1 code implementation • 22 Feb 2024 • Diana Cai, Chirag Modi, Loucas Pillaud-Vivien, Charles C. Margossian, Robert M. Gower, David M. Blei, Lawrence K. Saul

We analyze the convergence of BaM when the target distribution is Gaussian, and we prove that in the limit of infinite batch size the variational parameter updates converge exponentially quickly to the target mean and covariance.

1 code implementation • 10 Jan 2024 • Eli N. Weinstein, David M. Blei

Scientists often want to learn about cause and effect from hierarchical data, collected from subunits nested inside units.

1 code implementation • 4 Dec 2023 • Carolina Zheng, Keyon Vafa, David M. Blei

A recent line of work in natural language processing has aimed to combine language models and topic models.

no code implementations • 13 Oct 2023 • David M. Kaplan, David M. Blei

We develop a quantitative method to assess the style of American poems and to visualize a collection of poems in relation to one another.

1 code implementation • NeurIPS 2023 • Nino Scherrer, Claudia Shi, Amir Feder, David M. Blei

(2) We apply this method to study what moral beliefs are encoded in different LLMs, especially in ambiguous cases where the right choice is not obvious.

2 code implementations • 20 Jul 2023 • Charles C. Margossian, David M. Blei

We then show, on a broader class of models, how to expand the domain of AVI's inference function to improve its solution, and we provide examples, e. g. hidden Markov models, where the amortization gap cannot be closed.

1 code implementation • 21 Jun 2023 • Yookoon Park, David M. Blei

In this paper, we propose a novel criterion for reliable predictive uncertainty: a model's predictive variance should be grounded in the empirical density of the input.

1 code implementation • 31 May 2023 • Carolina Zheng, Claudia Shi, Keyon Vafa, Amir Feder, David M. Blei

In this paper, we show that the performance of controlled generation may be poor if the distributions of text in response to user prompts differ from the distribution the predictor was trained on.

no code implementations • NeurIPS 2021 • Yixin Wang, David M. Blei, John P. Cunningham

Unfortunately, variational autoencoders often suffer from posterior collapse: the posterior of the latent variables is equal to its prior, rendering the variational autoencoder useless as a means to produce meaningful representations.

no code implementations • 21 Nov 2022 • Linying Zhang, Lauren R. Richter, Yixin Wang, Anna Ostropolets, Noemie Elhadad, David M. Blei, George Hripcsak

Healthcare continues to grapple with the persistent issue of treatment disparities, sparking concerns regarding the equitable allocation of treatments in clinical practice.

1 code implementation • 14 Jun 2022 • Zhendong Wang, Ruijiang Gao, Mingzhang Yin, Mingyuan Zhou, David M. Blei

This paper proposes probabilistic conformal prediction (PCP), a predictive inference algorithm that estimates a target variable by a discontinuous predictive set.

1 code implementation • 16 Feb 2022 • Keyon Vafa, Emil Palikot, Tianyu Du, Ayush Kanodia, Susan Athey, David M. Blei

We fit CAREER to a dataset of 24 million job sequences from resumes, and adjust it on small longitudinal survey datasets.

1 code implementation • 3 Feb 2022 • Liyi Zhang, David M. Blei, Christian A. Naesseth

Variational inference often minimizes the "reverse" Kullbeck-Leibler (KL) KL(q||p) from the approximate distribution q to the posterior p. Recent work studies the "forward" KL KL(p||q), which unlike reverse KL does not lead to variational approximations that underestimate uncertainty.

1 code implementation • 7 Dec 2021 • Yookoon Park, Sangho Lee, Gunhee Kim, David M. Blei

We argue that the deep encoder should maximize its nonlinear expressivity on the data for downstream predictors to take full advantage of its representation power.

1 code implementation • 20 Oct 2021 • Gemma E. Moran, Dhanya Sridhar, Yixin Wang, David M. Blei

The underlying model is sparse in that each observed feature (i. e. each dimension of the data) depends on a small subset of the latent factors.

