AISTATS 2025
583 papers
A Causal Framework for Evaluating Deferring Systems
Filippo Palomba, Andrea Pugnana, Jose Manuel Alvarez, Salvatore Ruggieri A Primer on Linear Classification with Missing Data
Angel David REYERO Lobo, Alexis Ayme, Claire Boyer, Erwan Scornet A Subquadratic Time Approximation Algorithm for Individually Fair K-Center
Matthijs Ebbens, Nicole Funk, Jan Höckendorff, Christian Sohler, Vera Weil A Unified Evaluation Framework for Epistemic Predictions
Shireen Kudukkil Manchingal, Muhammad Mubashar, Kaizheng Wang, Fabio Cuzzolin A Unifying Framework for Action-Conditional Self-Predictive Reinforcement Learning
Khimya Khetarpal, Zhaohan Daniel Guo, Bernardo Avila Pires, Yunhao Tang, Clare Lyle, Mark Rowland, Nicolas Heess, Diana L Borsa, Arthur Guez, Will Dabney Active Feature Acquisition for Personalised Treatment Assignment
Julianna Piskorz, Nicolás Astorga, Jeroen Berrevoets, Mihaela Schaar Adapting to Online Distribution Shifts in Deep Learning: A Black-Box Approach
Dheeraj Baby, Boran Han, Shuai Zhang, Cuixiong Hu, Bernie Wang, Yu-Xiang Wang All Models Are Wrong, Some Are Useful: Model Selection with Limited Labels
Patrik Okanovic, Andreas Kirsch, Jannes Kasper, Torsten Hoefler, Andreas Krause, Nezihe Merve Gürel AlleNoise - Large-Scale Text Classification Benchmark Dataset with Real-World Label Noise
Alicja Rączkowska, Aleksandra Osowska-Kurczab, Jacek Szczerbiński, Kalina Jasinska-Kobus, Klaudia Nazarko Amortized Probabilistic Conditioning for Optimization, Simulation and Inference
Paul Edmund Chang, Nasrulloh Ratu Bagus Satrio Loka, Daolang Huang, Ulpu Remes, Samuel Kaski, Luigi Acerbi An Adaptive Method for Weak Supervision with Drifting Data
Alessio Mazzetto, Reza Esfandiarpoor, Akash Singirikonda, Eli Upfal, Stephen Bach Ant Colony Sampling with GFlowNets for Combinatorial Optimization
Minsu Kim, Sanghyeok Choi, Hyeonah Kim, Jiwoo Son, Jinkyoo Park, Yoshua Bengio Approximate Equivariance in Reinforcement Learning
Jung Yeon Park, Sujay Bhatt, Sihan Zeng, Lawson L.S. Wong, Alec Koppel, Sumitra Ganesh, Robin Walters Approximate Information Maximization for Bandit Games
Alex Barbier Chebbah, Christian L. Vestergaard, Jean-Baptiste Masson, Etienne Boursier Automatically Adaptive Conformal Risk Control
Vincent Blot, Anastasios Nikolas Angelopoulos, Michael Jordan, Nicolas J-B. Brunel Axiomatic Explainer Globalness via Optimal Transport
Davin Hill, Joshua Bone, Aria Masoomi, Max Torop, Jennifer Dy Balls-and-Bins Sampling for DP-SGD
Lynn Chua, Badih Ghazi, Charlie Harrison, Pritish Kamath, Ravi Kumar, Ethan Jacob Leeman, Pasin Manurangsi, Amer Sinha, Chiyuan Zhang Bayesian Circular Regression with Von Mises Quasi-Processes
Yarden Cohen, Alexandre Khae Wu Navarro, Jes Frellsen, Richard E. Turner, Raziel Riemer, Ari Pakman Bayesian Gaussian Process ODEs via Double Normalizing Flows
