Rajaraman, Nived

16 publications

COLT 2025 Computational Intractability of Strategizing Against Online Learners Angelos Assos, Yuval Dagan, Nived Rajaraman

ICLRW 2025 From Markov to Laplace: How Mamba In-Context Learns Markov Chains Marco Bondaschi, Nived Rajaraman, Xiuying Wei, Kannan Ramchandran, Razvan Pascanu, Caglar Gulcehre, Michael Gastpar, Ashok Vardhan Makkuva

ICML 2025 Scaling Test-Time Compute Without Verification or RL Is Suboptimal Amrith Setlur, Nived Rajaraman, Sergey Levine, Aviral Kumar

ICLRW 2025 Scaling Test-Time Compute Without Verification or RL Is Suboptimal Amrith Setlur, Nived Rajaraman, Sergey Levine, Aviral Kumar

COLT 2025 The Space Complexity of Learning-Unlearning Algorithms (extended Abstract) Yeshwanth Cherapanamjeri, Sumegba Garg, Nived Rajaraman, Ayush Sekhari, Abhishek Shetty

NeurIPS 2025 What One Cannot, Two Can: Two-Layer Transformers Provably Represent Induction Heads on Any-Order Markov Chains Chanakya Ekbote, Ashok Vardhan Makkuva, Marco Bondaschi, Nived Rajaraman, Michael Gastpar, Jason D. Lee, Paul Pu Liang

NeurIPS 2024 An Analysis of Tokenization: Transformers Under Markov Data Nived Rajaraman, Jiantao Jiao, Kannan Ramchandran

TMLR 2024 How Good Is Good-Turing for Markov Samples? Prafulla Chandra, Andrew Thangaraj, Nived Rajaraman

NeurIPS 2024 Transformers on Markov Data: Constant Depth Suffices Nived Rajaraman, Marco Bondaschi, Kannan Ramchandran, Michael Gastpar, Ashok Vardhan Makkuva

ICMLW 2024 Transformers on Markov Data: Constant Depth Suffices Nived Rajaraman, Marco Bondaschi, Ashok Vardhan Makkuva, Kannan Ramchandran, Michael Gastpar

NeurIPS 2023 Greedy Pruning with Group Lasso Provably Generalizes for Matrix Sensing Nived Rajaraman, Fnu Devvrit, Aryan Mokhtari, Kannan Ramchandran

TMLR 2023 Spectral Regularization Allows Data-Frugal Learning over Combinatorial Spaces Amirali Aghazadeh, Nived Rajaraman, Tony Tu, Kannan Ramchandran

NeurIPS 2022 Minimax Optimal Online Imitation Learning via Replay Estimation Gokul Swamy, Nived Rajaraman, Matt Peng, Sanjiban Choudhury, J. A. Bagnell, Steven Z. Wu, Jiantao Jiao, Kannan Ramchandran

NeurIPS 2022 Semi-Supervised Active Linear Regression Nived Rajaraman, Fnu Devvrit, Pranjal Awasthi

NeurIPS 2021 On the Value of Interaction and Function Approximation in Imitation Learning Nived Rajaraman, Yanjun Han, Lin Yang, Jingbo Liu, Jiantao Jiao, Kannan Ramchandran

NeurIPS 2020 Toward the Fundamental Limits of Imitation Learning Nived Rajaraman, Lin Yang, Jiantao Jiao, Kannan Ramchandran