← Authors

Ambuj Tewari

102 papers · 2006–2025 · 11 conferences · across top CS/AI conferences

Achievements

πŸ—ΊοΈ Taxonomy Completionist (33) πŸƒ Academic Marathon (19) 🧭 Keyword Pioneer πŸŒ‰ Interdisciplinary Bridge 🌍 Conference Polyglot (11) 🐣 Hot Topic Early Bird 🌈 Renaissance Researcher (6) 🐝 Cross-Pollinator (13) 🏠 Conference Loyalist (39) 🌟 Keyword Trendsetter Combo (3) πŸ† Keyword Champion (2) πŸ‘‘ Triple Crown 🌱 Topic Pioneer πŸ”¬ Deep Specialist (16) 🀝 Dynamic Duo (14) πŸ’Ž Century Club (102) πŸ—ƒοΈ Keyword Collector (178) πŸ“ˆ Trend Setter πŸš€ Conference Pioneer πŸ”₯ Unstoppable (16) ⚑ Prolific Year (10) ❓ The Questioner (3)

Conferences

NIPS (39) AISTATS (18) ICML (11) JMLR (10) COLT (7) UAI (7) ALT (4) ICLR (2) IJCAI (2) CLEAR (1) L4DC (1)

Top co-authors

Vinod Raman (14) UNIQUE SUBEDI (12) Karthik Sridharan (8) Ziping Xu (7) Kihyuk Hong (6) Yangyi Lu (5) Alexander Rakhlin (5) Inderjit S. Dhillon (5) Young Hun Jung (5) Amirhossein Meisami (5)

Research topics

Learning Paradigms (1) Optimization (1) Learning Paradigms (1) Differential Privacy (1) Privacy (1)

Keywords

online learning (25) regret bound (23) multiclass classification (8) convex optimization (7) multi-armed bandit (7) learning theory (6) rademacher complexity (5) markov decision process (5) thompson sampling (5) learning to rank (5) reinforcement learning (4) generalization bound (4) stochastic optimization (4) binary classification (4) regret analysis (3) pac learning (3) differential privacy (3) multi-class classification (3) causal inference (3) feature selection (3)

