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Shie Mannor

143 papers · 2003–2025 · 15 conferences · across top CS/AI conferences

Achievements

πŸ—ΊοΈ Taxonomy Completionist (40) πŸƒ Academic Marathon (22) 🧭 Keyword Pioneer πŸŒ‰ Interdisciplinary Bridge 🌍 Conference Polyglot (15) 🐣 Hot Topic Early Bird 🌈 Renaissance Researcher (7) 🐝 Cross-Pollinator (13) 🏠 Conference Loyalist (41) 🌟 Keyword Trendsetter Combo (5) πŸ† Keyword Champion (3) πŸ‘‘ Triple Crown 🌱 Topic Pioneer πŸ”¬ Deep Specialist (18) 🀝 Dynamic Duo (19) πŸ† Grand Slam πŸ—ƒοΈ Keyword Collector (210) ❓ The Questioner (2) πŸ“ˆ Trend Setter πŸš€ Conference Pioneer πŸ”₯ Unstoppable (18) ⚑ Prolific Year (10) πŸ’Ž Century Club (143)

Conferences

ICML (47) NIPS (41) COLT (13) AAAI (11) JMLR (10) ICLR (7) UAI (4) AISTATS (2) CVPR (2) ACML (1) ALT (1) CORL (1) IJCAI (1) RSS (1) WACV (1)

Top co-authors

Yonathan Efroni (19) Gal Dalal (15) Huan Xu (13) Gal Chechik (12) Constantine Caramanis (11) Guy Tennenholtz (10) Nadav Merlis (9) Assaf Hallak (8) Jeongyeol Kwon (8) Aviv Tamar (7)

Research topics

Statistics (1)

Keywords

reinforcement learning (32) online learning (25) regret bound (21) multi-armed bandit (13) markov decision process (12) policy gradient (11) robust optimization (9) regret minimization (8) stochastic optimization (8) sample complexity (6) contextual bandit (6) policy optimization (6) value function (6) model-based reinforcement learning (6) policy iteration (5) game theory (5) deep reinforcement learning (5) temporal difference learning (5) thompson sampling (5) robust markov decision process (5)

