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09/13/2023
Robustness for Spectral Clustering of General Graphs under Local Differential Privacy
Spectral clustering is a widely used algorithm to find clusters in netwo...
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06/06/2023
Asymptotics of Bayesian Uncertainty Estimation in Random Features Regression
In this paper we compare and contrast the behavior of the posterior pred...
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05/16/2023
Bi-Objective Lexicographic Optimization in Markov Decision Processes with Related Objectives
We consider lexicographic bi-objective problems on Markov Decision Proce...
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04/20/2023
Minimum Φ-distance estimators for finite mixing measures
Finite mixture models have long been used across a variety of fields in ...
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03/12/2023
Global Optimality of Elman-type RNN in the Mean-Field Regime
We analyze Elman-type Recurrent Reural Networks (RNNs) and their trainin...
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10/13/2022
Probabilistic Approach to Parameteric Inverse Problems Using Gibbs Posteriors
We propose a general framework for obtaining probabilistic solutions to ...
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06/22/2022
Concentration inequalities and optimal number of layers for stochastic deep neural networks
We state concentration and martingale inequalities for the output of the...
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04/19/2022
A Sheaf-Theoretic Construction of Shape Space
We present a sheaf-theoretic construction of shape space – the space of ...
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01/10/2022
A Statistical Analysis of Compositional Surveys
A common statistical problem is inference from positive-valued multivari...
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12/15/2021
Learning Graph Partitions
Given a partition of a graph into connected components, the membership o...
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11/01/2021
Extended probabilities in Statistics
We propose a new, more general definition of extended probability measur...
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08/20/2021
A Grover search-based algorithm for the list coloring problem
Graph coloring is a computationally difficult problem, and currently the...
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08/02/2021
Accelerating Markov Random Field Inference with Uncertainty Quantification
Statistical machine learning has widespread application in various domai...
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06/13/2021
A Large Deviation Approach to Posterior Consistency in Dynamical Systems
In this paper, we provide asymptotic results concerning (generalized) Ba...
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05/31/2021
Towards Explainable Convolutional Features for Music Audio Modeling
Audio signals are often represented as spectrograms and treated as 2D im...
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12/14/2020
At the Intersection of Deep Sequential Model Framework and State-space Model Framework: Study on Option Pricing
Inference and forecast problems of the nonlinear dynamical system have a...
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09/28/2020
Reachability for Updatable Timed Automata made faster and more effective
Updatable timed automata (UTA) are extensions of classic timed automata ...
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06/11/2020
Stanza: A Nonlinear State Space Model for Probabilistic Inference in Non-Stationary Time Series
Time series with long-term structure arise in a variety of contexts and ...
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03/13/2020
Ergodic theorems for imprecise probability kinematics
In a standard Bayesian setting, there is often ambiguity in prior choice...
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03/05/2020
Beyond Application End-Point Results: Quantifying Statistical Robustness of MCMC Accelerators
Statistical machine learning often uses probabilistic algorithms, such a...
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02/19/2020
On the expected number of components in a finite admixture model
In this paper we describe the growth rate of the expected number of comp...
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02/19/2020
On the expected number of components in a finite mixture model
In this paper we describe the growth rate of the expected number of comp...
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02/19/2020
On the geometric properties of finite mixture models
In this paper we relate the geometry of extremal points to properties of...
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11/11/2019
Bayesian Non-Parametric Factor Analysis for Longitudinal Spatial Surfaces
We introduce a Bayesian non-parametric spatial factor analysis model wit...
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10/27/2019
A Case for Quantifying Statistical Robustness of Specialized Probabilistic AI Accelerators
Statistical machine learning often uses probabilistic algorithms, such a...
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08/24/2019
Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma
As big spatial data becomes increasingly prevalent, classical spatiotemp...
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07/02/2019
Adaptive particle-based approximations of the Gibbs posterior for inverse problems
In this work, we adopt a general framework based on the Gibbs posterior ...
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04/18/2019
Fast algorithms for handling diagonal constraints in timed automata
A popular method for solving reachability in timed automata proceeds by ...
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01/24/2019
Gibbs posterior convergence and the thermodynamic formalism
In this paper we consider a Bayesian framework for making inferences abo...
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12/18/2018
Gaussian Process Mixtures for Estimating Heterogeneous Treatment Effects
We develop a Gaussian-process mixture model for heterogeneous treatment ...
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12/16/2018
Multiple testing with persistent homology
We propose a general null model for persistent homology barcodes from a ...
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11/15/2018
Subspace Clustering through Sub-Clusters
The problem of dimension reduction is of increasing importance in modern...
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06/28/2018
Reachability in timed automata with diagonal constraints
We consider the reachability problem for timed automata having diagonal ...
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05/24/2018
How Many Directions Determine a Shape and other Sufficiency Results for Two Topological Transforms
In this paper we consider two topological transforms based on Euler calc...
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03/12/2018
Scalable Algorithms for Learning High-Dimensional Linear Mixed Models
Linear mixed models (LMMs) are used extensively to model dependecies of ...
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02/21/2018
Subspace-Induced Gaussian Processes
We present a new Gaussian process (GP) regression model where the covari...
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08/12/2017
Classical Music Composition Using State Space Models
Algorithmic composition of music has a long history and with the develop...
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02/25/2017
Efficient Learning of Mixed Membership Models
We present an efficient algorithm for learning mixed membership models w...
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08/05/2015
Bayesian Approximate Kernel Regression with Variable Selection
Nonlinear kernel regression models are often used in statistics and mach...
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04/13/2015
Adaptive Randomized Dimension Reduction on Massive Data
The scalability of statistical estimators is of increasing importance in...
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11/11/2014
Bayesian group latent factor analysis with structured sparsity
Latent factor models are the canonical statistical tool for exploratory ...
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11/07/2012
Randomized Dimension Reduction on Massive Data
Scalability of statistical estimators is of increasing importance in mod...
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08/20/2010
Towards Stratification Learning through Homology Inference
A topological approach to stratification learning is developed for point...
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12/18/2009