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1 code implementation • 5 Feb 2024 • David R. Burt, Yunyi Shen, Tamara Broderick

Unfortunately, classical approaches for validation fail to handle mismatch between locations available for validation and (test) locations where we want to make predictions.

no code implementations • 11 Dec 2023 • Miriam Shiffman, Ryan Giordano, Tamara Broderick

We then overcome the inherent non-differentiability of gene set enrichment analysis to develop an additional approximation for the robustness of top gene sets.

1 code implementation • 11 Apr 2023 • Ryan Giordano, Martin Ingram, Tamara Broderick

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.

1 code implementation • 20 Feb 2023 • Renato Berlinghieri, Brian L. Trippe, David R. Burt, Ryan Giordano, Kaushik Srinivasan, Tamay Özgökmen, Junfei Xia, Tamara Broderick

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.

no code implementations • 1 Dec 2022 • Sameer K. Deshpande, Soumya Ghosh, Tin D. Nguyen, Tamara Broderick

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.

1 code implementation • 14 Jul 2022 • Vijay Gadepally, Gregory Angelides, Andrei Barbu, Andrew Bowne, Laura J. Brattain, Tamara Broderick, Armando Cabrera, Glenn Carl, Ronisha Carter, Miriam Cha, Emilie Cowen, Jesse Cummings, Bill Freeman, James Glass, Sam Goldberg, Mark Hamilton, Thomas Heldt, Kuan Wei Huang, Phillip Isola, Boris Katz, Jamie Koerner, Yen-Chen Lin, David Mayo, Kyle McAlpin, Taylor Perron, Jean Piou, Hrishikesh M. Rao, Hayley Reynolds, Kaira Samuel, Siddharth Samsi, Morgan Schmidt, Leslie Shing, Olga Simek, Brandon Swenson, Vivienne Sze, Jonathan Taylor, Paul Tylkin, Mark Veillette, Matthew L Weiss, Allan Wollaber, Sophia Yuditskaya, Jeremy Kepner

Through a series of federal initiatives and orders, the U. S. Government has been making a concerted effort to ensure American leadership in AI.

1 code implementation • 8 Jun 2022 • Brian L. Trippe, Jason Yim, Doug Tischer, David Baker, Tamara Broderick, Regina Barzilay, Tommi Jaakkola

Construction of a scaffold structure that supports a desired motif, conferring protein function, shows promise for the design of vaccines and enzymes.

1 code implementation • 23 Feb 2022 • Tin D. Nguyen, Brian L. Trippe, Tamara Broderick

In MCMC samplers of continuous random variables, Markov chain couplings can overcome bias.

no code implementations • 5 Dec 2021 • Tamara Broderick, Andrew Gelman, Rachael Meager, Anna L. Smith, Tian Zheng

Probabilistic machine learning increasingly informs critical decisions in medicine, economics, politics, and beyond.

no code implementations • NeurIPS 2021 • William T. Stephenson, Zachary Frangella, Madeleine Udell, Tamara Broderick

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.

no code implementations • NeurIPS 2021 • Brian L. Trippe, Hilary K. Finucane, Tamara Broderick

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.

no code implementations • 8 Jul 2021 • Ryan Giordano, Runjing Liu, Michael I. Jordan, Tamara Broderick

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.

1 code implementation • 23 Jun 2021 • Raj Agrawal, Tamara Broderick

Often, these effects are nonlinear and include interactions, so linear and additive methods can lead to poor estimation and variable selection.

no code implementations • 11 Jun 2021 • William T. Stephenson, Soumya Ghosh, Tin D. Nguyen, Mikhail Yurochkin, Sameer K. Deshpande, Tamara Broderick

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.

no code implementations • NeurIPS Workshop ICBINB 2020 • Tin D. Nguyen, Jonathan H. Huggins, Lorenzo Masoero, Lester Mackey, Tamara Broderick

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.

no code implementations • NeurIPS Workshop ICBINB 2020 • Diana Cai, Trevor Campbell, Tamara Broderick

Increasingly, though, data science papers suggest potential alternatives beyond vanilla FMMs, such as power posteriors, coarsening, and related methods.

no code implementations • 22 Sep 2020 • Tin D. Nguyen, Jonathan Huggins, Lorenzo Masoero, Lester Mackey, Tamara Broderick

We call our construction the automated independent finite approximation (AIFA).

no code implementations • NeurIPS 2020 • William T. Stephenson, Madeleine Udell, Tamara Broderick

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.

no code implementations • 8 Jul 2020 • Diana Cai, Trevor Campbell, Tamara Broderick

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.

1 code implementation • NeurIPS 2020 • Soumya Ghosh, William T. Stephenson, Tin D. Nguyen, Sameer K. Deshpande, Tamara Broderick

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.

no code implementations • pproximateinference AABI Symposium 2019 • Lorenzo Masoero, Federico Camerlenghi, Stefano Favaro, Tamara Broderick

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.