Jian Xu, Shian Du, Junmei Yang, Xinghao Ding, Delu Zeng, John Paisley Behavior-Inspired Neural Networks for Relational Inference
Yulong Yang, Bowen Feng, Keqin Wang, Naomi Leonard, Adji Bousso Dieng, Christine Allen-Blanchette Best-Arm Identification in Unimodal Bandits
Riccardo Poiani, Marc Jourdan, Emilie Kaufmann, Rémy Degenne Bridging the Theoretical Gap in Randomized Smoothing
Blaise Delattre, Paul Caillon, Quentin Barthélemy, Erwan Fagnou, Alexandre Allauzen Causal Discovery-Driven Change Point Detection in Time Series
Shanyun Gao, Raghavendra Addanki, Tong Yu, Ryan A. Rossi, Murat Kocaoglu Choice Is What Matters After Attention
Chenhan Fu, Guoming Wang, Juncheng Li, Rongxing Lu, Siliang Tang ChronosX: Adapting Pretrained Time Series Models with Exogenous Variables
Sebastian Pineda Arango, Pedro Mercado, Shubham Kapoor, Abdul Fatir Ansari, Lorenzo Stella, Huibin Shen, Hugo Henri Joseph Senetaire, Ali Caner Turkmen, Oleksandr Shchur, Danielle C. Maddix, Michael Bohlke-Schneider, Bernie Wang, Syama Sundar Rangapuram Class Imbalance in Anomaly Detection: Learning from an Exactly Solvable Model
Francesco Saverio Pezzicoli, Valentina Ros, François P. Landes, Marco Baity-Jesi Clustered Invariant Risk Minimization
Tomoya Murata, Atsushi Nitanda, Taiji Suzuki Clustering Context in Off-Policy Evaluation
Daniel Guzman Olivares, Philipp Schmidt, Jacek Golebiowski, Artur Bekasov ClusterSC: Advancing Synthetic Control with Donor Selection
Saeyoung Rho, Andrew Tang, Noah Bergam, Rachel Cummings, Vishal Misra Collaborative Non-Parametric Two-Sample Testing
Alejandro David De Concha Duarte, Nicolas Vayatis, Argyris Kalogeratos Composition and Control with Distilled Energy Diffusion Models and Sequential Monte Carlo
James Thornton, Louis Béthune, Ruixiang Zhang, Arwen Bradley, Preetum Nakkiran, Shuangfei Zhai Computation-Aware Kalman Filtering and Smoothing
Marvin Pförtner, Jonathan Wenger, Jon Cockayne, Philipp Hennig Conditional Prediction ROC Bands for Graph Classification
Yujia Wu, Bo Yang, Elynn Chen, Yuzhou Chen, Zheshi Zheng Conditioning Diffusion Models by Explicit Forward-Backward Bridging
Adrien Corenflos, Zheng Zhao, Thomas B. Schön, Simo Särkkä, Jens Sjölund Continuous Structure Constraint Integration for Robust Causal Discovery
Lyuzhou Chen, Taiyu Ban, Derui Lyu, Yijia Sun, Kangtao Hu, Xiangyu Wang, Huanhuan Chen Corruption Robust Offline Reinforcement Learning with Human Feedback
Debmalya Mandal, Andi Nika, Parameswaran Kamalaruban, Adish Singla, Goran Radanovic Cost-Aware Optimal Pairwise Pure Exploration
Di Wu, Chengshuai Shi, Ruida Zhou, Cong Shen Cost-Aware Simulation-Based Inference
Ayush Bharti, Daolang Huang, Samuel Kaski, Francois-Xavier Briol Counting Graphlets of Size K Under Local Differential Privacy
Vorapong Suppakitpaisarn, Donlapark Ponnoprat, Nicha Hirankarn, Quentin Hillebrand Covariance Selection over Networks
