Search Results for author:
Found 51 papers, 23 papers with code
Consistent Validation for Predictive Methods in Spatial Settings
Unfortunately, classical approaches for validation fail to handle mismatch between locations available for validation and (test) locations where we want to make predictions.
Could dropping a few cells change the takeaways from differential expression?
We then overcome the inherent non-differentiability of gene set enrichment analysis to develop an additional approximation for the robustness of top gene sets.
Black Box Variational Inference with a Deterministic Objective: Faster, More Accurate, and Even More Black Box
We show on a variety of real-world problems that DADVI reliably finds good solutions with default settings (unlike ADVI) and, together with LR covariances, is typically faster and more accurate than standard ADVI.
Gaussian processes at the Helm(holtz): A more fluid model for ocean currents
Given sparse observations of buoy velocities, oceanographers are interested in reconstructing ocean currents away from the buoys and identifying divergences in a current vector field.
Are you using test log-likelihood correctly?
Test log-likelihood is commonly used to compare different models of the same data or different approximate inference algorithms for fitting the same probabilistic model.
Developing a Series of AI Challenges for the United States Department of the Air Force
Through a series of federal initiatives and orders, the U. S. Government has been making a concerted effort to ensure American leadership in AI.
Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problem
Construction of a scaffold structure that supports a desired motif, conferring protein function, shows promise for the design of vaccines and enzymes.
Many processors, little time: MCMC for partitions via optimal transport couplings
In MCMC samplers of continuous random variables, Markov chain couplings can overcome bias.
Toward a Taxonomy of Trust for Probabilistic Machine Learning
Probabilistic machine learning increasingly informs critical decisions in medicine, economics, politics, and beyond.
Can we globally optimize cross-validation loss? Quasiconvexity in ridge regression
In the present paper, we show that, in the case of ridge regression, the CV loss may fail to be quasiconvex and thus may have multiple local optima.
For high-dimensional hierarchical models, consider exchangeability of effects across covariates instead of across datasets
While standard practice is to model regression parameters (effects) as (1) exchangeable across datasets and (2) correlated to differing degrees across covariates, we show that this approach exhibits poor statistical performance when the number of covariates exceeds the number of datasets.
Evaluating Sensitivity to the Stick-Breaking Prior in Bayesian Nonparametrics
Bayesian models based on the Dirichlet process and other stick-breaking priors have been proposed as core ingredients for clustering, topic modeling, and other unsupervised learning tasks.
The SKIM-FA Kernel: High-Dimensional Variable Selection and Nonlinear Interaction Discovery in Linear Time
Often, these effects are nonlinear and include interactions, so linear and additive methods can lead to poor estimation and variable selection.
Measuring the robustness of Gaussian processes to kernel choice
We demonstrate in both synthetic and real-world examples that decisions made with a GP can exhibit non-robustness to kernel choice, even when prior draws are qualitatively interchangeable to a user.
Power posteriors do not reliably learn the number of components in a finite mixture
Increasingly, though, data science papers suggest potential alternatives beyond vanilla FMMs, such as power posteriors, coarsening, and related methods.
Independent versus truncated finite approximations for Bayesian nonparametric inference
Bayesian nonparametric models based on completely random measures (CRMs) offers flexibility when the number of clusters or latent components in a data set is unknown.
Independent finite approximations for Bayesian nonparametric inference
We call our construction the automated independent finite approximation (AIFA).
Approximate Cross-Validation with Low-Rank Data in High Dimensions
Our second key insight is that, in the presence of ALR data, error in existing ACV methods roughly grows with the (approximate, low) rank rather than with the (full, high) dimension.
Finite mixture models do not reliably learn the number of components
In this paper, we add rigor to data-analysis folk wisdom by proving that under even the slightest model misspecification, the FMM component-count posterior diverges: the posterior probability of any particular finite number of components converges to 0 in the limit of infinite data.
Approximate Cross-Validation for Structured Models
But this existing ACV work is restricted to simpler models by the assumptions that (i) data across CV folds are independent and (ii) an exact initial model fit is available.
Genomic variety prediction via Bayesian nonparametrics
We consider the case where scientists have already conducted a pilot study to reveal some variants in a genome and are contemplating a follow-up study.
Validated Variational Inference via Practical Posterior Error Bounds
Finally, we demonstrate the utility of our proposed workflow and error bounds on a robust regression problem and on a real-data example with a widely used multilevel hierarchical model.
A Higher-Order Swiss Army Infinitesimal Jackknife
The first-order approximation is known as the "infinitesimal jackknife" in the statistics literature and has been the subject of recent interest in machine learning for approximate CV.
Approximate Cross-Validation in High Dimensions with Guarantees
Crucially, though, we are able to show, both empirically and theoretically, that one approximation can perform well in high dimensions -- in cases where the high-dimensional parameter exhibits sparsity.
LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data Approximations
Due to the ease of modern data collection, applied statisticians often have access to a large set of covariates that they wish to relate to some observed outcome.
