Search Results for author: David Blei

Found 28 papers, 10 papers with code

Estimating Social Influence from Observational Data

1 code implementation24 Mar 2022 Dhanya Sridhar, Caterina De Bacco, David Blei

We consider the problem of estimating social influence, the effect that a person's behavior has on the future behavior of their peers.

Posterior Collapse and Latent Variable Non-identifiability

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

Existing approaches to posterior collapse oftenattribute it to the use of neural networks or optimization issues dueto variational approximation.

Variational Combinatorial Sequential Monte Carlo Methods for Bayesian Phylogenetic Inference

1 code implementation31 May 2021 Antonio Khalil Moretti, Liyi Zhang, Christian A. Naesseth, Hadiah Venner, David Blei, Itsik Pe'er

Bayesian phylogenetic inference is often conducted via local or sequential search over topologies and branch lengths using algorithms such as random-walk Markov chain Monte Carlo (MCMC) or Combinatorial Sequential Monte Carlo (CSMC).

Hierarchical Inducing Point Gaussian Process for Inter-domain Observations

1 code implementation28 Feb 2021 Luhuan Wu, Andrew Miller, Lauren Anderson, Geoff Pleiss, David Blei, John Cunningham

In this work, we introduce the hierarchical inducing point GP (HIP-GP), a scalable inter-domain GP inference method that enables us to improve the approximation accuracy by increasing the number of inducing points to the millions.

Gaussian Processes

Invariant Representation Learning for Treatment Effect Estimation

1 code implementation24 Nov 2020 Claudia Shi, Victor Veitch, David Blei

To address this challenge, practitioners collect and adjust for the covariates, hoping that they adequately correct for confounding.

Causal Identification Causal Inference +1

Markovian Score Climbing: Variational Inference with KL(p||q)

no code implementations NeurIPS 2020 Christian A. Naesseth, Fredrik Lindsten, David Blei

Modern variational inference (VI) uses stochastic gradients to avoid intractable expectations, enabling large-scale probabilistic inference in complex models.

Variational Inference

Linear-time inference for Gaussian Processes on one dimension

no code implementations11 Mar 2020 Jackson Loper, David Blei, John P. Cunningham, Liam Paninski

Gaussian Processes (GPs) provide powerful probabilistic frameworks for interpolation, forecasting, and smoothing, but have been hampered by computational scaling issues.

Gaussian Processes Time Series

Counterfactual Inference for Consumer Choice Across Many Product Categories

no code implementations6 Jun 2019 Rob Donnelly, Francisco R. Ruiz, David Blei, Susan Athey

One source of the improvement is the ability of the model to accurately estimate heterogeneity in preferences (by pooling information across categories); another source of improvement is its ability to estimate the preferences of consumers who have rarely or never made a purchase in a given category in the training data.

Counterfactual Inference

Estimating Heterogeneous Consumer Preferences for Restaurants and Travel Time Using Mobile Location Data

no code implementations22 Jan 2018 Susan Athey, David Blei, Robert Donnelly, Francisco Ruiz, Tobias Schmidt

The data is used to identify users' approximate typical morning location, as well as their choices of lunchtime restaurants.

Variational Inference

Word2net: Deep Representations of Language

no code implementations ICLR 2018 Maja Rudolph, Francisco Ruiz, David Blei

Most embedding methods rely on a log-bilinear model to predict the occurrence of a word in a context of other words.

Word Embeddings

Variational Inference via \chi Upper Bound Minimization

no code implementations NeurIPS 2017 Adji Bousso Dieng, Dustin Tran, Rajesh Ranganath, John Paisley, David 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 $q$.

Variational Inference

Context Selection for Embedding Models

1 code implementation NeurIPS 2017 Liping Liu, Francisco Ruiz, Susan Athey, David Blei

Embedding models consider the probability of a target observation (a word or an item) conditioned on the elements in the context (other words or items).

Recommendation Systems Variational Inference +1

Structured Embedding Models for Grouped Data

1 code implementation NeurIPS 2017 Maja Rudolph, Francisco Ruiz, Susan Athey, David Blei

Here we develop structured exponential family embeddings (S-EFE), a method for discovering embeddings that vary across related groups of data.

Word Embeddings

Dynamic Bernoulli Embeddings for Language Evolution

1 code implementation23 Mar 2017 Maja Rudolph, David Blei

Word embeddings are a powerful approach for unsupervised analysis of language.

Word Embeddings

The Generalized Reparameterization Gradient

no code implementations NeurIPS 2016 Francisco R. Ruiz, Michalis Titsias Rc Aueb, David Blei

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

Variational Inference

Deep Survival Analysis

no code implementations6 Aug 2016 Rajesh Ranganath, Adler Perotte, Noémie Elhadad, David Blei

The electronic health record (EHR) provides an unprecedented opportunity to build actionable tools to support physicians at the point of care.

Survival Analysis

Correlated Random Measures

no code implementations2 Jul 2015 Rajesh Ranganath, David Blei

We develop correlated random measures, random measures where the atom weights can exhibit a flexible pattern of dependence, and use them to develop powerful hierarchical Bayesian nonparametric models.

Variational Inference

Content-based recommendations with Poisson factorization

3 code implementations NeurIPS 2014 Prem K. Gopalan, Laurent Charlin, David Blei

We develop collaborative topic Poisson factorization (CTPF), a generative model of articles and reader preferences.

Recommendation Systems Variational Inference

A Filtering Approach to Stochastic Variational Inference

no code implementations NeurIPS 2014 Neil Houlsby, David Blei

Stochastic variational inference (SVI) uses stochastic optimization to scale up Bayesian computation to massive data.

Stochastic Optimization Variational Inference

Variational Tempering

no code implementations7 Nov 2014 Stephan Mandt, James McInerney, Farhan Abrol, Rajesh Ranganath, David Blei

Lastly, we develop local variational tempering, which assigns a latent temperature to each data point; this allows for dynamic annealing that varies across data.

Variational Inference

Smoothed Gradients for Stochastic Variational Inference

no code implementations NeurIPS 2014 Stephan Mandt, David Blei

It uses stochastic optimization to fit a variational distribution, following easy-to-compute noisy natural gradients.

Stochastic Optimization Variational Inference

Efficient Online Inference for Bayesian Nonparametric Relational Models

no code implementations NeurIPS 2013 Dae Il Kim, Prem K. Gopalan, David Blei, Erik Sudderth

In large social networks, we expect entities to participate in multiple communities, and the number of communities to grow with the network size.

Link Prediction online learning +1

Modeling Overlapping Communities with Node Popularities

no code implementations NeurIPS 2013 Prem K. Gopalan, Chong Wang, David Blei

We evaluate the link prediction accuracy of our algorithm on eight real-world networks with up to 60, 000 nodes, and 24 benchmark networks.

Link Prediction Variational Inference

Variational Bayesian Inference with Stochastic Search

no code implementations27 Jun 2012 John Paisley, David Blei, Michael Jordan

This requires the ability to integrate a sum of terms in the log joint likelihood using this factorized distribution.

Bayesian Inference Stochastic Optimization +1

Continuous Time Dynamic Topic Models

no code implementations13 Jun 2012 Chong Wang, David Blei, David Heckerman

In contrast to the cDTM, the original discrete-time dynamic topic model (dDTM) requires that time be discretized.

Topic Models Variational Inference

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