← Authors

Ness Shroff

28 papers · 2017–2025 · 5 conferences · across top CS/AI conferences

Achievements

πŸŒ‰ Interdisciplinary Bridge 🐝 Cross-Pollinator (11) 🌈 Renaissance Researcher (7) πŸ—ΊοΈ Taxonomy Completionist (10) 🐣 Hot Topic Early Bird 🌍 Conference Polyglot (5) πŸƒ Academic Marathon (8) 🧭 Keyword Pioneer πŸ† Keyword Champion (2) 🀝 Dynamic Duo (10) πŸ‘‘ Triple Crown πŸ—ƒοΈ Keyword Collector (83) ❓ The Questioner ⚑ Prolific Year (6) πŸ’Ž Century Club (28) πŸ”₯ Unstoppable (9) πŸ“ˆ Trend Setter

Conferences

ICLR (9) ICML (8) NIPS (6) AISTATS (4) IJCAI (1)

Top co-authors

Peizhong Ju (10) Yingbin Liang (9) Arnob Ghosh (6) Xingyu Zhou (5) Sinong Wang (4) Sen Lin (4) Ming Shi (3) Wenbo Ren (3) Jia Liu (2) Fang Liu (2)

Keywords

regret bound (7) sample complexity (3) stochastic optimization (2) upper confidence bound (2) non-stationary environment (2) generalization error (2) linear function approximation (2) neural tangent kernel (2) online learning (2) active learning (2) learning theory (2) model-free reinforcement learning (2) coded computation (2) gradient descent (2) constrained markov decision process (2) sublinear regret (2) constrained reinforcement learning (1) convex optimization (1) catastrophic forgetting (1) policy optimization (1)

Papers

Broadening Target Distributions for Accelerated Diffusion Models via a Novel Analysis Approach ICLR 2025
How to Find the Exact Pareto Front for Multi-Objective MDPs? ICLR 2025
Unlocking the Power of Rehearsal in Continual Learning: A Theoretical Perspective ICML 2025
Provably Efficient RL for Linear MDPs under Instantaneous Safety Constraints in Non-Convex Feature Spaces ICML 2025
Theory on Score-Mismatched Diffusion Models and Zero-Shot Conditional Samplers ICLR 2025
Theory on Mixture-of-Experts in Continual Learning ICLR 2025
Achieving Fairness in Multi-Agent MDP Using Reinforcement Learning ICLR 2024
Towards Achieving Sub-linear Regret and Hard Constraint Violation in Model-free RL AISTATS 2024
Achieving Sample and Computational Efficient Reinforcement Learning by Action Space Reduction via Grouping ICLR 2024
A Near-Optimal Algorithm for Safe Reinforcement Learning Under Instantaneous Hard Constraints ICML 2023
Non-Convex Bilevel Optimization with Time-Varying Objective Functions NIPS 2023
Provably Efficient Model-Free Algorithms for Non-stationary CMDPs AISTATS 2023
Theoretical Characterization of the Generalization Performance of Overfitted Meta-Learning ICLR 2023
Achieving Sub-linear Regret in Infinite Horizon Average Reward Constrained MDP with Linear Function Approximation ICLR 2023
Near-Optimal Adversarial Reinforcement Learning with Switching Costs ICLR 2023
Theory on Forgetting and Generalization of Continual Learning ICML 2023
Weighted Gaussian Process Bandits for Non-stationary Environments AISTATS 2022
On the Generalization Power of the Overfitted Three-Layer Neural Tangent Kernel Model NIPS 2022
Provably Efficient Model-Free Constrained RL with Linear Function Approximation NIPS 2022
On the Generalization Power of Overfitted Two-Layer Neural Tangent Kernel Models ICML 2021
Sample Complexity Bounds for Active Ranking from Multi-wise Comparisons NIPS 2021
The Sample Complexity of Best-$k$ Items Selection from Pairwise Comparisons ICML 2020
Data Poisoning Attacks on Stochastic Bandits ICML 2019
On Sample Complexity Upper and Lower Bounds for Exact Ranking from Noisy Comparisons NIPS 2019
Computation Efficient Coded Linear Transform AISTATS 2019
UCBoost: A Boosting Approach to Tame Complexity and Optimality for Stochastic Bandits IJCAI 2018
Coded Sparse Matrix Multiplication ICML 2018
A New Alternating Direction Method for Linear Programming NIPS 2017