| SARAH: A novel method for machine learning problems using stochastic recursive gradient LM Nguyen, J Liu, K Scheinberg, M Takáč The 34th International Conference on Machine Learning (ICML 2017), 2017 | 631 | 2017 |
| SGD and Hogwild! Convergence Without the Bounded Gradients Assumption LM Nguyen, PH Nguyen, M van Dijk, P Richtárik, K Scheinberg, M Takác The 35th International Conference on Machine Learning (ICML 2018), 2018 | 236 | 2018 |
| ProxSARAH: An efficient algorithmic framework for stochastic composite nonconvex optimization NH Pham, LM Nguyen, DT Phan, Q Tran-Dinh Journal of Machine Learning Research 21 (110), 1-48, 2020 | 141 | 2020 |
| Stochastic recursive gradient algorithm for nonconvex optimization LM Nguyen, J Liu, K Scheinberg, M Takáč Technical Report, arXiv:1705.07261, 2017 | 113 | 2017 |
| PROVEN: Verifying Robustness of Neural Networks with a Probabilistic Approach TW Weng, PY Chen, LM Nguyen, MS Squillante, A Boopathy, I Oseledets, ... The 36th International Conference on Machine Learning (ICML 2019), 2019 | 89 | 2019 |
| Label-free Concept Bottleneck Models T Oikarinen, S Das, LM Nguyen, TW Weng The 11th International Conference on Learning Representations (ICLR 2023), 2023 | 81 | 2023 |
| Finite-Sum Smooth Optimization with SARAH LM Nguyen, M van Dijk, DT Phan, PH Nguyen, TW Weng, ... Computational Optimization and Applications, 2022 | 69* | 2022 |
| A unified convergence analysis for shuffling-type gradient methods LM Nguyen, Q Tran-Dinh, DT Phan, PH Nguyen, M van Dijk Journal of Machine Learning Research, 2021, 2021 | 67 | 2021 |
| New convergence aspects of stochastic gradient algorithms LM Nguyen, PH Nguyen, P Richtárik, K Scheinberg, M Takáč, M van Dijk Journal of Machine Learning Research 20 (176), 1-49, 2019 | 64 | 2019 |
| A hybrid stochastic optimization framework for composite nonconvex optimization Q Tran-Dinh, NH Pham, DT Phan, LM Nguyen Mathematical Programming 191 (2), 1005-1071, 2022 | 61 | 2022 |
| Inexact SARAH algorithm for stochastic optimization LM Nguyen, K Scheinberg, M Takáč Optimization Methods and Software 36 (1), 237-258, 2021 | 53 | 2021 |
| Hybrid Stochastic Gradient Descent Algorithms for Stochastic Nonconvex Optimization Q Tran-Dinh, NH Pham, DT Phan, LM Nguyen Technical Report, arXiv:1905.05920, 2019 | 53 | 2019 |
| A Scalable MIP-based Method for Learning Optimal Multivariate Decision Trees H Zhu, P Murali, DT Phan, LM Nguyen, JR Kalagnanam The 34th Conference on Neural Information Processing Systems (NeurIPS 2020), 2020 | 46 | 2020 |
| CEO Compensation: Does Financial Crisis Matter? P Vemala, L Nguyen, D Nguyen, A Kommasani International Business Research 7 (4), 125-131, 2014 | 38 | 2014 |
| Hybrid Variance-Reduced SGD Algorithms For Nonconvex-Concave Minimax Problems Q Tran-Dinh, D Liu, LM Nguyen The 34th Conference on Neural Information Processing Systems (NeurIPS 2020), 2020 | 35* | 2020 |
| Asynchronous Federated Learning with Reduced Number of Rounds and with Differential Privacy from Less Aggregated Gaussian Noise M van Dijk, NV Nguyen, TN Nguyen, LM Nguyen, Q Tran-Dinh, ... Technical Report, arXiv:2007.09208, 2020 | 33 | 2020 |
| FedDR–Randomized Douglas-Rachford Splitting Algorithms for Nonconvex Federated Composite Optimization Q Tran-Dinh, NH Pham, DT Phan, LM Nguyen The 35th Conference on Neural Information Processing Systems (NeurIPS 2021), 2021 | 32 | 2021 |
| Tight Dimension Independent Lower Bound on the Expected Convergence Rate for Diminishing Step Sizes in SGD PH Nguyen, LM Nguyen, M van Dijk The 33th Conference on Neural Information Processing Systems (NeurIPS 2019), 2019 | 32* | 2019 |
| Ensembling Graph Predictions for AMR Parsing HT Lam, G Picco, Y Hou, YS Lee, LM Nguyen, DT Phan, V López, ... The 35th Conference on Neural Information Processing Systems (NeurIPS 2021), 2021 | 29* | 2021 |
| Stochastic Gauss-Newton Algorithms for Nonconvex Compositional Optimization Q Tran-Dinh, NH Pham, LM Nguyen The 37th International Conference on Machine Learning (ICML 2020), 2020 | 28 | 2020 |
Katya ScheinbergCornell UniversityVerified email at cornell.edu
