Timezone: »
Probabilistic modeling is a foundation of modern data analysis  due in part to the flexibility and interpretability of these methods  and has been applied to numerous application domains, such as the biological sciences, social and political sciences, engineering, and health care. However, any probabilistic model relies on assumptions that are necessarily a simplification of complex reallife processes; thus, any such model is inevitably misspecified in practice. In addition, as data set sizes grow and probabilistic models become more complex, applying a probabilistic modeling analysis often relies on algorithmic approximations, such as approximate Bayesian inference, numerical approximations, or data summarization methods. Thus in many cases, approximations used for efficient computation lead to fitting a misspecified model by design (e.g., variational inference). Importantly, in some cases, this misspecification leads to useful model inferences, but in others it may lead to misleading and potentially harmful inferences that may then be used for important downstream tasks for, e.g., making scientific inferences or policy decisions.
The goal of the workshop is to bring together researchers focused on methods, applications, and theory to outline some of the core problems in specifying and applying probabilistic models in modern data contexts along with current stateoftheart solutions. Participants will leave the workshop with (i) exposure to recent advances in the field, (ii) an idea of the current major challenges in the field, and (iii) an introduction to methods meeting these challenges. These goals will be accomplished through a series of invited and contributed talks, poster spotlights, poster sessions, as well as ample time for discussion and live Q&A.
Tue 4:55 a.m.  5:00 a.m.

Welcome remarks
(Talk)

Diana Cai 
Tue 5:00 a.m.  5:30 a.m.

Invited Talk 1
(Talk)

Chris C Holmes 
Tue 5:30 a.m.  5:35 a.m.

Invite Talk 1 Q&A
(Q&A)


Tue 5:35 a.m.  6:05 a.m.

Invited Talk 2
(Talk)

Ilse Ipsen 
Tue 6:05 a.m.  6:10 a.m.

Invited Talk 2 Q&A
(Q&A)


Tue 6:10 a.m.  6:45 a.m.

Individual discussions in Gathertown
(Gathertown discussion)


Tue 6:45 a.m.  7:00 a.m.

Contributed talk 1
(Talk)

Michail Spitieris 
Tue 7:00 a.m.  7:15 a.m.

Contributed talk 2
(Talk)

Masha Naslidnyk 
Tue 10:30 a.m.  10:45 a.m.

Contributed talk 3
(Talk)

Maria Cervera 
Tue 10:45 a.m.  11:00 a.m.

Contributed talk 4
(Talk)

Jackson Killian 
Tue 11:00 a.m.  11:30 a.m.

Invited Talk 3
(Talk)

Andres Masegosa 
Tue 11:00 a.m.  11:05 a.m.

Invited Talk 3 Q&A
(Q&A)


Tue 11:35 a.m.  11:40 a.m.

Invited Talk 4 Q&A
(Q&A)


Tue 12:00 p.m.  12:15 p.m.

Contributed talk 5
(Talk)


Tue 12:15 p.m.  12:30 p.m.

Contributed talk 6
(Talk)

Eli N Weinstein 
Tue 12:30 p.m.  1:00 p.m.

Invited Talk 4
(Talk)

Jonathan Huggins 
Tue 1:30 p.m.  2:00 p.m.

Invited Talk 5
(Talk)

Lester Mackey 
Tue 2:00 p.m.  2:05 p.m.

Invited Talk 5 Q&A
(Q&A)


Tue 2:05 p.m.  2:35 p.m.

Invited Talk 6
(Talk)

Yixin Wang 
Tue 2:35 p.m.  2:40 p.m.

Invited Talk 6 Q&A
(Q&A)




Bayesian Data Selection
(Poster)
»
Insights into complex, highdimensional data can be obtained by discovering features of the data that match or do not match a model of interest. To formalize this task, we introduce the "data selection" problem: finding a lowerdimensional statistic  such as a subset of variables  that is well fit by a given parametric model of interest. A fully Bayesian approach to data selection would be to parametrically model the value of the statistic, nonparametrically model the remaining "background" components of the data, and perform standard Bayesian model selection for the choice of statistic. However, fitting a nonparametric model to highdimensional data tends to be highly inefficient, statistically and computationally. We propose a novel score for performing data selection, the "Stein volume criterion (SVC)", that does not require fitting a nonparametric model. The SVC takes the form of a generalized marginal likelihood with a kernelized Stein discrepancy in place of the KullbackLeibler divergence. We prove that the SVC is consistent for data selection. We apply the SVC to the analysis of singlecell RNA sequencing datasets using a spin glass model of gene regulation. 
Eli N Weinstein, Jeffrey Miller 


