Gartrell, Mike

10 publications

TMLR 2025 Differentially Private Gradient Flow Based on the Sliced Wasserstein Distance Ilana Sebag, Muni Sreenivas Pydi, Jean-Yves Franceschi, Alain Rakotomamonjy, Mike Gartrell, Jamal Atif, Alexandre Allauzen

AISTATS 2023 Learning from Multiple Sources for Data-to-Text and Text-to-Data Song Duong, Alberto Lumbreras, Mike Gartrell, Patrick Gallinari

NeurIPS 2023 Unifying GANs and Score-Based Diffusion as Generative Particle Models Jean-Yves Franceschi, Mike Gartrell, Ludovic Dos Santos, Thibaut Issenhuth, Emmanuel de Bézenac, Mickael Chen, Alain Rakotomamonjy

ICML 2022 Scalable MCMC Sampling for Nonsymmetric Determinantal Point Processes Insu Han, Mike Gartrell, Elvis Dohmatob, Amin Karbasi

ICLR 2022 Scalable Sampling for Nonsymmetric Determinantal Point Processes Insu Han, Mike Gartrell, Jennifer Gillenwater, Elvis Dohmatob, Amin Karbasi

ICLR 2021 Scalable Learning and MAP Inference for Nonsymmetric Determinantal Point Processes Mike Gartrell, Insu Han, Elvis Dohmatob, Jennifer Gillenwater, Victor-Emmanuel Brunel

NeurIPSW 2020 Wasserstein Learning of Determinantal Point Processes Lucas Anquetil, Mike Gartrell, Alain Rakotomamonjy, Ugo Tanielian, Clément Calauzènes

AISTATS 2019 Learning Determinantal Point Processes by Corrective Negative Sampling Zelda Mariet, Mike Gartrell, Suvrit Sra

NeurIPS 2019 Learning Nonsymmetric Determinantal Point Processes Mike Gartrell, Victor-Emmanuel Brunel, Elvis Dohmatob, Syrine Krichene

AAAI 2017 Low-Rank Factorization of Determinantal Point Processes Mike Gartrell, Ulrich Paquet, Noam Koenigstein