1 code implementation • 24 Sep 2021 • Mingzhang Yin, Yixin Wang, David M. Blei

This paper presents a new optimization approach to causal estimation.

2 code implementations • EMNLP 2021 • Keyon Vafa, Yuntian Deng, David M. Blei, Alexander M. Rush

Compared to existing baselines, greedy rationalization is best at optimizing the combinatorial objective and provides the most faithful rationales.

1 code implementation • ACL 2020 • Keyon Vafa, Suresh Naidu, David M. Blei

In this paper, we introduce the text-based ideal point model (TBIP), an unsupervised probabilistic topic model that analyzes texts to quantify the political positions of its authors.

no code implementations • 10 Mar 2020 • Yixin Wang, David M. Blei

Wang and Blei (2019) studies multiple causal inference and proposes the deconfounder algorithm.

1 code implementation • NeurIPS 2019 • Aaron Schein, Scott W. Linderman, Mingyuan Zhou, David M. Blei, Hanna Wallach

This paper presents the Poisson-randomized gamma dynamical system (PRGDS), a model for sequentially observed count tensors that encodes a strong inductive bias toward sparsity and burstiness.

no code implementations • 15 Oct 2019 • Yixin Wang, David M. Blei

Ogburn et al. (2019, arXiv:1910. 05438) discuss "The Blessings of Multiple Causes" (Wang and Blei, 2018, arXiv:1805. 06826).

2 code implementations • 9 Oct 2019 • Adji B. Dieng, Francisco J. R. Ruiz, David M. Blei, Michalis K. Titsias

Generative adversarial networks (GANs) are a powerful approach to unsupervised learning.

Ranked #2 on Image Generation on Stacked MNIST

no code implementations • 2 Aug 2019 • Gemma E. Moran, David M. Blei, Rajesh Ranganath

However, PPCs use the data twice -- both to calculate the posterior predictive and to evaluate it -- which can lead to overconfident assessments of the quality of a model.

1 code implementation • 12 Jul 2019 • Adji B. Dieng, Francisco J. R. Ruiz, David M. Blei

Topic modeling analyzes documents to learn meaningful patterns of words.

11 code implementations • TACL 2020 • Adji B. Dieng, Francisco J. R. Ruiz, David M. Blei

To this end, we develop the Embedded Topic Model (ETM), a generative model of documents that marries traditional topic models with word embeddings.

Ranked #4 on Topic Models on AG News

1 code implementation • 10 Jun 2019 • Wesley Tansey, Christopher Tosh, David M. Blei

The goal in each paired (cell line, drug) experiment is to map out the dose-response curve of the cell line as the dose level of the drug increases.

5 code implementations • NeurIPS 2019 • Claudia Shi, David M. Blei, Victor Veitch

We propose two adaptations based on insights from the statistical literature on the estimation of treatment effects.

Ranked #2 on Causal Inference on IHDP

no code implementations • 30 May 2019 • Yixin Wang, David M. Blei

Our results expand the theory in Wang & Blei (2018), justify the deconfounder for causal graphs, and extend the settings where it can be used.

4 code implementations • 29 May 2019 • Victor Veitch, Dhanya Sridhar, David M. Blei

To address this challenge, we develop causally sufficient embeddings, low-dimensional document representations that preserve sufficient information for causal identification and allow for efficient estimation of causal effects.

no code implementations • 26 May 2019 • Yixin Wang, Dhanya Sridhar, David M. Blei

Machine learning (ML) can automate decision-making by learning to predict decisions from historical data.

1 code implementation • NeurIPS 2019 • Yixin Wang, David M. Blei

As a consequence of these results, we find that the model misspecification error dominates the variational approximation error in VB posterior predictive distributions.

no code implementations • 3 Apr 2019 • Linying Zhang, Yixin Wang, Anna Ostropolets, Jami J. Mulgrave, David M. Blei, George Hripcsak

To adjust for unobserved confounders, we develop the medical deconfounder, a machine learning algorithm that unbiasedly estimates treatment effects from EHRs.