Uncertainty estimation under model misspecification in neural network regression
(Poster)
»
Although neural networks are powerful function approximators, the underlying modelling assumptions ultimately define the likelihood and thus the model class they are parameterizing. In classification, these assumptions are minimal as the commonly employed softmax is capable of representing any discrete distribution over a finite set of outcomes. In regression, however, restrictive assumptions on the type of continuous distribution to be realized are typically placed, like the dominant choice of training via meansquared error and its underlying Gaussianity assumption. Recently, modelling advances allow to be agnostic to the type of continuous distribution to be modelled, granting regression the flexibility of classification models. While past studies stress the benefit of such flexible regression models in terms of performance, here we study the effect of the model choice on uncertainty estimation. We highlight that under model misspecification, aleatoric uncertainty is not properly captured, and that a Bayesian treatment of a misspecified model leads to unreliable epistemic uncertainty estimates. Overall, our study provides an overview on how modelling choices in regression may influence uncertainty estimation and thus any downstream decision making process. 
Maria Cervera, Rafael Dätwyler, Francesco D'Angelo, Hamza Keurti, Benjamin F. Grewe, Christian Henning 


Fast approximate BayesBag model selection via Taylor expansions
(Poster)
»
BayesBag has been established as a useful tool for robust Bayesian model selection. However, computing BayesBag can be prohibitively expensive for large datasets. Here, we propose a fast approximation of BayesBag model selection. This approximationbased on Taylor approximations of the log marginal likelihoodcan achieve results comparable to BayesBag in a fraction of the time. 
Neil Spencer, Jeffrey Miller 


Diversity and Generalization in Neural Network Ensembles
(Poster)
»
Ensembles are widely used in machine learning and, usually, provide stateoftheart performance in many prediction tasks. From the very beginning, diversity of ensemble members has been identified as a key factor for the superior performance of an ensemble. But the exact role that diversity plays in an ensemble model is not fully understood and is still an open question. In this work, we employ a second order PACBayesian analysis to shed light on this problem in the context of neural network ensembles. More precisely, we provide sound theoretical answers to the following questions: how to measure diversity, how diversity relates to the generalization error and how diversity can be promoted by ensemble learning algorithms. This analysis covers three widely used loss functions, namely, the squared loss, the crossentropy loss, and the 01 loss; and two widely used model combination strategies, namely, model averaging and weighted majority vote. We empirically validate this theoretical analysis on ensembles of neural networks. 
Luis Antonio Ortega Andrés, Andres Masegosa, Rafael Cabañas Cabañas 


A shared parameter model accounting for dropout not at random in a predictive model for systolic bloodpressure using the HUNT study
(Poster)
»
This work proposes and evaluates a shared parameter model (SPM) to account for data being missing not at random (MNAR) for a predictive model based on a longitudinal population study. The aim is to model systolic blood pressure ten years ahead based on current observations and is inspired by and evaluated for data from the NordTrøndelag Health Study (HUNT). The proposed SPM consists of a linear model for the systolic blood pressure and a logistic model for the dropout process connected through a shared random effect. To evaluate the SPM we compare the parameter estimates and predictions of the SPM with a naive linear Bayesian model using the same explanatory variables while ignoring the dropout process. This corresponds to assuming data to be missing at random (MAR). In addition, a simulation study is performed in which the naive model and the SPM are tested on data with known parameters when missingness is assumed to be MNAR. The SPM indicates that participants with higher systolic blood pressure than expected from the explanatory variables at the time of the followup study have a higher probability of dropping out, suggesting that the data are MNAR. Further, the SPM and the naive model result in different parameter estimates for the explanatory variables. The simulation study validates that the SPM is identifiable for the estimates obtained by the predictive model based on the HUNT study 
Aurora Hofman 


