Sayan Mukherjee

research

∙ 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...

0 Sayan Mukherjee, et al. ∙

research

∙ 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...

0 Youngsoo Baek, et al. ∙

research

∙ 05/16/2023

Bi-Objective Lexicographic Optimization in Markov Decision Processes with Related Objectives

We consider lexicographic bi-objective problems on Markov Decision Proce...

0 Damien Busatto-Gaston, et al. ∙

research

∙ 04/20/2023

Minimum Φ-distance estimators for finite mixing measures

Finite mixture models have long been used across a variety of fields in ...

0 Yun Wei, et al. ∙

research

∙ 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...

0 Andrea Agazzi, et al. ∙

research

∙ 10/13/2022

Probabilistic Approach to Parameteric Inverse Problems Using Gibbs Posteriors

We propose a general framework for obtaining probabilistic solutions to ...

0 Youngsoo Baek, et al. ∙

research

∙ 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...

0 Michele Caprio, et al. ∙

research

∙ 04/19/2022

A Sheaf-Theoretic Construction of Shape Space

We present a sheaf-theoretic construction of shape space – the space of ...

0 Shreya Arya, et al. ∙

research

∙ 01/10/2022

A Statistical Analysis of Compositional Surveys

A common statistical problem is inference from positive-valued multivari...

0 Michelle Pistner Nixon, et al. ∙

research

∙ 12/15/2021

Learning Graph Partitions

Given a partition of a graph into connected components, the membership o...

0 Sayan Mukherjee, et al. ∙

research

∙ 11/01/2021

Extended probabilities in Statistics

We propose a new, more general definition of extended probability measur...

0 Michele Caprio, et al. ∙

research

∙ 08/20/2021

A Grover search-based algorithm for the list coloring problem

Graph coloring is a computationally difficult problem, and currently the...

0 Sayan Mukherjee, et al. ∙

research

∙ 08/02/2021

Accelerating Markov Random Field Inference with Uncertainty Quantification

Statistical machine learning has widespread application in various domai...

7 Ramin Bashizade, et al. ∙

research

∙ 06/13/2021

A Large Deviation Approach to Posterior Consistency in Dynamical Systems

In this paper, we provide asymptotic results concerning (generalized) Ba...

0 Langxuan Su, et al. ∙

research

∙ 05/31/2021

Towards Explainable Convolutional Features for Music Audio Modeling

Audio signals are often represented as spectrograms and treated as 2D im...

32 Anna K. Yanchenko, et al. ∙

research

∙ 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...

0 Ziyang Ding, et al. ∙

research

∙ 09/28/2020

Reachability for Updatable Timed Automata made faster and more effective

Updatable timed automata (UTA) are extensions of classic timed automata ...

0 Paul Gastin, et al. ∙

research

∙ 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 ...

0 Anna K. Yanchenko, et al. ∙

research

∙ 03/13/2020

Ergodic theorems for imprecise probability kinematics

In a standard Bayesian setting, there is often ambiguity in prior choice...

0 Michele Caprio, et al. ∙

research

∙ 03/05/2020

Beyond Application End-Point Results: Quantifying Statistical Robustness of MCMC Accelerators

Statistical machine learning often uses probabilistic algorithms, such a...

0 Xiangyu Zhang, et al. ∙

research

∙ 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...

0 Michele Caprio, et al. ∙

research

∙ 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...

0 Michele Caprio, et al. ∙

research

∙ 02/19/2020

On the geometric properties of finite mixture models

In this paper we relate the geometry of extremal points to properties of...

0 Michele Caprio, et al. ∙

research

∙ 11/11/2019

Bayesian Non-Parametric Factor Analysis for Longitudinal Spatial Surfaces

We introduce a Bayesian non-parametric spatial factor analysis model wit...

0 Samuel I. Berchuck, et al. ∙

research

∙ 10/27/2019

A Case for Quantifying Statistical Robustness of Specialized Probabilistic AI Accelerators

Statistical machine learning often uses probabilistic algorithms, such a...

0 Xiangyu Zhang, et al. ∙

research

∙ 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...

23 Samuel I. Berchuck, et al. ∙

research

∙ 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 ...

0 Zilong Zou, et al. ∙

research

∙ 04/18/2019

Fast algorithms for handling diagonal constraints in timed automata

A popular method for solving reachability in timed automata proceeds by ...

0 Paul Gastin, et al. ∙

research

∙ 01/24/2019

Gibbs posterior convergence and the thermodynamic formalism

In this paper we consider a Bayesian framework for making inferences abo...

0 Kevin McGoff, et al. ∙

research

∙ 12/18/2018

Gaussian Process Mixtures for Estimating Heterogeneous Treatment Effects

We develop a Gaussian-process mixture model for heterogeneous treatment ...

0 Abbas Zaidi, et al. ∙

research

∙ 12/16/2018

Multiple testing with persistent homology

We propose a general null model for persistent homology barcodes from a ...

0 Mikael Vejdemo-Johansson, et al. ∙

research

∙ 11/15/2018

Subspace Clustering through Sub-Clusters

The problem of dimension reduction is of increasing importance in modern...

0 Weiwei Li, et al. ∙

research

∙ 06/28/2018

Reachability in timed automata with diagonal constraints

We consider the reachability problem for timed automata having diagonal ...

0 Paul Gastin, et al. ∙

research

∙ 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...

0 Justin Curry, et al. ∙

research

∙ 03/12/2018

Scalable Algorithms for Learning High-Dimensional Linear Mixed Models

Linear mixed models (LMMs) are used extensively to model dependecies of ...

0 Zilong Tan, et al. ∙

research

∙ 02/21/2018

Subspace-Induced Gaussian Processes

We present a new Gaussian process (GP) regression model where the covari...

0 Zilong Tan, et al. ∙

research

∙ 08/12/2017

Classical Music Composition Using State Space Models

Algorithmic composition of music has a long history and with the develop...

0 Anna K. Yanchenko, et al. ∙

research

∙ 02/25/2017

Efficient Learning of Mixed Membership Models

We present an efficient algorithm for learning mixed membership models w...

0 Zilong Tan, et al. ∙

research

∙ 08/05/2015

Bayesian Approximate Kernel Regression with Variable Selection

Nonlinear kernel regression models are often used in statistics and mach...

0 Lorin Crawford, et al. ∙

research

∙ 04/13/2015

Adaptive Randomized Dimension Reduction on Massive Data

The scalability of statistical estimators is of increasing importance in...

0 Gregory Darnell, et al. ∙

research

∙ 11/11/2014

Bayesian group latent factor analysis with structured sparsity

Latent factor models are the canonical statistical tool for exploratory ...

0 Shiwen Zhao, et al. ∙

research

∙ 11/07/2012

Randomized Dimension Reduction on Massive Data

Scalability of statistical estimators is of increasing importance in mod...

0 Stoyan Georgiev, et al. ∙

research

∙ 08/20/2010

Towards Stratification Learning through Homology Inference

A topological approach to stratification learning is developed for point...

0 Paul Bendich, et al. ∙

research

∙ 12/18/2009

Geometric Representations of Random Hypergraphs

A parametrization of hypergraphs based on the geometry of points in R^d ...

0 Simón Lunagómez, et al. ∙

Sayan Mukherjee

Featured Co-authors