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09/10/2021
Knowledge-Aware Meta-learning for Low-Resource Text Classification
Meta-learning has achieved great success in leveraging the historical le...
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01/30/2021
On Data Efficiency of Meta-learning
Meta-learning has enabled learning statistical models that can be quickl...
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10/11/2020
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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04/07/2020
Learning from Imperfect Annotations
Many machine learning systems today are trained on large amounts of huma...
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05/31/2019
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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04/04/2019
Consistency by Agreement in Zero-shot Neural Machine Translation
Generalization and reliability of multilingual translation often highly ...
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02/18/2019
Regularizing Black-box Models for Improved Interpretability
Most work on interpretability in machine learning has focused on designi...
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11/16/2018
On the Complexity of Exploration in Goal-Driven Navigation
Building agents that can explore their environments intelligently is a c...
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06/17/2018
Learning Policy Representations in Multiagent Systems
Modeling agent behavior is central to understanding the emergence of com...
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02/14/2018
DiCE: The Infinitely Differentiable Monte-Carlo Estimator
The score function estimator is widely used for estimating gradients of ...
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01/30/2018
Personalized Survival Prediction with Contextual Explanation Networks
Accurate and transparent prediction of cancer survival times on the leve...
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01/30/2018
The Intriguing Properties of Model Explanations
Linear approximations to the decision boundary of a complex model have b...
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10/10/2017
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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09/13/2017
Learning with Opponent-Learning Awareness
Multi-agent settings are quickly gathering importance in machine learnin...
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05/29/2017
Contextual Explanation Networks
We introduce contextual explanation networks (CENs)---a class of models ...
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10/27/2016
Learning Scalable Deep Kernels with Recurrent Structure
Many applications in speech, robotics, finance, and biology deal with se...
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09/21/2016
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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11/14/2015
Stochastic Synapses Enable Efficient Brain-Inspired Learning Machines
Recent studies have shown that synaptic unreliability is a robust and su...
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12/23/2014
