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Handling Missing Data in Decision Trees: A Probabilistic Approach
Decision trees are a popular family of models due to their attractive pr...
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On Effective Parallelization of Monte Carlo Tree Search
Despite its groundbreaking success in Go and computer games, Monte Carlo...
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Off-Policy Deep Reinforcement Learning with Analogous Disentangled Exploration
Off-policy reinforcement learning (RL) is concerned with learning a rewa...
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LaTeS: Latent Space Distillation for Teacher-Student Driving Policy Learning
We describe a policy learning approach to map visual inputs to driving c...
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On Tractable Computation of Expected Predictions
Computing expected predictions has many interesting applications in area...
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What to Expect of Classifiers? Reasoning about Logistic Regression with Missing Features
While discriminative classifiers often yield strong predictive performan...
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Learning Logistic Circuits
This paper proposes a new classification model called logistic circuits....
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Horizon: Facebook's Open Source Applied Reinforcement Learning Platform
In this paper we present Horizon, Facebook's open source applied reinfor...
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Inference Aided Reinforcement Learning for Incentive Mechanism Design in Crowdsourcing
Incentive mechanisms for crowdsourcing are designed to incentivize finan...
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A Semantic Loss Function for Deep Learning with Symbolic Knowledge
This paper develops a novel methodology for using symbolic knowledge in ...
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