no code implementations • 7 May 2024 • Antonio Bikić, Sayan Mukherjee
Here, we review the connection between the model parameter selection in machine learning (ML) algorithms running on ANNs and the epistemological theory of neopragmatism focusing on the theory's utility and anti-representationalist aspects.
no code implementations • 5 Mar 2024 • Samuel I. Berchuck, Felipe A. Medeiros, Sayan Mukherjee, Andrea Agazzi
The generalized linear mixed model (GLMM) is a popular statistical approach for handling correlated data, and is used extensively in applications areas where big data is common, including biomedical data settings.
no code implementations • 12 Mar 2023 • Andrea Agazzi, Jianfeng Lu, Sayan Mukherjee
We analyze Elman-type Recurrent Reural Networks (RNNs) and their training in the mean-field regime.
no code implementations • 22 Jun 2022 • Michele Caprio, Sayan Mukherjee
We state concentration inequalities for the output of the hidden layers of a stochastic deep neural network (SDNN), as well as for the output of the whole SDNN.
no code implementations • 15 Dec 2021 • Xizhi Liu, Sayan Mukherjee
Given a partition of a graph into connected components, the membership oracle asserts whether any two vertices of the graph lie in the same component or not.
no code implementations • 2 Aug 2021 • Ramin Bashizade, Xiangyu Zhang, Sayan Mukherjee, Alvin R. Lebeck
In this paper, we propose a high-throughput accelerator for Markov Random Field (MRF) inference, a powerful model for representing a wide range of applications, using MCMC with Gibbs sampling.
1 code implementation • 31 May 2021 • Anna K. Yanchenko, Mohammadreza Soltani, Robert J. Ravier, Sayan Mukherjee, Vahid Tarokh
In this work, we instead take the perspective of relating deep features to well-studied, hand-crafted features that are meaningful for the application of interest.
no code implementations • 14 Dec 2020 • Ziyang Ding, Sayan Mukherjee
Reservoir computing and deep sequential models, on the one hand, have demonstrated efficient, robust, and superior performance in modeling simple and chaotic dynamical systems.
no code implementations • NeurIPS Workshop TDA_and_Beyond 2020 • Mikael Vejdemo-Johansson, Sayan Mukherjee
Multiple hypothesis testing requires a control procedure.
no code implementations • 11 Jun 2020 • Anna K. Yanchenko, Sayan Mukherjee
Stanza strikes a balance between competitive forecasting accuracy and probabilistic, interpretable inference for highly structured time series.
no code implementations • 5 Mar 2020 • Xiangyu Zhang, Ramin Bashizade, Yicheng Wang, Cheng Lyu, Sayan Mukherjee, Alvin R. Lebeck
Applying the framework to guide design space exploration shows that statistical robustness comparable to floating-point software can be achieved by slightly increasing the bit representation, without floating-point hardware requirements.
no code implementations • 27 Oct 2019 • Xiangyu Zhang, Sayan Mukherjee, Alvin R. Lebeck
Although a common approach is to compare the end-point result quality using community-standard benchmarks and metrics, we claim a probabilistic architecture should provide some measure (or guarantee) of statistical robustness.
1 code implementation • 24 Aug 2019 • Samuel I. Berchuck, Felipe A. Medeiros, Sayan Mukherjee
As big spatial data becomes increasingly prevalent, classical spatiotemporal (ST) methods often do not scale well.
no code implementations • 2 Jul 2019 • Zilong Zou, Sayan Mukherjee, Harbir Antil, Wilkins Aquino
To manage the computational cost of propagating increasing numbers of particles through the loss function, we employ a recently developed local reduced basis method to build an efficient surrogate loss function that is used in the Gibbs update formula in place of the true loss.
1 code implementation • 15 Nov 2018 • Weiwei Li, Jan Hannig, Sayan Mukherjee
The problem of dimension reduction is of increasing importance in modern data analysis.
1 code implementation • 12 Mar 2018 • Zilong Tan, Kimberly Roche, Xiang Zhou, Sayan Mukherjee
We provide theoretical guarantees for our learning algorithms, demonstrating the robustness of parameter estimation.
1 code implementation • 21 Feb 2018 • Zilong Tan, Sayan Mukherjee
We propose a representation of Gaussian processes (GPs) based on powers of the integral operator defined by a kernel function, we call these stochastic processes integral Gaussian processes (IGPs).
no code implementations • ICML 2017 • Zilong Tan, Sayan Mukherjee
We present an efficient algorithm for learning mixed membership models when the number of variables p is much larger than the number of hidden components k. This algorithm reduces the computational complexity of state-of-the-art tensor methods, which require decomposing an $O(p^3)$ tensor, to factorizing $O(p/k)$ sub-tensors each of size $O(k^3)$.
1 code implementation • 25 Feb 2017 • Zilong Tan, Sayan Mukherjee
We present an efficient algorithm for learning mixed membership models when the number of variables $p$ is much larger than the number of hidden components $k$.
2 code implementations • 21 Nov 2016 • Lorin Crawford, Anthea Monod, Andrew X. Chen, Sayan Mukherjee, Raúl Rabadán
We introduce a novel statistic, the smooth Euler characteristic transform (SECT), which is designed to integrate shape information into regression models by representing shapes and surfaces as a collection of curves.
Applications
no code implementations • 17 Mar 2016 • Shiwen Zhao, Barbara E. Engelhardt, Sayan Mukherjee, David B. Dunson
We illustrate the utility of our approach on simulated data, comparing results from MELD to alternative methods, and we show the promise of our approach through the application of MELD to several data sets.
1 code implementation • 5 Aug 2015 • Lorin Crawford, Kris C. Wood, Xiang Zhou, Sayan Mukherjee
State-of-the-art methods for genomic selection and association mapping are based on kernel regression and linear models, respectively.
no code implementations • 13 Apr 2015 • Gregory Darnell, Stoyan Georgiev, Sayan Mukherjee, Barbara E. Engelhardt
In this paper we develop an approach for dimension reduction that exploits the assumption of low rank structure in high dimensional data to gain both computational and statistical advantages.
1 code implementation • 11 Nov 2014 • Shiwen Zhao, Chuan Gao, Sayan Mukherjee, Barbara E. Engelhardt
Latent factor models are the canonical statistical tool for exploratory analyses of low-dimensional linear structure for an observation matrix with p features across n samples.
no code implementations • 29 Oct 2013 • Garvesh Raskutti, Sayan Mukherjee
Using this equivalence, it follows that (1) mirror descent is the steepest descent direction along the Riemannian manifold of the exponential family; (2) mirror descent with log-likelihood loss applied to parameter estimation in exponential families asymptotically achieves the classical Cram\'er-Rao lower bound and (3) natural gradient descent for manifolds corresponding to exponential families can be implemented as a first-order method through mirror descent.
no code implementations • 7 Nov 2012 • Stoyan Georgiev, Sayan Mukherjee
Scalability of statistical estimators is of increasing importance in modern applications and dimension reduction is often used to extract relevant information from data.
no code implementations • 18 Dec 2009 • Simón Lunagómez, Sayan Mukherjee, Robert L. Wolpert, Edoardo M. Airoldi
A parametrization of hypergraphs based on the geometry of points in $\mathbf{R}^d$ is developed.
no code implementations • NeurIPS 2008 • Qiang Wu, Sayan Mukherjee, Feng Liang
We developed localized sliced inverse regression for supervised dimension reduction.