Maruan Al-Shedivat

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Carnegie Mellon University

Ph.D. student in Machine Learning at Carnegie Mellon University

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Learning from Imperfect Annotations

Many machine learning systems today are trained on large amounts of huma...
04/07/2020 ∙ by Emmanouil Antonios Platanios, et al. ∙ 6 ∙ share

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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...
05/31/2019 ∙ by Gregory Plumb, et al. ∙ 5 ∙ share

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Consistency by Agreement in Zero-shot Neural Machine Translation

Generalization and reliability of multilingual translation often highly ...
04/04/2019 ∙ by Maruan Al-Shedivat, et al. ∙ 16 ∙ share

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Regularizing Black-box Models for Improved Interpretability

Most work on interpretability in machine learning has focused on designi...
02/18/2019 ∙ by Gregory Plumb, et al. ∙ 20 ∙ share

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On the Complexity of Exploration in Goal-Driven Navigation

Building agents that can explore their environments intelligently is a c...
11/16/2018 ∙ by Maruan Al-Shedivat, et al. ∙ 10 ∙ share

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Learning Policy Representations in Multiagent Systems

Modeling agent behavior is central to understanding the emergence of com...
06/17/2018 ∙ by Aditya Grover, et al. ∙ 0 ∙ share

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DiCE: The Infinitely Differentiable Monte-Carlo Estimator

The score function estimator is widely used for estimating gradients of ...
02/14/2018 ∙ by Jakob Foerster, et al. ∙ 0 ∙ share

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Personalized Survival Prediction with Contextual Explanation Networks

Accurate and transparent prediction of cancer survival times on the leve...
01/30/2018 ∙ by Maruan Al-Shedivat, et al. ∙ 0 ∙ share

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The Intriguing Properties of Model Explanations

Linear approximations to the decision boundary of a complex model have b...
01/30/2018 ∙ by Maruan Al-Shedivat, et al. ∙ 0 ∙ share

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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...
10/10/2017 ∙ by Maruan Al-Shedivat, et al. ∙ 0 ∙ share

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Learning with Opponent-Learning Awareness

Multi-agent settings are quickly gathering importance in machine learnin...
09/13/2017 ∙ by Jakob N. Foerster, et al. ∙ 0 ∙ share

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Contextual Explanation Networks

We introduce contextual explanation networks (CENs)---a class of models ...
05/29/2017 ∙ by Maruan Al-Shedivat, et al. ∙ 0 ∙ share

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Learning Scalable Deep Kernels with Recurrent Structure

Many applications in speech, robotics, finance, and biology deal with se...
10/27/2016 ∙ by Maruan Al-Shedivat, et al. ∙ 0 ∙ share

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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...
09/21/2016 ∙ by Kirthevasan Kandasamy, et al. ∙ 0 ∙ share

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Stochastic Synapses Enable Efficient Brain-Inspired Learning Machines

Recent studies have shown that synaptic unreliability is a robust and su...
11/14/2015 ∙ by Emre O. Neftci, et al. ∙ 0 ∙ share

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Learning Non-deterministic Representations with Energy-based Ensembles

The goal of a generative model is to capture the distribution underlying...
12/23/2014 ∙ by Maruan Al-Shedivat, et al. ∙ 0 ∙ share

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