MLJ 2023

164 papers

${{\mathrm {Latent}}Out}$: An Unsupervised Deep Anomaly Detection Approach Exploiting Latent Space Distribution Fabrizio Angiulli, Fabio Fassetti, Luca Ferragina

A Bayesian-Inspired, Deep Learning-Based, Semi-Supervised Domain Adaptation Technique for Land Cover Mapping Benjamin Lucas, Charlotte Pelletier, Daniel F. Schmidt, Geoffrey I. Webb, François Petitjean

A Comparison of Proximity-Based Methods for Detecting Temporal Anomalies in Business Processes Ioannis Mavroudopoulos, Anastasios Gounaris

A Deep Learning Approach Using Natural Language Processing and Time-Series Forecasting Towards Enhanced Food Safety Georgios Makridis, Philip Mavrepis, Dimosthenis Kyriazis

A Family of Pairwise Multi-Marginal Optimal Transports That Define a Generalized Metric Liang Mi, Azadeh Sheikholeslami, José Bento

A Geometric Framework for Multiclass Ensemble Classifiers Shengli Wu, Jinlong Li, Weimin Ding

A Hybrid Ensemble Method with Negative Correlation Learning for Regression Yun Bai, Ganglin Tian, Yanfei Kang, Suling Jia

A Moment-Matching Metric for Latent Variable Generative Models Cédric Beaulac

A New Large-Scale Learning Algorithm for Generalized Additive Models Bin Gu, Chenkang Zhang, Zhouyuan Huo, Heng Huang

A Parameter-Less Algorithm for Tensor Co-Clustering Elena Battaglia, Ruggero G. Pensa

A Viable Framework for Semi-Supervised Learning on Realistic Dataset Hao Chang, Guochen Xie, Jun Yu, Qiang Ling, Fang Gao, Ye Yu

A3T: Accuracy Aware Adversarial Training Enes Altinisik, Safa Messaoud, Husrev Taha Sencar, Sanjay Chawla

Adversarial Concept Drift Detection Under Poisoning Attacks for Robust Data Stream Mining Lukasz Korycki, Bartosz Krawczyk

Adversarial Learning for Counterfactual Fairness Vincent Grari, Sylvain Lamprier, Marcin Detyniecki

Adversarial Supervised Contrastive Learning Zhuorong Li, Daiwei Yu, Minghui Wu, Canghong Jin, Hongchuan Yu

Adversarial Vulnerability Bounds for Gaussian Process Classification Michael Thomas Smith, Kathrin Grosse, Michael Backes, Mauricio A. Álvarez

Algorithm Selection on a Meta Level Alexander Tornede, Lukas Gehring, Tanja Tornede, Marcel Wever, Eyke Hüllermeier

Also for K-Means: More Data Does Not Imply Better Performance Marco Loog, Jesse H. Krijthe, Manuele Bicego

An Accelerated Proximal Algorithm for Regularized Nonconvex and Nonsmooth Bi-Level Optimization Ziyi Chen, Bhavya Kailkhura, Yi Zhou

An Effective Negative Sampling Approach for Contrastive Learning of Sentence Embedding Qitao Tan, Xiaoying Song, Guanghui Ye, Chuan Wu

An Instance-Dependent Simulation Framework for Learning with Label Noise Keren Gu, Xander Masotto, Vandana Bachani, Balaji Lakshminarayanan, Jack Nikodem, Dong Yin

Are LSTMs Good Few-Shot Learners? Mike Huisman, Thomas M. Moerland, Aske Plaat, Jan N. van Rijn

Autoencoding Slow Representations for Semi-Supervised Data-Efficient Regression Oliver Struckmeier, Kshitij Tiwari, Ville Kyrki

Automated Imbalanced Classification via Layered Learning Vítor Cerqueira, Luís Torgo, Paula Branco, Colin Bellinger

Bayesian Optimization with Safety Constraints: Safe and Automatic Parameter Tuning in Robotics Felix Berkenkamp, Andreas Krause, Angela P. Schoellig

Beyond Confusion Matrix: Learning from Multiple Annotators with Awareness of Instance Features Jingzheng Li, Hailong Sun, Jiyi Li

Bimodal Variational Autoencoder for Audiovisual Speech Recognition Hadeer M. Sayed, Hesham E. ElDeeb, Shereen A. Taie

Biquality Learning: A Framework to Design Algorithms Dealing with Closed-Set Distribution Shifts Pierre Nodet, Vincent Lemaire, Alexis Bondu, Antoine Cornuéjols

