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Meyer Scetbon
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2020 – today
- 2024
- [j2]Tomas Geffner, Javier Antorán, Adam Foster, Wenbo Gong, Chao Ma, Emre Kiciman, Amit Sharma, Angus Lamb, Martin Kukla, Nick Pawlowski, Agrin Hilmkil, Joel Jennings, Meyer Scetbon, Miltiadis Allamanis, Cheng Zhang:
Deep End-to-end Causal Inference. Trans. Mach. Learn. Res. 2024 (2024) - [c15]Elvis Dohmatob, Meyer Scetbon:
Precise Accuracy / Robustness Tradeoffs in Regression: Case of General Norms. ICML 2024 - [c14]Meyer Scetbon, Joel Jennings, Agrin Hilmkil, Cheng Zhang, Chao Ma:
A Fixed-Point Approach for Causal Generative Modeling. ICML 2024 - [i19]Tarun Gupta, Wenbo Gong, Chao Ma, Nick Pawlowski, Agrin Hilmkil, Meyer Scetbon, Ade Famoti, Ashley Juan Llorens, Jianfeng Gao, Stefan Bauer, Danica Kragic, Bernhard Schölkopf, Cheng Zhang:
The Essential Role of Causality in Foundation World Models for Embodied AI. CoRR abs/2402.06665 (2024) - [i18]Meyer Scetbon, Joel Jennings, Agrin Hilmkil, Cheng Zhang, Chao Ma:
FiP: a Fixed-Point Approach for Causal Generative Modeling. CoRR abs/2404.06969 (2024) - [i17]Divyat Mahajan, Jannes Gladrow, Agrin Hilmkil, Cheng Zhang, Meyer Scetbon:
Zero-Shot Learning of Causal Models. CoRR abs/2410.06128 (2024) - 2023
- [c13]Meyer Scetbon, Elvis Dohmatob:
Robust Linear Regression: Gradient-descent, Early-stopping, and Beyond. AISTATS 2023: 11583-11607 - [c12]Meyer Scetbon, Michal Klein, Giovanni Palla, Marco Cuturi:
Unbalanced Low-rank Optimal Transport Solvers. NeurIPS 2023 - [i16]Meyer Scetbon, Elvis Dohmatob:
Robust Linear Regression: Gradient-descent, Early-stopping, and Beyond. CoRR abs/2301.13486 (2023) - [i15]Meyer Scetbon:
Polynomial-Time Solvers for the Discrete ∞-Optimal Transport Problems. CoRR abs/2304.13467 (2023) - [i14]Meyer Scetbon, Michal Klein, Giovanni Palla, Marco Cuturi:
Unbalanced Low-rank Optimal Transport Solvers. CoRR abs/2305.19727 (2023) - [i13]Elvis Dohmatob, Meyer Scetbon:
Robust Linear Regression: Phase-Transitions and Precise Tradeoffs for General Norms. CoRR abs/2308.00556 (2023) - 2022
- [c11]Nicholas J. Irons, Meyer Scetbon, Soumik Pal, Zaïd Harchaoui:
Triangular Flows for Generative Modeling: Statistical Consistency, Smoothness Classes, and Fast Rates. AISTATS 2022: 10161-10195 - [c10]Meyer Scetbon, Laurent Meunier, Yaniv Romano:
An Asymptotic Test for Conditional Independence using Analytic Kernel Embeddings. ICML 2022: 19328-19346 - [c9]Meyer Scetbon, Gabriel Peyré, Marco Cuturi:
Linear-Time Gromov Wasserstein Distances using Low Rank Couplings and Costs. ICML 2022: 19347-19365 - [c8]Meyer Scetbon, Marco Cuturi:
Low-rank Optimal Transport: Approximation, Statistics and Debiasing. NeurIPS 2022 - [i12]Meyer Scetbon, Marco Cuturi:
Low-rank Optimal Transport: Approximation, Statistics and Debiasing. CoRR abs/2205.12365 (2022) - 2021
- [j1]Meyer Scetbon
, Michael Elad
Deep K-SVD Denoising. IEEE Trans. Image Process. 30: 5944-5955 (2021) - [c7]Meyer Scetbon, Laurent Meunier, Jamal Atif, Marco Cuturi:
Equitable and Optimal Transport with Multiple Agents. AISTATS 2021: 2035-2043 - [c6]Meyer Scetbon, Zaïd Harchaoui:
A Spectral Analysis of Dot-product Kernels. AISTATS 2021: 3394-3402 - [c5]Laurent Meunier, Meyer Scetbon, Rafael Pinot, Jamal Atif, Yann Chevaleyre:
Mixed Nash Equilibria in the Adversarial Examples Game. ICML 2021: 7677-7687 - [c4]Meyer Scetbon, Marco Cuturi, Gabriel Peyré:
Low-Rank Sinkhorn Factorization. ICML 2021: 9344-9354 - [i11]Laurent Meunier, Meyer Scetbon, Rafael Pinot, Jamal Atif, Yann Chevaleyre:
Mixed Nash Equilibria in the Adversarial Examples Game. CoRR abs/2102.06905 (2021) - [i10]Meyer Scetbon, Marco Cuturi, Gabriel Peyré:
Low-Rank Sinkhorn Factorization. CoRR abs/2103.04737 (2021) - [i9]Meyer Scetbon, Gabriel Peyré, Marco Cuturi:
Linear-Time Gromov Wasserstein Distances using Low Rank Couplings and Costs. CoRR abs/2106.01128 (2021) - [i8]Meyer Scetbon, Laurent Meunier, Yaniv Romano:
An 𝓁p-based Kernel Conditional Independence Test. CoRR abs/2110.14868 (2021) - [i7]Nicholas J. Irons, Meyer Scetbon, Soumik Pal, Zaïd Harchaoui:
Triangular Flows for Generative Modeling: Statistical Consistency, Smoothness Classes, and Fast Rates. CoRR abs/2112.15595 (2021) - 2020
- [c3]Meyer Scetbon, Zaïd Harchaoui:
Harmonic Decompositions of Convolutional Networks. ICML 2020: 8522-8532 - [c2]Meyer Scetbon, Marco Cuturi:
Linear Time Sinkhorn Divergences using Positive Features. NeurIPS 2020 - [i6]Meyer Scetbon, Zaïd Harchaoui:
Risk Bounds for Multi-layer Perceptrons through Spectra of Integral Operators. CoRR abs/2002.12640 (2020) - [i5]Meyer Scetbon, Zaïd Harchaoui:
Harmonic Decompositions of Convolutional Networks. CoRR abs/2003.12756 (2020) - [i4]Meyer Scetbon, Marco Cuturi:
Linear Time Sinkhorn Divergences using Positive Features. CoRR abs/2006.07057 (2020) - [i3]Meyer Scetbon, Laurent Meunier, Jamal Atif, Marco Cuturi:
Handling Multiple Costs in Optimal Transport: Strong Duality and Efficient Computation. CoRR abs/2006.07260 (2020)
2010 – 2019
- 2019
- [c1]Meyer Scetbon, Gaël Varoquaux:
Comparing distributions: 퓁1 geometry improves kernel two-sample testing. NeurIPS 2019: 12306-12316 - [i2]Meyer Scetbon, Gaël Varoquaux:
Comparing distributions: 𝓁1 geometry improves kernel two-sample testing. CoRR abs/1909.09264 (2019) - [i1]Meyer Scetbon, Michael Elad, Peyman Milanfar:
Deep K-SVD Denoising. CoRR abs/1909.13164 (2019)
Coauthor Index
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