3 code implementations • NeurIPS 2019 • Victor Veitch, Yixin Wang, David M. Blei

We validate the method with experiments on a semi-synthetic social network dataset.

1 code implementation • 13 Dec 2018 • Wesley Tansey, Kathy Li, Haoran Zhang, Scott W. Linderman, Raul Rabadan, David M. Blei, Chris H. Wiggins

Personalized cancer treatments based on the molecular profile of a patient's tumor are an emerging and exciting class of treatments in oncology.

Applications

no code implementations • 1 Dec 2018 • Andrew C. Miller, Ziad Obermeyer, David M. Blei, John P. Cunningham, Sendhil Mullainathan

An electrocardiogram (EKG) is a common, non-invasive test that measures the electrical activity of a patient's heart.

3 code implementations • 1 Nov 2018 • Wesley Tansey, Victor Veitch, Haoran Zhang, Raul Rabadan, David M. Blei

We propose the holdout randomization test (HRT), an approach to feature selection using black box predictive models.

Methodology

no code implementations • 27 Sep 2018 • Adji B. Dieng, Kyunghyun Cho, David M. Blei, Yann Lecun

Furthermore, the reflective likelihood objective prevents posterior collapse when used to train stochastic auto-encoders with amortized inference.

no code implementations • 20 Aug 2018 • Yixin Wang, Dawen Liang, Laurent Charlin, David M. Blei

To this end, we develop a causal approach to recommendation, one where watching a movie is a "treatment" and a user's rating is an "outcome."

no code implementations • 12 Jul 2018 • Adji B. Dieng, Yoon Kim, Alexander M. Rush, David M. Blei

VAEs can capture complex distributions, but they can also suffer from an issue known as "latent variable collapse," especially if the likelihood model is powerful.

1 code implementation • 27 Jun 2018 • Victor Veitch, Morgane Austern, Wenda Zhou, David M. Blei, Peter Orbanz

We solve this problem using recent ideas from graph sampling theory to (i) define an empirical risk for relational data and (ii) obtain stochastic gradients for this empirical risk that are automatically unbiased.

no code implementations • ICML 2018 • Wesley Tansey, Yixin Wang, David M. Blei, Raul Rabadan

BB-FDR learns a series of black box predictive models to boost power and control the false discovery rate (FDR) at two stages of study analysis.

2 code implementations • 17 May 2018 • Yixin Wang, David M. Blei

Causal inference from observational data often assumes "ignorability," that all confounders are observed.

no code implementations • ICML 2018 • Adji B. Dieng, Rajesh Ranganath, Jaan Altosaar, David M. Blei

On the Penn Treebank, the method with Noisin more quickly reaches state-of-the-art performance.

no code implementations • 24 Mar 2018 • Kriste Krstovski, David M. Blei

Qualitatively, we found that equation embeddings provide coherent semantic representations of equations and can capture semantic similarity to other equations and to words.

1 code implementation • ICML 2018 • Francisco J. R. Ruiz, Michalis K. Titsias, Adji B. Dieng, David M. Blei

It maximizes a lower bound on the marginal likelihood of the data.

no code implementations • ICLR 2018 • Adji B. Dieng, Jaan Altosaar, Rajesh Ranganath, David M. Blei

We develop a noise-based regularization method for RNNs.

2 code implementations • 9 Nov 2017 • Francisco J. R. Ruiz, Susan Athey, David M. Blei

We develop SHOPPER, a sequential probabilistic model of shopping data.

no code implementations • ICLR 2018 • Dustin Tran, David M. Blei

For the first, we describe implicit causal models, a class of causal models that leverages neural architectures with an implicit density.

no code implementations • ICML 2017 • Alp Kucukelbir, Yixin Wang, David M. Blei

We propose to evaluate a model through posterior dispersion.

no code implementations • ICML 2017 • Li-Ping Liu, David M. Blei

In this paper, we develop zero-inflated embeddings, a new embedding method that is designed to learn from sparse observations.

