Sanjay Shakkottai

papers

520

total citations

papers (14)

Solving Linear Inverse Problems Provably via Posterior Sampling with Latent Diffusion Models

NEURIPS 2023arXiv

151

citations

FedAvg with Fine Tuning: Local Updates Lead to Representation Learning

NEURIPS 2022arXiv

118

citations

Semantic Image Inversion and Editing using Rectified Stochastic Differential Equations

ICLR 2025arXiv

106

citations

Beyond First-Order Tweedie: Solving Inverse Problems using Latent Diffusion

CVPR 2024arXiv

citations

Finite-Sample Analysis of Off-Policy TD-Learning via Generalized Bellman Operators

NEURIPS 2021arXiv

citations

Non-Stationary Bandits under Recharging Payoffs: Improved Planning with Sublinear Regret

NEURIPS 2022arXiv

citations

Mix and Match: An Optimistic Tree-Search Approach for Learning Models from Mixture Distributions

NEURIPS 2020arXiv

citations

Constrained Posterior Sampling: Time Series Generation with Hard Constraints

NEURIPS 2025arXiv

citations

Finite-Sample Analysis of Contractive Stochastic Approximation Using Smooth Convex Envelopes

NEURIPS 2020

citations

Provable Meta-Learning with Low-Rank Adaptations

NEURIPS 2025arXiv

citations

Sanjay Shakkottai

papers (14)

Solving Linear Inverse Problems Provably via Posterior Sampling with Latent Diffusion Models

FedAvg with Fine Tuning: Local Updates Lead to Representation Learning

Semantic Image Inversion and Editing using Rectified Stochastic Differential Equations

Beyond First-Order Tweedie: Solving Inverse Problems using Latent Diffusion

Finite-Sample Analysis of Off-Policy TD-Learning via Generalized Bellman Operators

Minimax Regret for Cascading Bandits

Task-Robust Model-Agnostic Meta-Learning

Provable Multi-Task Representation Learning by Two-Layer ReLU Neural Networks

Applications of Common Entropy for Causal Inference

Non-Stationary Bandits under Recharging Payoffs: Improved Planning with Sublinear Regret

Mix and Match: An Optimistic Tree-Search Approach for Learning Models from Mixture Distributions

Constrained Posterior Sampling: Time Series Generation with Hard Constraints

Finite-Sample Analysis of Contractive Stochastic Approximation Using Smooth Convex Envelopes

Provable Meta-Learning with Low-Rank Adaptations

papers (14)

Solving Linear Inverse Problems Provably via Posterior Sampling with Latent Diffusion Models

FedAvg with Fine Tuning: Local Updates Lead to Representation Learning

Semantic Image Inversion and Editing using Rectified Stochastic Differential Equations

Beyond First-Order Tweedie: Solving Inverse Problems using Latent Diffusion

Finite-Sample Analysis of Off-Policy TD-Learning via Generalized Bellman Operators

Minimax Regret for Cascading Bandits

Task-Robust Model-Agnostic Meta-Learning

Provable Multi-Task Representation Learning by Two-Layer ReLU Neural Networks

Applications of Common Entropy for Causal Inference

Non-Stationary Bandits under Recharging Payoffs: Improved Planning with Sublinear Regret

Mix and Match: An Optimistic Tree-Search Approach for Learning Models from Mixture Distributions

Constrained Posterior Sampling: Time Series Generation with Hard Constraints

Finite-Sample Analysis of Contractive Stochastic Approximation Using Smooth Convex Envelopes

Provable Meta-Learning with Low-Rank Adaptations