Robust Aggregation for Federated Learning K Pillutla, SM Kakade, Z Harchaoui IEEE Transactions on Signal Processing 70, 1142-1154, 2022 | 590 | 2022 |
MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers K Pillutla, S Swayamdipta, R Zellers, J Thickstun, S Welleck, Y Choi, ... Advances in Neural Information Processing Systems 34, 4816-4828, 2021 | 250 | 2021 |
Federated Learning with Partial Model Personalization K Pillutla, K Malik, AR Mohamed, M Rabbat, M Sanjabi, L Xiao International Conference on Machine Learning, 17716-17758, 2022 | 140 | 2022 |
Federated Learning with Superquantile Aggregation for Heterogeneous Data K Pillutla, Y Laguel, J Malick, Z Harchaoui Machine Learning, 1-68, 2023 | 77* | 2023 |
A Markov Chain Theory Approach to Characterizing the Minimax Optimality of Stochastic Gradient Descent (for Least Squares) P Jain, SM Kakade, R Kidambi, P Netrapalli, VK Pillutla, A Sidford arXiv preprint arXiv:1710.09430, 2017 | 43 | 2017 |
A Smoother Way to Train Structured Prediction Models VK Pillutla, V Roulet, SM Kakade, Z Harchaoui Advances in Neural Information Processing Systems 31, 2018 | 24 | 2018 |
Superquantiles at work: Machine learning applications and efficient subgradient computation Y Laguel, K Pillutla, J Malick, Z Harchaoui Set-Valued and Variational Analysis 29 (4), 967-996, 2021 | 22 | 2021 |
Unleashing the power of randomization in auditing differentially private ml K Pillutla, G Andrew, P Kairouz, HB McMahan, A Oprea, S Oh Advances in Neural Information Processing Systems 36, 2024 | 16 | 2024 |
Data driven resource allocation for distributed learning T Dick, M Li, VK Pillutla, C White, N Balcan, A Smola Artificial Intelligence and Statistics, 662-671, 2017 | 15 | 2017 |
LLC: Accurate, multi-purpose learnt low-dimensional binary codes A Kusupati, M Wallingford, V Ramanujan, R Somani, JS Park, K Pillutla, ... Advances in neural information processing systems 34, 23900-23913, 2021 | 12 | 2021 |
Correlated noise provably beats independent noise for differentially private learning CA Choquette-Choo, K Dvijotham, K Pillutla, A Ganesh, T Steinke, ... arXiv preprint arXiv:2310.06771, 2023 | 11 | 2023 |
Divergence frontiers for generative models: Sample complexity, quantization effects, and frontier integrals L Liu, K Pillutla, S Welleck, S Oh, Y Choi, Z Harchaoui Advances in Neural Information Processing Systems 34, 12930-12942, 2021 | 11 | 2021 |
User inference attacks on large language models N Kandpal, K Pillutla, A Oprea, P Kairouz, CA Choquette-Choo, Z Xu arXiv preprint arXiv:2310.09266, 2023 | 10* | 2023 |
Mauve scores for generative models: Theory and practice K Pillutla, L Liu, J Thickstun, S Welleck, S Swayamdipta, R Zellers, S Oh, ... Journal of Machine Learning Research 24 (356), 1-92, 2023 | 10 | 2023 |
Reconstructing cancer drug response networks using multitask learning M Ruffalo, P Stojanov, VK Pillutla, R Varma, Z Bar-Joseph BMC Systems Biology 11, 1-15, 2017 | 10 | 2017 |
Towards federated foundation models: Scalable dataset pipelines for group-structured learning Z Charles, N Mitchell, K Pillutla, M Reneer, Z Garrett Advances in Neural Information Processing Systems 36, 2024 | 9 | 2024 |
Stochastic optimization for spectral risk measures R Mehta, V Roulet, K Pillutla, L Liu, Z Harchaoui International Conference on Artificial Intelligence and Statistics, 10112-10159, 2023 | 9 | 2023 |
Modified Gauss-Newton Algorithms under Noise K Pillutla, V Roulet, SM Kakade, Z Harchaoui 2023 IEEE Statistical Signal Processing Workshop (SSP), 51-55, 2023 | 5* | 2023 |
On Skewed Multi-dimensional Distributions: the FusionRP Model, Algorithms, and Discoveries VK Pillutla, Z Fang, P Devineni, C Faloutsos, D Koutra, J Tang Proceedings of the 2016 SIAM International Conference on Data Mining, 783-791, 2016 | 5 | 2016 |
Distributionally robust optimization with bias and variance reduction R Mehta, V Roulet, K Pillutla, Z Harchaoui arXiv preprint arXiv:2310.13863, 2023 | 4 | 2023 |