Boundary-Restricted Metric Learning Shuo Chen, Chen Gong, Xiang Li, Jian Yang, Gang Niu, Masashi Sugiyama

Byzantine-Robust Distributed Sparse Learning for M-Estimation Jiyuan Tu, Weidong Liu, Xiaojun Mao

Can Language Models Automate Data Wrangling? Gonzalo Jaimovitch-López, Cèsar Ferri, José Hernández-Orallo, Fernando Martínez-Plumed, María José Ramírez-Quintana

Causal Discovery with a Mixture of DAGs Eric V. Strobl

Cautious Policy Programming: Exploiting KL Regularization for Monotonic Policy Improvement in Reinforcement Learning Lingwei Zhu, Takamitsu Matsubara

Circular-Symmetric Correlation Layer Bahar Azari, Deniz Erdogmus

Classifier Calibration: A Survey on How to Assess and Improve Predicted Class Probabilities Telmo de Menezes e Silva Filho, Hao Song, Miquel Perelló-Nieto, Raúl Santos-Rodríguez, Meelis Kull, Peter A. Flach

Considerations When Learning Additive Explanations for Black-Box Models Sarah Tan, Giles Hooker, Paul Koch, Albert Gordo, Rich Caruana

Constrained Regret Minimization for Multi-Criterion Multi-Armed Bandits Anmol Kagrecha, Jayakrishnan Nair, Krishna Jagannathan

Contrastive Counterfactual Visual Explanations with Overdetermination Adam White, Kwun Ho Ngan, James Phelan, Kevin Ryan, Saman Sadeghi Afgeh, Constantino Carlos Reyes-Aldasoro, Artur S. d'Avila Garcez

Correction to: Model-Free Inverse Reinforcement Learning with Multi-Intention, Unlabeled, and Overlapping Demonstrations Ariyan Bighashdel, Pavol Jancura, Gijs Dubbelman

Correlated Product of Experts for Sparse Gaussian Process Regression Manuel Schürch, Dario Azzimonti, Alessio Benavoli, Marco Zaffalon

Cross-Model Consensus of Explanations and Beyond for Image Classification Models: An Empirical Study Xuhong Li, Haoyi Xiong, Siyu Huang, Shilei Ji, Dejing Dou

DAFS: A Domain Aware Few Shot Generative Model for Event Detection Nan Xia, Hang Yu, Yin Wang, Junyu Xuan, Xiangfeng Luo

Data Driven Discovery of Systems of Ordinary Differential Equations Using Nonconvex Multitask Learning Clément Lejeune, Josiane Mothe, Adil Soubki, Olivier Teste

Data-Aware Process Discovery for Malware Detection: An Empirical Study Mario Luca Bernardi, Marta Cimitile, Fabrizio Maria Maggi

Decentralized Bayesian Learning with Metropolis-Adjusted Hamiltonian Monte Carlo Vyacheslav Kungurtsev, Adam D. Cobb, Tara Javidi, Brian Jalaian

Deep Intrinsically Motivated Exploration in Continuous Control Baturay Saglam, Suleyman S. Kozat

Deep Learning's Shallow Gains: A Comparative Evaluation of Algorithms for Automatic Music Generation Zongyu Yin, Federico Reuben, Susan Stepney, Tom Collins

Diametrical Risk Minimization: Theory and Computations Matthew D. Norton, Johannes O. Royset

Differentiable Learning of Matricized DNFs and Its Application to Boolean Networks Taisuke Sato, Katsumi Inoue

Distilling Ensemble of Explanations for Weakly-Supervised Pre-Training of Image Segmentation Models Xuhong Li, Haoyi Xiong, Yi Liu, Dingfu Zhou, Zeyu Chen, Yaqing Wang, Dejing Dou

Diverse and Consistent Multi-View Networks for Semi-Supervised Regression Cuong Manh Nguyen, Arun Raja, Le Zhang, Xun Xu, Balagopal Unnikrishnan, Mohamed Ragab, Kangkang Lu, Chuan-Sheng Foo

Domain Adversarial Neural Networks for Domain Generalization: When It Works and How to Improve Anthony Sicilia, Xingchen Zhao, Seong Jae Hwang

Dynamic Customer Segmentation via Hierarchical Fragmentation-Coagulation Processes Ling Luo, Bin Li, Xuhui Fan, Yang Wang, Irena Koprinska, Fang Chen

