Submodular Maximization Under A Matroid Constraint: Asking more from an old friend, the Greedy Algorithm. - Nived Rajaraman; Rahul Vaze (CoRR, 2018)

Robust Correlation Clustering. - Devvrit; Ravishankar Krishnaswamy; Nived Rajaraman (APPROX-RANDOM, 2019)

Convergence of Chao Unseen Species Estimator. - Nived Rajaraman; Prafulla Chandra; Andrew Thangaraj; Ananda Theertha Suresh (ISIT, 2019)

Missing Mass of Markov Chains. - Prafulla Chandra; Andrew Thangaraj; Nived Rajaraman (ISIT, 2020)

Toward the Fundamental Limits of Imitation Learning. - Nived Rajaraman; Lin F. Yang; Jiantao Jiao; Kannan Ramchandran (NeurIPS, 2020)

FastSecAgg: Scalable Secure Aggregation for Privacy-Preserving Federated Learning. - Swanand Kadhe; Nived Rajaraman; Onur Ozan Koyluoglu; Kannan Ramchandran (CoRR, 2020)

FastShare: Scalable Secret Sharing by Leveraging Locality. - Swanand Kadhe; Nived Rajaraman; Kannan Ramchandran (ISIT, 2021)

On the Value of Interaction and Function Approximation in Imitation Learning. - Nived Rajaraman; Yanjun Han; Lin Yang 011; Jingbo Liu; Jiantao Jiao; Kannan Ramchandran (NeurIPS, 2021)

Missing Mass of Rank-2 Markov Chains. - Prafulla Chandra; Andrew Thangaraj; Nived Rajaraman (CoRR, 2021)

Provably Breaking the Quadratic Error Compounding Barrier in Imitation Learning, Optimally. - Nived Rajaraman; Yanjun Han; Lin F. Yang; Kannan Ramchandran; Jiantao Jiao (CoRR, 2021)

Semi-supervised Active Regression. - Devvrit; Nived Rajaraman; Pranjal Awasthi (CoRR, 2021)

Not Just Age but Age and Quality of Information. - Nived Rajaraman; Rahul Vaze; Goonwanth Reddy (IEEE J. Sel. Areas Commun., 2021)

Missing Mass Estimation from Sticky Channels. - Prafulla Chandra; Andrew Thangaraj; Nived Rajaraman (ISIT, 2022)

Semi-supervised Active Linear Regression. - Nived Rajaraman; Devvrit; Pranjal Awasthi (NeurIPS, 2022)

Minimax Optimal Online Imitation Learning via Replay Estimation. - Gokul Swamy; Nived Rajaraman; Matthew Peng; Sanjiban Choudhury; J. Andrew Bagnell; Steven Wu 001; Jiantao Jiao; Kannan Ramchandran (NeurIPS, 2022)

Greedy Pruning with Group Lasso Provably Generalizes for Matrix Sensing. - Nived Rajaraman; Devvrit; Aryan Mokhtari; Kannan Ramchandran (NeurIPS, 2023)

Sample Efficient Deep Reinforcement Learning via Local Planning. - Dong Yin; Sridhar Thiagarajan; Nevena Lazic; Nived Rajaraman; Botao Hao; Csaba Szepesvári (CoRR, 2023)

Beyond UCB: Statistical Complexity and Optimal Algorithms for Non-linear Ridge Bandits. - Nived Rajaraman; Yanjun Han; Jiantao Jiao; Kannan Ramchandran (CoRR, 2023)

Greedy Pruning with Group Lasso Provably Generalizes for Matrix Sensing and Neural Networks with Quadratic Activations. - Nived Rajaraman; Devvrit; Aryan Mokhtari; Kannan Ramchandran (CoRR, 2023)

Spectral Regularization Allows Data-frugal Learning over Combinatorial Spaces. - Amirali Aghazadeh; Nived Rajaraman; Tony Tu; Kannan Ramchandran (Trans. Mach. Learn. Res., 2023)

Transformers on Markov data: Constant depth suffices. - Nived Rajaraman; Marco Bondaschi; Ashok Vardhan Makkuva; Kannan Ramchandran; Michael Gastpar (NeurIPS, 2024)

An Analysis of Tokenization: Transformers under Markov Data. - Nived Rajaraman; Jiantao Jiao; Kannan Ramchandran (NeurIPS, 2024)

Toward a Theory of Tokenization in LLMs. - Nived Rajaraman; Jiantao Jiao; Kannan Ramchandran (CoRR, 2024)

How good is Good-Turing for Markov samples? - Prafulla Chandra; Andrew Thangaraj; Nived Rajaraman (Trans. Mach. Learn. Res., 2024)

Computational Intractability of Strategizing against Online Learners. - Angelos Assos; Yuval Dagan; Nived Rajaraman (COLT, 2025)

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

Scaling Test-Time Compute Without Verification or RL is Suboptimal. - Amrith Setlur; Nived Rajaraman; Sergey Levine; Aviral Kumar (CoRR, 2025)

The Space Complexity of Learning-Unlearning Algorithms. - Yeshwanth Cherapanamjeri; Sumegha Garg; Nived Rajaraman; Ayush Sekhari; Abhishek Shetty (CoRR, 2025)

SPECS: Faster Test-Time Scaling through Speculative Drafts. - Mert Cemri; Nived Rajaraman; Rishabh Tiwari; Xiaoxuan Liu; Kurt Keutzer; Ion Stoica; Kannan Ramchandran; Ahmad Beirami; Ziteng Sun (CoRR, 2025)

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