Wenfu Xia, Fengpei Li, Ying Sun, Ziping Zhao Credal Two-Sample Tests of Epistemic Uncertainty
Siu Lun Chau, Antonin Schrab, Arthur Gretton, Dino Sejdinovic, Krikamol Muandet Credibility-Aware Multimodal Fusion Using Probabilistic Circuits
Sahil Sidheekh, Pranuthi Tenali, Saurabh Mathur, Erik Blasch, Kristian Kersting, Sriraam Natarajan Decision from Suboptimal Classifiers: Excess Risk Pre- and Post-Calibration
Alexandre Perez-Lebel, Gael Varoquaux, Sanmi Koyejo, Matthieu Doutreligne, Marine Le Morvan Decision-Point Guided Safe Policy Improvement
Abhishek Sharma, Leo Benac, Sonali Parbhoo, Finale Doshi-Velez Deep Generative Quantile Bayes
Jungeum Kim, Percy S. Zhai, Veronika Rockova Deep Optimal Sensor Placement for Black Box Stochastic Simulations
Paula Cordero Encinar, Tobias Schröder, Peter Yatsyshin, Andrew B. Duncan Density Ratio-Based Proxy Causal Learning Without Density Ratios
Bariscan Bozkurt, Ben Deaner, Dimitri Meunier, Liyuan Xu, Arthur Gretton Density-Dependent Group Testing
Rahil Morjaria, Saikiran Bulusu, Venkata Gandikota, Sidharth Jaggi Differentiable Causal Structure Learning with Identifiability by NOTIME
Jeroen Berrevoets, Jakob Raymaekers, Mihaela Schaar, Tim Verdonck, Ruicong Yao Diffusion Models as Constrained Samplers for Optimization with Unknown Constraints
Lingkai Kong, Yuanqi Du, Wenhao Mu, Kirill Neklyudov, Valentin De Bortoli, Dongxia Wu, Haorui Wang, Aaron M Ferber, Yian Ma, Carla P Gomes, Chao Zhang Distributional Adversarial Loss
Saba Ahmadi, Siddharth Bhandari, Avrim Blum, Chen Dan, Prabhav Jain Dynamic DBSCAN with Euler Tour Sequences
Seiyun Shin, Ilan Shomorony, Peter Macgregor Efficient and Asymptotically Unbiased Constrained Decoding for Large Language Models
Haotian Ye, Himanshu Jain, Chong You, Ananda Theertha Suresh, Haowei Lin, James Zou, Felix Yu Efficient Exploitation of Hierarchical Structure in Sparse Reward Reinforcement Learning
Gianluca Drappo, Arnaud Robert, Marcello Restelli, Aldo A. Faisal, Alberto Maria Metelli, Ciara Pike-Burke Efficient Optimization Algorithms for Linear Adversarial Training
Antonio H. Ribeiro, Thomas B. Schön, Dave Zachariah, Francis Bach Enhancing Feature-Specific Data Protection via Bayesian Coordinate Differential Privacy
Maryam Aliakbarpour, Syomantak Chaudhuri, Thomas Courtade, Alireza Fallah, Michael Jordan Estimation of Large Zipfian Distributions with Sort and Snap
Peter Matthew Jacobs, Anirban Bhattacharya, Debdeep Pati, Lekha Patel, Jeff M. Phillips Evidential Uncertainty Probes for Graph Neural Networks
Linlin Yu, Kangshuo Li, Pritom Kumar Saha, Yifei Lou, Feng Chen Explaining ViTs Using Information Flow
Chase Walker, Md Rubel Ahmed, Sumit Kumar Jha, Rickard Ewetz Exposing Privacy Gaps: Membership Inference Attack on Preference Data for LLM Alignment
Qizhang Feng, Siva Rajesh Kasa, Santhosh Kumar Kasa, Hyokun Yun, Choon Hui Teo, Sravan Babu Bodapati Fast Convergence of SoftMax Policy Mirror Ascent
