Arora, Raman

65 publications

ICML 2025 Backdoor Attacks in Token Selection of Attention Mechanism Yunjuan Wang, Raman Arora

ICML 2025 Policy-Regret Minimization in Markov Games with Function Approximation Thanh Nguyen-Tang, Raman Arora

NeurIPS 2025 When Does Curriculum Learning Help? a Theoretical Perspective Raman Arora, Yunjuan Wang, Kaibo Zhang

ICML 2024 Adversarially Robust Hypothesis Transfer Learning Yunjuan Wang, Raman Arora

NeurIPS 2024 Adversarially Robust Multi-Task Representation Learning Austin Watkins, Thanh Nguyen-Tang, Enayat Ullah, Raman Arora

ICML 2024 Benign Overfitting in Adversarial Training of Neural Networks Yunjuan Wang, Kaibo Zhang, Raman Arora

ALT 2024 Differentially Private Non-Convex Optimization Under the KL Condition with Optimal Rates Michael Menart, Enayat Ullah, Raman Arora, Raef Bassily, Cristobal Guzman

NeurIPS 2024 Learning in Markov Games with Adaptive Adversaries: Policy Regret, Fundamental Barriers, and Efficient Algorithms Thanh Nguyen-Tang, Raman Arora

NeurIPS 2024 Offline Multitask Representation Learning for Reinforcement Learning Haque Ishfaq, Thanh Nguyen-Tang, Songtao Feng, Raman Arora, Mengdi Wang, Ming Yin, Doina Precup

NeurIPS 2024 On the Stability and Generalization of Meta-Learning Yunjuan Wang, Raman Arora

ICML 2024 On the Statistical Complexity of Offline Decision-Making Thanh Nguyen-Tang, Raman Arora

NeurIPS 2024 Public-Data Assisted Private Stochastic Optimization: Power and Limitations Enayat Ullah, Michael Menart, Raef Bassily, Cristóbal Guzmán, Raman Arora

NeurIPS 2024 Stability and Generalization of Adversarial Training for Shallow Neural Networks with Smooth Activation Kaibo Zhang, Yunjuan Wang, Raman Arora

AAAI 2023 A Risk-Sensitive Approach to Policy Optimization Jared Markowitz, Ryan W. Gardner, Ashley J. Llorens, Raman Arora, I-Jeng Wang

TMLR 2023 Clustering Using Approximate Nearest Neighbour Oracles Enayat Ullah, Harry Lang, Raman Arora, Vladimir Braverman

ICML 2023 Faster Rates of Convergence to Stationary Points in Differentially Private Optimization Raman Arora, Raef Bassily, Tomás González, Cristóbal A Guzmán, Michael Menart, Enayat Ullah

ICML 2023 From Adaptive Query Release to Machine Unlearning Enayat Ullah, Raman Arora

TMLR 2023 Generalization Bounds for Kernel Canonical Correlation Analysis Enayat Ullah, Raman Arora

NeurIPS 2023 Multi-Agent Learning with Heterogeneous Linear Contextual Bandits Anh Do, Thanh Nguyen-Tang, Raman Arora

AAAI 2023 On Instance-Dependent Bounds for Offline Reinforcement Learning with Linear Function Approximation Thanh Nguyen-Tang, Ming Yin, Sunil Gupta, Svetha Venkatesh, Raman Arora

NeurIPS 2023 On Sample-Efficient Offline Reinforcement Learning: Data Diversity, Posterior Sampling and Beyond Thanh Nguyen-Tang, Raman Arora

NeurIPS 2023 Optimistic Rates for Multi-Task Representation Learning Austin Watkins, Enayat Ullah, Thanh Nguyen-Tang, Raman Arora

NeurIPS 2023 Robustness Guarantees for Adversarially Trained Neural Networks Poorya Mianjy, Raman Arora

ICLR 2023 VIPeR: Provably Efficient Algorithm for Offline RL with Neural Function Approximation Thanh Nguyen-Tang, Raman Arora

NeurIPS 2022 Adversarial Robustness Is at Odds with Lazy Training Yunjuan Wang, Enayat Ullah, Poorya Mianjy, Raman Arora

NeurIPS 2022 Differentially Private Generalized Linear Models Revisited Raman Arora, Raef Bassily, Cristóbal Guzmán, Michael Menart, Enayat Ullah

AISTATS 2021 Corralling Stochastic Bandit Algorithms Raman Arora, Teodor Vanislavov Marinov, Mehryar Mohri

AISTATS 2021 Differentially Private Analysis on Graph Streams Jalaj Upadhyay, Sarvagya Upadhyay, Raman Arora

ICML 2021 Dropout: Explicit Forms and Capacity Control Raman Arora, Peter Bartlett, Poorya Mianjy, Nathan Srebro

COLT 2021 Machine Unlearning via Algorithmic Stability Enayat Ullah, Tung Mai, Anup Rao, Ryan A. Rossi, Raman Arora

ICML 2021 Robust Learning for Data Poisoning Attacks Yunjuan Wang, Poorya Mianjy, Raman Arora

NeurIPS 2020 Adversarial Robustness of Supervised Sparse Coding Jeremias Sulam, Ramchandran Muthukumar, Raman Arora

