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07/26/2023
Evaluating the Moral Beliefs Encoded in LLMs
This paper presents a case study on the design, administration, post-pro...
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07/20/2023
Amortized Variational Inference: When and Why?
Amortized variational inference (A-VI) is a method for approximating the...
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06/30/2023
Practical and Asymptotically Exact Conditional Sampling in Diffusion Models
Diffusion models have been successful on a range of conditional generati...
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06/21/2023
Density Uncertainty Layers for Reliable Uncertainty Estimation
Assessing the predictive uncertainty of deep neural networks is crucial ...
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06/01/2023
Nonparametric Identifiability of Causal Representations from Unknown Interventions
We study causal representation learning, the task of inferring latent ca...
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05/31/2023
An Invariant Learning Characterization of Controlled Text Generation
Controlled generation refers to the problem of creating text that contai...
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02/24/2023
On the Misspecification of Linear Assumptions in Synthetic Control
The synthetic control (SC) method is a popular approach for estimating t...
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06/14/2022
Probabilistic Conformal Prediction Using Conditional Random Samples
This paper proposes probabilistic conformal prediction (PCP), a predicti...
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02/16/2022
Learning Transferrable Representations of Career Trajectories for Economic Prediction
Understanding career trajectories – the sequences of jobs that individua...
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02/14/2022
Mapping Interstellar Dust with Gaussian Processes
Interstellar dust corrupts nearly every stellar observation, and account...
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02/03/2022
Transport Score Climbing: Variational Inference Using Forward KL and Adaptive Neural Transport
Variational inference often minimizes the "reverse" Kullbeck-Leibler (KL...
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12/10/2021
On the Assumptions of Synthetic Control Methods
Synthetic control (SC) methods have been widely applied to estimate the ...
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12/07/2021
Unsupervised Representation Learning via Neural Activation Coding
We present neural activation coding (NAC) as a novel approach for learni...
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12/06/2021
Posterior Predictive Null Checks
Bayesian model criticism is an important part of the practice of Bayesia...
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09/24/2021
Optimization-based Causal Estimation from Heterogenous Environments
This paper presents a new optimization approach to causal estimation. Gi...
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09/14/2021
Rationales for Sequential Predictions
Sequence models are a critical component of modern NLP systems, but thei...
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05/08/2020
Text-Based Ideal Points
Ideal point models analyze lawmakers' votes to quantify their political ...
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03/10/2020
Towards Clarifying the Theory of the Deconfounder
Wang and Blei (2019) studies multiple causal inference and proposes the ...
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10/28/2019
Poisson-Randomized Gamma Dynamical Systems
This paper presents the Poisson-randomized gamma dynamical system (PRGDS...
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10/15/2019
The Blessings of Multiple Causes: A Reply to Ogburn et al. (2019)
Ogburn et al. (2019, arXiv:1910.05438) discuss "The Blessings of Multipl...
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10/09/2019
Prescribed Generative Adversarial Networks
Generative adversarial networks (GANs) are a powerful approach to unsupe...
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08/02/2019
Population Predictive Checks
Bayesian modeling has become a staple for researchers analyzing data. Th...
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07/12/2019
The Dynamic Embedded Topic Model
Topic modeling analyzes documents to learn meaningful patterns of words....
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07/08/2019
Topic Modeling in Embedding Spaces
Topic modeling analyzes documents to learn meaningful patterns of words....
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06/10/2019
Bayesian Tensor Filtering: Smooth, Locally-Adaptive Factorization of Functional Matrices
We consider the problem of functional matrix factorization, finding low-...
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06/05/2019
Adapting Neural Networks for the Estimation of Treatment Effects
This paper addresses the use of neural networks for the estimation of tr...
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05/30/2019
Multiple Causes: A Causal Graphical View
Unobserved confounding is a major hurdle for causal inference from obser...
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05/29/2019
Using Text Embeddings for Causal Inference
We address causal inference with text documents. For example, does addin...
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05/26/2019
Equal Opportunity and Affirmative Action via Counterfactual Predictions
Machine learning (ML) can automate decision-making by learning to predic...
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05/26/2019
Variational Bayes under Model Misspecification
Variational Bayes (VB) is a scalable alternative to Markov chain Monte C...
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04/03/2019
The Medical Deconfounder: Assessing Treatment Effect with Electronic Health Records (EHRs)
Causal estimation of treatment effect has an important role in guiding p...
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02/11/2019
Using Embeddings to Correct for Unobserved Confounding
We consider causal inference in the presence of unobserved confounding. ...
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12/13/2018
Dose-response modeling in high-throughput cancer drug screenings: A case study with recommendations for practitioners
Personalized cancer treatments based on the molecular profile of a patie...
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12/01/2018
A Probabilistic Model of Cardiac Physiology and Electrocardiograms
An electrocardiogram (EKG) is a common, non-invasive test that measures ...
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11/01/2018
The Holdout Randomization Test: Principled and Easy Black Box Feature Selection
We consider the problem of feature selection using black box predictive ...
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08/20/2018
The Deconfounded Recommender: A Causal Inference Approach to Recommendation
The goal of a recommender system is to show its users items that they wi...
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07/12/2018
Avoiding Latent Variable Collapse With Generative Skip Models
Variational autoencoders (VAEs) learn distributions of high-dimensional ...
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06/27/2018
Empirical Risk Minimization and Stochastic Gradient Descent for Relational Data
Empirical risk minimization is the principal tool for prediction problem...
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06/08/2018
Black Box FDR
Analyzing large-scale, multi-experiment studies requires scientists to t...
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05/17/2018
The Blessings of Multiple Causes
Causal inference from observation data often assumes "strong ignorabilit...
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05/03/2018
Noisin: Unbiased Regularization for Recurrent Neural Networks
Recurrent neural networks (RNNs) are powerful models of sequential data....
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03/24/2018
Equation Embeddings
We present an unsupervised approach for discovering semantic representat...
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02/12/2018
Augment and Reduce: Stochastic Inference for Large Categorical Distributions
Categorical distributions are ubiquitous in machine learning, e.g., in c...
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11/09/2017
SHOPPER: A Probabilistic Model of Consumer Choice with Substitutes and Complements
We develop SHOPPER, a sequential probabilistic model of market baskets. ...
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10/30/2017
Implicit Causal Models for Genome-wide Association Studies
Progress in probabilistic generative models has accelerated, developing ...
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05/31/2017
Variational Sequential Monte Carlo
Variational inference underlies many recent advances in large scale prob...
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05/24/2017
Proximity Variational Inference
Variational inference is a powerful approach for approximate posterior i...
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05/09/2017
Frequentist Consistency of Variational Bayes
A key challenge for modern Bayesian statistics is how to perform scalabl...
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04/13/2017
Stochastic Gradient Descent as Approximate Bayesian Inference
Stochastic Gradient Descent with a constant learning rate (constant SGD)...
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02/28/2017