Reza Asad, Reza Babanezhad Harikandeh, Issam H. Laradji, Nicolas Le Roux, Sharan Vaswani Faster WIND: Accelerating Iterative Best-of-$n$ Distillation for LLM Alignment
Tong Yang, Jincheng Mei, Hanjun Dai, Zixin Wen, Shicong Cen, Dale Schuurmans, Yuejie Chi, Bo Dai Feasible Learning
Juan Ramirez, Ignacio Hounie, Juan Elenter, Jose Gallego-Posada, Meraj Hashemizadeh, Alejandro Ribeiro, Simon Lacoste-Julien Fundamental Computational Limits of Weak Learnability in High-Dimensional Multi-Index Models
Emanuele Troiani, Yatin Dandi, Leonardo Defilippis, Lenka Zdeborova, Bruno Loureiro, Florent Krzakala Fundamental Limits of Perfect Concept Erasure
Somnath Basu Roy Chowdhury, Kumar Avinava Dubey, Ahmad Beirami, Rahul Kidambi, Nicholas Monath, Amr Ahmed, Snigdha Chaturvedi Gaussian Mean Testing Under Truncation
Clement Louis Canonne, Themis Gouleakis, Yuhao Wang, Qiping Yang Geometry-Aware Generative Autoencoders for Warped Riemannian Metric Learning and Generative Modeling on Data Manifolds
Xingzhi Sun, Danqi Liao, Kincaid MacDonald, Yanlei Zhang, Guillaume Huguet, Guy Wolf, Ian Adelstein, Tim G. J. Rudner, Smita Krishnaswamy Get Rid of Your Constraints and Reparametrize: A Study in NNLS and Implicit Bias
Hung-Hsu Chou, Johannes Maly, Claudio Mayrink Verdun, Bernardo Freitas Paulo Costa, Heudson Mirandola Hierarchical Bias-Driven Stratification for Interpretable Causal Effect Estimation
Lucile Ter-Minassian, Liran Szlak, Ehud Karavani, Christopher C. Holmes, Yishai Shimoni How Well Can Transformers Emulate In-Context Newton’s Method?
Angeliki Giannou, Liu Yang, Tianhao Wang, Dimitris Papailiopoulos, Jason D. Lee Implicit Diffusion: Efficient Optimization Through Stochastic Sampling
Pierre Marion, Anna Korba, Peter Bartlett, Mathieu Blondel, Valentin De Bortoli, Arnaud Doucet, Felipe Llinares-López, Courtney Paquette, Quentin Berthet Importance-Weighted Positive-Unlabeled Learning for Distribution Shift Adaptation
Atsutoshi Kumagai, Tomoharu Iwata, Hiroshi Takahashi, Taishi Nishiyama, Yasuhiro Fujiwara Independent Learning in Performative Markov Potential Games
Rilind Sahitaj, Paulius Sasnauskas, Yiğit Yalın, Debmalya Mandal, Goran Radanovic Infinite Width Limits of Self Supervised Neural Networks
Maximilian Fleissner, Gautham Govind Anil, Debarghya Ghoshdastidar Infinite-Dimensional Diffusion Bridge Simulation via Operator Learning
Gefan Yang, Elizabeth Louise Baker, Michael Lind Severinsen, Christy Anna Hipsley, Stefan Sommer InfoNCE: Identifying the Gap Between Theory and Practice
Evgenia Rusak, Patrik Reizinger, Attila Juhos, Oliver Bringmann, Roland S. Zimmermann, Wieland Brendel Information Transfer Across Clinical Tasks via Adaptive Parameter Optimisation
Anshul Thakur, Elena Gal, Soheila Molaei, Xiao Gu, Patrick Schwab, Danielle Belgrave, Kim Branson, David A. Clifton Is Gibbs Sampling Faster than Hamiltonian Monte Carlo on GLMs?