Influential Observations in Bayesian Regression Tree Models
(Poster)
»
BCART (Bayesian Classification and Regression Trees) and BART (Bayesian Additive Regression Trees) are popular modern regression models. Their popularity is intimately tied to the ability to flexibly model complex responses depending on highdimensional inputs while simultaneously being able to quantify uncertainties. However, surprisingly little work has been done to evaluate the sensitivity of these modern regression models to violations of modeling assumptions. In particular, we consider influential observations and propose methods for detecting influentials and adjusting predictions to not be unduly affected by such problematic data. We consider two detection diagnostics for Bayesian tree models, one an analogue of Cook's distance and the other taking the form of a divergence measure, and then propose an importance sampling algorithm to reweight previously sampled posterior draws so as to remove the effects of influential data. Finally, our methods are demonstrated on realworld data where blind application of models can lead to poor predictions. 
Matthew Pratola 


Invariant Priors for Bayesian Quadrature
(Poster)
»
Bayesian quadrature (BQ) is a modelbased numerical integration method that is able to increase sample efficiency by encoding and leveraging known structure of the integration task at hand. In this paper, we explore priors that encode invariance of the integrand under a set of bijective transformations in the input domain, in particular some unitary transformations, such as rotations, axisflips, or point symmetries. We show initial results on superior performance in comparison to standard Bayesian quadrature on several synthetic and one real world application. 
Masha Naslidnyk, Javier González, Maren Mahsereci 


Composite Goodnessoffit Tests with Kernels
(Poster)
»
Model misspecification can create significant challenges for the implementation of probabilistic models, and this has led to development of a range of inference methods which directly account for this issue. However, whether these more involved methods are required will depend on whether the model is really misspecified, and there is a lack of generally applicable methods to answer this question. One set of tools which can help are goodnessoffit tests, where we test whether a dataset could have been generated by a fixed distribution. Kernelbased tests have been developed to for this problem, and these are popular due to their flexibility, strong theoretical guarantees and ease of implementation in a wide range of scenarios. In this paper, we extend this line of work to the more challenging composite goodnessoffit problem, where we are instead interested in whether the data comes from any distribution in some parametric family. This is equivalent to testing whether a parametric model is wellspecified for the data. 
Oscar Key, Tamara Fernandez, Arthur Gretton, FrancoisXavier Briol 


Inferior Clusterings in Misspecified Gaussian Mixture Models
(Poster)
»
Gaussian Mixture Model (GMM) is a widely used probabilistic model for clustering. In many practical settings, the true data distribution, which is unknown, may be nonGaussian and may be contaminated by noise or outliers. In such cases, clustering may still be done with a misspecified GMM. However, this may lead to incorrect classification of the underlying subpopulations. In this work, we examine the performance of both Expectation Maximization (EM) and Gradient Descent (GD) on unconstrained Gaussian Mixture Models when there is misspecification. Our simulation study reveals a previously unreported class of \textit{inferior} clustering solutions, different from spurious solutions, that occurs due to asymmetry in the fitted component variances. 
Siva Rajesh Kasa, Vaibhav Rajan 


Blindness of scorebased methods to isolated components and mixing proportions
(Poster)
»
Abstract Statistical tasks such as density estimation and approximate Bayesian inference often involve densities with unknown normalising constants. Scorebased methods, including score matching, are popular techniques as they are free of normalising constants. Although these methods enjoy theoretical guarantees, a littleknown fact is that they suffer from practical failure modes when the unnormalised distribution of interest has isolated components  they cannot discover isolated components or identify the correct mixing proportions between components. We demonstrate these findings using simple distributions and present heuristic attempts to address these issues. We hope to bring the attention of theoreticians and practitioners to these issues when developing new algorithms and applications. 
Li Kevin Wenliang, Heishiro Kanagawa 


