objects
Raja Sunkara and Tie Luo
SAViR-T: Spatially Attentive Visual Reasoning with Transformers
Pritish Sahu, Kalliopi Basioti, and Vladimir Pavlovic
A Scaling Law for Syn2real Transfer: How Much Is Your Pre-training Effective?
Hiroaki Mikami, Kenji Fukumizu, Shogo Murai, Shuji Suzuki, Yuta Kikuchi, Taiji Suzuki, Shin-ichi Maeda, and Kohei Hayashi
Submodular Meta Data Compiling for Meta Optimization
Fengguang Su, Yu Zhu, Ou Wu, and Yingjun Deng
Supervised Contrastive Learning for Few-Shot Action Classification
Hongfeng Han, Nanyi Fei, Zhiwu Lu, and Ji-Rong Wen
A Novel Data Augmentation Technique for Out-of-Distribution Sample Detection using Compounded Corruptions
Ramya Hebbalaguppe, Suvra Ghosal, Prakash, Khadilkar, and Arora
Charge Own Job: Saliency Map and Visual Word Encoder for Image-Level Semantic Segmentation
Yuhui Guo, Xun Liang, Hui Tang, Xiangping Zheng, Bo Wu, and Xuan Zhang
Understanding Adversarial Robustness of Vision Transformers via Cauchy Problem
Zheng Wang and Wenjie Ruan
Automatic Feature Engineering through Monte Carlo Tree Search
Yiran Huang, Yexu Zhou, Michael Hefenbrock, Till Riedel, Likun Fang, and Michael Beigl
Adversarial Projections to Tackle Support-Query Shifts in Few-Shot Meta-Learning
Aroof Aimen, Bharat Ladrecha, and Narayanan C. Krishnan
Discovering wiring patterns influencing neural network performance
Aleksandra I. Nowak and Romuald A. Janik
Context Abstraction to Improve Decentralized Machine Learning in Structured Sensing Environments
Massinissa Hamidi and Aomar Osmani
Efficient Automated Deep Learning for Time Series Forecasting
Difan Deng, Florian Karl, Frank Hutter, Bernd Bischl, and Marius Lindauer
Coupling User Preference with External Rewards to Enable Driver-centered and Resource-aware EV Charging Recommendation
Chengyin Li, Dong, Fisher, and Zhu
Multi-Objective Actor-Critics for Real-Time Bidding in Display Advertising
Haolin Zhou, Chaoqi Yang, Xiaofeng Gao, Gongsheng Liu, and Guihai Chen
Oracle-SAGE: Planning Ahead in Graph-Based Deep Reinforcement Learning
Andrew Chester, Michael Dann, Fabio Zambetta, and John Thangarajah
Reducing the Planning Horizon through Reinforcement Learning
Logan Dunbar, Benjamin Rosman, Anthony G. Cohn, and Matteo Leonetti
State Representation Learning for Goal-Conditioned Reinforcement Learning
Lorenzo Steccanella and Anders Jonsson
Bootstrap State Representation using Style Transfer for Better Generalization in Deep Reinforcement Learning
Md Masudur Rahman and Yexiang Xue
Safe Exploration Method for Reinforcement Learning under Existence of Disturbance
Yoshihiro Okawa, Tomotake Sasaki, Hitoshi Yanami, and Toru Namerikawa
Model Selection in Reinforcement Learning with General Function Approximations
Avishek Ghosh and Sayak Ray Chowdhury
Heterogeneity Breaks the Game: Evaluating Cooperation-Competition with Multisets of Agents
Yue Zhao, Jos'e Hern'andez-Orallo
Constrained Multiagent Reinforcement Learning for Large Agent Population
Jiajing Ling, Arambam James Singh, Nguyen Duc Thien, and Akshat Kumar
Reinforcement Learning for Multi-Agent Stochastic Resource Collection
Niklas Strauss, David Winkel, Max Berrendorf, and Matthias Schubert
Team-Imitate-Synchronize for Solving Dec-POMDPs
Eliran Abdoo, Ronen I. Brafman, Guy Shani, and Nitsan Soffair
DistSPECTRL: Distributing Specifications in Multi-Agent Reinforcement Learning Systems
Joe Eappen and Suresh Jagannathan
MAVIPER: Learning Decision Tree Policies for Interpretable Multi-Agent Reinforcement Learning
Stephanie Milani, Zhicheng Zhang, Nicholay Topin, Zheyuan Ryan Shi, Charles Kamhoua, Evangelos E. Papalexakis, and Fei Fang
