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On Data Efficiency of Meta-learning
Meta-learning has enabled learning statistical models that can be quickl...
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Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms
Federated learning is typically approached as an optimization problem, w...
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Learning from Imperfect Annotations
Many machine learning systems today are trained on large amounts of huma...
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Regularizing Black-box Models for Improved Interpretability (HILL 2019 Version)
Most of the work on interpretable machine learning has focused on design...
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Consistency by Agreement in Zero-shot Neural Machine Translation
Generalization and reliability of multilingual translation often highly ...
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Regularizing Black-box Models for Improved Interpretability
Most work on interpretability in machine learning has focused on designi...
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On the Complexity of Exploration in Goal-Driven Navigation
Building agents that can explore their environments intelligently is a c...
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Learning Policy Representations in Multiagent Systems
Modeling agent behavior is central to understanding the emergence of com...
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DiCE: The Infinitely Differentiable Monte-Carlo Estimator
The score function estimator is widely used for estimating gradients of ...
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Personalized Survival Prediction with Contextual Explanation Networks
Accurate and transparent prediction of cancer survival times on the leve...
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The Intriguing Properties of Model Explanations
Linear approximations to the decision boundary of a complex model have b...
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Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments
Ability to continuously learn and adapt from limited experience in nonst...
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Learning with Opponent-Learning Awareness
Multi-agent settings are quickly gathering importance in machine learnin...
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Contextual Explanation Networks
We introduce contextual explanation networks (CENs)---a class of models ...
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Learning Scalable Deep Kernels with Recurrent Structure
Many applications in speech, robotics, finance, and biology deal with se...
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Learning HMMs with Nonparametric Emissions via Spectral Decompositions of Continuous Matrices
Recently, there has been a surge of interest in using spectral methods f...
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Stochastic Synapses Enable Efficient Brain-Inspired Learning Machines
Recent studies have shown that synaptic unreliability is a robust and su...
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Learning Non-deterministic Representations with Energy-based Ensembles
The goal of a generative model is to capture the distribution underlying...
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