Ramchandran, Kannan

43 publications

ICLR 2025 EmbedLLM: Learning Compact Representations of Large Language Models Richard Zhuang, Tianhao Wu, Zhaojin Wen, Andrew Li, Jiantao Jiao, Kannan Ramchandran

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

ICLR 2025 Looped Transformers for Length Generalization Ying Fan, Yilun Du, Kannan Ramchandran, Kangwook Lee

AISTATS 2025 Online Assortment and Price Optimization Under Contextual Choice Models Yigit Efe Erginbas, Thomas Courtade, Kannan Ramchandran

NeurIPS 2025 ProxySPEX: Inference-Efficient Interpretability via Sparse Feature Interactions in LLMs Landon Butler, Abhineet Agarwal, Justin Singh Kang, Yigit Efe Erginbas, Bin Yu, Kannan Ramchandran

ICML 2025 SPEX: Scaling Feature Interaction Explanations for LLMs Justin Singh Kang, Landon Butler, Abhineet Agarwal, Yigit Efe Erginbas, Ramtin Pedarsani, Bin Yu, Kannan Ramchandran

ICLRW 2025 SPEX: Scaling Feature Interaction Explanations for LLMs Justin Singh Kang, Landon Butler, Abhineet Agarwal, Yigit Efe Erginbas, Ramtin Pedarsani, Bin Yu, Kannan Ramchandran

ICML 2025 VersaPRM: Multi-Domain Process Reward Model via Synthetic Reasoning Data Thomas Zeng, Shuibai Zhang, Shutong Wu, Christian Classen, Daewon Chae, Ethan Ewer, Minjae Lee, Heeju Kim, Wonjun Kang, Jackson Kunde, Ying Fan, Jungtaek Kim, Hyung Il Koo, Kannan Ramchandran, Dimitris Papailiopoulos, Kangwook Lee

NeurIPS 2025 Why Do Multi-Agent LLM Systems Fail? Mert Cemri, Melissa Z Pan, Shuyi Yang, Lakshya A Agrawal, Bhavya Chopra, Rishabh Tiwari, Kurt Keutzer, Aditya Parameswaran, Dan Klein, Kannan Ramchandran, Matei Zaharia, Joseph E. Gonzalez, Ion Stoica

ICLRW 2025 Why Do Multiagent Systems Fail? Melissa Z Pan, Mert Cemri, Lakshya A Agrawal, Shuyi Yang, Bhavya Chopra, Rishabh Tiwari, Kurt Keutzer, Aditya Parameswaran, Kannan Ramchandran, Dan Klein, Joseph E. Gonzalez, Matei Zaharia, Ion Stoica

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

ICMLW 2024 Discrete Diffusion Posterior Sampling for Protein Design Mert Cemri, Ajil Jalal, Kannan Ramchandran

NeurIPS 2024 Learning to Understand: Identifying Interactions via the Möbius Transform Justin Singh Kang, Yigit Efe Erginbas, Landon Butler, Ramtin Pedarsani, Kannan Ramchandran

NeurIPSW 2024 Looped Transformers for Length Generalization Ying Fan, Yilun Du, Kannan Ramchandran, Kangwook Lee

TMLR 2024 The Fair Value of Data Under Heterogeneous Privacy Constraints in Federated Learning Justin Singh Kang, Ramtin Pedarsani, Kannan Ramchandran

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

ICMLW 2023 Competing Bandits in Non-Stationary Matching Markets Avishek Ghosh, Abishek Sankararaman, Kannan Ramchandran, Tara Javidi, Arya Mazumdar

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

AISTATS 2023 Interactive Learning with Pricing for Optimal and Stable Allocations in Markets Yigit Efe Erginbas, Soham Phade, Kannan Ramchandran

NeurIPS 2023 Learning a 1-Layer Conditional Generative Model in Total Variation Ajil Jalal, Justin Kang, Ananya Uppal, Kannan Ramchandran, Ecprice