Hypothesis Transfer in Bandits by Weighted Models
Steven Bilaj, Sofien Dhouib, and Setareh Maghsudi
Multi-Agent Heterogeneous Stochastic Linear Bandits
Avishek Ghosh, Abishek Sankararaman, and Kannan Ramchandran
On the Complexity of All ε-Best Arms Identification
Aymen Al Marjani, Tomas Kocak, and Aurélien Garivier
Improved Regret Bounds for Online Kernel Selection under Bandit Feedback
Junfan Li and Shizhong Liao
Exploring Latent Sparse Graph for Large-Scale Semi-supervised Learning
Zitong Wang, Li Wang, Raymond Chan, and Tieyong Zeng
Near out-of-distribution detection for low-resolution radar micro-Doppler signatures
Martin Bauw, Santiago Velasco-Forero, Jesus Angulo, Claude Adnet, and Olivier Airiau
SemiITE: Semi-supervised Individual Treatment Effect Estimation via Disagreement-Based Co-training
Qiang Huang, Jing Ma, Jundong Li, Huiyan Sun, and Yi Chang
Multi-Task Adversarial Learning for Semi-Supervised Trajectory-User Linking
Sen Zhang, Senzhang Wang, Xiang Wang, Shigeng Zhang, Hao Miao, and Junxing Zhu
Consistent and Tractable Algorithm for Markov Network learning
Vojtech Franc, Daniel Prusa, and Andrii Yermakov
Automatic Detection of Mercury's Bow Shock and Magnetopause
Sahib Julka, Nikolas Kirschstein, Michael Granitzer, Alexander Lavrukhin, and Ute Amerstorfer
A Stopping Criterion for Transductive Active Learning
Daniel Kottke, Christoph Sandrock, Georg Krempl, and Bernhard Sick
Multi-domain Active Learning for Semi-supervised Anomaly Detection
Vincent Vercruyssen, Lorenzo Perini, Wannes Meert, and Jesse Davis
CMG: A Class-Mixed Generation Approach to Out-of-Distribution Detection
Mengyu Wang, Yijia Shao, Haowei Lin, Wenpeng Hu, and Bing Liu
GraphMixup: Improving Class-Imbalanced Node Classification by Reinforcement Mixup and Self-supervised Context Prediction
Lirong Wu, Jun Xia, Zhangyang Gao, Haitao Lin, Cheng Tan, and Stan Z. Li
Non-IID Distributed Learning with Optimal Mixture Weights
Jian Li, Bojian Wei, Yong Liu, and Weiping Wang
Marginal Release under Multi-Party Personalized Differential Privacy
Peng Tang, Rui Chen, Chongshi Jin, Gaoyuan Liu, and Shanqing Guo
Beyond Random Selection: A Perspective from Model Inversion in Personalized Federated Learning
Zichen Ma, Yu Lu, Wenye Li, and Shuguang Cui
Noise-efficient Learning of Differentially Private Partitioning Machine Ensembles
Zhanliang Huang, Yunwen Lei, and Ata Kab'an
Differentially Private Bayesian Neural Networks on Accuracy, Privacy and Reliability
Qiyiwen Zhang, Zhiqi Bu, Kan Chen, and Qi Long
Differentially Private Federated Combinatorial Bandits with Constraints
Sambhav Solanki, Samhita Kanaparthy, Sankarshan Damle, and Sujit Gujar
LCDB 1.0: An Extensive Learning Curves Database for Classification Tasks
Felix Mohr, Tom J. Viering, Marco Loog, and Jan N. van Rijn
Factorized Structured Regression for Large-Scale Varying Coefficient Models
David Rugamer, Andreas Bender, Simon Wiegrebe, Daniel Racek, Bernd Bischl, Christian L. Muller, and Clemens Stachl
Ordinal Quantification through Regularization
Mirko Bunse, Alejandro Moreo, Fabrizio Sebastiani, and Martin Senz
Truly Unordered Probabilistic Rule Sets for Multi-class Classification
Lincen Yang and Matthijs van Leeuwen
From graphs to DAGs: a low-complexity model and a scalable algorithm
Shuyu Dong and Mich`ele Sebag
Sparse Horseshoe Estimation via Expectation-Maximisation
Shu Tew, Daniel F Schmidt, and Enes Makalic
Structure-preserving Gaussian Process Dynamics
Katharina Ensinger, Friedrich Solowjow, Sebastian Ziesche, Michael Tiemann, and Sebastian Trimpe
Summarizing Data Structures with Gaussian Process and Robust Neighborhood Preservation