Early Anomaly Detection in Time Series: A Hierarchical Approach for Predicting Critical Health Episodes Vítor Cerqueira, Luís Torgo, Carlos Soares

Efficient Generator of Mathematical Expressions for Symbolic Regression Sebastian Meznar, Saso Dzeroski, Ljupco Todorovski

Efficient Learning of Large Sets of Locally Optimal Classification Rules Van Quoc Phuong Huynh, Johannes Fürnkranz, Florian Beck

Embedding to Reference T-SNE Space Addresses Batch Effects in Single-Cell Classification Pavlin G. Policar, Martin Strazar, Blaz Zupan

Ensemble and Continual Federated Learning for Classification Tasks Fernando E. Casado, Dylan Lema, Roberto Iglesias, Carlos Vázquez Regueiro, Senén Barro

Explaining Classifiers by Constructing Familiar Concepts Johannes Schneider, Michalis Vlachos

Explaining Short Text Classification with Diverse Synthetic Exemplars and Counter-Exemplars Orestis Lampridis, Laura State, Riccardo Guidotti, Salvatore Ruggieri

Explanatory Machine Learning for Sequential Human Teaching Lun Ai, Johannes Langer, Stephen H. Muggleton, Ute Schmid

Explicit Explore, Exploit, or Escape (e4): Near-Optimal Safety-Constrained Reinforcement Learning in Polynomial Time David M. Bossens, Nicholas Bishop

FAC-Fed: Federated Adaptation for Fairness and Concept Drift Aware Stream Classification Maryam Badar, Wolfgang Nejdl, Marco Fisichella

FairSwiRL: Fair Semi-Supervised Classification with Representation Learning Shuyi Yang, Mattia Cerrato, Dino Ienco, Ruggero G. Pensa, Roberto Esposito

Faster Riemannian Newton-Type Optimization by Subsampling and Cubic Regularization Yian Deng, Tingting Mu

Feature Ranking for Semi-Supervised Learning Matej Petkovic, Saso Dzeroski, Dragi Kocev

FFNSL: Feed-Forward Neural-Symbolic Learner Daniel Cunnington, Mark Law, Jorge Lobo, Alessandra Russo

Forecasting Upper Atmospheric Scalars Advection Using Deep Learning: An O3 Experiment Luiz Angelo Steffenel, Vagner Anabor, Damaris Kirsch Pinheiro, Lissette Guzman, Gabriela Dornelles Bittencourt, Hassan Bencherif

Fully Convolutional Open Set Segmentation Hugo N. Oliveira, Caio C. V. da Silva, Gabriel L. S. Machado, Keiller Nogueira, Jefersson A. dos Santos

Generalizing P-Laplacian: Spectral Hypergraph Theory and a Partitioning Algorithm Shota Saito, Mark Herbster

Generalizing Universal Adversarial Perturbations for Deep Neural Networks Yanghao Zhang, Wenjie Ruan, Fu Wang, Xiaowei Huang

Generating Probabilistic Safety Guarantees for Neural Network Controllers Sydney M. Katz, Kyle D. Julian, Christopher A. Strong, Mykel J. Kochenderfer

Global Optimization of Objective Functions Represented by ReLU Networks Christopher A. Strong, Haoze Wu, Aleksandar Zeljic, Kyle D. Julian, Guy Katz, Clark W. Barrett, Mykel J. Kochenderfer

Heterogeneous Graph Embedding with Single-Level Aggregation and Infomax Encoding Nuttapong Chairatanakul, Xin Liu, Nguyen Thai Hoang, Tsuyoshi Murata

Heterogeneous Multi-Task Gaussian Cox Processes Feng Zhou, Quyu Kong, Zhijie Deng, Fengxiang He, Peng Cui, Jun Zhu

Heuristic Search of Optimal Machine Teaching Curricula Manuel Garcia-Piqueras, José Hernández-Orallo

Hierarchically Structured Task-Agnostic Continual Learning Heinke Hihn, Daniel A. Braun

How to Be Fair? a Study of Label and Selection Bias Marco Favier, Toon Calders, Sam Pinxteren, Jonathan Meyer

Imbalanced Regression Using Regressor-Classifier Ensembles Oghenejokpeme I. Orhobor, Nastasiya F. Grinberg, Larisa N. Soldatova, Ross D. King

Improving Fairness Generalization Through a Sample-Robust Optimization Method Julien Ferry, Ulrich Aïvodji, Sébastien Gambs, Marie-José Huguet, Mohamed Siala