ICLR 2020 Dropout: Explicit Forms and Capacity Control Raman Arora, Peter Bartlett, Poorya Mianjy, Nathan Srebro

ICML 2020 FetchSGD: Communication-Efficient Federated Learning with Sketching Daniel Rothchild, Ashwinee Panda, Enayat Ullah, Nikita Ivkin, Ion Stoica, Vladimir Braverman, Joseph Gonzalez, Raman Arora

NeurIPS 2020 On Convergence and Generalization of Dropout Training Poorya Mianjy, Raman Arora

NeurIPS 2019 Bandits with Feedback Graphs and Switching Costs Raman Arora, Teodor Vanislavov Marinov, Mehryar Mohri

NeurIPS 2019 Communication-Efficient Distributed SGD with Sketching Nikita Ivkin, Daniel Rothchild, Enayat Ullah, Vladimir Braverman, Ion Stoica, Raman Arora

NeurIPS 2019 Efficient Convex Relaxations for Streaming PCA Raman Arora, Teodor Vanislavov Marinov

NeurIPS 2019 On Differentially Private Graph Sparsification and Applications Raman Arora, Jalaj Upadhyay

ICML 2019 On Dropout and Nuclear Norm Regularization Poorya Mianjy, Raman Arora

UAI 2019 On Fast Convergence of Proximal Algorithms for SQRT-Lasso Optimization: Don’t Worry About Its Nonsmooth Loss Function Xinguo Li, Haoming Jiang, Jarvis Haupt, Raman Arora, Han Liu, Mingyi Hong, Tuo Zhao

NeurIPS 2018 Differentially Private Robust Low-Rank Approximation Raman Arora, Vladimir Braverman, Jalaj Upadhyay

ICML 2018 On the Implicit Bias of Dropout Poorya Mianjy, Raman Arora, Rene Vidal

NeurIPS 2018 Policy Regret in Repeated Games Raman Arora, Michael Dinitz, Teodor Vanislavov Marinov, Mehryar Mohri

ICML 2018 Stochastic PCA with $\ell_2$ and $\ell_1$ Regularization Poorya Mianjy, Raman Arora

NeurIPS 2018 Streaming Kernel PCA with $\tilde{O}(\sqrt{n})$ Random Features Enayat Ullah, Poorya Mianjy, Teodor Vanislavov Marinov, Raman Arora

ICML 2018 Streaming Principal Component Analysis in Noisy Setting Teodor Vanislavov Marinov, Poorya Mianjy, Raman Arora

NeurIPS 2018 The Physical Systems Behind Optimization Algorithms Lin Yang, Raman Arora, Vladimir Braverman, Tuo Zhao

ICLR 2018 Understanding Deep Neural Networks with Rectified Linear Units Raman Arora, Amitabh Basu, Poorya Mianjy, Anirbit Mukherjee

NeurIPS 2017 Stochastic Approximation for Canonical Correlation Analysis Raman Arora, Teodor Vanislavov Marinov, Poorya Mianjy, Nati Srebro

AISTATS 2016 An Improved Convergence Analysis of Cyclic Block Coordinate Descent-Type Methods for Strongly Convex Minimization Xingguo Li, Tuo Zhao, Raman Arora, Han Liu, Mingyi Hong

NeurIPS 2016 Disease Trajectory Maps Peter Schulam, Raman Arora

ICML 2016 Stochastic Optimization for Multiview Representation Learning Using Partial Least Squares Raman Arora, Poorya Mianjy, Teodor Marinov

ICML 2016 Stochastic Variance Reduced Optimization for Nonconvex Sparse Learning Xingguo Li, Tuo Zhao, Raman Arora, Han Liu, Jarvis Haupt

ICML 2015 On Deep Multi-View Representation Learning Weiran Wang, Raman Arora, Karen Livescu, Jeff Bilmes

NeurIPS 2014 Accelerated Mini-Batch Randomized Block Coordinate Descent Method Tuo Zhao, Mo Yu, Yiming Wang, Raman Arora, Han Liu

AISTATS 2014 Robust Stochastic Principal Component Analysis John Goes, Teng Zhang, Raman Arora, Gilad Lerman

AISTATS 2013 Consensus Ranking with Signed Permutations Raman Arora, Marina Meila

ICML 2013 Deep Canonical Correlation Analysis Galen Andrew, Raman Arora, Jeff Bilmes, Karen Livescu

JMLR 2013 Similarity-Based Clustering by Left-Stochastic Matrix Factorization Raman Arora, Maya R. Gupta, Amol Kapila, Maryam Fazel

NeurIPS 2013 Stochastic Optimization of PCA with Capped MSG Raman Arora, Andy Cotter, Nati Srebro

UAI 2012 Deterministic MDPs with Adversarial Rewards and Bandit Feedback Raman Arora, Ofer Dekel, Ambuj Tewari

ICML 2012 Online Bandit Learning Against an Adaptive Adversary: From Regret to Policy Regret Ofer Dekel, Ambuj Tewari, Raman Arora

ICML 2011 Clustering by Left-Stochastic Matrix Factorization Raman Arora, Maya R. Gupta, Amol Kapila, Maryam Fazel

NeurIPS 2009 On Learning Rotations Raman Arora