Koshi Watanabe, Keisuke Maeda, Takahiro Ogawa, and Miki Haseyama
Optimization of Annealed Importance Sampling Hyperparameters
Shirin Goshtasbpour and Fernando Perez-Cruz
Bayesian Nonparametrics for Sparse Dynamic Networks
Cian Naik, Franc cois Caron, Judith Rousseau, Yee Whye Teh, and Konstantina Palla
A Pre-Screening Approach for Faster Bayesian Network Structure Learning
Thibaud Rahier, Sylvain Mari'e, and Florence Forbes
A Non-Parametric Bayesian Approach for Uplift Discretization and Feature Selection
Mina Rafla, Nicolas Voisine, Bruno Crémilleux, and Marc Boullé
Bounding the Family-Wise Error Rate in Local Causal Discovery using Rademacher Averages
Dario Simionato and Fabio Vandin
Learning Optimal Transport Between two Empirical Distributions with Normalizing Flows
Florentin Coeurdoux, Nicolas Dobigeon, and Pierre Chainais
Feature-Robust Optimal Transport for High-Dimensional Data
Mathis Petrovich, Chao Liang, Ryoma Sato, Yanbin Liu, Yao-Hung Hubert Tsai, Linchao Zhu, Yi Yang, Ruslan Salakhutdinov, and Makoto Yamada
Learning to Control Local Search for Combinatorial Optimization
Jonas K. Falkner, Daniela Thyssens, Ahmad Bdeir, and Lars Schmidt-Thieme
Branch Ranking for Efficient Mixed-Integer Programming via Offline Ranking-based Policy Learning
Zeren Huang, Wenhao Chen, Weinan Zhang, Chuhan Shi, Furui Liu, Hui-Ling Zhen, Mingxuan Yuan, Jianye Hao, Yong Yu, and Jun Wang
Learning Optimal Decision Trees Under Memory Constraints
Gael Aglin, Siegfried Nijssen, and Pierre Schaus
SaDe: Learning Models that Provably Satisfy Domain Constraints
Kshitij Goyal, Sebastijan Dumancic, and Hendrik Blockeel
On the Generalization of Neural Combinatorial Optimization Heuristics
Sahil Manchanda, Sofia Michel, Darko Drakulic, and Jean-Marc Andreoli
Time constrained DL8.5 using Limited Discrepancy Search
Harold Kiossou, Pierre Schaus, Siegfried Nijssen, and Ratheil Vinasetan Houndji
Block-Level Surrogate Models for Inference Time Estimation in Hardware-Aware Neural Architecture Search
Kurt Stolle, Sebastian Vogel, Fons van der Sommen, and Willem Sanberg
FASE: A Fast, Accurate and Seamless Emulator for Custom Numerical Formats
John Osorio, Adri`a Armejach, Eric Petit, Greg Henry, and Marc Casas
GNNSampler: Bridging the Gap between Sampling Algorithms of GNN and Hardware
Xin Liu, Mingyu Yan, Shuhan Song, Zhengyang Lv, Wenming Li, Guangyu Sun, Xiaochun Ye, and Dongrui Fan
Training Parameterized Quantum Circuits with Triplet Loss
Christof Wendenius, Eileen Kuehn, and Achim Streit
Immediate Split Trees: Immediate Encoding of Floating Point Split Values in Random Forests
Christian Hakert, Kuan-Hsun Chen, and Jian-Jia Chen
CGPM: Poverty Mapping Framework based on Multi-Modal Geographic Knowledge Integration and Macroscopic Social Network Mining
Zhao Geng, Gao Ziqing, Tsai Chihsu, and Lu Jiamin
Bayesian Multi-Head Convolutional Neural Networks with Bahdanau Attention for Forecasting Daily Precipitation in Climate Change Monitoring
Firas Gerges, Michel C. Boufadel, Elie Bou-Zeid, Ankit Darekar, Hani Nassif, and Jason T. L. Wang
Cubism: Co-Balanced Mixup for Unsupervised Volcano-Seismic Knowledge Transfer
Mahsa Keramati, Mohammad A. Tayebi, Zahra Zohrevand, Juan Anzieta, and Glyn Williams-Jones
Go green: A decision-tree framework to select optimal box-sizes for product shipments
Karthik S. Gurumoorthy and Abhiraj Hinge
An Improved Yaw Control Algorithm for Wind Turbines via Reinforcement Learning
Alban Puech and Jesse Read
Few-Shot Forecasting of Time-Series with Heterogeneous Channels
Lukas Brinkmeyer, Rafael Rego Drumond, Johannes Burchert, and Lars Schmidt-Thieme
Online Adaptive Multivariate Time Series Forecasting