Incremental Permutation Feature Importance (iPFI): Towards Online Explanations on Data Streams Fabian Fumagalli, Maximilian Muschalik, Eyke Hüllermeier, Barbara Hammer

Inference over Radiative Transfer Models Using Variational and Expectation Maximization Methods Daniel Heestermans Svendsen, Daniel Hernández-Lobato, Luca Martino, Valero Laparra, Álvaro Moreno-Martínez, Gustau Camps-Valls

Information Bottleneck and Selective Noise Supervision for Zero-Shot Learning Lei Zhou, Yang Liu, Pengcheng Zhang, Xiao Bai, Lin Gu, Jun Zhou, Yazhou Yao, Tatsuya Harada, Jin Zheng, Edwin R. Hancock

Interpreting Machine-Learning Models in Transformed Feature Space with an Application to Remote-Sensing Classification Alexander Brenning

Inverse Learning in Hilbert Scales Abhishake Rastogi, Peter Mathé

Inverse Reinforcement Learning Through Logic Constraint Inference Mattijs Baert, Sam Leroux, Pieter Simoens

LADDER: Latent Boundary-Guided Adversarial Training Xiaowei Zhou, Ivor W. Tsang, Jie Yin

Lagrangian Objective Function Leads to Improved Unforeseen Attack Generalization Mohammad Azizmalayeri, Mohammad Hossein Rohban

Large Scale Multi-Output Multi-Class Classification Using Gaussian Processes Chunchao Ma, Mauricio A. Álvarez

Learning Biologically-Interpretable Latent Representations for Gene Expression Data Ioulia Karagiannaki, Krystallia Gourlia, Vincenzo Lagani, Yannis Pantazis, Ioannis Tsamardinos

Learning Domain Invariant Representations of Heterogeneous Image Data Mihailo Obrenovic, Thomas Andrew Lampert, Milos R. Ivanovic, Pierre Gançarski

Learning from Crowds with Sparse and Imbalanced Annotations Ye Shi, Shao-Yuan Li, Sheng-Jun Huang

Learning from Self-Discrepancy via Multiple Co-Teaching for Cross-Domain Person Re-Identification Suncheng Xiang, Yuzhuo Fu, Mengyuan Guan, Ting Liu

Learning Key Steps to Attack Deep Reinforcement Learning Agents Chien-Min Yu, Ming-Hsin Chen, Hsuan-Tien Lin

Learning Logic Programs by Explaining Their Failures Rolf Morel, Andrew Cropper

Learning Multi-Agent Coordination Through Connectivity-Driven Communication Emanuele Pesce, Giovanni Montana

Learning Programs with Magic Values Céline Hocquette, Andrew Cropper

Learning System Parameters from Turing Patterns David Schnörr, Christoph Schnörr

Learning to Increase the Power of Conditional Randomization Tests Shalev Shaer, Yaniv Romano

Limits of Multi-Relational Graphs Juan Alvarado, Yuyi Wang, Jan Ramon

Local2Global: A Distributed Approach for Scaling Representation Learning on Graphs Lucas G. S. Jeub, Giovanni Colavizza, Xiaowen Dong, Marya Bazzi, Mihai Cucuringu

Machine Learning from Casual Conversation Awrad Mohammed Ali, Avelino J. Gonzalez

Machine Truth Serum: A Surprisingly Popular Approach to Improving Ensemble Methods Tianyi Luo, Yang Liu

MAP Inference Algorithms Without Approximation for Collective Graphical Models on Path Graphs via Discrete Difference of Convex Algorithm Yasunori Akagi, Naoki Marumo, Hideaki Kim, Takeshi Kurashima, Hiroyuki Toda

MapFlow: Latent Transition via Normalizing Flow for Unsupervised Domain Adaptation Hossein Askari, Yasir Latif, Hongfu Sun

Metrics and Methods for Robustness Evaluation of Neural Networks with Generative Models Igor Buzhinsky, Arseny Nerinovsky, Stavros Tripakis

Mirror Variational Transport: A Particle-Based Algorithm for Distributional Optimization on Constrained Domains Dai Hai Nguyen, Tetsuya Sakurai

Model-Free Inverse Reinforcement Learning with Multi-Intention, Unlabeled, and Overlapping Demonstrations Ariyan Bighashdel, Pavol Jancura, Gijs Dubbelman

Multi-Armed Bandits with Censored Consumption of Resources Viktor Bengs, Eyke Hüllermeier

