
Ensembles of Locally Independent Prediction Models
Many ensemble methods encourage their constituent models to be diverse, ...
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An Evaluation of the HumanInterpretability of Explanation
Recent years have seen a boom in interest in machine learning systems th...
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Quality of Uncertainty Quantification for Bayesian Neural Network Inference
Bayesian Neural Networks (BNNs) place priors over the parameters in a ne...
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Regional Tree Regularization for Interpretability in Black Box Models
The lack of interpretability remains a barrier to the adoption of deep n...
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Evaluating Reinforcement Learning Algorithms in Observational Health Settings
Much attention has been devoted recently to the development of machine l...
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Defining Admissible Rewards for High Confidence Policy Evaluation
A key impediment to reinforcement learning (RL) in real applications wit...
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Beyond Sparsity: Tree Regularization of Deep Models for Interpretability
The lack of interpretability remains a key barrier to the adoption of de...
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Accountability of AI Under the Law: The Role of Explanation
The ubiquity of systems using artificial intelligence or "AI" has brough...
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Decomposition of Uncertainty for Active Learning and Reliable Reinforcement Learning in Stochastic Systems
Bayesian neural networks (BNNs) with latent variables are probabilistic ...
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Weighted Tensor Decomposition for Learning Latent Variables with Partial Data
Tensor decomposition methods are popular tools for learning latent varia...
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Rollback Hamiltonian Monte Carlo
We propose a new framework for Hamiltonian Monte Carlo (HMC) on truncate...
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PredictionConstrained Training for SemiSupervised Mixture and Topic Models
Supervisory signals have the potential to make lowdimensional data repr...
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Uncertainty Decomposition in Bayesian Neural Networks with Latent Variables
Bayesian neural networks (BNNs) with latent variables are probabilistic ...
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Robust and Efficient Transfer Learning with HiddenParameter Markov Decision Processes
We introduce a new formulation of the Hidden Parameter Markov Decision P...
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Model Selection in Bayesian Neural Networks via Horseshoe Priors
Bayesian Neural Networks (BNNs) have recently received increasing attent...
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Transfer Learning Across Patient Variations with Hidden Parameter Markov Decision Processes
Due to physiological variation, patients diagnosed with the same conditi...
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Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations
Neural networks are among the most accurate supervised learning methods ...
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Towards A Rigorous Science of Interpretable Machine Learning
As machine learning systems become ubiquitous, there has been a surge of...
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Increasing the Interpretability of Recurrent Neural Networks Using Hidden Markov Models
As deep neural networks continue to revolutionize various application do...
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Rapid Posterior Exploration in Bayesian Nonnegative Matrix Factorization
Nonnegative Matrix Factorization (NMF) is a popular tool for data explo...
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PredictionConstrained Topic Models for Antidepressant Recommendation
Supervisory signals can help topic models discover lowdimensional data ...
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An Empirical Comparison of Sampling Quality Metrics: A Case Study for Bayesian Nonnegative Matrix Factorization
In this work, we empirically explore the question: how can we assess the...
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Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural Networks
We present an algorithm for modelbased reinforcement learning that comb...
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Hidden Parameter Markov Decision Processes: A Semiparametric Regression Approach for Discovering Latent Task Parametrizations
Control applications often feature tasks with similar, but not identical...
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GraphSparse LDA: A Topic Model with Structured Sparsity
Originally designed to model text, topic modeling has become a powerful ...
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Correlated NonParametric Latent Feature Models
We are often interested in explaining data through a set of hidden facto...
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Prior matters: simple and general methods for evaluating and improving topic quality in topic modeling
Latent Dirichlet Allocation (LDA) models trained without stopword remova...
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Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradients
Deep neural networks have proven remarkably effective at solving many cl...
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Unsupervised Grammar Induction with Depthbounded PCFG
There has been recent interest in applying cognitively or empirically mo...
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How do Humans Understand Explanations from Machine Learning Systems? An Evaluation of the HumanInterpretability of Explanation
Recent years have seen a boom in interest in machine learning systems th...
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A particlebased variational approach to Bayesian Nonnegative Matrix Factorization
Bayesian Nonnegative Matrix Factorization (NMF) is a promising approach...
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HumanintheLoop Interpretability Prior
We often desire our models to be interpretable as well as accurate. Prio...
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Representation Balancing MDPs for OffPolicy Policy Evaluation
We study the problem of offpolicy policy evaluation (OPPE) in RL. In co...
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Structured Variational Learning of Bayesian Neural Networks with Horseshoe Priors
Bayesian Neural Networks (BNNs) have recently received increasing attent...
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Learning Qualitatively Diverse and Interpretable Rules for Classification
There has been growing interest in developing accurate models that can a...
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Behaviour Policy Estimation in OffPolicy Policy Evaluation: Calibration Matters
In this work, we consider the problem of estimating a behaviour policy f...
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Depthbounding is effective: Improvements and evaluation of unsupervised PCFG induction
There have been several recent attempts to improve the accuracy of gramm...
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Latent Projection BNNs: Avoiding weightspace pathologies by learning latent representations of neural network weights
While modern neural networks are making remarkable gains in terms of pre...
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Improving Sepsis Treatment Strategies by Combining Deep and KernelBased Reinforcement Learning
Sepsis is the leading cause of mortality in the ICU. It is challenging t...
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Truly Batch Apprenticeship Learning with Deep Successor Features
We introduce a novel apprenticeship learning algorithm to learn an exper...
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Combining Parametric and Nonparametric Models for OffPolicy Evaluation
We consider a modelbased approach to perform batch offpolicy evaluatio...
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OutputConstrained Bayesian Neural Networks
Bayesian neural network (BNN) priors are defined in parameter space, mak...
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A general method for regularizing tensor decomposition methods via pseudodata
Tensor decomposition methods allow us to learn the parameters of latent ...
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Exploring Computational User Models for Agent Policy Summarization
AI agents are being developed to support high stakes decisionmaking pro...
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DiversityInducing Policy Gradient: Using Maximum Mean Discrepancy to Find a Set of Diverse Policies
Standard reinforcement learning methods aim to master one way of solving...
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Optimizing for Interpretability in Deep Neural Networks with Tree Regularization
Deep models have advanced prediction in many domains, but their lack of ...
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Prediction Focused Topic Models via Vocab Selection
Supervised topic models are often sought to balance prediction quality a...
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Learning Deep Bayesian Latent Variable Regression Models that Generalize: When Nonidentifiability is a Problem
Bayesian Neural Networks with Latent Variables (BNN+LV's) provide uncert...
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Prediction Focused Topic Models for Electronic Health Records
Electronic Health Record (EHR) data can be represented as discrete count...
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Towards Expressive Priors for Bayesian Neural Networks: Poisson Process Radial Basis Function Networks
While Bayesian neural networks have many appealing characteristics, curr...
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Finale DoshiVelez
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Assistant Professor of Computer Science at Harvard Paulson School of Engineering and Applied Sciences, Harvard University, MSc from the University of Cambridge as a Marshall Scholar, her PhD from MIT, and her postdoc at Harvard Medical School.