Search Results for author:
Found 28 papers, 10 papers with code
Estimating Social Influence from Observational Data
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
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
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
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.
Invariant Representation Learning for Treatment Effect Estimation
To address this challenge, practitioners collect and adjust for the covariates, hoping that they adequately correct for confounding.
Markovian Score Climbing: Variational Inference with KL(p||q)
Modern variational inference (VI) uses stochastic gradients to avoid intractable expectations, enabling large-scale probabilistic inference in complex models.
Linear-time inference for Gaussian Processes on one dimension
Gaussian Processes (GPs) provide powerful probabilistic frameworks for interpolation, forecasting, and smoothing, but have been hampered by computational scaling issues.
Counterfactual Inference for Consumer Choice Across Many Product Categories
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.
Augment and Reduce: Stochastic Inference for Large Categorical Distributions
It maximizes a lower bound on the marginal likelihood of the data.
Estimating Heterogeneous Consumer Preferences for Restaurants and Travel Time Using Mobile Location Data
The data is used to identify users' approximate typical morning location, as well as their choices of lunchtime restaurants.
Word2net: Deep Representations of Language
Most embedding methods rely on a log-bilinear model to predict the occurrence of a word in a context of other words.
Variational Inference via \chi Upper Bound Minimization
In this paper we propose CHIVI, a black-box variational inference algorithm that minimizes $D_{\chi}(p || q)$, the $\chi$-divergence from $p$ to $q$.
Context Selection for Embedding Models
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).
Structured Embedding Models for Grouped Data
Here we develop structured exponential family embeddings (S-EFE), a method for discovering embeddings that vary across related groups of data.
Dynamic Bernoulli Embeddings for Language Evolution
Word embeddings are a powerful approach for unsupervised analysis of language.
The Generalized Reparameterization Gradient
The reparameterization gradient has become a widely used method to obtain Monte Carlo gradients to optimize the variational objective.
Deep Survival Analysis
The electronic health record (EHR) provides an unprecedented opportunity to build actionable tools to support physicians at the point of care.
The Population Posterior and Bayesian Modeling on Streams
Many modern data analysis problems involve inferences from streaming data.
Correlated Random Measures
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.
Content-based recommendations with Poisson factorization
We develop collaborative topic Poisson factorization (CTPF), a generative model of articles and reader preferences.
A Filtering Approach to Stochastic Variational Inference
Stochastic variational inference (SVI) uses stochastic optimization to scale up Bayesian computation to massive data.
Variational Tempering
Lastly, we develop local variational tempering, which assigns a latent temperature to each data point; this allows for dynamic annealing that varies across data.
Smoothed Gradients for Stochastic Variational Inference
It uses stochastic optimization to fit a variational distribution, following easy-to-compute noisy natural gradients.
Efficient Online Inference for Bayesian Nonparametric Relational Models
In large social networks, we expect entities to participate in multiple communities, and the number of communities to grow with the network size.
Modeling Overlapping Communities with Node Popularities
We evaluate the link prediction accuracy of our algorithm on eight real-world networks with up to 60, 000 nodes, and 24 benchmark networks.
Variational Bayesian Inference with Stochastic Search
This requires the ability to integrate a sum of terms in the log joint likelihood using this factorized distribution.
Continuous Time Dynamic Topic Models
In contrast to the cDTM, the original discrete-time dynamic topic model (dDTM) requires that time be discretized.
