| Robust Aggregation for Federated Learning K Pillutla, SM Kakade, Z Harchaoui IEEE Transactions on Signal Processing 70, 1142 - 1154, 2022 | 260 | 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, 2021 | 88 | 2021 |
| A Superquantile Approach to Federated Learning with Heterogeneous Devices Y Laguel, K Pillutla, J Malick, Z Harchaoui 2021 55th Annual Conference on Information Sciences and Systems (CISS), 1-6, 2021 | 39* | 2021 |
| 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 | 30 | 2017 |
| 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 | 29 | 2022 |
| A Smoother Way to Train Structured Prediction Models VK Pillutla, V Roulet, SM Kakade, Z Harchaoui Advances in Neural Information Processing Systems 31, 2018 | 18 | 2018 |
| Superquantiles at Work: Machine Learning Applications and Efficient Subgradient Computation Y Laguel, K Pillutla, J Malick, Z Harchaoui Set Valued and Variational Analysis, 2021 | 14 | 2021 |
| 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 | 14 | 2017 |
| 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 |
| 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 | 7 | 2021 |
| 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 | 5 | 2021 |
| 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 |
| Robust aggregation for federated learning. arXiv 2019 K Pillutla, SM Kakade, Z Harchaoui arXiv preprint arXiv:1912.13445, 0 | 5 | |
| Federated learning with heterogeneous data: A superquantile optimization approach K Pillutla, Y Laguel, J Malick, Z Harchaoui arXiv preprint arXiv:2112.09429, 2021 | 4 | 2021 |
| MAUVE Scores for Generative Models: Theory and Practice K Pillutla, L Liu, J Thickstun, S Welleck, S Swayamdipta, R Zellers, S Oh, ... arXiv preprint arXiv:2212.14578, 2022 | 2 | 2022 |
| Differentially Private Federated Quantiles with the Distributed Discrete Gaussian Mechanism K Pillutla, Y Laguel, J Malick, Z Harchaoui International Workshop on Federated Learning: Recent Advances and New Challenges, 2022 | 1 | 2022 |
| Modified Gauss-Newton Algorithms under Noise K Pillutla, V Roulet, S Kakade, Z Harchaoui arXiv preprint arXiv:2305.10634, 2023 | | 2023 |
| Federated learning with superquantile aggregation for heterogeneous data K Pillutla, Y Laguel, J Malick, Z Harchaoui Machine Learning, 1-68, 2023 | | 2023 |
| Influence Diagnostics under Self-concordance J Fisher, L Liu, K Pillutla, Y Choi, Z Harchaoui International Conference on Artificial Intelligence and Statistics, 10028-10076, 2023 | | 2023 |
| 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 | | 2023 |
Zaid HarchaouiUniversity of WashingtonPotvrđena adresa e-pošte na uw.edu