Papers

On Bits and Bandits: Quantifying the Regret-Information Trade-off ICLR 2025
Policy Gradient with Tree Expansion ICML 2025
RL-RC-DoT: A Block-level RL agent for Task-Aware Video Compression CVPR 2025
A Classification View on Meta Learning Bandits ICML 2025
Reinforcement Learning with Segment Feedback ICML 2025
Global Convergence of Policy Gradient in Average Reward MDPs ICLR 2025
Efficient Value Iteration for s-rectangular Robust Markov Decision Processes ICML 2024
Sobolev Space Regularised Pre Density Models ICML 2024
Exploration-Driven Policy Optimization in RLHF: Theoretical Insights on Efficient Data Utilization ICML 2024
Bring Your Own (Non-Robust) Algorithm to Solve Robust MDPs by Estimating The Worst Kernel ICML 2024
Improving Token-Based World Models with Parallel Observation Prediction ICML 2024
Solving Non-rectangular Reward-Robust MDPs via Frequency Regularization AAAI 2024
Tree Search-Based Policy Optimization under Stochastic Execution Delay ICLR 2024
RL in Latent MDPs is Tractable: Online Guarantees via Off-Policy Evaluation NIPS 2024
Prospective Side Information for Latent MDPs ICML 2024
Train Hard, Fight Easy: Robust Meta Reinforcement Learning NIPS 2023
Learning Hidden Markov Models When the Locations of Missing Observations are Unknown ICML 2023
Planning and Learning with Adaptive Lookahead AAAI 2023
PPG Reloaded: An Empirical Study on What Matters in Phasic Policy Gradient ICML 2023
Learning to Initiate and Reason in Event-Driven Cascading Processes ICML 2023
Reward-Mixing MDPs with Few Latent Contexts are Learnable ICML 2023
Representation-Driven Reinforcement Learning ICML 2023
Optimization or Architecture: How to Hack Kalman Filtering NIPS 2023
Individualized Dosing Dynamics via Neural Eigen Decomposition NIPS 2023
Policy Gradient for Rectangular Robust Markov Decision Processes NIPS 2023
DiffStack: A Differentiable and Modular Control Stack for Autonomous Vehicles CORL 2022
Analysis of Stochastic Processes through Replay Buffers ICML 2022
Locality Matters: A Scalable Value Decomposition Approach for Cooperative Multi-Agent Reinforcement Learning AAAI 2022
On Covariate Shift of Latent Confounders in Imitation and Reinforcement Learning ICLR 2022
Online Apprenticeship Learning AAAI 2022
Reinforcement Learning for Datacenter Congestion Control AAAI 2022
Uncertainty Estimation Using Riemannian Model Dynamics for Offline Reinforcement Learning NIPS 2022
Tractable Optimality in Episodic Latent MABs NIPS 2022
Finite Sample Analysis Of Dynamic Regression Parameter Learning NIPS 2022
Coordinated Attacks against Contextual Bandits: Fundamental Limits and Defense Mechanisms ICML 2022
Optimizing Tensor Network Contraction Using Reinforcement Learning ICML 2022
The Geometry of Robust Value Functions ICML 2022
Actor-Critic based Improper Reinforcement Learning ICML 2022
Efficient Risk-Averse Reinforcement Learning NIPS 2022
Reinforcement Learning with a Terminator NIPS 2022
Bandits with partially observable confounded data UAI 2021
Action redundancy in reinforcement learning UAI 2021
Robust Value Iteration for Continuous Control Tasks RSS 2021
Reinforcement Learning in Reward-Mixing MDPs NIPS 2021
Improve Agents without Retraining: Parallel Tree Search with Off-Policy Correction NIPS 2021
Sim and Real: Better Together NIPS 2021
Twice regularized MDPs and the equivalence between robustness and regularization NIPS 2021
RL for Latent MDPs: Regret Guarantees and a Lower Bound NIPS 2021
Reinforcement Learning with Trajectory Feedback AAAI 2021
Lenient Regret for Multi-Armed Bandits AAAI 2021
Online Limited Memory Neural-Linear Bandits with Likelihood Matching ICML 2021
Controlling Graph Dynamics with Reinforcement Learning and Graph Neural Networks ICML 2021
Over-the-Air Adversarial Flickering Attacks Against Video Recognition Networks CVPR 2021
Optimizing Memory Placement using Evolutionary Graph Reinforcement Learning ICLR 2021
Acting in Delayed Environments with Non-Stationary Markov Policies ICLR 2021
Value Iteration in Continuous Actions, States and Time ICML 2021
Detecting Rewards Deterioration in Episodic Reinforcement Learning ICML 2021
Confidence-Budget Matching for Sequential Budgeted Learning ICML 2021
Known unknowns: Learning novel concepts using reasoning-by-elimination UAI 2021
Tight Lower Bounds for Combinatorial Multi-Armed Bandits COLT 2020
An adaptive stochastic optimization algorithm for resource allocation ALT 2020
Online Planning with Lookahead Policies NIPS 2020
Adaptive Trust Region Policy Optimization: Global Convergence and Faster Rates for Regularized MDPs AAAI 2020
Optimistic Policy Optimization with Bandit Feedback ICML 2020
Topic Modeling via Full Dependence Mixtures ICML 2020
Off-Policy Evaluation in Partially Observable Environments AAAI 2020
Scalable Detection of Offensive and Non-compliant Content / Logo in Product Images WACV 2020
Tight Regret Bounds for Model-Based Reinforcement Learning with Greedy Policies NIPS 2019
Batch-Size Independent Regret Bounds for the Combinatorial Multi-Armed Bandit Problem COLT 2019
