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Raman Arora

64 papers · 2009–2025 · 12 conferences · across top CS/AI conferences

Achievements

🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🌍 Conference Polyglot (12) πŸƒ Academic Marathon (16) 🌈 Renaissance Researcher (9) πŸŒ‰ Interdisciplinary Bridge πŸ—ΊοΈ Taxonomy Completionist (21) 🐝 Cross-Pollinator (14) 🏠 Conference Loyalist (27) 🀝 Dynamic Duo (13) πŸ‘‘ Triple Crown 🌱 Topic Pioneer πŸ† Keyword Champion (3) πŸ† Grand Slam πŸ”¬ Deep Specialist (22) πŸ—ƒοΈ Keyword Collector (72) πŸ”₯ Unstoppable (13) πŸš€ Conference Pioneer ⚑ Prolific Year (9) πŸ“ˆ Trend Setter πŸ’Ž Century Club (64)

Conferences

NIPS (27) ICML (18) AISTATS (5) AAAI (2) ACL (2) ICLR (2) JMLR (2) NAACL (2) ALT (1) COLT (1) INTERSPEECH (1) UAI (1)

Top co-authors

Poorya Mianjy (13) Enayat Ullah (12) Thanh Nguyen-Tang (10) Teodor Vanislavov Marinov (7) Yunjuan Wang (7) Tuo Zhao (6) Han Liu (5) Raef Bassily (4) Michael Menart (4) Vladimir braverman (4)

Research topics

Differential Privacy (5)

Keywords

differential privacy (8) stochastic optimization (7) representation learning (7) neural network (7) stochastic gradient descent (7) principal component analysis (6) convex optimization (5) generalization bound (4) regret bound (4) gradient descent (4) transfer learning (4) canonical correlation analysis (4) streaming algorithm (4) adversarial training (3) robust optimization (3) distributed learning (3) multi-view learning (3) online learning (3) offline reinforcement learning (3) adversarial robustness (3)

Papers

Backdoor Attacks in Token Selection of Attention Mechanism ICML 2025
Policy-Regret Minimization in Markov Games with Function Approximation ICML 2025
Differentially Private Non-Convex Optimization under the KL Condition with Optimal Rates ALT 2024
On The Statistical Complexity of Offline Decision-Making ICML 2024
Adversarially Robust Multi-task Representation Learning NIPS 2024
On the Stability and Generalization of Meta-Learning NIPS 2024
Adversarially Robust Hypothesis Transfer Learning ICML 2024
Offline Multitask Representation Learning for Reinforcement Learning NIPS 2024
Learning in Markov Games with Adaptive Adversaries: Policy Regret, Fundamental Barriers, and Efficient Algorithms NIPS 2024
Stability and Generalization of Adversarial Training for Shallow Neural Networks with Smooth Activation NIPS 2024
Public-data Assisted Private Stochastic Optimization: Power and Limitations NIPS 2024
Benign Overfitting in Adversarial Training of Neural Networks ICML 2024
On Sample-Efficient Offline Reinforcement Learning: Data Diversity, Posterior Sampling and Beyond NIPS 2023
VIPeR: Provably Efficient Algorithm for Offline RL with Neural Function Approximation ICLR 2023
Optimistic Rates for Multi-Task Representation Learning NIPS 2023
Robustness Guarantees for Adversarially Trained Neural Networks NIPS 2023
Multi-Agent Learning with Heterogeneous Linear Contextual Bandits NIPS 2023
On Instance-Dependent Bounds for Offline Reinforcement Learning with Linear Function Approximation AAAI 2023
A Risk-Sensitive Approach to Policy Optimization AAAI 2023
From Adaptive Query Release to Machine Unlearning ICML 2023
Faster Rates of Convergence to Stationary Points in Differentially Private Optimization ICML 2023
Adversarial Robustness is at Odds with Lazy Training NIPS 2022
Differentially Private Generalized Linear Models Revisited NIPS 2022
Machine Unlearning via Algorithmic Stability COLT 2021
Corralling Stochastic Bandit Algorithms AISTATS 2021
Differentially Private Analysis on Graph Streams AISTATS 2021
Dropout: Explicit Forms and Capacity Control ICML 2021
Robust Learning for Data Poisoning Attacks ICML 2021
FetchSGD: Communication-Efficient Federated Learning with Sketching ICML 2020
On Convergence and Generalization of Dropout Training NIPS 2020
Adversarial Robustness of Supervised Sparse Coding NIPS 2020
On Differentially Private Graph Sparsification and Applications NIPS 2019
On Dropout and Nuclear Norm Regularization ICML 2019
On Fast Convergence of Proximal Algorithms for SQRT-Lasso Optimization: Don’t Worry About its Nonsmooth Loss Function UAI 2019
Communication-efficient Distributed SGD with Sketching NIPS 2019
Bandits with Feedback Graphs and Switching Costs NIPS 2019
Efficient Convex Relaxations for Streaming PCA NIPS 2019
Deep Generalized Canonical Correlation Analysis ACL 2019
Differentially Private Robust Low-Rank Approximation NIPS 2018
The Physical Systems Behind Optimization Algorithms NIPS 2018
Streaming Kernel PCA with $\tilde{O}(\sqrt{n})$ Random Features NIPS 2018
Policy Regret in Repeated Games NIPS 2018
Understanding Deep Neural Networks with Rectified Linear Units ICLR 2018
Streaming Principal Component Analysis in Noisy Setting ICML 2018
Stochastic PCA with $\ell_2$ and $\ell_1$ Regularization ICML 2018
On the Implicit Bias of Dropout ICML 2018
On Faster Convergence of Cyclic Block Coordinate Descent-type Methods for Strongly Convex Minimization JMLR 2018
Stochastic Approximation for Canonical Correlation Analysis NIPS 2017
A Speaker Adaptive DNN Training Approach for Speaker-Independent Acoustic Inversion INTERSPEECH 2017
Embedding Lexical Features via Low-Rank Tensors NAACL 2016
An Improved Convergence Analysis of Cyclic Block Coordinate Descent-type Methods for Strongly Convex Minimization AISTATS 2016
Learning Multiview Embeddings of Twitter Users ACL 2016
Disease Trajectory Maps NIPS 2016
Stochastic Optimization for Multiview Representation Learning using Partial Least Squares ICML 2016
Stochastic Variance Reduced Optimization for Nonconvex Sparse Learning ICML 2016
On Deep Multi-View Representation Learning ICML 2015
Multiview LSA: Representation Learning via Generalized CCA NAACL 2015
Accelerated Mini-batch Randomized Block Coordinate Descent Method NIPS 2014
Robust Stochastic Principal Component Analysis AISTATS 2014
Deep Canonical Correlation Analysis ICML 2013
Stochastic Optimization of PCA with Capped MSG NIPS 2013
Similarity-based Clustering by Left-Stochastic Matrix Factorization JMLR 2013
Consensus Ranking with Signed Permutations AISTATS 2013
On Learning Rotations NIPS 2009