| FastSecAgg: Scalable Secure Aggregation for Privacy-Preserving Federated Learning S Kadhe, N Rajaraman, OO Koyluoglu, K Ramchandran arXiv preprint arXiv:2009.11248, 2020 | 215 | 2020 |
| Toward the fundamental limits of imitation learning N Rajaraman, L Yang, J Jiao, K Ramchandran Advances in Neural Information Processing Systems 33, 2914-2924, 2020 | 93 | 2020 |
| Not just age but age and quality of information N Rajaraman, R Vaze, G Reddy IEEE Journal on Selected Areas in Communications 39 (5), 1325-1338, 2021 | 53 | 2021 |
| On the Value of Interaction and Function Approximation in Imitation Learning N Rajaraman, Y Han, L Yang, J Liu, J Jiao, K Ramchandran Advances in Neural Information Processing Systems 34, 1325-1336, 2021 | 24 | 2021 |
| Minimax Optimal Online Imitation Learning via Replay Estimation G Swamy, N Rajaraman, M Peng, S Choudhury, J Bagnell, SZ Wu, J Jiao, ... Advances in Neural Information Processing Systems 35, 7077-7088, 2022 | 20 | 2022 |
| Toward a Theory of Tokenization in LLMs N Rajaraman, J Jiao, K Ramchandran arXiv preprint arXiv:2404.08335, 2024 | 17 | 2024 |
| Provably Breaking the Quadratic Error Compounding Barrier in Imitation Learning, Optimally N Rajaraman, Y Han, LF Yang, K Ramchandran, J Jiao arXiv preprint arXiv:2102.12948, 2021 | 12 | 2021 |
| Submodular maximization under a matroid constraint: Asking more from an old friend, the greedy algorithm N Rajaraman, R Vaze arXiv preprint arXiv:1810.12861, 2018 | 12 | 2018 |
| Sample Efficient Deep Reinforcement Learning via Local Planning D Yin, S Thiagarajan, N Lazic, N Rajaraman, B Hao, C Szepesvari arXiv preprint arXiv:2301.12579, 2023 | 8 | 2023 |
| Statistical complexity and optimal algorithms for nonlinear ridge bandits N Rajaraman, Y Han, J Jiao, K Ramchandran The Annals of Statistics 52 (6), 2557-2582, 2024 | 7* | 2024 |
| Transformers on Markov data: Constant depth suffices N Rajaraman, M Bondaschi, AV Makkuva, K Ramchandran, M Gastpar ICML 2024 Workshop on Mechanistic Interpretability, 2024 | 6 | 2024 |
| Missing mass estimation from sticky channels P Chandra, A Thangaraj, N Rajaraman 2022 IEEE International Symposium on Information Theory (ISIT), 910-915, 2022 | 5* | 2022 |
| Robust Correlation Clustering R Krishnaswamy, N Rajaraman Approximation, Randomization, and Combinatorial Optimization. Algorithms and …, 2019 | 5* | 2019 |
| Greedy Pruning with Group Lasso Provably Generalizes for Matrix Sensing N Rajaraman, F Devvrit, A Mokhtari, K Ramchandran Thirty-seventh Conference on Neural Information Processing Systems, 2023 | 2 | 2023 |
| How good is Good-Turing for Markov samples? P Chandra, A Thangaraj, N Rajaraman arXiv preprint arXiv:2102.01938, 2021 | 2 | 2021 |
| Spectral Regularization Allows Data-frugal Learning over Combinatorial Spaces A Aghazadeh, N Rajaraman, T Tu, K Ramchandran arXiv preprint arXiv:2210.02604, 2022 | 1 | 2022 |
| Semi-supervised Active Linear Regression N Rajaraman, F Devvrit, P Awasthi Advances in Neural Information Processing Systems, 2022 | 1 | 2022 |
| Missing mass of Markov chains P Chandra, A Thangaraj, N Rajaraman 2020 IEEE International Symposium on Information Theory (ISIT), 1207-1212, 2020 | 1 | 2020 |
| Scaling Test-Time Compute Without Verification or RL is Suboptimal A Setlur, N Rajaraman, S Levine, A Kumar arXiv preprint arXiv:2502.12118, 2025 | | 2025 |
| From Markov to Laplace: How Mamba In-Context Learns Markov Chains M Bondaschi, N Rajaraman, X Wei, K Ramchandran, R Pascanu, ... arXiv preprint arXiv:2502.10178, 2025 | | 2025 |
Kannan RamchandranProfessor of Electrical Engineering and Computer Science, UC BerkeleyOverená e-mailová adresa na: eecs.berkeley.edu