The Kernel Interaction Trick: Fast Bayesian Discovery of Pairwise Interactions in High Dimensions
Discovering interaction effects on a response of interest is a fundamental problem faced in biology, medicine, economics, and many other scientific disciplines.
Reconstructing probabilistic trees of cellular differentiation from single-cell RNA-seq data
Specifically, we extend the framework of the classical Dirichlet diffusion tree to simultaneously infer branch topology and latent cell states along continuous trajectories over the full tree.
Evaluating Sensitivity to the Stick-Breaking Prior in Bayesian Nonparametrics
Bayesian models based on the Dirichlet process and other stick-breaking priors have been proposed as core ingredients for clustering, topic modeling, and other unsupervised learning tasks.
Methodology
Data-dependent compression of random features for large-scale kernel approximation
Random feature maps (RFMs) and the Nystrom method both consider low-rank approximations to the kernel matrix as a potential solution.
Practical bounds on the error of Bayesian posterior approximations: A nonasymptotic approach
Bayesian inference typically requires the computation of an approximation to the posterior distribution.
Scalable Gaussian Process Inference with Finite-data Mean and Variance Guarantees
We develop an approach to scalable approximate GP regression with finite-data guarantees on the accuracy of pointwise posterior mean and variance estimates.
A Swiss Army Infinitesimal Jackknife
This linear approximation is sometimes known as the "infinitesimal jackknife" in the statistics literature, where it is mostly used to as a theoretical tool to prove asymptotic results.
Methodology
Minimal I-MAP MCMC for Scalable Structure Discovery in Causal DAG Models
Learning a Bayesian network (BN) from data can be useful for decision-making or discovering causal relationships.
Bayesian Coreset Construction via Greedy Iterative Geodesic Ascent
Coherent uncertainty quantification is a key strength of Bayesian methods.
Automated Scalable Bayesian Inference via Hilbert Coresets
We begin with an intuitive reformulation of Bayesian coreset construction as sparse vector sum approximation, and demonstrate that its automation and performance-based shortcomings arise from the use of the supremum norm.
PASS-GLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference
We provide theoretical guarantees on the quality of point (MAP) estimates, the approximate posterior, and posterior mean and uncertainty estimates.
Covariances, Robustness, and Variational Bayes
The estimates for MFVB posterior covariances rely on a result from the classical Bayesian robustness literature relating derivatives of posterior expectations to posterior covariances and include the Laplace approximation as a special case.
Methodology
Edge-exchangeable graphs and sparsity (NIPS 2016)
Many popular network models rely on the assumption of (vertex) exchangeability, in which the distribution of the graph is invariant to relabelings of the vertices.
Boosting Variational Inference
Variational inference (VI) provides fast approximations of a Bayesian posterior in part because it formulates posterior approximation as an optimization problem: to find the closest distribution to the exact posterior over some family of distributions.
Fast robustness quantification with variational Bayes
Bayesian hierarchical models are increasing popular in economics.
Coresets for Scalable Bayesian Logistic Regression
We demonstrate the efficacy of our approach on a number of synthetic and real-world datasets, and find that, in practice, the size of the coreset is independent of the original dataset size.
Edge-exchangeable graphs and sparsity
We show that, unlike node exchangeability, edge exchangeability encompasses models that are known to provide a projective sequence of random graphs that circumvent the Aldous-Hoover Theorem and exhibit sparsity, i. e., sub-quadratic growth of the number of edges with the number of nodes.
Completely random measures for modeling power laws in sparse graphs
Since individual network datasets continue to grow in size, it is necessary to develop models that accurately represent the real-life scaling properties of networks.
Linear Response Methods for Accurate Covariance Estimates from Mean Field Variational Bayes
We call our method linear response variational Bayes (LRVB).
Covariance Matrices and Influence Scores for Mean Field Variational Bayes
We develop a fast, general methodology for exponential families that augments MFVB to deliver accurate uncertainty estimates for model variables -- both for individual variables and coherently across variables.
Covariance Matrices for Mean Field Variational Bayes
We develop a fast, general methodology for exponential families that augments MFVB to deliver accurate uncertainty estimates for model variables -- both for individual variables and coherently across variables.
Variational Bayes for Merging Noisy Databases
Bayesian entity resolution merges together multiple, noisy databases and returns the minimal collection of unique individuals represented, together with their true, latent record values.
Optimistic Concurrency Control for Distributed Unsupervised Learning
Research on distributed machine learning algorithms has focused primarily on one of two extremes - algorithms that obey strict concurrency constraints or algorithms that obey few or no such constraints.
Streaming Variational Bayes
We present SDA-Bayes, a framework for (S)treaming, (D)istributed, (A)synchronous computation of a Bayesian posterior.
Combinatorial clustering and the beta negative binomial process
We show that the NBP is conjugate to the beta process, and we characterize the posterior distribution under the beta-negative binomial process (BNBP) and hierarchical models based on the BNBP (the HBNBP).
Faster solutions of the inverse pairwise Ising problem
Recent work has shown that probabilistic models based on pairwise interactions-in the simplest case, the Ising model-provide surprisingly accurate descriptions of experiments on real biological networks ranging from neurons to genes.