1 code implementation • 9 Oct 2019 • Jonathan H. Huggins, Mikołaj Kasprzak, Trevor Campbell, Tamara Broderick

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.

1 code implementation • 28 Jul 2019 • Ryan Giordano, Michael. I. Jordan, Tamara Broderick

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.

1 code implementation • 31 May 2019 • William T. Stephenson, Tamara Broderick

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.

no code implementations • 17 May 2019 • Brian L. Trippe, Jonathan H. Huggins, Raj Agrawal, Tamara Broderick

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.

1 code implementation • 16 May 2019 • Raj Agrawal, Jonathan H. Huggins, Brian Trippe, Tamara Broderick

Discovering interaction effects on a response of interest is a fundamental problem faced in biology, medicine, economics, and many other scientific disciplines.

no code implementations • 28 Nov 2018 • Miriam Shiffman, William T. Stephenson, Geoffrey Schiebinger, Jonathan Huggins, Trevor Campbell, Aviv Regev, Tamara Broderick

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.

4 code implementations • 15 Oct 2018 • Runjing Liu, Ryan Giordano, Michael. I. Jordan, Tamara Broderick

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

no code implementations • 9 Oct 2018 • Raj Agrawal, Trevor Campbell, Jonathan H. Huggins, Tamara Broderick

Random feature maps (RFMs) and the Nystrom method both consider low-rank approximations to the kernel matrix as a potential solution.

no code implementations • 25 Sep 2018 • Jonathan H. Huggins, Trevor Campbell, Mikołaj Kasprzak, Tamara Broderick

Bayesian inference typically requires the computation of an approximation to the posterior distribution.

no code implementations • 26 Jun 2018 • Jonathan H. Huggins, Trevor Campbell, Mikołaj Kasprzak, Tamara Broderick

We develop an approach to scalable approximate GP regression with finite-data guarantees on the accuracy of pointwise posterior mean and variance estimates.

3 code implementations • 1 Jun 2018 • Ryan Giordano, Will Stephenson, Runjing Liu, Michael. I. Jordan, Tamara Broderick

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

1 code implementation • ICML 2018 • Raj Agrawal, Tamara Broderick, Caroline Uhler

Learning a Bayesian network (BN) from data can be useful for decision-making or discovering causal relationships.

1 code implementation • ICML 2018 • Trevor Campbell, Tamara Broderick

Coherent uncertainty quantification is a key strength of Bayesian methods.

2 code implementations • 13 Oct 2017 • Trevor Campbell, Tamara Broderick

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.

1 code implementation • NeurIPS 2017 • Jonathan H. Huggins, Ryan P. Adams, Tamara Broderick

We provide theoretical guarantees on the quality of point (MAP) estimates, the approximate posterior, and posterior mean and uncertainty estimates.

4 code implementations • 8 Sep 2017 • Ryan Giordano, Tamara Broderick, Michael. I. Jordan

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

no code implementations • 16 Dec 2016 • Diana Cai, Trevor Campbell, Tamara Broderick

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.

no code implementations • 17 Nov 2016 • Fangjian Guo, Xiangyu Wang, Kai Fan, Tamara Broderick, David B. Dunson

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.

no code implementations • 23 Jun 2016 • Ryan Giordano, Tamara Broderick, Rachael Meager, Jonathan Huggins, Michael Jordan

Bayesian hierarchical models are increasing popular in economics.

2 code implementations • NeurIPS 2016 • Jonathan H. Huggins, Trevor Campbell, Tamara Broderick

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.

no code implementations • 22 Mar 2016 • Diana Cai, Tamara Broderick

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.

no code implementations • NeurIPS 2016 • Tamara Broderick, Diana Cai

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.

1 code implementation • NeurIPS 2015 • Ryan Giordano, Tamara Broderick, Michael Jordan

We call our method linear response variational Bayes (LRVB).

no code implementations • 26 Feb 2015 • Ryan Giordano, Tamara Broderick

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.

no code implementations • 24 Oct 2014 • Ryan Giordano, Tamara Broderick

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.

no code implementations • 17 Oct 2014 • Tamara Broderick, Rebecca C. Steorts

Bayesian entity resolution merges together multiple, noisy databases and returns the minimal collection of unique individuals represented, together with their true, latent record values.

no code implementations • NeurIPS 2013 • Xinghao Pan, Joseph E. Gonzalez, Stefanie Jegelka, Tamara Broderick, Michael. I. Jordan

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.

2 code implementations • NeurIPS 2013 • Tamara Broderick, Nicholas Boyd, Andre Wibisono, Ashia C. Wilson, Michael. I. Jordan

We present SDA-Bayes, a framework for (S)treaming, (D)istributed, (A)synchronous computation of a Bayesian posterior.

no code implementations • 8 Nov 2011 • Tamara Broderick, Lester Mackey, John Paisley, Michael. I. Jordan

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).

1 code implementation • 14 Dec 2007 • Tamara Broderick, Miroslav Dudik, Gasper Tkacik, Robert E. Schapire, William Bialek

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.

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