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06/01/2023
From Temporal to Contemporaneous Iterative Causal Discovery in the Presence of Latent Confounders
We present a constraint-based algorithm for learning causal structures f...
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11/06/2022
Wall Street Tree Search: Risk-Aware Planning for Offline Reinforcement Learning
Offline reinforcement-learning (RL) algorithms learn to make decisions u...
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11/03/2022
Learning Control by Iterative Inversion
We formulate learning for control as an inverse problem – inverting a dy...
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10/07/2022
CLEAR: Causal Explanations from Attention in Neural Recommenders
We present CLEAR, a method for learning session-specific causal graphs, ...
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11/07/2021
Iterative Causal Discovery in the Possible Presence of Latent Confounders and Selection Bias
We present a sound and complete algorithm, called iterative causal disco...
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11/01/2021
Validate on Sim, Detect on Real – Model Selection for Domain Randomization
A practical approach to learning robot skills, often termed sim2real, is...
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07/11/2021
Improving Efficiency and Accuracy of Causal Discovery Using a Hierarchical Wrapper
Causal discovery from observational data is an important tool in many br...
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05/10/2021
Efficient Self-Supervised Data Collection for Offline Robot Learning
A practical approach to robot reinforcement learning is to first collect...
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12/14/2020
A Single Iterative Step for Anytime Causal Discovery
We present a sound and complete algorithm for recovering causal graphs f...
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10/27/2019
Neural Network Distiller: A Python Package For DNN Compression Research
This paper presents the philosophy, design and feature-set of Neural Net...
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05/30/2019
Modeling Uncertainty by Learning a Hierarchy of Deep Neural Connections
Quantifying and measuring uncertainty in deep neural networks, despite r...
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09/13/2018
Bayesian Structure Learning by Recursive Bootstrap
We address the problem of Bayesian structure learning for domains with h...
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06/24/2018