Son Luu, Zuheng Xu, Nikola Surjanovic, Miguel Biron-Lattes, Trevor Campbell, Alexandre Bouchard-Cote Koopman-Equivariant Gaussian Processes
Petar Bevanda, Max Beier, Alexandre Capone, Stefan Georg Sosnowski, Sandra Hirche, Armin Lederer Learning High-Dimensional Gaussians from Censored Data
Arnab Bhattacharyya, Constantinos Costis Daskalakis, Themis Gouleakis, Yuhao Wang Learning Identifiable Structures Helps Avoid Bias in DNN-Based Supervised Causal Learning
Jiaru Zhang, Rui Ding, Qiang Fu, Huang Bojun, Zizhen Deng, Yang Hua, Haibing Guan, Shi Han, Dongmei Zhang Learning Laplacian Positional Encodings for Heterophilous Graphs
Michael Ito, Jiong Zhu, Dexiong Chen, Danai Koutra, Jenna Wiens Learning to Negotiate via Voluntary Commitment
Shuhui Zhu, Baoxiang Wang, Sriram Ganapathi Subramanian, Pascal Poupart Learning Visual-Semantic Subspace Representations
Gabriel Moreira, Manuel Marques, Joao Costeira, Alexander G Hauptmann LITE: Efficiently Estimating Gaussian Probability of Maximality
Nicolas Menet, Jonas Hübotter, Parnian Kassraie, Andreas Krause Local Stochastic Sensitivity Analysis for Dynamical Systems
Nishant Panda, Jehanzeb H Chaudhry, Natalie Klein, James Carzon, Troy Butler Locally Optimal Descent for Dynamic Stepsize Scheduling
Gilad Yehudai, Alon Cohen, Amit Daniely, Yoel Drori, Tomer Koren, Mariano Schain Locally Private Sampling with Public Data
Behnoosh Zamanlooy, Mario Diaz, Shahab Asoodeh M-HOF-Opt: Multi-Objective Hierarchical Output Feedback Optimization via Multiplier Induced Loss Landscape Scheduling
Xudong Sun, Nutan Chen, Alexej Gossmann, Yu Xing, Matteo Wohlrapp, Emilio Dorigatti, Carla Feistner, Felix Drost, Daniele Scarcella, Lisa Helen Beer, Carsten Marr Max-Rank: Efficient Multiple Testing for Conformal Prediction
Alexander Timans, Christoph-Nikolas Straehle, Kaspar Sakmann, Christian A. Naesseth, Eric Nalisnick MDP Geometry, Normalization and Reward Balancing Solvers
Arsenii Mustafin, Aleksei Pakharev, Alex Olshevsky, Ioannis Paschalidis Mean-Field Microcanonical Gradient Descent
Marcus Häggbom, Morten Karlsmark, Joakim Andén Memorization in Attention-Only Transformers
Léo Dana, Muni Sreenivas Pydi, Yann Chevaleyre Microfoundation Inference for Strategic Prediction
Daniele Bracale, Subha Maity, Felipe Maia Polo, Seamus Somerstep, Moulinath Banerjee, Yuekai Sun MING: A Functional Approach to Learning Molecular Generative Models
Van Khoa Nguyen, Maciej Falkiewicz, Giangiacomo Mercatali, Alexandros Kalousis Model Selection for Behavioral Learning Data and Applications to Contextual Bandits
Julien Aubert, Louis Köhler, Luc Lehéricy, Giulia Mezzadri, Patricia Reynaud-Bouret MODL: Multilearner Online Deep Learning
Antonios Valkanas, Boris N. Oreshkin, Mark Coates Multi-Agent Credit Assignment with Pretrained Language Models
Wenhao Li, Dan Qiao, Baoxiang Wang, Xiangfeng Wang, Wei Yin, Hao Shen, Bo Jin, Hongyuan Zha Neural Point Processes for Pixel-Wise Regression
Chengzhi Shi, Gözde Özcan, Miquel Sirera Perelló, Yuanyuan Li, Nina Iftikhar Shamsi, Stratis Ioannidis New User Event Prediction Through the Lens of Causal Inference