Multiclass Optimal Classification Trees with SVM-Splits Víctor Blanco, Alberto Japón, Justo Puerto

Multimodal Deep Learning for Cetacean Distribution Modeling of Fin Whales (Balaenoptera Physalus) in the Western Mediterranean Sea Dorian Cazau, Paul Nguyen Hong Duc, J.-N. Druon, S. Matwins, Ronan Fablet

Multiscale Principle of Relevant Information for Hyperspectral Image Classification Yantao Wei, Shujian Yu, Luis G. Sánchez Giraldo, José C. Príncipe

NaCL: Noise-Robust Cross-Domain Contrastive Learning for Unsupervised Domain Adaptation Jingzheng Li, Hailong Sun

Naive Automated Machine Learning Felix Mohr, Marcel Wever

Neural Predictor-Based Automated Graph Classifier Framework Babatounde Moctard Oloulade, Jianliang Gao, Jiamin Chen, Raeed Al-Sabri, Tengfei Lyu

On the Discrepancy Between Kleinberg's Clustering Axioms and K-Means Clustering Algorithm Behavior Mieczyslaw Alojzy Klopotek, Robert Albert Klopotek

On the Sample Complexity of Actor-Critic Method for Reinforcement Learning with Function Approximation Harshat Kumar, Alec Koppel, Alejandro Ribeiro

Online AutoML: An Adaptive AutoML Framework for Online Learning Bilge Celik, Prabhant Singh, Joaquin Vanschoren

Online Learning of Network Bottlenecks via Minimax Paths Niklas Åkerblom, Fazeleh Sadat Hoseini, Morteza Haghir Chehreghani

PAC-Learning with Approximate Predictors Andrew James Turner, Ata Kabán

Parameter Identifiability of a Deep Feedforward ReLU Neural Network Joachim Bona-Pellissier, François Bachoc, François Malgouyres

Partially Hidden Markov Chain Multivariate Linear Autoregressive Model: Inference and Forecasting - Application to Machine Health Prognostics Fatoumata Dama, Christine Sinoquet

Positive-Unlabeled Classification Under Class-Prior Shift: A Prior-Invariant Approach Based on Density Ratio Estimation Shota Nakajima, Masashi Sugiyama

Pruning During Training by Network Efficacy Modeling Mohit Rajpal, Yehong Zhang, Bryan Kian Hsiang Low

Ranking-Preserved Generative Label Enhancement Yunan Lu, Weiwei Li, Huaxiong Li, Xiuyi Jia

Reconciling Privacy and Utility: An Unscented Kalman Filter-Based Framework for Differentially Private Machine Learning Kunsheng Tang, Ping Li, Yide Song, Tian Luo

Reducing Classifier Overconfidence Against Adversaries Through Graph Algorithms Leonardo Teixeira, Brian Jalaian, Bruno Ribeiro

Refining Neural Network Predictions Using Background Knowledge Alessandro Daniele, Emile van Krieken, Luciano Serafini, Frank van Harmelen

Relational Data Embeddings for Feature Enrichment with Background Information Alexis Cvetkov-Iliev, Alexandre Allauzen, Gaël Varoquaux

Responsible Model Deployment via Model-Agnostic Uncertainty Learning Preethi Lahoti, P. Krishna Gummadi, Gerhard Weikum

ROAD-R: The Autonomous Driving Dataset with Logical Requirements Eleonora Giunchiglia, Mihaela Catalina Stoian, Salman Khan, Fabio Cuzzolin, Thomas Lukasiewicz

Robust Estimation in Regression and Classification Methods for Large Dimensional Data Chunming Zhang, Lixing Zhu, Yanbo Shen

Robust Federated Learning Under Statistical Heterogeneity via Hessian-Weighted Aggregation Adnan Ahmad, Wei Luo, Antonio Robles-Kelly

Robust Generative Adversarial Network Shufei Zhang, Zhuang Qian, Kaizhu Huang, Rui Zhang, Jimin Xiao, Yuan He, Canyi Lu

Robust Matrix Estimations Meet Frank-Wolfe Algorithm Naimin Jing, Ethan X. Fang, Cheng Yong Tang

Root-Finding Approaches for Computing Conformal Prediction Set Eugène Ndiaye, Ichiro Takeuchi

SAED: Self-Attentive Energy Disaggregation Nikolaos Virtsionis Gkalinikis, Christoforos Nalmpantis, Dimitris Vrakas

