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Found 96 papers, 43 papers with code
Posterior Collapse and Latent Variable Non-identifiability
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.
A Bayesian Causal Inference Approach for Assessing Fairness in Clinical Decision-Making
Fairness in clinical decision-making is a critical element of health equity, but assessing fairness of clinical decisions from observational data is challenging.
Probabilistic Conformal Prediction Using Conditional Random Samples
This paper proposes probabilistic conformal prediction (PCP), a predictive inference algorithm that estimates a target variable by a discontinuous predictive set.
CAREER: Transfer Learning for Economic Prediction of Labor Sequence Data
We fit CAREER to a dataset of 24 million job sequences from resumes, and fine-tune its representations on longitudinal survey datasets.
Transport Score Climbing: Variational Inference Using Forward KL and Adaptive Neural Transport
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.
Unsupervised Representation Learning via Neural Activation Coding
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.
Identifiable Deep Generative Models via Sparse Decoding
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.
Optimization-based Causal Estimation from Heterogenous Environments
This paper presents a new optimization approach to causal estimation.
Rationales for Sequential Predictions
Compared to existing baselines, greedy rationalization is best at optimizing the combinatorial objective and provides the most faithful rationales.
Text-Based Ideal Points
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.
Towards Clarifying the Theory of the Deconfounder
Wang and Blei (2019) studies multiple causal inference and proposes the deconfounder algorithm.
Poisson-Randomized Gamma Dynamical Systems
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.
The Blessings of Multiple Causes: A Reply to Ogburn et al. (2019)
Ogburn et al. (2019, arXiv:1910. 05438) discuss "The Blessings of Multiple Causes" (Wang and Blei, 2018, arXiv:1805. 06826).
Prescribed Generative Adversarial Networks
Generative adversarial networks (GANs) are a powerful approach to unsupervised learning.
Ranked #2 on
Image Generation
on Stacked MNIST
Population Predictive Checks
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.
The Dynamic Embedded Topic Model
Topic modeling analyzes documents to learn meaningful patterns of words.
Topic Modeling in Embedding Spaces
To this end, we develop the Embedded Topic Model (ETM), a generative model of documents that marries traditional topic models with word embeddings.
A Bayesian Model of Dose-Response for Cancer Drug Studies
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.
Adapting Neural Networks for the Estimation of Treatment Effects
We propose two adaptations based on insights from the statistical literature on the estimation of treatment effects.
Multiple Causes: A Causal Graphical View
Our results expand the theory in Wang & Blei (2018), justify the deconfounder for causal graphs, and extend the settings where it can be used.
Adapting Text Embeddings for Causal Inference
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.
Variational Bayes under Model Misspecification
As a consequence of these results, we find that the model misspecification error dominates the variational approximation error in VB posterior predictive distributions.
Equal Opportunity and Affirmative Action via Counterfactual Predictions
Machine learning (ML) can automate decision-making by learning to predict decisions from historical data.
The Medical Deconfounder: Assessing Treatment Effects with Electronic Health Records
To adjust for unobserved confounders, we develop the medical deconfounder, a machine learning algorithm that unbiasedly estimates treatment effects from EHRs.
Using Embeddings to Correct for Unobserved Confounding in Networks
We validate the method with experiments on a semi-synthetic social network dataset.
Dose-response modeling in high-throughput cancer drug screenings: An end-to-end approach
Personalized cancer treatments based on the molecular profile of a patient's tumor are an emerging and exciting class of treatments in oncology.
Applications
A Probabilistic Model of Cardiac Physiology and Electrocardiograms
An electrocardiogram (EKG) is a common, non-invasive test that measures the electrical activity of a patient's heart.
The Holdout Randomization Test for Feature Selection in Black Box Models
We propose the holdout randomization test (HRT), an approach to feature selection using black box predictive models.
Methodology
Learning with Reflective Likelihoods
Furthermore, the reflective likelihood objective prevents posterior collapse when used to train stochastic auto-encoders with amortized inference.