Amal Saadallah, Hanna Mykula, and Katharina Morik
U-Net Inspired Transformer Architecture for Far Horizon Time Series Forecasting
Kiran Madhusudhanan, Johannes Burchert, Nghia Duong-Trung, Stefan Born, and Lars Schmidt-Thieme
Learning Perceptual Position-aware Shapelets for Time series Classification
Xuan-May Le, Minh-Tuan Tran, and Van-Nam Huynh
Finding Local Groupings of Time Series
Zed Lee, Marco Trincavelli, and Panagiotis Papapetrou
TS-MIoU: A Time Series Similarity Metric Without Mapping
Azim Ahmadzadeh, Yang Chen, Krishna Rukmini Puthucode, Ruizhe Ma, and Rafal A. Angryk
Distributional Correlation–Aware Knowledge Distillation for Stock Trading Volume Prediction
Lei Li, Zhiyuan Zhang, Ruihan Bao, Keiko Harimoto, and Xu Sun
Banksformer: A Deep Generative Model for Synthetic Transaction Sequences
Kyle Nickerson, Terrence Tricco, Antonina Kolokolova, Farzaneh Shoeleh, Charles Robertson, John Hawkin, and Ting Hu
Stock Trading Volume Prediction with Dual-Process Meta-Learning
Ruibo Chen, Wei Li, Zhiyuan Zhang, Ruihan Bao, Keiko Harimoto, and Xu Sun
Uncertainty Awareness for Predicting Noisy Stock Price Movements
Yun-Hsuan Lien, Yu-Syuan Lin, and Yu-Shuen Wang
A Prescriptive Machine Learning Approach for Assessing Goodwill in the Automotive Domain
Stefan Haas and Eyke Hullermeier
Risk-Aware Reinforcement Learning for Multi-Period Portfolio Selection
David Winkel, Niklas Strauss, Matthias Schubert, and Thomas Seidl
Waypoint Generation in Row-based Crops with Deep Learning and Contrastive Clustering
Francesco Salvetti, Simone Angarano, Mauro Martini, Simone Cerrato, and Marcello Chiaberge
Grasping Partially Occluded Objects Using Autoencoder-Based Point Cloud Inpainting
Alexander Koebler, Ralf Gross, Florian Buettner, and Ingo Thon
Is this bug severe? A text-cum-graph based model for bug severity prediction
Rima Hazra, Arpit Dwivedi, and Animesh Mukherjee
Physically Invertible System Identification for Monitoring System Edges with Unobservability
Jingyi Yuan and Yang Weng
GALG: Linking Addresses in Tracking Ecosystem Using Graph Autoencoder with Link Generation
Tianyu Cui, Gang Xiong, Chang Liu, Junzheng Shi, Peipei Fu, and Gaopeng Gou
Automatic Grading of Student Code with Similarity Measurement
Dongxia Wang, En Zhang, and Xuesong Lu
Meta Hierarchical Reinforced Learning to Rank for Recommendation: A Comprehensive Study in MOOCs
Yuchen Li, Haoyi Xiong, Linghe Kong, Rui Zhang, and Dejing Dou
Recognizing Cognitive Load by a Hybrid Spatio-Temporal Causal Model from Multivariate Physiological Data
Zirui Yong, Guoxin Su, Xiaohu Li, Lingyun Sun, Zejian Li, and Li Liu
Placing (Historical) Facts on a Timeline: A Classification cum Coref Resolution Approach
Sayantan Adak, Altaf Ahmad, Aditya Basu, and Animesh Mukherjee
'John ate 5 apples' != 'John ate some apples': Self-Supervised Paraphrase Quality Detection for Algebraic Word Problems
Rishabh Gupta, Venktesh V, Mukesh Mohania, and Vikram Goyal
Looking Beyond the Past: Analyzing the Intrinsic Playing Style of Soccer Teams
Jeroen Clijmans, Maaike Van Roy, and Jesse Davis
Recognizing Non-Small Cell Lung Cancer Subtypes by a Constraint-Based Causal Network from CT Images
Zhengqiao Deng, Shuang Qian, Jing Qi, Li Liu, and Bo Xu
Detection of ADHD based on Eye Movements during Natural Viewing
Shuwen Deng, Paul Prasse, David R. Reich, Sabine Dziemian, Maja Stegenwallner-Schu
FFBDNet: Feature Fusion and Bipartite Decision Networks for Recommending Medication Combination
Zisen Wang, Ying Liang, and Zhengjun Liu
Towards Federated COVID-19 Vaccine Side Effect Prediction
Jiaqi Wang, Cheng Qian, Suhan Cui, Lucas Glass, and Fenglong Ma
