| Testing ising models C Daskalakis, N Dikkala, G Kamath IEEE Transactions on Information Theory 65 (11), 6829-6852, 2019 | 97 | 2019 |
| Minimax estimation of conditional moment models N Dikkala, G Lewis, L Mackey, V Syrgkanis Advances in Neural Information Processing Systems 33, 12248-12262, 2020 | 66 | 2020 |
| Tight hardness results for maximum weight rectangles A Backurs, N Dikkala, C Tzamos arXiv preprint arXiv:1602.05837, 2016 | 46 | 2016 |
| From soft classifiers to hard decisions: How fair can we be? R Canetti, A Cohen, N Dikkala, G Ramnarayan, S Scheffler, A Smith Proceedings of the conference on fairness, accountability, and transparency …, 2019 | 38 | 2019 |
| Regression from dependent observations C Daskalakis, N Dikkala, I Panageas Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing …, 2019 | 25 | 2019 |
| Concentration of multilinear functions of the Ising model with applications to network data C Daskalakis, N Dikkala, G Kamath Advances in Neural Information Processing Systems 30, 2017 | 25 | 2017 |
| Learning from weakly dependent data under dobrushin’s condition Y Dagan, C Daskalakis, N Dikkala, S Jayanti Conference on Learning Theory, 914-928, 2019 | 24 | 2019 |
| Testing symmetric markov chains from a single trajectory C Daskalakis, N Dikkala, N Gravin Conference On Learning Theory, 385-409, 2018 | 22 | 2018 |
| Hogwild!-gibbs can be panaccurate C Daskalakis, N Dikkala, S Jayanti Advances in Neural Information Processing Systems 31, 2018 | 16 | 2018 |
| Estimating ising models from one sample Y Dagan, C Daskalakis, N Dikkala, AV Kandiros arXiv preprint arXiv:2004.09370, 2020 | 13 | 2020 |
| Do more negative samples necessarily hurt in contrastive learning? P Awasthi, N Dikkala, P Kamath International Conference on Machine Learning, 1101-1116, 2022 | 9 | 2022 |
| Logistic regression with peer-group effects via inference in higher-order Ising models C Daskalakis, N Dikkala, I Panageas International Conference on Artificial Intelligence and Statistics, 3653-3663, 2020 | 9 | 2020 |
| Learning Ising models from one or multiple samples Y Dagan, C Daskalakis, N Dikkala, AV Kandiros Proceedings of the 53rd Annual ACM SIGACT Symposium on Theory of Computing …, 2021 | 7 | 2021 |
| Statistical estimation from dependent data V Kandiros, Y Dagan, N Dikkala, S Goel, C Daskalakis International Conference on Machine Learning, 5269-5278, 2021 | 5 | 2021 |
| Can Credit Increase Revenue? N Dikkala, É Tardos Web and Internet Economics: 9th International Conference, WINE 2013 …, 2013 | 5 | 2013 |
| A theoretical view on sparsely activated networks C Baykal, N Dikkala, R Panigrahy, C Rashtchian, X Wang arXiv preprint arXiv:2208.04461, 2022 | 4 | 2022 |
| For manifold learning, deep neural networks can be locality sensitive hash functions N Dikkala, G Kaplun, R Panigrahy arXiv preprint arXiv:2103.06875, 2021 | 4 | 2021 |
| A Quantum Computing Approach to Part-of-Speech Tagging: A Quantum Viterbi decoding Algorithm V Singh, N Dikkala, P Bhattacharyya Semantic Scholar, 2015 | 1 | 2015 |
| The Power of External Memory in Increasing Predictive Model Capacity C Baykal, DJ Cutler, N Dikkala, N Ghosh, R Panigrahy, X Wang arXiv preprint arXiv:2302.00003, 2023 | | 2023 |
| Alternating Updates for Efficient Transformers C Baykal, D Cutler, N Dikkala, N Ghosh, R Panigrahy, X Wang arXiv preprint arXiv:2301.13310, 2023 | | 2023 |