The Deconfounded Recommender: A Causal Inference Approach to Recommendation
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."
Avoiding Latent Variable Collapse With Generative Skip Models
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.
Empirical Risk Minimization and Stochastic Gradient Descent for Relational Data
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.
Black Box FDR
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.
The Blessings of Multiple Causes
Causal inference from observational data often assumes "ignorability," that all confounders are observed.
Noisin: Unbiased Regularization for Recurrent Neural Networks
On the Penn Treebank, the method with Noisin more quickly reaches state-of-the-art performance.
Equation Embeddings
Qualitatively, we found that equation embeddings provide coherent semantic representations of equations and can capture semantic similarity to other equations and to words.
Augment and Reduce: Stochastic Inference for Large Categorical Distributions
It maximizes a lower bound on the marginal likelihood of the data.
Noise-Based Regularizers for Recurrent Neural Networks
We develop a noise-based regularization method for RNNs.
SHOPPER: A Probabilistic Model of Consumer Choice with Substitutes and Complements
We develop SHOPPER, a sequential probabilistic model of shopping data.
Implicit Causal Models for Genome-wide Association Studies
For the first, we describe implicit causal models, a class of causal models that leverages neural architectures with an implicit density.
Zero-Inflated Exponential Family Embeddings
In this paper, we develop zero-inflated embeddings, a new embedding method that is designed to learn from sparse observations.
Evaluating Bayesian Models with Posterior Dispersion Indices
We propose to evaluate a model through posterior dispersion.
Variational Sequential Monte Carlo
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.
Proximity Variational Inference
Consequently, PVI is less sensitive to initialization and optimization quirks and finds better local optima.
Frequentist Consistency of Variational Bayes
The theorem leverages the theoretical characterizations of frequentist variational approximations to understand asymptotic properties of VB.
Stochastic Gradient Descent as Approximate Bayesian Inference
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.
Hierarchical Implicit Models and Likelihood-Free Variational Inference
Implicit probabilistic models are a flexible class of models defined by a simulation process for data.
Deep Probabilistic Programming
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.
Variational Inference via $χ$-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 $q$.
Edward: A library for probabilistic modeling, inference, and criticism
Probabilistic modeling is a powerful approach for analyzing empirical information.
Operator Variational Inference
Though this divergence has been widely used, the resultant posterior approximation can suffer from undesirable statistical properties.
Recurrent switching linear dynamical systems
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.
Reparameterization Gradients through Acceptance-Rejection Sampling Algorithms
Variational inference using the reparameterization trick has enabled large-scale approximate Bayesian inference in complex probabilistic models, leveraging stochastic optimization to sidestep intractable expectations.
The Generalized Reparameterization Gradient
The reparameterization gradient has become a widely used method to obtain Monte Carlo gradients to optimize the variational objective.
Exponential Family Embeddings
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.
Robust Probabilistic Modeling with Bayesian Data Reweighting
We propose a way to systematically detect and mitigate mismatch of a large class of probabilistic models.
Bayesian Poisson Tucker Decomposition for Learning the Structure of International Relations
We introduce Bayesian Poisson Tucker decomposition (BPTD) for modeling country--country interaction event data.
Overdispersed Black-Box Variational Inference
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.
A Variational Analysis of Stochastic Gradient Algorithms
With constant learning rates, it is a stochastic process that, after an initial phase of convergence, generates samples from a stationary distribution.
Variational Inference: A Review for Statisticians
One of the core problems of modern statistics is to approximate difficult-to-compute probability densities.
The Variational Gaussian Process
Variational inference is a powerful tool for approximate inference, and it has been recently applied for representation learning with deep generative models.
Modeling User Exposure in Recommendation
The exposure is modeled as a latent variable and the model infers its value from data.