Bounding Wasserstein distance with couplings
(Poster)
»
Markov chain Monte Carlo (MCMC) methods are a powerful tool in Bayesian computation. They provide asymptotically consistent estimates as the number of iterations tends to infinity. However, in large data applications, MCMC can be computationally expensive per iteration. This has catalyzed interest in sampling methods such as approximate MCMC, which trade off asymptotic consistency for improved computational speed. In this article, we propose estimators based on couplings of Markov chains to assess the quality of such asymptotically biased sampling methods. The estimators give empirical upper bounds of the Wassertein distance between the limiting distribution of the asymptotically biased sampling method and the original target distribution of interest. We establish theoretical guarantees for our upper bounds and show that our estimators can remain effective in high dimensions. We apply our sample quality measures to stochastic gradient MCMC, variational Bayes, and Laplace approximations for tall data and to approximate MCMC for highdimensional linear regression and highdimensional logistic regression. 
Niloy Biswas, Lester Mackey 


Relaxing the I.I.D. Assumption: Adaptively Minimax Optimal Regret via RootEntropic Regularization
(Poster)
»
We introduce the semiadversarial framework for sequential prediction with expert advice, where data are generated from distributions varying arbitrarily within an unknown constraint set. We quantify relaxations of the classical i.i.d. assumption along a spectrum induced by this framework, with i.i.d. sequences at one extreme and adversarial mechanisms at the other. The Hedge algorithm, which corresponds to using an expertvalued Bayesian power posterior to make decisions, was recently shown to be simultaneously optimal for both i.i.d. and adversarial data. We demonstrate that Hedge is suboptimal at all points of the spectrum in between these endpoints. Further, we introduce a novel algorithm and prove that it achieves the minimax optimal rate of regret at all points along the semiadversarial spectrumwithout advance knowledge of the constraint set. This algorithm corresponds to followtheregularizedleader, constructed by replacing the Shannon entropy regularizer of Hedge with the squareroot of the Shannon entropy. 
Blair Bilodeau, Jeffrey Negrea, Daniel Roy 


Measuring the sensitivity of Gaussian processes to kernel choice
(Poster)
»
Gaussian processes (GPs) are used to make medical and scientific decisions, including in cardiac care and monitoring of carbon dioxide emissions. But the choice of GP kernel is often somewhat arbitrary. In particular, uncountably many kernels typically align with qualitative prior knowledge (e.g. function smoothness or stationarity). But in practice, data analysts choose among a handful of convenient standard kernels (e.g. squared exponential). In the present work, we ask: Would decisions made with a GP differ under other, qualitatively interchangeable kernels? We show how to formulate this sensitivity analysis as a constrained optimization problem over a finitedimensional space. We can then use standard optimizers to identify substantive changes in relevant decisions made with a GP. We demonstrate in both synthetic and realworld examples that decisions made with a GP can exhibit substantial sensitivity to kernel choice, even when prior draws are qualitatively interchangeable to a user. 
Will Stephenson, Soumya Ghosh, Tin Nguyen, Mikhail Yurochkin, Sameer Deshpande, Tamara Broderick 


Robust Bayesian Inference for Simulatorbased Models via the MMD Posterior Bootstrap
(Poster)
»
Simulatorbased models are models for which the likelihood is intractable but simulation of synthetic data is possible. Such models are often used to describe complex realworld phenomena, and as such can often be misspecified in practice. Unfortunately, existing Bayesian approaches for simulators are known to perform poorly in misspecified settings. In this paper, we propose a novel approach based on the posterior bootstrap which gives a highlyparallelisable Bayesian inference algorithm for simulatorbased models. Our approach is based on maximum mean discrepancy estimators, which also allows us to inherit their robustness properties. 
Charita Dellaporta, Jeremias Knoblauch, Theodoros Damoulas, FrancoisXavier Briol 


Your Bandit Model is Not Perfect: Introducing Robustness to Restless Bandits Enabled by Deep Reinforcement Learning
(Poster)
»
Restless multiarm bandits (RMABs) are receiving renewed attention for their potential to model realworld planning problems under resource constraints. However, few RMAB models have surpassed theoretical interest, since they make the limiting assumption that model parameters are perfectly known. In the real world, model parameters often must be estimated via historical data or expert input, introducing uncertainty. In this light, we introduce a new paradigm, \emph{Robust RMABs}, a challenging generalization of RMABs that incorporates interval uncertainty over parameters of the dynamic model of each arm. This uncovers several new challenges for RMABs and inspires new algorithmic techniques of general interest. Our contributions are: (i)~We introduce the Robust Restless Bandit problem with interval uncertainty and solve a minimax regret objective; (ii)~We tackle the complexity of the robust objective via a double oracle (DO) approach and analyze its convergence; (iii)~To enable our DO approach, we introduce RMABPPO, a novel deep reinforcement learning (RL) algorithm for solving RMABs, of potential general interest.; (iv)~We design the first adversary algorithm for RMABs, required to implement the notoriously difficult minimax regret adversary oracle and also of general interest, by formulating it as a multiagent RL problem and solving with a multiagent extension of RMABPPO. 
Jackson Killian, Lily Xu, Arpita Biswas, Milind Tambe 


