
Generalization Bounds and Representation Learning for Estimation of Potential Outcomes and Causal Effects
Practitioners in diverse fields such as healthcare, economics and educat...
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Open Set Medical Diagnosis
Machinelearned diagnosis models have shown promise as medical aides but...
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Robustly Extracting Medical Knowledge from EHRs: A Case Study of Learning a Health Knowledge Graph
Increasingly large electronic health records (EHRs) provide an opportuni...
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Support and Invertibility in DomainInvariant Representations
Learning domaininvariant representations has become a popular approach ...
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Estimation of UtilityMaximizing Bounds on Potential Outcomes
Estimation of individual treatment effects is often used as the basis fo...
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Knowledge Base Completion for Constructing ProblemOriented Medical Records
Both electronic health records and personal health records are typically...
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Counterfactual OffPolicy Evaluation with GumbelMax Structural Causal Models
We introduce an offpolicy evaluation procedure for highlighting episode...
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Characterization of Overlap in Observational Studies
Overlap between treatment groups is required for nonparametric estimatio...
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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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Prototypical Clustering Networks for Dermatological Disease Diagnosis
We consider the problem of image classification for the purpose of aidin...
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Overcomplete Independent Component Analysis via SDP
We present a novel algorithm for overcomplete independent components ana...
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Deep Kalman Filters
Kalman Filters are one of the most influential models of timevarying ph...
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Benefits of Overparameterization in SingleLayer Latent Variable Generative Models
One of the most surprising and exciting discoveries in supervising learn...
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Causal Effect Inference with Deep LatentVariable Models
Learning individuallevel causal effects from observational data, such a...
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Structured Inference Networks for Nonlinear State Space Models
Gaussian state space models have been used for decades as generative mod...
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Identifiable Phenotyping using Constrained NonNegative Matrix Factorization
This work proposes a new algorithm for automated and simultaneous phenot...
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Clinical Tagging with Joint Probabilistic Models
We describe a method for parameter estimation in bipartite probabilistic...
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Estimating individual treatment effect: generalization bounds and algorithms
There is intense interest in applying machine learning to problems of ca...
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Recurrent Neural Networks for Multivariate Time Series with Missing Values
Multivariate time series data in practical applications, such as health ...
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Learning Representations for Counterfactual Inference
Observational studies are rising in importance due to the widespread acc...
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Lifted TreeReweighted Variational Inference
We analyze variational inference for highly symmetric graphical models s...
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Anchored Discrete Factor Analysis
We present a semisupervised learning algorithm for learning discrete fa...
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Barrier FrankWolfe for Marginal Inference
We introduce a globallyconvergent algorithm for optimizing the treerew...
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Train and Test Tightness of LP Relaxations in Structured Prediction
Structured prediction is used in areas such as computer vision and natur...
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Incorporating Type II Error Probabilities from Independence Tests into ScoreBased Learning of Bayesian Network Structure
We give a new consistent scoring function for structure learning of Baye...
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Tight Error Bounds for Structured Prediction
Structured prediction tasks in machine learning involve the simultaneous...
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Tightening LP Relaxations for MAP using Message Passing
Linear Programming (LP) relaxations have become powerful tools for findi...
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Unsupervised Learning of NoisyOr Bayesian Networks
This paper considers the problem of learning the parameters in Bayesian ...
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SparsityBoost: A New Scoring Function for Learning Bayesian Network Structure
We give a new consistent scoring function for structure learning of Baye...
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A Practical Algorithm for Topic Modeling with Provable Guarantees
Topic models provide a useful method for dimensionality reduction and ex...
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Efficiently Searching for Frustrated Cycles in MAP Inference
Dual decomposition provides a tractable framework for designing algorith...
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Grounded Recurrent Neural Networks
In this work, we present the Grounded Recurrent Neural Network (GRNN), a...
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DiscourseBased Objectives for Fast Unsupervised Sentence Representation Learning
This work presents a novel objective function for the unsupervised train...
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Simultaneous Learning of Trees and Representations for Extreme Classification and Density Estimation
We consider multiclass classification where the predictor has a hierarc...
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CharacterAware Neural Language Models
We describe a simple neural language model that relies only on character...
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Alphaexpansion is Exact on Stable Instances
Approximate algorithms for structured prediction problemssuch as the ...
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Learning Weighted Representations for Generalization Across Designs
Predictive models that generalize well under distributional shift are of...
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SemiAmortized Variational Autoencoders
Amortized variational inference (AVI) replaces instancespecific local i...
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Why Is My Classifier Discriminatory?
Recent attempts to achieve fairness in predictive models focus on the ba...
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Block Stability for MAP Inference
To understand the empirical success of approximate MAP inference, recent...
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David Sontag
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Assistant Professor in the Department of Electrical Engineering and Computer Science (EECS) MIT, Assistant Professor in Computer Science and Data Science at New York University’s Courant Institute of Mathematical Sciences from 2011 to 2016, Sprowls award for outstanding doctoral thesis in Computer Science at MIT in 2010, best paper awards at the conferences Empirical Methods in Natural Language Processing (EMNLP), Uncertainty in Artificial Intelligence (UAI), and Neural Information Processing Systems (NIPS), faculty awards from Google, Facebook, and Adobe, and a NSF CAREER Award.