Papers

A Theoretical Framework for Partially-Observed Reward States in RLHF ICLR 2025
Learning Infinite-Horizon Average-Reward Linear Mixture MDPs of Bounded Span AISTATS 2025
On the Benefits of Active Data Collection in Operator Learning ICML 2025
Generation through the lens of learning theory COLT 2025
A Computationally Efficient Algorithm for Infinite-Horizon Average-Reward Linear MDPs ICML 2025
Leveraging Offline Data in Linear Latent Contextual Bandits ICML 2025
A Unified Theory of Supervised Online Learnability ALT 2025
Reinforcement Learning for Infinite-Horizon Average-Reward Linear MDPs via Approximation by Discounted-Reward MDPs AISTATS 2025
The Complexity of Sequential Prediction in Dynamical Systems L4DC 2025
A Primal-Dual Algorithm for Offline Constrained Reinforcement Learning with Linear MDPs ICML 2024
Variational Inference with Coverage Guarantees in Simulation-Based Inference ICML 2024
A Characterization of Multioutput Learnability JMLR 2024
On the Computational Complexity of Private High-dimensional Model Selection NIPS 2024
Online Classification with Predictions NIPS 2024
Smoothed Online Classification can be Harder than Batch Classification NIPS 2024
A Primal-Dual-Critic Algorithm for Offline Constrained Reinforcement Learning AISTATS 2024
Sequence Length Independent Norm-Based Generalization Bounds for Transformers AISTATS 2024
Offline Policy Evaluation and Optimization Under Confounding AISTATS 2024
Conformal Contextual Robust Optimization AISTATS 2024
Multiclass Online Learnability under Bandit Feedback ALT 2024
Online Infinite-Dimensional Regression: Learning Linear Operators ALT 2024
Apple Tasting: Combinatorial Dimensions and Minimax Rates COLT 2024
Online Learning with Set-valued Feedback COLT 2024
Sample Efficient Myopic Exploration Through Multitask Reinforcement Learning with Diverse Tasks ICLR 2024
On Proper Learnability between Average- and Worst-case Robustness NIPS 2023
Thompson Sampling for High-Dimensional Sparse Linear Contextual Bandits ICML 2023
An Optimization-based Algorithm for Non-stationary Kernel Bandits without Prior Knowledge AISTATS 2023
On the Learnability of Multilabel Ranking NIPS 2023
Learning Mixtures of Markov Chains and MDPs ICML 2023
Learning in online MDPs: is there a price for handling the communicating case? UAI 2023
Multiclass Online Learning and Uniform Convergence COLT 2023
Online Agnostic Multiclass Boosting NIPS 2022
Weighted Gaussian Process Bandits for Non-stationary Environments AISTATS 2022
Efficient Reinforcement Learning with Prior Causal Knowledge CLEAR 2022
Adaptive Sampling for Discovery NIPS 2022
On the Statistical Benefits of Curriculum Learning ICML 2022
Balancing adaptability and non-exploitability in repeated games UAI 2022
Low-Rank Generalized Linear Bandit Problems AISTATS 2021
Decision Making Problems with Funnel Structure: A Multi-Task Learning Approach with Application to Email Marketing Campaigns AISTATS 2021
Thompson sampling for Markov games with piecewise stationary opponent policies UAI 2021
Causal Bandits with Unknown Graph Structure NIPS 2021
Representation Learning Beyond Linear Prediction Functions NIPS 2021
Randomized Exploration for Non-Stationary Stochastic Linear Bandits UAI 2020
On the Equivalence between Online and Private Learnability beyond Binary Classification NIPS 2020
Reinforcement Learning in Factored MDPs: Oracle-Efficient Algorithms and Tighter Regret Bounds for the Non-Episodic Setting NIPS 2020
TorsionNet: A Reinforcement Learning Approach to Sequential Conformer Search NIPS 2020
Sample Complexity of Reinforcement Learning using Linearly Combined Model Ensembles AISTATS 2020
Regret Analysis of Bandit Problems with Causal Background Knowledge UAI 2020
What You See May Not Be What You Get: UCB Bandit Algorithms Robust to $\varepsilon$-Contamination UAI 2020
No-regret Exploration in Contextual Reinforcement Learning UAI 2020
Online Multiclass Boosting with Bandit Feedback AISTATS 2019
Generalization Bounds in the Predict-then-Optimize Framework NIPS 2019
On the Optimality of Perturbations in Stochastic and Adversarial Multi-armed Bandit Problems NIPS 2019
Regret Bounds for Thompson Sampling in Episodic Restless Bandit Problems NIPS 2019
Online Learning via the Differential Privacy Lens NIPS 2019
Cost-Sensitive Learning with Noisy Labels JMLR 2018
Beyond the Hazard Rate: More Perturbation Algorithms for Adversarial Multi-armed Bandits JMLR 2018
Markov Decision Processes with Continuous Side Information ALT 2018
But How Does It Work in Theory? Linear SVM with Random Features NIPS 2018
Active Learning for Non-Parametric Regression Using Purely Random Trees NIPS 2018
Online Boosting Algorithms for Multi-label Ranking AISTATS 2018
Action Centered Contextual Bandits NIPS 2017
Online Learning to Rank with Top-k Feedback JMLR 2017
Online multiclass boosting NIPS 2017
On Structural Properties of MDPs that Bound Loss Due to Shallow Planning IJCAI 2016
Mixture Proportion Estimation via Kernel Embeddings of Distributions ICML 2016
Phased Exploration with Greedy Exploitation in Stochastic Combinatorial Partial Monitoring Games NIPS 2016
Predtron: A Family of Online Algorithms for General Prediction Problems NIPS 2015
Online Ranking with Top-1 Feedback AISTATS 2015
Online Learning via Sequential Complexities JMLR 2015
Fighting Bandits with a New Kind of Smoothness NIPS 2015
Generalization error bounds for learning to rank: Does the length of document lists matter? ICML 2015
Alternating Minimization for Regression Problems with Vector-valued Outputs NIPS 2015
Convex Calibrated Surrogates for Hierarchical Classification ICML 2015
Online Linear Optimization via Smoothing COLT 2014
Prediction and Clustering in Signed Networks: A Local to Global Perspective JMLR 2014
On Iterative Hard Thresholding Methods for High-dimensional M-Estimation NIPS 2014
Learning with Noisy Labels NIPS 2013
Convex Calibrated Surrogates for Low-Rank Loss Matrices with Applications to Subset Ranking Losses NIPS 2013
On Robust Estimation of High Dimensional Generalized Linear Models IJCAI 2013
Perturbation based Large Margin Approach for Ranking AISTATS 2012
Regularization Techniques for Learning with Matrices JMLR 2012
Feature Clustering for Accelerating Parallel Coordinate Descent NIPS 2012
Online Learning: Beyond Regret COLT 2011
Improved Regret Guarantees for Online Smooth Convex Optimization with Bandit Feedback AISTATS 2011
Stochastic Methods for -regularized Loss Minimization JMLR 2011
On NDCG Consistency of Listwise Ranking Methods AISTATS 2011
Greedy Algorithms for Structurally Constrained High Dimensional Problems NIPS 2011
On the Universality of Online Mirror Descent NIPS 2011
Online Learning: Stochastic, Constrained, and Smoothed Adversaries NIPS 2011
Orthogonal Matching Pursuit with Replacement NIPS 2011
Nearest Neighbor based Greedy Coordinate Descent NIPS 2011
Complexity-Based Approach to Calibration with Checking Rules COLT 2011
Learning Exponential Families in High-Dimensions: Strong Convexity and Sparsity AISTATS 2010
Online Learning: Random Averages, Combinatorial Parameters, and Learnability NIPS 2010
Smoothness, Low Noise and Fast Rates NIPS 2010
On the Complexity of Linear Prediction: Risk Bounds, Margin Bounds, and Regularization NIPS 2008
On the Generalization Ability of Online Strongly Convex Programming Algorithms NIPS 2008
Sparseness vs Estimating Conditional Probabilities: Some Asymptotic Results JMLR 2007
Optimistic Linear Programming gives Logarithmic Regret for Irreducible MDPs NIPS 2007
On the Consistency of Multiclass Classification Methods JMLR 2007
Sample Complexity of Policy Search with Known Dynamics NIPS 2006