Sanjay Shakkottai
39 papers · 2016–2025 · 7 conferences · across top CS/AI conferences
Achievements
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π§ Keyword Pioneer π Renaissance Researcher (5) π Interdisciplinary Bridge πΊοΈ Taxonomy Completionist (19) π£ Hot Topic Early Bird
π§
Keyword Pioneer
π£
Hot Topic Early Bird
π
Renaissance Researcher
(5)
π¬
Deep Specialist
(10)
π
Keyword Champion
(2)
π€
Dynamic Duo
(13)
ποΈ
Keyword Collector
(163)
β‘
Prolific Year
(9)
π
Century Club
(39)
π₯
Unstoppable
(10)
Conferences
NIPS (13)
ICML (12)
AISTATS (6)
COLT (3)
ICLR (3)
CVPR (1)
JMLR (1)
Top co-authors
Research topics
Keywords
regret bound
(11)
multi-armed bandit
(8)
contextual bandit
(4)
online learning
(4)
representation learning
(4)
causal inference
(4)
upper confidence bound
(3)
hierarchical partitioning
(2)
conditional independence
(2)
finite-sample analysis
(2)
stochastic gradient descent
(2)
ucb algorithm
(2)
multi-task learning
(2)
stochastic optimization
(2)
reinforcement learning
(2)
importance sampling
(2)
stochastic approximation
(2)
non-convex optimization
(2)
black-box optimization
(2)
posterior sampling
(2)
Papers
RB-Modulation: Training-Free Stylization using Reference-Based Modulation
ICLR 2025
Semantic Image Inversion and Editing using Rectified Stochastic Differential Equations
ICLR 2025
Infilling Score: A Pretraining Data Detection Algorithm for Large Language Models
ICLR 2025
Beyond First-Order Tweedie: Solving Inverse Problems using Latent Diffusion
CVPR 2024
In-Context Learning with Transformers: Softmax Attention Adapts to Function Lipschitzness
NIPS 2024
Provable Multi-Task Representation Learning by Two-Layer ReLU Neural Networks
ICML 2024
Beyond Uniform Smoothness: A Stopped Analysis of Adaptive SGD
COLT 2023
PAC Generalization via Invariant Representations
ICML 2023
InfoNCE Loss Provably Learns Cluster-Preserving Representations
COLT 2023
Solving Linear Inverse Problems Provably via Posterior Sampling with Latent Diffusion Models
NIPS 2023
Collaborative Multi-Agent Heterogeneous Multi-Armed Bandits
ICML 2023
MAML and ANIL Provably Learn Representations
ICML 2022
Regret Bounds for Stochastic Shortest Path Problems with Linear Function Approximation
ICML 2022
Linear Bandit Algorithms with Sublinear Time Complexity
ICML 2022
FedAvg with Fine Tuning: Local Updates Lead to Representation Learning
NIPS 2022
Non-Stationary Bandits under Recharging Payoffs: Improved Planning with Sublinear Regret
NIPS 2022
Minimax Regret for Cascading Bandits
NIPS 2022
Improved Algorithms for Misspecified Linear Markov Decision Processes
AISTATS 2022
The Power of Adaptivity in SGD: Self-Tuning Step Sizes with Unbounded Gradients and Affine Variance
COLT 2022
Asymptotically-Optimal Gaussian Bandits with Side Observations
ICML 2022
Exploiting Shared Representations for Personalized Federated Learning
ICML 2021
Finite-Sample Analysis of Off-Policy TD-Learning via Generalized Bellman Operators
NIPS 2021
Combinatorial Blocking Bandits with Stochastic Delays
ICML 2021
Contextual Blocking Bandits
AISTATS 2021
The Gossiping Insert-Eliminate Algorithm for Multi-Agent Bandits
AISTATS 2020
Finite-Sample Analysis of Contractive Stochastic Approximation Using Smooth Convex Envelopes
NIPS 2020
Task-Robust Model-Agnostic Meta-Learning
NIPS 2020
Applications of Common Entropy for Causal Inference
NIPS 2020
Mix and Match: An Optimistic Tree-Search Approach for Learning Models from Mixture Distributions
NIPS 2020
Pareto Optimal Streaming Unsupervised Classification
ICML 2019
Blocking Bandits
NIPS 2019
Noisy Blackbox Optimization using Multi-fidelity Queries: A Tree Search Approach
AISTATS 2019
The Search Problem in Mixture Models
JMLR 2018
Contextual Bandits with Stochastic Experts
AISTATS 2018
Multi-Fidelity Black-Box Optimization with Hierarchical Partitions
ICML 2018
Identifying Best Interventions through Online Importance Sampling
ICML 2017
Contextual Bandits with Latent Confounders: An NMF Approach
AISTATS 2017
Model-Powered Conditional Independence Test
NIPS 2017
Regret of Queueing Bandits
NIPS 2016