NeurIPS 2023 Online Pricing for Multi-User Multi-Item Markets Yigit Efe Erginbas, Thomas Courtade, Kannan Ramchandran, Soham Phade

NeurIPSW 2023 Pairwise Proximal Policy Optimization: Harnessing Relative Feedback for LLM Alignment Tianhao Wu, Banghua Zhu, Ruoyu Zhang, Zhaojin Wen, Kannan Ramchandran, Jiantao Jiao

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

NeurIPSW 2023 The Fair Value of Data Under Heterogeneous Privacy Constraints in Federated Learning Justin Singh Kang, Kannan Ramchandran, Ramtin Pedarsani

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

ECML-PKDD 2022 Multi-Agent Heterogeneous Stochastic Linear Bandits Avishek Ghosh, Abishek Sankararaman, Kannan Ramchandran

UAI 2021 LocalNewton: Reducing Communication Rounds for Distributed Learning Vipul Gupta, Avishek Ghosh, Michał Dereziński, Rajiv Khanna, Kannan Ramchandran, Michael W. Mahoney

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 2021 Taxonomizing Local Versus Global Structure in Neural Network Loss Landscapes Yaoqing Yang, Liam Hodgkinson, Ryan Theisen, Joe Zou, Joseph E Gonzalez, Kannan Ramchandran, Michael W. Mahoney

NeurIPS 2020 An Efficient Framework for Clustered Federated Learning Avishek Ghosh, Jichan Chung, Dong Yin, Kannan Ramchandran

NeurIPS 2020 Boundary Thickness and Robustness in Learning Models Yaoqing Yang, Rajiv Khanna, Yaodong Yu, Amir Gholami, Kurt Keutzer, Joseph E Gonzalez, Kannan Ramchandran, Michael W. Mahoney

ECML-PKDD 2020 Reprogramming GANs via Input Noise Design Kangwook Lee, Changho Suh, Kannan Ramchandran

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

AISTATS 2018 Approximate Ranking from Pairwise Comparisons Reinhard Heckel, Max Simchowitz, Kannan Ramchandran, Martin J. Wainwright

AISTATS 2018 Gradient Diversity: A Key Ingredient for Scalable Distributed Learning Dong Yin, Ashwin Pananjady, Maximilian Lam, Dimitris S. Papailiopoulos, Kannan Ramchandran, Peter L. Bartlett

ICML 2017 The Sample Complexity of Online One-Class Collaborative Filtering Reinhard Heckel, Kannan Ramchandran

NeurIPS 2016 Cyclades: Conflict-Free Asynchronous Machine Learning Xinghao Pan, Maximilian Lam, Stephen Tu, Dimitris Papailiopoulos, Ce Zhang, Michael I Jordan, Kannan Ramchandran, Christopher Ré

JMLR 2016 Estimation from Pairwise Comparisons: Sharp Minimax Bounds with Topology Dependence Nihar B. Shah, Sivaraman Balakrishnan, Joseph Bradley, Abhay Parekh, Kannan Ramchandran, Martin J. Wainwright

ICML 2016 Metadata-Conscious Anonymous Messaging Giulia Fanti, Peter Kairouz, Sewoong Oh, Kannan Ramchandran, Pramod Viswanath

NeurIPS 2015 An Active Learning Framework Using Sparse-Graph Codes for Sparse Polynomials and Graph Sketching Xiao Li, Kannan Ramchandran

AISTATS 2015 Estimation from Pairwise Comparisons: Sharp Minimax Bounds with Topology Dependence Nihar B. Shah, Sivaraman Balakrishnan, Joseph K. Bradley, Abhay Parekh, Kannan Ramchandran, Martin J. Wainwright

NeurIPS 2015 Parallel Correlation Clustering on Big Graphs Xinghao Pan, Dimitris Papailiopoulos, Samet Oymak, Benjamin Recht, Kannan Ramchandran, Michael I Jordan