3k citations · 24.2k across the 299 of their papers we have counts for
22 papers · 1 filter
QuTE: decentralized multiple testing on sensor networks with false discovery rate control
Aaditya Ramdas, Jianbo Chen, Martin J. Wainwright +1
This paper designs methods for decentralized multiple hypothesis testing on graphs that are equipped with provable guarantees on the false discovery rate (FDR). We consider the set…
Evaluating Sensitivity to the Stick-Breaking Prior in Bayesian Nonparametrics
Ryan Giordano, Runjing Liu, Michael I. Jordan +1
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 learnin…
The Power of Batching in Multiple Hypothesis Testing
Tijana Zrnic, Daniel L. Jiang, Aaditya Ramdas +1
One important partition of algorithms for controlling the false discovery rate (FDR) in multiple testing is into offline and online algorithms. The first generally achieve signific…
Asynchronous Online Testing of Multiple Hypotheses
Tijana Zrnic, Aaditya Ramdas, Michael I. Jordan
We consider the problem of asynchronous online testing, aimed at providing control of the false discovery rate (FDR) during a continual stream of data collection and testing, where…
Evaluating Sensitivity to the Stick-Breaking Prior in Bayesian Nonparametrics
Ryan Giordano, Runjing Liu, Michael I. Jordan +1
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 learnin…
A Swiss Army Infinitesimal Jackknife
Ryan Giordano, Will Stephenson, Runjing Liu +2
The error or variability of machine learning algorithms is often assessed by repeatedly re-fitting a model with different weighted versions of the observed data. The ubiquitous too…
SAFFRON: an adaptive algorithm for online control of the false discovery rate
Aaditya Ramdas, Tijana Zrnic, Martin Wainwright +1
In the online false discovery rate (FDR) problem, one observes a possibly infinite sequence of -values , each testing a different null hypothesis, and an algorith…
Online control of the false discovery rate with decaying memory
Aaditya Ramdas, Fanny Yang, Martin J. Wainwright +1
In the online multiple testing problem, p-values corresponding to different null hypotheses are observed one by one, and the decision of whether or not to reject the current hypoth…
DAGGER: A sequential algorithm for FDR control on DAGs
Aaditya Ramdas, Jianbo Chen, Martin J. Wainwright +1
We propose a linear-time, single-pass, top-down algorithm for multiple testing on directed acyclic graphs (DAGs), where nodes represent hypotheses and edges specify a partial order…
Covariances, Robustness, and Variational Bayes
Ryan Giordano, Tamara Broderick, Michael I. Jordan
Mean-field Variational Bayes (MFVB) is an approximate Bayesian posterior inference technique that is increasingly popular due to its fast runtimes on large-scale datasets. However,…
A unified treatment of multiple testing with prior knowledge using the p-filter
Aaditya Ramdas, Rina Foygel Barber, Martin J. Wainwright +1
There is a significant literature on methods for incorporating knowledge into multiple testing procedures so as to improve their power and precision. Some common forms of prior kno…
Fast Measurements of Robustness to Changing Priors in Variational Bayes
Ryan Giordano, Tamara Broderick, Michael Jordan
In Bayesian analysis, the posterior follows from the data and a choice of a prior and a likelihood. One hopes that the posterior is robust to reasonable variation in the choice of…
Joint modeling of multiple time series via the beta process with application to motion capture segmentation
Emily B. Fox, Michael C. Hughes, Erik B. Sudderth +1
We propose a Bayesian nonparametric approach to the problem of jointly modeling multiple related time series. Our model discovers a latent set of dynamical behaviors shared among t…
A Scalable Bootstrap for Massive Data
Ariel Kleiner, Ameet Talwalkar, Purnamrita Sarkar +1
The bootstrap provides a simple and powerful means of assessing the quality of estimators. However, in settings involving large datasets---which are increasingly prevalent---the co…
Joint Modeling of Multiple Related Time Series via the Beta Process
Emily B. Fox, Erik B. Sudderth, Michael I. Jordan +1
We propose a Bayesian nonparametric approach to the problem of jointly modeling multiple related time series. Our approach is based on the discovery of a set of latent, shared dyna…
Combinatorial clustering and the beta negative binomial process
Tamara Broderick, Lester Mackey, John Paisley +1
We develop a Bayesian nonparametric approach to a general family of latent class problems in which individuals can belong simultaneously to multiple classes and where each class ca…
Beta processes, stick-breaking, and power laws
Tamara Broderick, Michael I. Jordan, Jim Pitman
The beta-Bernoulli process provides a Bayesian nonparametric prior for models involving collections of binary-valued features. A draw from the beta process yields an infinite colle…
Cluster Forests
Donghui Yan, Aiyou Chen, Michael I. Jordan
With inspiration from Random Forests (RF) in the context of classification, a new clustering ensemble method---Cluster Forests (CF) is proposed. Geometrically, CF randomly probes a…
Multiway Spectral Clustering: A Margin-Based Perspective
Zhihua Zhang, Michael I. Jordan
Spectral clustering is a broad class of clustering procedures in which an intractable combinatorial optimization formulation of clustering is "relaxed" into a tractable eigenvector…
Tree-Structured Stick Breaking Processes for Hierarchical Data
Ryan Prescott Adams, Zoubin Ghahramani, Michael I. Jordan
Many data are naturally modeled by an unobserved hierarchical structure. In this paper we propose a flexible nonparametric prior over unknown data hierarchies. The approach uses ne…
Bayesian Nonparametric Inference of Switching Linear Dynamical Systems
Emily B. Fox, Erik B. Sudderth, Michael I. Jordan +1
Many complex dynamical phenomena can be effectively modeled by a system that switches among a set of conditionally linear dynamical modes. We consider two such models: the switchin…
A sticky HDP-HMM with application to speaker diarization
Emily B. Fox, Erik B. Sudderth, Michael I. Jordan +1
We consider the problem of speaker diarization, the problem of segmenting an audio recording of a meeting into temporal segments corresponding to individual speakers. The problem i…