| 2025 | AISTATS | Bayesian Off-Policy Evaluation and Learning for Large Action Spaces. | Imad Aouali, Victor-Emmanuel Brunel, David Rohde, Anna Korba |
| 2024 | ALT | Concentration of empirical barycenters in metric spaces. | Victor-Emmanuel Brunel, Jordan Serres |
| 2024 | UAI | Unified PAC-Bayesian Study of Pessimism for Offline Policy Learning with Regularized Importance Sampling. | Imad Aouali, Victor-Emmanuel Brunel, David Rohde, Anna Korba |
| 2023 | COLT | Geodesically convex M-estimation in metric spaces. | Victor-Emmanuel Brunel |
| 2023 | ICML | Exponential Smoothing for Off-Policy Learning. | Imad Aouali, Victor-Emmanuel Brunel, David Rohde, Anna Korba |
| 2021 | ALT | Statistical guarantees for generative models without domination. | Nicolas Schreuder, Victor-Emmanuel Brunel, Arnak S. Dalalyan |
| 2021 | ICLR | Scalable Learning and MAP Inference for Nonsymmetric Determinantal Point Processes. | Mike Gartrell, Insu Han, Elvis Dohmatob, Jennifer Gillenwater, Victor-Emmanuel Brunel |
| 2020 | AISTATS | A nonasymptotic law of iterated logarithm for general M-estimators. | Arnak S. Dalalyan, Nicolas Schreuder, Victor-Emmanuel Brunel |
| 2019 | COLT | Learning rates for Gaussian mixtures under group action. | Victor-Emmanuel Brunel |
| 2017 | COLT | Rates of estimation for determinantal point processes. | Victor-Emmanuel Brunel, Ankur Moitra, Philippe Rigollet, John Urschel |
| 2017 | ICML | Learning Determinantal Point Processes with Moments and Cycles. | John Urschel, Victor-Emmanuel Brunel, Ankur Moitra, Philippe Rigollet |