Bayesian Calibration of imperfect computer models using Physicsinformed priors
(Poster)
»
We introduce a computational efficient datadriven framework suitable for the quantification of the uncertainty in physical parameters of computer models, represented by differential equations. We construct physicsinformed priors for timedependent differential equations, which are multioutput Gaussian process (GP) priors that encode the model's structure in the covariance function. We extend this into a fully Bayesian framework which allows quantifying the uncertainty of physical parameters and model predictions. Since physical models are usually imperfect descriptions of the real process, we allow the model to deviate from the observed data by considering a discrepancy function. To obtain the posterior distributions we use Hamiltonian Monte Carlo (HMC) sampling. This work is primarily motivated by the need for interpretable parameters for the hemodynamics of the heart for personal treatment of hypertension. The model used is the arterial Windkessel model, which represents the hemodynamics of the heart through differential equations with physically interpretable parameters of medical interest. As most physical models, the Windkessel model is an imperfect description of the real process. To demonstrate our approach we simulate noisy data from a more complex physical model with known mathematical connections to our modeling choice. We show that without accounting for discrepancy, the posterior of the physical parameters deviates from the true value while accounting for discrepancy gives reasonable quantification of physical parameters uncertainty and reduces the uncertainty in subsequent model predictions. 
Michail Spitieris 


Forcing a model to be correct for classification
(Poster)
»
Scientists have long recognized deficiencies in their models, particularly in those that seek to describe the full distribution of a set of data. Statistics is replete with ways to address these deficiencies, including adjusting the data (e.g., removing outliers), expanding the class of models under consideration, and the use of robust methods. In this work, we pursue a different path, searching for a recognizable portion of a model that is approximately correct and which aligns with the goal of inference. Once such a model portion has been found, traditional statistical theory applies and suggests effective methods. We illustrate this approach with linear discriminant analysis and show much better performance than one gets by ignoring the deficiency in the model or by working in a large enough space to capture the main deficiency in the model. 
Jiae Kim, Steve MacEachern 


Make crossvalidation Bayes again
(Poster)
»
There are two orthogonal paradigms for hyperparameter inference: either to make a joint estimation in a larger hierarchical Bayesian model or to optimize the tuning parameter with respect to crossvalidation metrics. Both are limited: the “full Bayes” strategy is conceptually unjustified in misspecified models, and may severely under or overfit observations; The crossvalidation strategy, besides its computation cost, typically results in a point estimate, ignoring the uncertainty in hyperparameters. To bridge the two extremes, we present a general paradigm: a fullBayes model on top of the crossvalidated log likelihood. This predictionaware approach incorporates additional regularization during hyperparameter tuning, and facilities Bayesian workflow in many otherwise blackbox learning algorithms. We develop theory justification and discuss its application in a model averaging example. 
Yuling Yao, Aki Vehtari 


Evaluating Bayesian Hierarchical Models for scRNA seq Data
(Poster)
»
A Bayesian hierarchical model (BHM) is typically formulated specifying the data model, the parameters model and the prior distributions. The posterior inference of a BHM depends both on the model specification and on the computation algorithm used. The most straightforward way to test the reliability of a BHM inference is to compare the posterior distributions with the ground truth value of the model parameters, when available. However, when dealing with experimental data, the true value of the underlying parameters is typically unknown. In these situations, numerical experiments based on synthetic datasets generated from the model itself offer a natural approach to check model performance and posterior estimates. Surprisingly, validation of BHMswith highdimensional parameter spaces and nonGaussian distributions is unexplored. In this paper, we show how to test the reliability of a BHM. We introduce a change in the model assumptions to allow for prior contamination and develop a simulationbased evaluation framework to assess the reliability of the inference of a given BHM. We illustrate our approach on a specific BHM used for the analysis of Singlecell Sequencing Data (BASiCS). 
Sijia Li 