Henry Yuchi, Shixiang Zhu, Li Dong, Yigit M. Arisoy, Matthew C. Spencer No-Regret Bayesian Optimization with Stochastic Observation Failures
Shogo Iwazaki, Tomohiko Tanabe, Mitsuru Irie, Shion Takeno, Kota Matsui, Yu Inatsu Nonparametric Factor Analysis and Beyond
Yujia Zheng, Yang Liu, Jiaxiong Yao, Yingyao Hu, Kun Zhang Nyström Kernel Stein Discrepancy
Florian Kalinke, Zoltán Szabó, Bharath Sriperumbudur On Local Posterior Structure in Deep Ensembles
Mikkel Jordahn, Jonas Vestergaard Jensen, Mikkel N. Schmidt, Michael Riis Andersen On the Asymptotic Mean Square Error Optimality of Diffusion Models
Benedikt Fesl, Benedikt Böck, Florian Strasser, Michael Baur, Michael Joham, Wolfgang Utschick On the Consistent Recovery of Joint Distributions from Conditionals
Mahbod Majid, Rattana Pukdee, Vishwajeet Agrawal, Burak Varıcı, Pradeep Kumar Ravikumar On the Difficulty of Constructing a Robust and Publicly-Detectable Watermark
Jaiden Fairoze, Guillermo Ortiz-Jimenez, Mel Vecerik, Somesh Jha, Sven Gowal On the Identifiability of Causal Abstractions
Xiusi Li, Sékou-Oumar Kaba, Siamak Ravanbakhsh Optimal Downsampling for Imbalanced Classification with Generalized Linear Models
Yan Chen, Jose Blanchet, Krzysztof Dembczynski, Laura Fee Nern, Aaron Eliasib Flores Optimising Clinical Federated Learning Through Mode Connectivity-Based Model Aggregation
Anshul Thakur, Soheila Molaei, Patrick Schwab, Danielle Belgrave, Kim Branson, David A. Clifton Parabolic Continual Learning
Haoming Yang, Ali Hasan, Vahid Tarokh Paths and Ambient Spaces in Neural Loss Landscapes
Daniel Dold, Julius Kobialka, Nicolai Palm, Emanuel Sommer, David Rügamer, Oliver Dürr Performative Prediction on Games and Mechanism Design
António Góis, Mehrnaz Mofakhami, Fernando P. Santos, Gauthier Gidel, Simon Lacoste-Julien Personalized Convolutional Dictionary Learning of Physiological Time Series
Axel Roques, Samuel Gruffaz, Kyurae Kim, Alain Oliviero Durmus, Laurent Oudre Personalizing Low-Rank Bayesian Neural Networks via Federated Learning
Boning Zhang, Dongzhu Liu, Osvaldo Simeone, Guanchu Wang, Dimitrios Pezaros, Guangxu Zhu Policy Teaching via Data Poisoning in Learning from Human Preferences
Andi Nika, Jonathan Nöther, Debmalya Mandal, Parameswaran Kamalaruban, Adish Singla, Goran Radanovic Posteriordb: Testing, Benchmarking and Developing Bayesian Inference Algorithms
Måns Magnusson, Jakob Torgander, Paul-Christian Bürkner, Lu Zhang, Bob Carpenter, Aki Vehtari Powerful Batch Conformal Prediction for Classification
Ulysse Gazin, Ruth Heller, Etienne Roquain, Aldo Solari Prediction-Centric Uncertainty Quantification via MMD
Zheyang Shen, Jeremias Knoblauch, Samuel Power, Chris J. Oates Prior-Fitted Networks Scale to Larger Datasets When Treated as Weak Learners
Yuxin Wang, Botian Jiang, Yiran Guo, Quan Gan, David Wipf, Xuanjing Huang, Xipeng Qiu Privacy in Metalearning and Multitask Learning: Modeling and Separations
Maryam Aliakbarpour, Konstantina Bairaktari, Adam Smith, Marika Swanberg, Jonathan Ullman Provable Benefits of Task-Specific Prompts for In-Context Learning