Safety-Constrained Reinforcement Learning with a Distributional Safety Critic Qisong Yang, Thiago D. Simão, Simon H. Tindemans, Matthijs T. J. Spaan

Satellite Derived Bathymetry Using Deep Learning Mahmoud Al Najar, Gregoire Thoumyre, Erwin W. J. Bergsma, Rafael Almar, Rachid Benshila, Dennis G. Wilson

Scalable Clustering of Segmented Trajectories Within a Continuous Time Framework: Application to Maritime Traffic Data Pierre Gloaguen, Laetitia Chapel, Chloé Friguet, Romain Tavenard

Scale-Preserving Automatic Concept Extraction (SPACE) Andres Felipe Posada-Moreno, Lukas Kreisköther, Tassilo Glander, Sebastian Trimpe

Scenic: A Language for Scenario Specification and Data Generation Daniel J. Fremont, Edward Kim, Tommaso Dreossi, Shromona Ghosh, Xiangyu Yue, Alberto L. Sangiovanni-Vincentelli, Sanjit A. Seshia

SETAR-Tree: A Novel and Accurate Tree Algorithm for Global Time Series Forecasting Rakshitha Godahewa, Geoffrey I. Webb, Daniel F. Schmidt, Christoph Bergmeir

Shift of Pairwise Similarities for Data Clustering Morteza Haghir Chehreghani

SLISEMAP: Supervised Dimensionality Reduction Through Local Explanations Anton Björklund, Jarmo Mäkelä, Kai Puolamäki

Spike2CGR: An Efficient Method for Spike Sequence Classification Using Chaos Game Representation Taslim Murad, Sarwan Ali, Imdadullah Khan, Murray Patterson

State-Based Episodic Memory for Multi-Agent Reinforcement Learning Xiao Ma, Wu-Jun Li

STUDD: A Student-Teacher Method for Unsupervised Concept Drift Detection Vítor Cerqueira, Heitor Murilo Gomes, Albert Bifet, Luís Torgo

Surrogate Models of Radiative Transfer Codes for Atmospheric Trace Gas Retrievals from Satellite Observations Jure Brence, Jovan Tanevski, Jennifer Adams, Edward Malina, Saso Dzeroski

Synthesizing Explainable Counterfactual Policies for Algorithmic Recourse with Program Synthesis Giovanni De Toni, Bruno Lepri, Andrea Passerini

Tensor Completion with Noisy Side Information Dimitris Bertsimas, Colin Pawlowski

The Role of Mutual Information in Variational Classifiers Matías Vera, Leonardo Rey Vega, Pablo Piantanida

Trajectory Test-Train Overlap in Next-Location Prediction Datasets Massimiliano Luca, Luca Pappalardo, Bruno Lepri, Gianni Barlacchi

Tree-Based Dynamic Classifier Chains Eneldo Loza Mencía, Moritz Kulessa, Simon Bohlender, Johannes Fürnkranz

Trimming Stability Selection Increases Variable Selection Robustness Tino Werner

Troubleshooting Image Segmentation Models with Human-in-the-Loop Haotao Wang, Tianlong Chen, Zhangyang Wang, Kede Ma

UCoDe: Unified Community Detection with Graph Convolutional Networks Atefeh Moradan, Andrew Draganov, Davide Mottin, Ira Assent

Unified SVM Algorithm Based on LS-DC Loss Shuisheng Zhou, Wendi Zhou

Unsupervised Discretization by Two-Dimensional MDL-Based Histogram Lincen Yang, Mitra Baratchi, Matthijs van Leeuwen

Weakly Supervised Change Detection Using Guided Anisotropic Diffusion Rodrigo Caye Daudt, Bertrand Le Saux, Alexandre Boulch, Yann Gousseau

WEASEL 2.0: A Random Dilated Dictionary Transform for Fast, Accurate and Memory Constrained Time Series Classification Patrick Schäfer, Ulf Leser

Weighted Neural Tangent Kernel: A Generalized and Improved Network-Induced Kernel Lei Tan, Shutong Wu, Wenxing Zhou, Xiaolin Huang

WINTENDED: WINdowed TENsor Decomposition for Densification Event Detection in Time-Evolving Networks Sofia Fernandes, Hadi Fanaee-T, João Gama, Leo Tisljaric, Tomislav Smuc

αILP: Thinking Visual Scenes as Differentiable Logic Programs Hikaru Shindo, Viktor Pfanschilling, Devendra Singh Dhami, Kristian Kersting