1 code implementation • 31 May 2017 • Christian A. Naesseth, Scott W. Linderman, Rajesh Ranganath, David M. Blei

The success of variational approaches depends on (i) formulating a flexible parametric family of distributions, and (ii) optimizing the parameters to find the member of this family that most closely approximates the exact posterior.

1 code implementation • 24 May 2017 • Jaan Altosaar, Rajesh Ranganath, David M. Blei

Consequently, PVI is less sensitive to initialization and optimization quirks and finds better local optima.

no code implementations • 9 May 2017 • Yixin Wang, David M. Blei

The theorem leverages the theoretical characterizations of frequentist variational approximations to understand asymptotic properties of VB.

1 code implementation • 13 Apr 2017 • Stephan Mandt, Matthew D. Hoffman, David M. Blei

Specifically, we show how to adjust the tuning parameters of constant SGD to best match the stationary distribution to a posterior, minimizing the Kullback-Leibler divergence between these two distributions.

no code implementations • NeurIPS 2017 • Dustin Tran, Rajesh Ranganath, David M. Blei

Implicit probabilistic models are a flexible class of models defined by a simulation process for data.

no code implementations • 13 Jan 2017 • Dustin Tran, Matthew D. Hoffman, Rif A. Saurous, Eugene Brevdo, Kevin Murphy, David M. Blei

By treating inference as a first class citizen, on a par with modeling, we show that probabilistic programming can be as flexible and computationally efficient as traditional deep learning.

no code implementations • 1 Nov 2016 • Adji B. Dieng, Dustin Tran, Rajesh Ranganath, John Paisley, David M. Blei

In this paper we propose CHIVI, a black-box variational inference algorithm that minimizes $D_{\chi}(p || q)$, the $\chi$-divergence from $p$ to $q$.

no code implementations • 31 Oct 2016 • Dustin Tran, Alp Kucukelbir, Adji B. Dieng, Maja Rudolph, Dawen Liang, David M. Blei

Probabilistic modeling is a powerful approach for analyzing empirical information.

no code implementations • NeurIPS 2016 • Rajesh Ranganath, Jaan Altosaar, Dustin Tran, David M. Blei

Though this divergence has been widely used, the resultant posterior approximation can suffer from undesirable statistical properties.

1 code implementation • 26 Oct 2016 • Scott W. Linderman, Andrew C. Miller, Ryan P. Adams, David M. Blei, Liam Paninski, Matthew J. Johnson

Many natural systems, such as neurons firing in the brain or basketball teams traversing a court, give rise to time series data with complex, nonlinear dynamics.

2 code implementations • 18 Oct 2016 • Christian A. Naesseth, Francisco J. R. Ruiz, Scott W. Linderman, David M. Blei

Variational inference using the reparameterization trick has enabled large-scale approximate Bayesian inference in complex probabilistic models, leveraging stochastic optimization to sidestep intractable expectations.

no code implementations • NeurIPS 2016 • Francisco J. R. Ruiz, Michalis K. Titsias, David M. Blei

The reparameterization gradient has become a widely used method to obtain Monte Carlo gradients to optimize the variational objective.

no code implementations • NeurIPS 2016 • Maja R. Rudolph, Francisco J. R. Ruiz, Stephan Mandt, David M. Blei

In this paper, we develop exponential family embeddings, a class of methods that extends the idea of word embeddings to other types of high-dimensional data.

1 code implementation • ICML 2017 • Yixin Wang, Alp Kucukelbir, David M. Blei

We propose a way to systematically detect and mitigate mismatch of a large class of probabilistic models.

