| Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration J Altschuler, J Weed, P Rigollet NeurIPS, 2017 | 719 | 2017 |
| Massively scalable Sinkhorn distances via the Nyström method J Altschuler, F Bach, A Rudi, J Niles-Weed NeurIPS, 2019 | 145* | 2019 |
| Greedy column subset selection: new bounds and distributed algorithms J Altschuler, A Bhaskara, G Fu, V Mirrokni, A Rostamizadeh, ... International Conference on Machine Learning, 2539-2548, 2016 | 88 | 2016 |
| Privacy of Noisy Stochastic Gradient Descent: More Iterations without More Privacy Loss JM Altschuler, K Talwar NeurIPS, 2022 | 65 | 2022 |
| Wasserstein barycenters are NP-hard to compute JM Altschuler, E Boix-Adsera SIAM Journal on Mathematics of Data Science 4 (1), 179-203, 2022 | 65 | 2022 |
| Best arm identification for contaminated bandits J Altschuler, VE Brunel, A Malek Journal of Machine Learning Research 20 (91), 1-39, 2019 | 63 | 2019 |
| Wasserstein barycenters can be computed in polynomial time in fixed dimension JM Altschuler, E Boix-Adsera Journal of Machine Learning Research 22, 1-19, 2021 | 57 | 2021 |
| Averaging on the Bures-Wasserstein manifold: dimension-free convergence of gradient descent JM Altschuler, S Chewi, P Gerber, AJ Stromme NeurIPS, 2021 | 53 | 2021 |
| Faster high-accuracy log-concave sampling via algorithmic warm starts JM Altschuler, S Chewi Journal of the ACM 71 (3), 1-55, 2024 | 51 | 2024 |
| Online learning over a finite action set with limited switching JM Altschuler, K Talwar Mathematics of Operations Research 46 (1), 179-203, 2021 | 45 | 2021 |
| Polynomial-time algorithms for Multimarginal Optimal Transport problems with structure JM Altschuler, E Boix-Adsera Mathematical Programming 199 (1), 1107–1178, 2023 | 44 | 2023 |
| Asymptotics for semi-discrete entropic optimal transport JM Altschuler, J Niles-Weed, AJ Stromme SIAM Journal on Mathematical Analysis 54 (2), 1718-1741, 2022 | 40 | 2022 |
| Hardness results for Multimarginal Optimal Transport problems JM Altschuler, E Boix-Adsera Discrete Optimization 42, 100669, 2021 | 37 | 2021 |
| Resolving the Mixing Time of the Langevin Algorithm to its Stationary Distribution for Log-Concave Sampling JM Altschuler, K Talwar Conference on Learning Theory 195, 2509-2510, 2023 | 34 | 2023 |
| Acceleration by Stepsize Hedging: Multi-Step Descent and the Silver Stepsize Schedule J Altschuler, P Parrilo Journal of the ACM, 2023 | 34 | 2023 |
| Acceleration by stepsize hedging: Silver Stepsize Schedule for smooth convex optimization JM Altschuler, PA Parrilo Mathematical Programming, 1-14, 2024 | 29 | 2024 |
| Greed, hedging, and acceleration in convex optimization JM Altschuler Massachusetts Institute of Technology, 2018 | 15 | 2018 |
| Shifted Interpolation for Differential Privacy J Bok, W Su, JM Altschuler International Conference on Machine Learning, 2024 | 10 | 2024 |
| Near-linear convergence of the Random Osborne algorithm for Matrix Balancing JM Altschuler, PA Parrilo Mathematical Programming 198 (1), 363–397, 2023 | 10* | 2023 |
| Concentration of the Langevin Algorithm's Stationary Distribution JM Altschuler, K Talwar arXiv preprint arXiv:2212.12629, 2022 | 8 | 2022 |