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42 papers · 1 filter
Probabilistic Model Incorporating Auxiliary Covariates to Control FDR
Lin Qiu, Nils Murrugarra-Llerena, Vítor Silva +2
Controlling False Discovery Rate (FDR) while leveraging the side information of multiple hypothesis testing is an emerging research topic in modern data science. Existing methods r…
Uniform Concentration Bounds toward a Unified Framework for Robust Clustering
Debolina Paul, Saptarshi Chakraborty, Swagatam Das +1
Recent advances in center-based clustering continue to improve upon the drawbacks of Lloyd's celebrated -means algorithm over years after its introduction. Various methods…
Understanding Deflation Process in Over-parametrized Tensor Decomposition
Rong Ge, Yunwei Ren, Xiang Wang +1
In this paper we study the training dynamics for gradient flow on over-parametrized tensor decomposition problems. Empirically, such training process often first fits larger compon…
Scalable nonparametric Bayesian learning for heterogeneous and dynamic velocity fields
Sunrit Chakraborty, Aritra Guha, Rayleigh Lei +1
Analysis of heterogeneous patterns in complex spatio-temporal data finds usage across various domains in applied science and engineering, including training autonomous vehicles to…
A Statistician Teaches Deep Learning
G. Jogesh Babu, David Banks, Hyunsoon Cho +3
Deep learning (DL) has gained much attention and become increasingly popular in modern data science. Computer scientists led the way in developing deep learning techniques, so the…
Minimax Pareto Fairness: A Multi Objective Perspective
Natalia Martinez, Martin Bertran, Guillermo Sapiro
In this work we formulate and formally characterize group fairness as a multi-objective optimization problem, where each sensitive group risk is a separate objective. We propose a…