1 code implementation • 6 Jun 2016 • Aaron Schein, Mingyuan Zhou, David M. Blei, Hanna Wallach

We introduce Bayesian Poisson Tucker decomposition (BPTD) for modeling country--country interaction event data.

no code implementations • 24 May 2016 • Alp Kucukelbir, David M. Blei

We propose to evaluate a model through posterior dispersion.

no code implementations • 3 Mar 2016 • Francisco J. R. Ruiz, Michalis K. Titsias, David M. Blei

Instead of taking samples from the variational distribution, we use importance sampling to take samples from an overdispersed distribution in the same exponential family as the variational approximation.

4 code implementations • 2 Mar 2016 • Alp Kucukelbir, Dustin Tran, Rajesh Ranganath, Andrew Gelman, David M. Blei

Probabilistic modeling is iterative.

no code implementations • 8 Feb 2016 • Stephan Mandt, Matthew D. Hoffman, David M. Blei

With constant learning rates, it is a stochastic process that, after an initial phase of convergence, generates samples from a stationary distribution.

6 code implementations • 4 Jan 2016 • David M. Blei, Alp Kucukelbir, Jon D. McAuliffe

One of the core problems of modern statistics is to approximate difficult-to-compute probability densities.

no code implementations • 20 Nov 2015 • Dustin Tran, Rajesh Ranganath, David M. Blei

Variational inference is a powerful tool for approximate inference, and it has been recently applied for representation learning with deep generative models.

1 code implementation • 7 Nov 2015 • Rajesh Ranganath, Dustin Tran, David M. Blei

We study HVMs on a variety of deep discrete latent variable models.

1 code implementation • 23 Oct 2015 • Dawen Liang, Laurent Charlin, James McInerney, David M. Blei

The exposure is modeled as a latent variable and the model infers its value from data.

no code implementations • 17 Oct 2015 • Chong Wang, David M. Blei

Robust Bayesian models are appealing alternatives to standard models, providing protection from data that contains outliers or other departures from the model assumptions.

no code implementations • 15 Sep 2015 • Laurent Charlin, Rajesh Ranganath, James McInerney, David M. Blei

Models for recommender systems use latent factors to explain the preferences and behaviors of users with respect to a set of items (e. g., movies, books, academic papers).

2 code implementations • 19 Jul 2015 • James McInerney, Rajesh Ranganath, David M. Blei

Many modern data analysis problems involve inferences from streaming data.

no code implementations • 24 Jun 2015 • Maja R. Rudolph, Joseph G. Ellis, David M. Blei

In this paper, we develop a probabilistic method to learn a profitable strategy to set the reserve price.

no code implementations • NeurIPS 2015 • Alp Kucukelbir, Rajesh Ranganath, Andrew Gelman, David M. Blei

With ADVI we can use variational inference on any model we write in Stan.

no code implementations • NeurIPS 2015 • Dustin Tran, David M. Blei, Edoardo M. Airoldi

We develop a general variational inference method that preserves dependency among the latent variables.

1 code implementation • 10 Jun 2015 • Aaron Schein, John Paisley, David M. Blei, Hanna Wallach

We demonstrate that our model's predictive performance is better than that of standard non-negative tensor factorization methods.

no code implementations • 10 Nov 2014 • Rajesh Ranganath, Linpeng Tang, Laurent Charlin, David M. Blei

We describe \textit{deep exponential families} (DEFs), a class of latent variable models that are inspired by the hidden structures used in deep neural networks.

1 code implementation • 2 Nov 2014 • Alp Kucukelbir, David M. Blei

We develop population empirical Bayes (POP-EB), a hierarchical framework that explicitly models the empirical population distribution as part of Bayesian analysis.

no code implementations • 16 Apr 2014 • Matthew D. Hoffman, David M. Blei

Stochastic variational inference makes it possible to approximate posterior distributions induced by large datasets quickly using stochastic optimization.

2 code implementations • 31 Dec 2013 • Rajesh Ranganath, Sean Gerrish, David M. Blei

We evaluate our method against the corresponding black box sampling based methods.

4 code implementations • 7 Nov 2013 • Prem Gopalan, Jake M. Hofman, David M. Blei

This is an efficient algorithm that iterates over the observed entries and adjusts an approximate posterior over the user/item representations.