On Counterfactual Analysis of Differentiable Functionals
(Poster)
»
Structural econometric models are used to combine economic theory and data to estimate parameters and counterfactuals, e.g. the effect of a policy change. These models typically make functional form assumptions, e.g. the distribution of latent variables. I propose a framework to characterize the sensitivity of structural estimands with respect to misspecification of distributional assumptions of the model. Specifically, I characterize the lower and upper bounds on the estimand as the assumption is perturbed infinitesimally on the tangent space and locally in a neighborhood of the model's assumption. I compute bounds by finding the gradient of the estimand, and integrate these iteratively to construct the gradient flow curve through neighborhoods of the model's assumption. My framework covers models with general smooth dependence on the distributional assumption, allows sensitivity perturbations over neighborhoods described by a general metric, and is computationally tractable, in particular, it is not required to resolve the model under any alternative distributional assumption. I illustrate the framework with an application to the Rust (1987) model of optimal replacement of bus engines. 
Yaroslav Mukhin 


Robust Generalised Bayesian Inference for Intractable Likelihoods
(Poster)
»
Generalised Bayesian inference updates prior beliefs using a loss function, rather than a likelihood, and can therefore be used to confer robustness against possible misspecification of the likelihood. Here we consider generalised Bayesian inference with a Stein discrepancy as a loss function, motivated by applications in which the likelihood contains an intractable normalisation constant. In this context, the Stein discrepancy circumvents evaluation of the normalisation constant and produces generalised posteriors that are either closed form or accessible using standard Markov chain Monte Carlo. 
Takuo Matsubara, Jeremias Knoblauch, FrancoisXavier Briol, Chris Oates 


Boosting heterogeneous VAEs via multiobjective optimization
(Poster)
»
Variational autoencoders (VAEs) have been successfully applied to complex input data such as images and videos. Counterintuitively, their application to simpler, heterogeneous data—where features are of different types, often leads to underwhelming results. While the goal in the heterogeneous case is to accurately approximate all observed features, VAEs often perform poorly in a subset of them. In this work, we study this feature overlooking problem through the lens of multitask learning (MTL), relating it to the problem of negative transfer and the interaction between gradients from different features. With these new insights, we propose to train VAEs by leveraging offtheshelf solutions from the MTL literature based on multiobjective optimization. Furthermore, we empirically demonstrate how these solutions significantly boost the performance of different VAE models and training objectives on a large variety of heterogeneous datasets. 
Adrián Javaloy, Maryam Meghdadi, Isabel Valera 


PAC^mBayes: Narrowing the Empirical Risk Gap in the Misspecified Bayesian Regime
(Poster)
»
The Bayesian posterior minimizes the "inferential risk" which itself bounds the "predictive risk." This bound is tight when the likelihood and prior are wellspecified. However since misspecification induces a gap, the Bayesian posterior predictive distribution may have poor generalization performance. This work develops a multisample loss (PAC^m) which can close the gap by spanning a tradeoff between the two risks. The loss is computationally favorable and offers PAC generalization guarantees. Empirical study demonstrates improvement to the predictive distribution. 
Joshua V Dillon, Warren R Morningstar, Alex Alemi 
Author Information
Diana Cai (Princeton University)
Sameer Deshpande (Wharton Statistics)
Mike Hughes (Tufts University)
Tamara Broderick (MIT)
Trevor Campbell (UBC)
Nick Foti (Apple & University of Washington)
Barbara Engelhardt (Princeton University)
Sinead Williamson (University of Texas at Austin)
More from the Same Authors