A General Method for Robust Bayesian Modeling
Robust Bayesian models are appealing alternatives to standard models, providing protection from data that contains outliers or other departures from the model assumptions.
Dynamic Poisson Factorization
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).
The Population Posterior and Bayesian Inference on Streams
Many modern data analysis problems involve inferences from streaming data.
Objective Variables for Probabilistic Revenue Maximization in Second-Price Auctions with Reserve
In this paper, we develop a probabilistic method to learn a profitable strategy to set the reserve price.
Automatic Variational Inference in Stan
With ADVI we can use variational inference on any model we write in Stan.
Copula variational inference
We develop a general variational inference method that preserves dependency among the latent variables.
Bayesian Poisson Tensor Factorization for Inferring Multilateral Relations from Sparse Dyadic Event Counts
We demonstrate that our model's predictive performance is better than that of standard non-negative tensor factorization methods.
Deep Exponential Families
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.
Population Empirical Bayes
We develop population empirical Bayes (POP-EB), a hierarchical framework that explicitly models the empirical population distribution as part of Bayesian analysis.
Structured Stochastic Variational Inference
Stochastic variational inference makes it possible to approximate posterior distributions induced by large datasets quickly using stochastic optimization.
Black Box Variational Inference
We evaluate our method against the corresponding black box sampling based methods.
Scalable Recommendation with Poisson Factorization
This is an efficient algorithm that iterates over the observed entries and adjusts an approximate posterior over the user/item representations.
Efficient discovery of overlapping communities in massive networks
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.
Truncation-free Online Variational Inference for Bayesian Nonparametric Models
We present a truncation-free online variational inference algorithm for Bayesian nonparametric models.
How They Vote: Issue-Adjusted Models of Legislative Behavior
We develop a probabilistic model of legislative data that uses the text of the bills to uncover lawmakers' positions on specific political issues.
Scalable Inference of Overlapping Communities
We develop a scalable algorithm for posterior inference of overlapping communities in large networks.
Nested Hierarchical Dirichlet Processes
We develop a nested hierarchical Dirichlet process (nHDP) for hierarchical topic modeling.
Stochastic Variational Inference
We develop stochastic variational inference, a scalable algorithm for approximating posterior distributions.
Spatial distance dependent Chinese restaurant processes for image segmentation
The distance dependent Chinese restaurant process (ddCRP) was recently introduced to accommodate random partitions of non-exchangeable data.
Online Learning for Latent Dirichlet Allocation
We develop an online variational Bayes (VB) algorithm for Latent Dirichlet Allocation (LDA).
Nonparametric Density Estimation for Stochastic Optimization with an Observable State Variable
Those similar to the current state are used to create a convex, deterministic approximation of the objective function.
Estimating Spatial Layout of Rooms using Volumetric Reasoning about Objects and Surfaces
There has been a recent push in extraction of 3D spatial layout of scenes.
Supervised Topic Models
We introduce supervised latent Dirichlet allocation (sLDA), a statistical model of labelled documents.
Decoupling Sparsity and Smoothness in the Discrete Hierarchical Dirichlet Process
We present a nonparametric hierarchical Bayesian model of document collections that decouples sparsity and smoothness in the component distributions (i. e., the ``topics).
A Bayesian Analysis of Dynamics in Free Recall
We develop a probabilistic model of human memory performance in free recall experiments.
Variational Inference for the Nested Chinese Restaurant Process
The nested Chinese restaurant process (nCRP) is a powerful nonparametric Bayesian model for learning tree-based hierarchies from data.
Mixed Membership Stochastic Blockmodels
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.
Relative Performance Guarantees for Approximate Inference in Latent Dirichlet Allocation
In this paper we provide the beginnings of such understanding.
A correlated topic model of Science
This limitation stems from the use of the Dirichlet distribution to model the variability among the topic proportions.
Applications
Latent Dirichlet Allocation
Each topic is, in turn, modeled as an infinite mixture over an underlying set of topic probabilities.
