
Action Redundancy in Reinforcement Learning
Maximum Entropy (MaxEnt) reinforcement learning is a powerful learning p...
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GELATO: Geometrically Enriched Latent Model for Offline Reinforcement Learning
Offline reinforcement learning approaches can generally be divided to pr...
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Reinforcement Learning for Datacenter Congestion Control
We approach the task of network congestion control in datacenters using ...
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Improper Learning with Gradientbased Policy Optimization
We consider an improper reinforcement learning setting where the learner...
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Online Apprenticeship Learning
In Apprenticeship Learning (AL), we are given a Markov Decision Process ...
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RL for Latent MDPs: Regret Guarantees and a Lower Bound
In this work, we consider the regret minimization problem for reinforcem...
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Dimension Free Generalization Bounds for Non Linear Metric Learning
In this work we study generalization guarantees for the metric learning ...
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Online Limited Memory NeuralLinear Bandits with Likelihood Matching
We study neurallinear bandits for solving problems where both explorati...
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Acting in Delayed Environments with NonStationary Markov Policies
The standard Markov Decision Process (MDP) formulation hinges on the ass...
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The Architectural Implications of Distributed Reinforcement Learning on CPUGPU Systems
With deep reinforcement learning (RL) methods achieving results that exc...
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Drift Detection in Episodic Data: Detect When Your Agent Starts Faltering
Detection of deterioration of agent performance in dynamic environments ...
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How to Stop Epidemics: Controlling Graph Dynamics with Reinforcement Learning and Graph Neural Networks
We consider the problem of monitoring and controlling a partiallyobserv...
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Reinforcement Learning with Trajectory Feedback
The computational model of reinforcement learning is based upon the abil...
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Lenient Regret for MultiArmed Bandits
We consider the MultiArmed Bandit (MAB) problem, where the agent sequen...
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The Pendulum Arrangement: Maximizing the Escape Time of Heterogeneous Random Walks
We identify a fundamental phenomenon of heterogeneous one dimensional ra...
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Optimizing Memory Placement using Evolutionary Graph Reinforcement Learning
As modern neural networks have grown to billions of parameters, meeting ...
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Bandits with Partially Observable Offline Data
We study linear contextual bandits with access to a large, partially obs...
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Distributional Robustness and Regularization in Reinforcement Learning
Distributionally Robust Optimization (DRO) has enabled to prove the equi...
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ExplorationExploitation in Constrained MDPs
In many sequential decisionmaking problems, the goal is to optimize a u...
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Stealing BlackBox Functionality Using The Deep Neural Tree Architecture
This paper makes a substantial step towards cloning the functionality of...
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Optimistic Policy Optimization with Bandit Feedback
Policy optimization methods are one of the most widely used classes of R...
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Kalman meets Bellman: Improving Policy Evaluation through Value Tracking
Policy evaluation is a key process in Reinforcement Learning (RL). It as...
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Tight Lower Bounds for Combinatorial MultiArmed Bandits
The Combinatorial MultiArmed Bandit problem is a sequential decisionma...
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Patternless Adversarial Attacks on Video Recognition Networks
Deep neural networks for classification of videos, just like image class...
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Maximizing the Total Reward via Reward Tweaking
In reinforcement learning, the discount factor γ controls the agent's ef...
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Stabilizing OffPolicy Reinforcement Learning with Conservative Policy Gradients
In recent years, advances in deep learning have enabled the application ...
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Natural Language State Representation for Reinforcement Learning
Recent advances in Reinforcement Learning have highlighted the difficult...
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MultiStep Greedy and Approximate Real Time Dynamic Programming
Real Time Dynamic Programming (RTDP) is a wellknown Dynamic Programming...
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OffPolicy Evaluation in Partially Observable Environments
This work studies the problem of batch offpolicy evaluation for Reinfor...
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Adaptive Trust Region Policy Optimization: Global Convergence and Faster Rates for Regularized MDPs
Trust region policy optimization (TRPO) is a popular and empirically suc...
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Practical Risk Measures in Reinforcement Learning
Practical application of Reinforcement Learning (RL) often involves risk...
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Topic Modeling via Full Dependence Mixtures
We consider the topic modeling problem for large datasets. For this prob...
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Variance Estimation For Online Regression via Spectrum Thresholding
We consider the online linear regression problem, where the predictor ve...
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Tight Regret Bounds for ModelBased Reinforcement Learning with Greedy Policies
Stateoftheart efficient modelbased Reinforcement Learning (RL) algor...
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Distributional Policy Optimization: An Alternative Approach for Continuous Control
We identify a fundamental problem in policy gradientbased methods in co...
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Inverse Reinforcement Learning in Contextual MDPs
We consider the Inverse Reinforcement Learning (IRL) problem in Contextu...
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Action Assembly: Sparse Imitation Learning for Text Based Games with Combinatorial Action Spaces
We propose a computationally efficient algorithm that combines compresse...
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A Bayesian Approach to Robust Reinforcement Learning
Robust Markov Decision Processes (RMDPs) intend to ensure robustness wit...
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BatchSize Independent Regret Bounds for the Combinatorial MultiArmed Bandit Problem
We consider the combinatorial multiarmed bandit (CMAB) problem, where t...
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Image Matters: Detecting Offensive and NonCompliant Content / Logo in Product Images
In ecommerce, product content, especially product images have a signifi...
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A ProblemAdaptive Algorithm for Resource Allocation
We consider a sequential stochastic resource allocation problem under th...
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The Natural Language of Actions
We introduce Act2Vec, a general framework for learning contextbased act...
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Value Propagation for Decentralized Networked Deep Multiagent Reinforcement Learning
We consider the networked multiagent reinforcement learning (MARL) prob...
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Action Robust Reinforcement Learning and Applications in Continuous Control
A policy is said to be robust if it maximizes the reward while consideri...
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Deep Neural Linear Bandits: Overcoming Catastrophic Forgetting through Likelihood Matching
We study the neurallinear bandit model for solving sequential decision...
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Trust Region Value Optimization using Kalman Filtering
Policy evaluation is a key process in reinforcement learning. It assesse...
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Multi Instance Learning For Unbalanced Data
In the context of Multi Instance Learning, we analyze the Single Instanc...
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Revisiting ExplorationConscious Reinforcement Learning
The objective of Reinforcement Learning is to learn an optimal policy by...
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Inspiration Learning through Preferences
Current imitation learning techniques are too restrictive because they r...
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OnLine Learning of Linear Dynamical Systems: Exponential Forgetting in Kalman Filters
Kalman filter is a key tool for timeseries forecasting and analysis. We...
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Shie Mannor
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Faculty member at the Department of Electrical Engineering at the Technion where I am a member of the Technion Machine Learning Center and the Grand Technion Energy Program.