2021 Datasets and Benchmarks: Dataset and Benchmark Track 1 »
Joaquin Vanschoren · Serena Yeung · Maria Xenochristou 
2021 Poster: Slice Sampling Reparameterization Gradients »
David M Zoltowski · Diana Cai · Ryan Adams 
2021 Spotlight: Slice Sampling Reparameterization Gradients »
David M Zoltowski · Diana Cai · Ryan Adams 
2021 Poster: Dynamical Wasserstein Barycenters for Timeseries Modeling »
Kevin Cheng · Shuchin Aeron · Michael Hughes · Eric L Miller 
2021 Poster: Can we globally optimize crossvalidation loss? Quasiconvexity in ridge regression »
Will Stephenson · Zachary Frangella · Madeleine Udell · Tamara Broderick 
2021 Poster: For highdimensional hierarchical models, consider exchangeability of effects across covariates instead of across datasets »
Brian Trippe · Hilary Finucane · Tamara Broderick 
2021 Workshop: Learning Meaningful Representations of Life (LMRL) »
Elizabeth Wood · Adji Bousso Dieng · Aleksandrina Goeva · Anshul Kundaje · Barbara Engelhardt · Chang Liu · David Van Valen · Debora Marks · Edward Boyden · Eli N Weinstein · Lorin Crawford · Mor Nitzan · Romain Lopez · Tamara Broderick · Ray Jones · Wouter Boomsma · Yixin Wang 
2020 Poster: Bayesian Pseudocoresets »
Dionysis Manousakas · Zuheng Xu · Cecilia Mascolo · Trevor Campbell 
2020 Poster: Approximate CrossValidation for Structured Models »
Soumya Ghosh · Will Stephenson · Tin Nguyen · Sameer Deshpande · Tamara Broderick 
2020 Poster: Approximate CrossValidation with LowRank Data in High Dimensions »
Will Stephenson · Madeleine Udell · Tamara Broderick 
2019 Workshop: Learning Meaningful Representations of Life »
Elizabeth Wood · Yakir Reshef · Jonathan Bloom · Jasper Snoek · Barbara Engelhardt · Scott Linderman · Suchi Saria · Alexander Wiltschko · Casey Greene · Chang Liu · Kresten LindorffLarsen · Debora Marks 
2019 Poster: Sparse Variational Inference: Bayesian Coresets from Scratch »
Trevor Campbell · Boyan Beronov 
2019 Poster: Universal Boosting Variational Inference »
Trevor Campbell · Xinglong Li 
2018 Workshop: Machine Learning for Health (ML4H): Moving beyond supervised learning in healthcare »
Andrew Beam · Tristan Naumann · Marzyeh Ghassemi · Matthew McDermott · Madalina Fiterau · Irene Y Chen · Brett BeaulieuJones · Michael Hughes · Farah Shamout · Corey Chivers · Jaz Kandola · Alexandre Yahi · Samuel Finlayson · Bruno Jedynak · Peter Schulam · Natalia Antropova · Jason Fries · Adrian Dalca · Irene Chen 
2018 Workshop: All of Bayesian Nonparametrics (Especially the Useful Bits) »
Diana Cai · Trevor Campbell · Michael Hughes · Tamara Broderick · Nick Foti · Sinead Williamson 
2018 Poster: A Bayesian Nonparametric View on CountMin Sketch »
Diana Cai · Michael Mitzenmacher · Ryan Adams 
2017 Workshop: Advances in Approximate Bayesian Inference »
Francisco Ruiz · Stephan Mandt · Cheng Zhang · James McInerney · James McInerney · Dustin Tran · Dustin Tran · David Blei · Max Welling · Tamara Broderick · Michalis Titsias 
2017 Workshop: Machine Learning for Health (ML4H)  What Parts of Healthcare are Ripe for Disruption by Machine Learning Right Now? »
Jason Fries · Alex Wiltschko · Andrew Beam · Isaac S Kohane · Jasper Snoek · Peter Schulam · Madalina Fiterau · David Kale · Rajesh Ranganath · Bruno Jedynak · Michael Hughes · Tristan Naumann · Natalia Antropova · Adrian Dalca · SHUBHI ASTHANA · Prateek Tandon · Jaz Kandola · Uri Shalit · Marzyeh Ghassemi · Tim Althoff · Alexander Ratner · Jumana Dakka 
2017 Poster: PASSGLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference »
