
Learn What Not to Learn: Action Elimination with Deep Reinforcement Learning
Learning how to act when there are many available actions in each state ...
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Finite Sample Analyses for TD(0) with Function Approximation
TD(0) is one of the most commonly used algorithms in reinforcement learn...
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Shallow Updates for Deep Reinforcement Learning
Deep reinforcement learning (DRL) methods such as the Deep QNetwork (DQ...
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Unit Commitment using Nearest Neighbor as a ShortTerm Proxy
We devise the Unit Commitment Nearest Neighbor (UCNN) algorithm to be us...
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Situational Awareness by RiskConscious Skills
Hierarchical Reinforcement Learning has been previously shown to speed u...
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Deep Robust Kalman Filter
A Robust Markov Decision Process (RMDP) is a sequential decision making ...
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Rotting Bandits
The MultiArmed Bandits (MAB) framework highlights the tension between a...
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Consistent OnLine OffPolicy Evaluation
The problem of online offpolicy evaluation (OPE) has been actively stu...
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Deep Reinforcement Learning Discovers Internal Models
Deep Reinforcement Learning (DRL) is a trending field of research, showi...
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Outlier Robust Online Learning
We consider the problem of learning from noisy data in practical setting...
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Adaptive Lambda LeastSquares Temporal Difference Learning
Temporal Difference learning or TD(λ) is a fundamental algorithm in the ...
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Hierarchical Decision Making In Electricity Grid Management
The power grid is a complex and vital system that necessitates careful r...
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Iterative Hierarchical Optimization for Misspecified Problems (IHOMP)
For complex, highdimensional Markov Decision Processes (MDPs), it may b...
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Graying the black box: Understanding DQNs
In recent years there is a growing interest in using deep representation...
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A nonparametric sequential test for online randomized experiments
We propose a nonparametric sequential test that aims to address two prac...
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Bayesian Reinforcement Learning: A Survey
Bayesian methods for machine learning have been widely investigated, yie...
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Situationally Aware Options
Hierarchical abstractions, also known as options  a type of temporally...
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Bootstrapping Skills
The monolithic approach to policy representation in Markov Decision Proc...
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RiskSensitive and Robust DecisionMaking: a CVaR Optimization Approach
In this paper we address the problem of decision making within a Markov ...
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How to Allocate Resources For Features Acquisition?
We study classification problems where features are corrupted by noise a...
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Visualizing Dynamics: from tSNE to SEMIMDPs
Deep Reinforcement Learning (DRL) is a trending field of research, showi...
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Clustering Time Series and the Surprising Robustness of HMMs
Suppose that we are given a time series where consecutive samples are be...
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Adaptive Skills, Adaptive Partitions (ASAP)
We introduce the Adaptive Skills, Adaptive Partitions (ASAP) framework t...
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Generalized Emphatic Temporal Difference Learning: BiasVariance Analysis
We consider the offpolicy evaluation problem in Markov decision process...
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Emphatic TD Bellman Operator is a Contraction
Recently, SuttonMW15 introduced the emphatic temporal differences (ETD) ...
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Overlapping Communities Detection via Measure Space Embedding
We present a new algorithm for community detection. The algorithm uses r...
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Actively Learning to Attract Followers on Twitter
Twitter, a popular social network, presents great opportunities for onl...
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Policy Gradient for Coherent Risk Measures
Several authors have recently developed risksensitive policy gradient m...
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Offpolicy evaluation for MDPs with unknown structure
Offpolicy learning in dynamic decision problems is essential for provid...
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Contextual Markov Decision Processes
We consider a planning problem where the dynamics and rewards of the env...
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Implicit Temporal Differences
In reinforcement learning, the TD(λ) algorithm is a fundamental policy e...
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Nonstochastic MultiArmed Bandits with GraphStructured Feedback
We present and study a partialinformation model of online learning, whe...
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Distributed Robust Learning
We propose a framework for distributed robust statistical learning on b...
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Ensemble Robustness and Generalization of Stochastic Deep Learning Algorithms
The question why deep learning algorithms generalize so well has attract...
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Thompson Sampling for Learning Parameterized Markov Decision Processes
We consider reinforcement learning in parameterized Markov Decision Proc...
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Optimizing the CVaR via Sampling
Conditional Value at Risk (CVaR) is a prominent risk measure that is bei...
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Approachability in unknown games: Online learning meets multiobjective optimization
In the standard setting of approachability there are two players and a t...
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MeanVariance Optimization in Markov Decision Processes
We consider finite horizon Markov decision processes under performance m...
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Scaling Up Robust MDPs by Reinforcement Learning
We consider largescale Markov decision processes (MDPs) with parameter ...
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A Primal Condition for Approachability with Partial Monitoring
In approachability with full monitoring there are two types of condition...
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Adaptive Bases for Reinforcement Learning
We consider the problem of reinforcement learning using function approxi...
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Robust High Dimensional Sparse Regression and Matching Pursuit
We consider high dimensional sparse regression, and develop strategies a...
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Policy Evaluation with Variance Related Risk Criteria in Markov Decision Processes
In this paper we extend temporal difference policy evaluation algorithms...
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The Perturbed Variation
We introduce a new discrepancy score between two distributions that give...
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How to sample if you must: on optimal functional sampling
We examine a fundamental problem that models various active sampling set...
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Policy Gradients with Variance Related Risk Criteria
Managing risk in dynamic decision problems is of cardinal importance in ...
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From Bandits to Experts: On the Value of SideObservations
We consider an adversarial online learning setting where a decision make...
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The Sample Complexity of Dictionary Learning
A large set of signals can sometimes be described sparsely using a dicti...
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A Geometric Proof of Calibration
We provide yet another proof of the existence of calibrated forecasters;...
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Is a picture worth a thousand words? A Deep MultiModal Fusion Architecture for Product Classification in ecommerce
Classifying products into categories precisely and efficiently is a majo...
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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.