Xiangyu Chang, Yingcong Li, Muti Kara, Samet Oymak, Amit Roy-Chowdhury Proximal Sampler with Adaptive Step Size
Bo Yuan, Jiaojiao Fan, Jiaming Liang, Yongxin Chen Pure Exploration with Feedback Graphs
Alessio Russo, Yichen Song, Aldo Pacchiano Q-Function Decomposition with Intervention Semantics for Factored Action Spaces
Junkyu Lee, Tian Gao, Elliot Nelson, Miao Liu, Debarun Bhattacharjya, Songtao Lu Quantifying Knowledge Distillation Using Partial Information Decomposition
Pasan Dissanayake, Faisal Hamman, Barproda Halder, Ilia Sucholutsky, Qiuyi Zhang, Sanghamitra Dutta Quantile Additive Trend Filtering
Zhi Zhang, Kyle Ritscher, Oscar Hernan Madrid Padilla Rate of Model Collapse in Recursive Training
Ananda Theertha Suresh, Andrew Thangaraj, Aditya Nanda Kishore Khandavally Reinforcement Learning for Adaptive MCMC
Congye Wang, Wilson Ye Chen, Heishiro Kanagawa, Chris J. Oates Restructuring Tractable Probabilistic Circuits
Honghua Zhang, Benjie Wang, Marcelo Arenas, Guy Van Broeck RetroDiff: Retrosynthesis as Multi-Stage Distribution Interpolation
Yiming Wang, Yuxuan Song, Yiqun Wang, Minkai Xu, Rui Wang, Hao Zhou, Wei-Ying Ma Riemann$^2$: Learning Riemannian Submanifolds from Riemannian Data
Leonel Rozo, Miguel González-Duque, Noémie Jaquier, Søren Hauberg Risk-Sensitive Bandits: Arm Mixture Optimality and Regret-Efficient Algorithms
Meltem Tatlı, Arpan Mukherjee, L. A. Prashanth, Karthikeyan Shanmugam, Ali Tajer Robust Fair Clustering with Group Membership Uncertainty Sets
Sharmila Duppala, Juan Luque, John P Dickerson, Seyed A. Esmaeili Robust Score Matching
Richard Schwank, Andrew McCormack, Mathias Drton RTD-Lite: Scalable Topological Analysis for Comparing Weighted Graphs in Learning Tasks
Eduard Tulchinskii, Daria Voronkova, Ilya Trofimov, Evgeny Burnaev, Serguei Barannikov S-CFE: Simple Counterfactual Explanations
Shpresim Sadiku, Moritz Wagner, Sai Ganesh Nagarajan, Sebastian Pokutta Safe Exploration in Reproducing Kernel Hilbert Spaces
Abdullah Tokmak, Kiran G. Krishnan, Thomas B. Schön, Dominik Baumann Scalable Implicit Graphon Learning
Ali Azizpour, Nicolas Zilberstein, Santiago Segarra Scalable Out-of-Distribution Robustness in the Presence of Unobserved Confounders
Parjanya Prajakta Prashant, Seyedeh Baharan Khatami, Bruno Ribeiro, Babak Salimi Sequential Kernelized Stein Discrepancy
Diego Martinez-Taboada, Aaditya Ramdas Signature Isolation Forest
Marta Campi, Guillaume Staerman, Gareth W. Peters, Tomoko Masui Signed Graph Autoencoder for Explainable and Polarization-Aware Network Embeddings
Nikolaos Nakis, Chrysoula Kosma, Giannis Nikolentzos, Michail Chatzianastasis, Iakovos Evdaimon, Michalis Vazirgiannis Sparse Activations as Conformal Predictors
Margarida M Campos, João Cálem, Sophia Sklaviadis, Mario A. T. Figueiredo, Andre Martins Statistical Guarantees for Lifelong Reinforcement Learning Using PAC-Bayes Theory
Zhi Zhang, Chris Chow, Yasi Zhang, Yanchao Sun, Haochen Zhang, Eric Hanchen Jiang, Han Liu, Furong Huang, Yuchen Cui, Oscar Hernan Madrid Padilla Statistical Test for Auto Feature Engineering by Selective Inference