1 code implementation • PNAS 2013 2013 • Prem K. Gopalan, David M. Blei

Our approach is based on a Bayesian model of networks that allows nodes to participate in multiple communities, and a corresponding algorithm that naturally interleaves subsampling from the network and updating an estimate of its communities.

no code implementations • NeurIPS 2012 • Sean Gerrish, David M. Blei

We develop a probabilistic model of legislative data that uses the text of the bills to uncover lawmakers' positions on specific political issues.

no code implementations • NeurIPS 2012 • Chong Wang, David M. Blei

We present a truncation-free online variational inference algorithm for Bayesian nonparametric models.

no code implementations • NeurIPS 2012 • Prem K. Gopalan, Sean Gerrish, Michael Freedman, David M. Blei, David M. Mimno

We develop a scalable algorithm for posterior inference of overlapping communities in large networks.

no code implementations • 25 Oct 2012 • John Paisley, Chong Wang, David M. Blei, Michael. I. Jordan

We develop a nested hierarchical Dirichlet process (nHDP) for hierarchical topic modeling.

2 code implementations • 29 Jun 2012 • Matt Hoffman, David M. Blei, Chong Wang, John Paisley

We develop stochastic variational inference, a scalable algorithm for approximating posterior distributions.

no code implementations • NeurIPS 2011 • Soumya Ghosh, Andrei B. Ungureanu, Erik B. Sudderth, David M. Blei

The distance dependent Chinese restaurant process (ddCRP) was recently introduced to accommodate random partitions of non-exchangeable data.

no code implementations • NeurIPS 2010 • Lauren Hannah, Warren Powell, David M. Blei

Those similar to the current state are used to create a convex, deterministic approximation of the objective function.

no code implementations • NeurIPS 2010 • Matthew Hoffman, Francis R. Bach, David M. Blei

We develop an online variational Bayes (VB) algorithm for Latent Dirichlet Allocation (LDA).

no code implementations • NeurIPS 2010 • Abhinav Gupta, Martial Hebert, Takeo Kanade, David M. Blei

There has been a recent push in extraction of 3D spatial layout of scenes.

1 code implementation • NeurIPS 2007 • David M. Blei, Jon D. McAuliffe

We introduce supervised latent Dirichlet allocation (sLDA), a statistical model of labelled documents.

no code implementations • NeurIPS 2009 • Chong Wang, David M. Blei

The nested Chinese restaurant process (nCRP) is a powerful nonparametric Bayesian model for learning tree-based hierarchies from data.

no code implementations • NeurIPS 2009 • Chong Wang, David M. Blei

We present a nonparametric hierarchical Bayesian model of document collections that decouples sparsity and smoothness in the component distributions (i. e., the ``topics).

no code implementations • NeurIPS 2009 • Richard Socher, Samuel Gershman, Per Sederberg, Kenneth Norman, Adler J. Perotte, David M. Blei

We develop a probabilistic model of human memory performance in free recall experiments.

no code implementations • NeurIPS 2008 • Edo M. Airoldi, David M. Blei, Stephen E. Fienberg, Eric P. Xing

Observations consisting of measurements on relationships for pairs of objects arise in many settings, such as protein interaction and gene regulatory networks, collections of author-recipient email, and social networks.

no code implementations • NeurIPS 2008 • Jordan L. Boyd-Graber, David M. Blei

We develop \name\ (STM), a nonparametric Bayesian model of parsed documents.

no code implementations • NeurIPS 2008 • Indraneel Mukherjee, David M. Blei

In this paper we provide the beginnings of such understanding.

no code implementations • 27 Aug 2007 • David M. Blei, John D. Lafferty

This limitation stems from the use of the Dirichlet distribution to model the variability among the topic proportions.

Applications

2 code implementations • 1 Jan 2003 • David M. Blei, Andrew Y. Ng, Michael I. Jordan

Each topic is, in turn, modeled as an infinite mixture over an underlying set of topic probabilities.

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