MepoGNN: Metapopulation Epidemic Forecasting with Graph Neural Networks
Qi Cao, Renhe Jiang, Chuang Yang, Zipei Fan, Xuan Song, and Ryosuke Shibasaki
EpiGNN: Exploring Spatial Transmission with Graph Neural Network for Regional Epidemic Forecasting
Feng Xie, Zhong Zhang, Liang Li, Bin Zhou, and Yusong Tan
Route to Time and Time to Route: Travel Time Estimation from Sparse Trajectories
Zhiwen Zhang, Hongjun Wang, Zipei Fan, Jiyuan Chen, Xuan Song, and Ryosuke Shibasaki
Attention, Filling in The Gaps for Generalization in Routing Problems
Ahmad Bdeir, Jonas K. Falkner, and Lars Schmidt-Thieme
Can we Learn from Outliers? Unsupervised Optimization of Intelligent Vehicle Traffic Management Systems
Tom Mertens and Marwan Hassani
A Bayesian Markov Model for Station-Level Origin-Destination Matrix Reconstruction
Victor Amblard, Amir Dib, Noelie Cherrier, and Guillaume Barthe
BusWTE: Realtime Bus Waiting Time Estimation of GPS Missing via Multi-Task Learning
Yuecheng Rong, Jun Liu, Zhilin Xu, Jian Ding, Chuangming Zhang, and Jiaxiang Gao
PathOracle: A Deep Learning Based Trip Planner for Daily Commuters
Md. Tareq Mahmood, Mohammed Eunus Ali, Muhammad Aamir Cheema, Syed Md. Mukit Rashid, and Timos Sellis
Logistics, Graphs, and Transformers: Towards improving Travel Time Estimation
Natalia Semenova, Vadim Porvatov, Vladislav Tishin, Artyom Sosedka, and Vladislav Zamkovoy
Explainable Anomaly Detection System for Categorical Sensor Data in Internet of Things
Peng Yuan, Lu-An Tang, Haifeng Chen, Moto Sato, and Kevin Woodward
AGG: An Automated Genogram Generator by Discovering Information in Clinical Texts
Nuria Garc'i a-Santa and Kendrick Cetina
TAMOR: Tier-Aware Multi-Objective Recommendation for Ant Fortune Financial Marketing
Xu Min, Xiaolu Zhang, Jun Zhou, Changxun Fan, and Junlin Yu
Benchmarking GNNs with GenCAT Workbench
Seiji Maekawa, Yuya Sasaki, George Fletcher, and Makoto Onizuka
SLISEMAP: Combining supervised dimensionality reduction with local explanations
Anton Bjorklund, Makela, and Puolamaki
A camera-based system to detect driver hands on the steering wheel in semi-autonomous vehicles
Raphael Morvillier, Christophe Prat, and Saifeddine Aloui
ADEPT: Anomaly Detection, Explanation and Processing for Time Series with a Focus on Energy Consumption Data
Benedikt Tobias Müller, Marvin Ender, Jan Erik Swiadek, Mengcheng Jin, Simon Winkel, Dominik Niedziela, Bin Li, Jelle Hüntelmann, and Emmanuel Müller
RE-Tagger: A light-weight Real-Estate Image Classifier
Prateek Chhikara, Anil Goyal, and Chirag Sharma
An Embedded Continual Learning System for Facial Emotion Recognition
Olivier Antoni, Marion Mainsant, Christelle Godin, Martial Mermillod, and Marina Reyboz
CAGE: A Hybrid Framework for Closed-Domain Conversational Agents
Edward Burgin, Sourav Dutta, Haytham Assem, and Raj Nath Patel
Cloud-Based Real-Time Molecular Screening Platform with MolFormer
Brian Belgodere, Vijil Chenthamarakshan, Payel Das, Pierre Dognin, Toby Kurien, Igor Melnyk, Youssef Mroueh, Inkit Padhi, Mattia Rigotti, Jarret Ross, Yair Schiff, and Richard A. Young
ImbalancedLearningRegression - A Python package to tackle the imbalanced regression problem.
Wenglei Wu, Nicholas Kunz, and Paula Branco
A Light Weight Cardiac Monitoring System for On-device ECG Analysis
Rohan Banerjee and Avik Ghose
Urban Traveller Preference Miner: modelling transport choices with survey data streams
Maciej Grzenda, Marcin Luckner, and Przemysl aw Wrona
Interactive Toolbox for two-dimensional Gaussian Mixture Modeling
Michael C. Thrun, Quirin Stier, and Alfred Ultsch
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