Action Robust Reinforcement Learning and Applications in Continuous Control ICML 2019
Reward Constrained Policy Optimization ICLR 2019
The Natural Language of Actions ICML 2019
Exploration Conscious Reinforcement Learning Revisited ICML 2019
A Bayesian Approach to Robust Reinforcement Learning UAI 2019
Nonlinear Distributional Gradient Temporal-Difference Learning ICML 2019
On-Line Learning of Linear Dynamical Systems: Exponential Forgetting in Kalman Filters AAAI 2019
How to Combine Tree-Search Methods in Reinforcement Learning AAAI 2019
Value Propagation for Decentralized Networked Deep Multi-agent Reinforcement Learning NIPS 2019
Distributional Policy Optimization: An Alternative Approach for Continuous Control NIPS 2019
Learn What Not to Learn: Action Elimination with Deep Reinforcement Learning NIPS 2018
Multiple-Step Greedy Policies in Approximate and Online Reinforcement Learning NIPS 2018
Beyond the One-Step Greedy Approach in Reinforcement Learning ICML 2018
A General Approach to Multi-Armed Bandits Under Risk Criteria COLT 2018
Finite Sample Analysis of Two-Timescale Stochastic Approximation with Applications to Reinforcement Learning COLT 2018
Multi-objective Bandits: Optimizing the Generalized Gini Index ICML 2017
Rotting Bandits NIPS 2017
End-to-End Differentiable Adversarial Imitation Learning ICML 2017
Approximate Value Iteration with Temporally Extended Actions (Extended Abstract) IJCAI 2017
Ignoring Is a Bliss: Learning with Large Noise Through Reweighting-Minimization COLT 2017
Shallow Updates for Deep Reinforcement Learning NIPS 2017
Consistent On-Line Off-Policy Evaluation ICML 2017
Adaptive Skills Adaptive Partitions (ASAP) NIPS 2016
Heteroscedastic Sequences: Beyond Gaussianity ICML 2016
Graying the black box: Understanding DQNs ICML 2016
Hierarchical Decision Making In Electricity Grid Management ICML 2016
Learning the Variance of the Reward-To-Go JMLR 2016
Regularized Policy Iteration with Nonparametric Function Spaces JMLR 2016
Policy Gradient for Coherent Risk Measures NIPS 2015
Community Detection via Measure Space Embedding NIPS 2015
Risk-Sensitive and Robust Decision-Making: a CVaR Optimization Approach NIPS 2015
Dynamic Sensing: Better Classification under Acquisition Constraints ICML 2015
Off-policy Model-based Learning under Unknown Factored Dynamics ICML 2015
Thompson Sampling for Learning Parameterized Markov Decision Processes COLT 2015
Sensor Selection for Crowdsensing Dynamical Systems AISTATS 2015
Online Learning for Adversaries with Memory: Price of Past Mistakes NIPS 2015
Set-Valued Approachability and Online Learning with Partial Monitoring JMLR 2014
Robust Logistic Regression and Classification NIPS 2014
Time-Regularized Interrupting Options (TRIO) ICML 2014
How hard is my MDP?" The distribution-norm to the rescue" NIPS 2014
Concept Drift Detection Through Resampling ICML 2014
Scaling Up Robust MDPs using Function Approximation ICML 2014
Approachability in unknown games: Online learning meets multi-objective optimization COLT 2014
Latent Bandits. ICML 2014
Scaling Up Approximate Value Iteration with Options: Better Policies with Fewer Iterations ICML 2014
Thompson Sampling for Complex Online Problems ICML 2014
Reinforcement Learning in Robust Markov Decision Processes NIPS 2013
Robust Sparse Regression under Adversarial Corruption ICML 2013
Temporal Difference Methods for the Variance of the Reward To Go ICML 2013
Approachability, fast and slow COLT 2013
Online Learning for Time Series Prediction COLT 2013
Opportunistic Strategies for Generalized No-Regret Problems COLT 2013
Learning Multiple Models via Regularized Weighting NIPS 2013
Online PCA for Contaminated Data NIPS 2013
The Perturbed Variation NIPS 2012
More Is Better: Large Scale Partially-supervised Sentiment Classification ACML 2012
Statistical Optimization in High Dimensions AISTATS 2012
The Sample Complexity of Dictionary Learning JMLR 2011
Does an Efficient Calibrated Forecasting Strategy Exist? COLT 2011
The Sample Complexity of Dictionary Learning COLT 2011
Robust approachability and regret minimization in games with partial monitoring COLT 2011
From Bandits to Experts: On the Value of Side-Observations NIPS 2011
Committing Bandits NIPS 2011
Distributionally Robust Markov Decision Processes NIPS 2010
Online Classification with Specificity Constraints NIPS 2010
Robustness and Regularization of Support Vector Machines JMLR 2009
Online Learning with Sample Path Constraints JMLR 2009
Regularized Policy Iteration NIPS 2008
Robust Regression and Lasso NIPS 2008
The Robustness-Performance Tradeoff in Markov Decision Processes NIPS 2006
Action Elimination and Stopping Conditions for the Multi-Armed Bandit and Reinforcement Learning Problems JMLR 2006
A Geometric Approach to Multi-Criterion Reinforcement Learning JMLR 2004
The Sample Complexity of Exploration in the Multi-Armed Bandit Problem JMLR 2004
Greedy Algorithms for Classification -- Consistency, Convergence Rates, and Adaptivity JMLR 2003