Tatsuya Matsukawa, Tomohiro Shiraishi, Shuichi Nishino, Teruyuki Katsuoka, Ichiro Takeuchi SteinDreamer: Variance Reduction for Text-to-3D Score Distillation via Stein Identity
Peihao Wang, Zhiwen Fan, Dejia Xu, Dilin Wang, Sreyas Mohan, Forrest Iandola, Rakesh Ranjan, Yilei Li, Qiang Liu, Zhangyang Wang, Vikas Chandra Steinmetz Neural Networks for Complex-Valued Data
Shyam Venkatasubramanian, Ali Pezeshki, Vahid Tarokh Stochastic Weight Sharing for Bayesian Neural Networks
Moule Lin, Shuhao Guan, Weipeng Jing, Goetz Botterweck, Andrea Patane Strategic Conformal Prediction
Daniel Csillag, Claudio Jose Struchiner, Guilherme Tegoni Goedert The Hardness of Validating Observational Studies with Experimental Data
Jake Fawkes, Michael O’Riordan, Athanasios Vlontzos, Oriol Corcoll, Ciarán Mark Gilligan-Lee The Sample Complexity of Stackelberg Games
Francesco Bacchiocchi, Matteo Bollini, Matteo Castiglioni, Alberto Marchesi, Nicola Gatti The VampPrior Mixture Model
Andrew A. Stirn, David A. Knowles Theoretical Convergence Guarantees for Variational Autoencoders
Sobihan Surendran, Antoine Godichon-Baggioni, Sylvain Le Corff Time-Series Attribution Maps with Regularized Contrastive Learning
Steffen Schneider, Rodrigo González Laiz, Anastasiia Filippova, Markus Frey, Mackenzie W Mathis Towards Fair Graph Learning Without Demographic Information
Zichong Wang, Nhat Hoang, Xingyu Zhang, Kevin Bello, Xiangliang Zhang, Sundararaja Sitharama Iyengar, Wenbin Zhang Training LLMs with MXFP4
Albert Tseng, Tao Yu, Youngsuk Park Training Neural Samplers with Reverse Diffusive KL Divergence
Jiajun He, Wenlin Chen, Mingtian Zhang, David Barber, José Miguel Hernández-Lobato Transfer Neyman-Pearson Algorithm for Outlier Detection
Mohammadreza Mousavi Kalan, Eitan J. Neugut, Samory Kpotufe Transformers Are Provably Optimal In-Context Estimators for Wireless Communications
Vishnu Teja Kunde, Vicram Rajagopalan, Chandra Shekhara Kaushik Valmeekam, Krishna Narayanan, Jean-Francois Chamberland, Dileep Kalathil, Srinivas Shakkottai Understanding the Effect of GCN Convolutions in Regression Tasks
Juntong Chen, Johannes Schmidt-Hieber, Claire Donnat, Olga Klopp UNHaP: Unmixing Noise from Hawkes Processes
Virginie Loison, Guillaume Staerman, Thomas Moreau Unifying Feature-Based Explanations with Functional ANOVA and Cooperative Game Theory
Fabian Fumagalli, Maximilian Muschalik, Eyke Hüllermeier, Barbara Hammer, Julia Herbinger Unveiling the Role of Randomization in Multiclass Adversarial Classification: Insights from Graph Theory
Lucas Gnecco Heredia, Matteo Sammut, Muni Sreenivas Pydi, Rafael Pinot, Benjamin Negrevergne, Yann Chevaleyre Variational Combinatorial Sequential Monte Carlo for Bayesian Phylogenetics in Hyperbolic Space
Alex Chen, Philippe Chlenski, Kenneth Munyuza, Antonio Khalil Moretti, Christian A. Naesseth, Itsik Pe’er Variational Schrödinger Momentum Diffusion
Kevin Rojas, Yixin Tan, Molei Tao, Yuriy Nevmyvaka, Wei Deng Weighted Sum of Gaussian Process Latent Variable Models
James A C Odgers, Ruby Sedgwick, Chrysoula Dimitra Kappatou, Ruth Misener, Sarah Lucie Filippi