
Bootstrapped MetaLearning
Metalearning empowers artificial intelligence to increase its efficienc...
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Emphatic Algorithms for Deep Reinforcement Learning
Offpolicy learning allows us to learn about possible policies of behavi...
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Reward is enough for convex MDPs
Maximising a cumulative reward function that is Markov and stationary, i...
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Online Apprenticeship Learning
In Apprenticeship Learning (AL), we are given a Markov Decision Process ...
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Discovery of Options via MetaLearned Subgoals
Temporal abstractions in the form of options have been shown to help rei...
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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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Balancing Constraints and Rewards with MetaGradient D4PG
Deploying Reinforcement Learning (RL) agents to solve realworld applica...
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Learning to Ask Medical Questions using Reinforcement Learning
We propose a novel reinforcement learningbased approach for adaptive an...
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SelfTuning Deep Reinforcement Learning
Reinforcement learning (RL) algorithms often require expensive manual or...
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Deep learning reconstruction of ultrashort pulses from 2D spatial intensity patterns recorded by an allinline system in a singleshot
We propose a simple allinline singleshot scheme for diagnostics of ul...
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Apprenticeship Learning via FrankWolfe
We consider the applications of the FrankWolfe (FW) algorithm for Appre...
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Inverse Reinforcement Learning in Contextual MDPs
We consider the Inverse Reinforcement Learning (IRL) problem in Contextu...
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Average reward reinforcement learning with unknown mixing times
We derive and analyze learning algorithms for policy evaluation, apprent...
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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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Planning in Hierarchical Reinforcement Learning: Guarantees for Using Local Policies
We consider a settings of hierarchical reinforcement learning, in which ...
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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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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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Deep Learning Reconstruction of UltraShort Pulses
Ultrashort laser pulses with femtosecond to attosecond pulse duration a...
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Hierarchical Reinforcement Learning: Approximating Optimal Discounted TSP Using Local Policies
In this work, we provide theoretical guarantees for reward decomposition...
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Train on Validation: Squeezing the Data Lemon
Model selection on validation data is an essential step in machine 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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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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Visualizing Dynamics: from tSNE to SEMIMDPs
Deep Reinforcement Learning (DRL) is a trending field of research, showi...
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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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Graying the black box: Understanding DQNs
In recent years there is a growing interest in using deep representation...
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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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Tom Zahavy
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