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| 2025 | Causal Representation Learning from General Environments under Nonparametric Mixing. | Ignavier Ng, Shaoan Xie, Xinshuai Dong, Peter Spirtes, Kun Zhang |
| 2025 | Wasserstein Gradient Flow over Variational Parameter Space for Variational Inference. | Dai Hai Nguyen, Tetsuya Sakurai, Hiroshi Mamitsuka |
| 2025 | MING: A Functional Approach to Learning Molecular Generative Models. | Van Khoa Nguyen, Maciej Falkiewicz, Giangiacomo Mercatali, Alexandros Kalousis |
| 2025 | Memory-Efficient Optimization with Factorized Hamiltonian Descent. | Son Nguyen, Lizhang Chen, Bo Liu, Qiang Liu |
| 2025 | HAVER: Instance-Dependent Error Bounds for Maximum Mean Estimation and Applications to Q-Learning and Monte Carlo Tree Search. | Tuan Nguyen, Jay Barrett, Kwang-Sung Jun |
| 2025 | Prior-Dependent Allocations for Bayesian Fixed-Budget Best-Arm Identification in Structured Bandits. | Nicolas Nguyen, Imad Aouali, Andrs Gyrgy, Claire Vernade |
| 2025 | Offline RL via Feature-Occupancy Gradient Ascent. | Gergely Neu, Nneka Okolo |
| 2025 | Logarithmic Neyman Regret for Adaptive Estimation of the Average Treatment Effect. | Ojash Neopane, Aaditya Ramdas, Aarti Singh |
| 2025 | Theoretically Grounded Pruning of Large Ground Sets for Constrained, Discrete Optimization. | Ankur Nath, Alan Kuhnle |
| 2025 | Signed Graph Autoencoder for Explainable and Polarization-Aware Network Embeddings. | Nikolaos Nakis, Chrysoula Kosma, Giannis Nikolentzos, Michail Chatzianastasis, Iakovos Evdaimon, Michalis Vazirgiannis |
| 2025 | Bayesian Decision Theory on Decision Trees: Uncertainty Evaluation and Interpretability. | Yuta Nakahara, Shota Saito, Naoki Ichijo, Koki Kazama, Toshiyasu Matsushima |
| 2025 | Parallel Backpropagation for Inverse of a Convolution with Application to Normalizing Flows. | Sandeep Nagar, Girish Varma |
| 2025 | MDP Geometry, Normalization and Reward Balancing Solvers. | Arsenii Mustafin, Aleksei Pakharev, Alex Olshevsky, Ioannis Paschalidis |
| 2025 | Clustered Invariant Risk Minimization. | Tomoya Murata, Atsushi Nitanda, Taiji Suzuki |
| 2025 | Representer Theorems for Metric and Preference Learning: Geometric Insights and Algorithms. | Peyman Morteza |
| 2025 | Density-Dependent Group Testing. | Rahil Morjaria, Saikiran Bulusu, Venkata Gandikota, Sidharth Jaggi |
| 2025 | Learning Visual-Semantic Subspace Representations. | Gabriel Moreira, Manuel Marques, Joo Paulo Costeira, Alexander G. Hauptmann |
| 2025 | Batch, match, and patch: low-rank approximations for score-based variational inference. | Chirag Modi, Diana Cai, Lawrence K. Saul |
| 2025 | Level Set Teleportation: An Optimization Perspective. | Aaron Mishkin, Alberto Bietti, Robert M. Gower |
| 2025 | An Empirical Bernstein Inequality for Dependent Data in Hilbert Spaces and Applications. | Erfan Mirzaei, Andreas Maurer, Vladimir R. Kostic, Massimiliano Pontil |
| 2025 | Wasserstein Distributionally Robust Bayesian Optimization with Continuous Context. | Francesco Micheli, Efe C. Balta, Anastasios Tsiamis, John Lygeros |
| 2025 | Differentially private algorithms for linear queries via stochastic convex optimization. | Giorgio Micali, Clment Lezane, Annika Betken |
| 2025 | Bayes without Underfitting: Fully Correlated Deep Learning Posteriors via Alternating Projections. | Marco Miani, Hrittik Roy, Sren Hauberg |
| 2025 | LITE: Efficiently Estimating Gaussian Probability of Maximality. | Nicolas Menet, Jonas Hbotter, Parnian Kassraie, Andreas Krause |