Jonathan Huggins · Ryan Adams · Tamara Broderick 
2017 Spotlight: PASSGLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference »
Jonathan Huggins · Ryan Adams · Tamara Broderick 
2017 Poster: Reducing Reparameterization Gradient Variance »
Andrew Miller · Nick Foti · Alexander D'Amour · Ryan Adams 
2016 Workshop: Machine Learning in Computational Biology »
Gerald Quon · Sara Mostafavi · James Y Zou · Barbara Engelhardt · Oliver Stegle · Nicolo Fusi 
2016 Workshop: Advances in Approximate Bayesian Inference »
Tamara Broderick · Stephan Mandt · James McInerney · Dustin Tran · David Blei · Kevin Murphy · Andrew Gelman · Michael I Jordan 
2016 Workshop: Practical Bayesian Nonparametrics »
Nick Foti · Tamara Broderick · Trevor Campbell · Michael Hughes · Jeffrey Miller · Aaron Schein · Sinead Williamson · Yanxun Xu 
2016 Poster: Variance Reduction in Stochastic Gradient Langevin Dynamics »
Kumar Avinava Dubey · Sashank J. Reddi · Sinead Williamson · Barnabas Poczos · Alexander Smola · Eric Xing 
2016 Poster: Coresets for Scalable Bayesian Logistic Regression »
Jonathan Huggins · Trevor Campbell · Tamara Broderick 
2016 Poster: Edgeexchangeable graphs and sparsity »
Diana Cai · Trevor Campbell · Tamara Broderick 
2015 Workshop: Bayesian Nonparametrics: The Next Generation »
Tamara Broderick · Nick Foti · Aaron Schein · Alex Tank · Hanna Wallach · Sinead Williamson 
2015 Workshop: Advances in Approximate Bayesian Inference »
Dustin Tran · Tamara Broderick · Stephan Mandt · James McInerney · Shakir Mohamed · Alp Kucukelbir · Matthew D. Hoffman · Neil Lawrence · David Blei 
2015 Poster: Linear Response Methods for Accurate Covariance Estimates from Mean Field Variational Bayes »
Ryan Giordano · Tamara Broderick · Michael Jordan 
2015 Spotlight: Linear Response Methods for Accurate Covariance Estimates from Mean Field Variational Bayes »
Ryan Giordano · Tamara Broderick · Michael Jordan 
2015 Poster: Scalable Adaptation of State Complexity for Nonparametric Hidden Markov Models »
Michael Hughes · William Stephenson · Erik Sudderth 
2014 Workshop: Advances in Variational Inference »
David Blei · Shakir Mohamed · Michael Jordan · Charles Blundell · Tamara Broderick · Matthew D. Hoffman 
2014 Workshop: 3rd NIPS Workshop on Probabilistic Programming »
Daniel Roy · Josh Tenenbaum · Thomas Dietterich · Stuart J Russell · YI WU · Ulrik R Beierholm · Alp Kucukelbir · Zenna Tavares · Yura Perov · Daniel Lee · Brian Ruttenberg · Sameer Singh · Michael Hughes · Marco Gaboardi · Alexey Radul · Vikash Mansinghka · Frank Wood · Sebastian Riedel · Prakash Panangaden 
2014 Poster: Stochastic variational inference for hidden Markov models »
Nick Foti · Jason Xu · Dillon Laird · Emily Fox 
2014 Poster: Dependent nonparametric trees for dynamic hierarchical clustering »
Kumar Avinava Dubey · Qirong Ho · Sinead Williamson · Eric Xing 
2013 Poster: Optimistic Concurrency Control for Distributed Unsupervised Learning »
Xinghao Pan · Joseph Gonzalez · Stefanie Jegelka · Tamara Broderick · Michael Jordan 
2013 Poster: Memoized Online Variational Inference for Dirichlet Process Mixture Models »
Michael Hughes · Erik Sudderth 
2013 Poster: Streaming Variational Bayes »
Tamara Broderick · Nicholas Boyd · Andre Wibisono · Ashia C Wilson · Michael Jordan 
2012 Poster: Effective SplitMerge Monte Carlo Methods for Nonparametric Models of Sequential Data »
Michael Hughes · Emily Fox · Erik Sudderth 
2012 Poster: Slice sampling normalized kernelweighted completely random measure mixture models »
Nick Foti · Sinead Williamson