| 2025 | RTD-Lite: Scalable Topological Analysis for Comparing Weighted Graphs in Learning Tasks. | Eduard Tulchinskii, Daria Voronkova, Ilya Trofimov, Evgeny Burnaev, Serguei Barannikov |
| 2025 | Fair Resource Allocation in Weakly Coupled Markov Decision Processes. | Xiaohui Tu, Yossiri Adulyasak, Nima Akbarzadeh, Erick Delage |
| 2025 | Training LLMs with MXFP4. | Albert Tseng, Tao Yu, Youngsuk Park |
| 2025 | Fundamental computational limits of weak learnability in high-dimensional multi-index models. | Emanuele Troiani, Yatin Dandi, Leonardo Defilippis, Lenka Zdeborov, Bruno Loureiro, Florent Krzakala |
| 2025 | Effective Bayesian Causal Inference via Structural Marginalisation and Autoregressive Orders. | Christian Toth, Christian Knoll, Franz Pernkopf, Robert Peharz |
| 2025 | Learning to Forget: Bayesian Time Series Forecasting using Recurrent Sparse Spectrum Signature Gaussian Processes. | Csaba Tth, Masaki Adachi, Michael A. Osborne, Harald Oberhauser |
| 2025 | Safe exploration in reproducing kernel Hilbert spaces. | Abdullah Tokmak, Kiran G. Krishnan, Thomas B. Schn, Dominik Baumann |
| 2025 | Max-Rank: Efficient Multiple Testing for Conformal Prediction. | Alexander Timans, Christoph-Nikolas Straehle, Kaspar Sakmann, Christian A. Naesseth, Eric T. Nalisnick |
| 2025 | Invariant Link Selector for Spatial-Temporal Out-of-Distribution Problem. | Katherine Tieu, Dongqi Fu, Jun Wu, Jingrui He |
| 2025 | Narrowing the Gap between Adversarial and Stochastic MDPs via Policy Optimization. | Daniil Tiapkin, Evgenii Chzhen, Gilles Stoltz |
| 2025 | Composition and Control with Distilled Energy Diffusion Models and Sequential Monte Carlo. | James Thornton, Louis Bthune, Ruixiang Zhang, Arwen Bradley, Preetum Nakkiran, Shuangfei Zhai |
| 2025 | Optimising Clinical Federated Learning through Mode Connectivity-based Model Aggregation. | Anshul Thakur, Soheila Molaei, Patrick Schwab, Danielle Belgrave, Kim Branson, David A. Clifton |
| 2025 | 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 |
| 2025 | Learning Geometrically-Informed Lyapunov Functions with Deep Diffeomorphic RBF Networks. | Samuel Tesfazgi, Leonhard Sprandl, Sandra Hirche |
| 2025 | Hierarchical Bias-Driven Stratification for Interpretable Causal Effect Estimation. | Lucile Ter-Minassian, Liran Szlak, Ehud Karavani, Christopher C. Holmes, Yishai Shimoni |
| 2025 | Learning from biased positive-unlabeled data via threshold calibration. | Pawel Teisseyre, Timo Martens, Jessa Bekker, Jesse Davis |
| 2025 | Risk-sensitive Bandits: Arm Mixture Optimality and Regret-efficient Algorithms. | Meltem Tatli, Arpan Mukherjee, Prashanth L. A., Karthikeyan Shanmugam, Ali Tajer |
| 2025 | A High Dimensional Statistical Model for Adversarial Training: Geometry and Trade-Offs. | Kasimir Tanner, Matteo Vilucchio, Bruno Loureiro, Florent Krzakala |
| 2025 | Energy-consistent Neural Operators for Hamiltonian and Dissipative Partial Differential Equations. | Yusuke Tanaka, Takaharu Yaguchi, Tomoharu Iwata, Naonori Ueda |
| 2025 | Data-Driven Upper Confidence Bounds with Near-Optimal Regret for Heavy-Tailed Bandits. | Ambrus Tams, Szabolcs Szentpteri, Balzs Csand Csji |
| 2025 | Regularity in Canonicalized Models: A Theoretical Perspective. | Behrooz Tahmasebi, Stefanie Jegelka |
| 2025 | A Convex Relaxation Approach to Generalization Analysis for Parallel Positively Homogeneous Networks. | Uday Kiran Reddy Tadipatri, Benjamin David Haeffele, Joshua Agterberg, Ren Vidal |
| 2025 | Rate of Model Collapse in Recursive Training. | Ananda Theertha Suresh, Andrew Thangaraj, Aditya Nanda Kishore Khandavally |
| 2025 | Theoretical Convergence Guarantees for Variational Autoencoders. | Sobihan Surendran, Antoine Godichon-Baggioni, Sylvain Le Corff |
| 2025 | Counting Graphlets of Size k under Local Differential Privacy. | Vorapong Suppakitpaisarn, Donlapark Ponnoprat, Nicha Hirankarn, Quentin Hillebrand |