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Found 34 papers, 13 papers with code
Content-based recommendations with Poisson factorization
We develop collaborative topic Poisson factorization (CTPF), a generative model of articles and reader preferences.
Variational Inference with Gaussian Score Matching
We study how GSM-VI behaves as a function of the problem dimensionality, the condition number of the target covariance matrix (when the target is Gaussian), and the degree of mismatch between the approximating and exact posterior distribution.
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.
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).
Variational Inference for Infinitely Deep Neural Networks
We introduce the unbounded depth neural network (UDN), an infinitely deep probabilistic model that adapts its complexity to the training data.
Dynamic Bernoulli Embeddings for Language Evolution
Word embeddings are a powerful approach for unsupervised analysis of language.
Augment and Reduce: Stochastic Inference for Large Categorical Distributions
It maximizes a lower bound on the marginal likelihood of the data.
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.
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).
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.
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.
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.
Deep Survival Analysis
The electronic health record (EHR) provides an unprecedented opportunity to build actionable tools to support physicians at the point of care.
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.
Continuous Time Dynamic Topic Models
In contrast to the cDTM, the original discrete-time dynamic topic model (dDTM) requires that time be discretized.
Smoothed Gradients for Stochastic Variational Inference
It uses stochastic optimization to fit a variational distribution, following easy-to-compute noisy natural gradients.
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.
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 $q$.
The Generalized Reparameterization Gradient
The reparameterization gradient has become a widely used method to obtain Monte Carlo gradients to optimize the variational objective.
The Population Posterior and Bayesian Modeling on Streams
Many modern data analysis problems involve inferences from streaming data.
A Filtering Approach to Stochastic Variational Inference
Stochastic variational inference (SVI) uses stochastic optimization to scale up Bayesian computation to massive data.
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.
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.
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.
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.
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.
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.
Reconstructing the Universe with Variational self-Boosted Sampling
We show that after a short initial warm-up and training phase, VBS generates better quality of samples than simple VI approaches and reduces the correlation length in the sampling phase by a factor of 10-50 over using only HMC to explore the posterior of initial conditions in 64$^3$ and 128$^3$ dimensional problems, with larger gains for high signal-to-noise data observations.
Forget-me-not! Contrastive Critics for Mitigating Posterior Collapse
Variational autoencoders (VAEs) suffer from posterior collapse, where the powerful neural networks used for modeling and inference optimize the objective without meaningfully using the latent representation.
Data Augmentations for Improved (Large) Language Model Generalization
The reliance of text classifiers on spurious correlations can lead to poor generalization at deployment, raising concerns about their use in safety-critical domains such as healthcare.
Stable Differentiable Causal Discovery
We find that SDCD outperforms existing methods in both convergence speed and accuracy and can scale to thousands of variables.
Extending Mean-Field Variational Inference via Entropic Regularization: Theory and Computation
Variational inference (VI) has emerged as a popular method for approximate inference for high-dimensional Bayesian models.
