1k citations · 2.6k across the 149 of their papers we have counts for
18 papers · 1 filter
Understanding challenges to the interpretation of disaggregated evaluations of algorithmic fairness
Stephen R. Pfohl, Natalie Harris, Chirag Nagpal +12
Disaggregated evaluation across subgroups is critical for assessing the fairness of machine learning models, but its uncritical use can mislead practitioners. We show that equal pe…
Metric Elicitation; Moving from Theory to Practice
Safinah Ali, Sohini Upadhyay, Gaurush Hiranandani +2
Metric Elicitation (ME) is a framework for eliciting classification metrics that better align with implicit user preferences based on the task and context. The existing ME strategy…
A Nonconvex Framework for Structured Dynamic Covariance Recovery
Katherine Tsai, Mladen Kolar, Oluwasanmi Koyejo
We propose a flexible yet interpretable model for high-dimensional data with time-varying second order statistics, motivated and applied to functional neuroimaging data. Motivated…
Quadratic Metric Elicitation for Fairness and Beyond
Gaurush Hiranandani, Jatin Mathur, Harikrishna Narasimhan +1
Metric elicitation is a recent framework for eliciting classification performance metrics that best reflect implicit user preferences based on the task and context. However, availa…
Bayesian Coresets: Revisiting the Nonconvex Optimization Perspective
Jacky Y. Zhang, Rajiv Khanna, Anastasios Kyrillidis +1
Bayesian coresets have emerged as a promising approach for implementing scalable Bayesian inference. The Bayesian coreset problem involves selecting a (weighted) subset of the data…
Fair Performance Metric Elicitation
Gaurush Hiranandani, Harikrishna Narasimhan, Oluwasanmi Koyejo
What is a fair performance metric? We consider the choice of fairness metrics through the lens of metric elicitation -- a principled framework for selecting performance metrics tha…
Learning Sparse Distributions using Iterative Hard Thresholding
Jacky Y. Zhang, Rajiv Khanna, Anastasios Kyrillidis +1
Iterative hard thresholding (IHT) is a projected gradient descent algorithm, known to achieve state of the art performance for a wide range of structured estimation problems, such…
Consistent Classification with Generalized Metrics
Xiaoyan Wang, Ran Li, Bowei Yan +1
We propose a framework for constructing and analyzing multiclass and multioutput classification metrics, i.e., involving multiple, possibly correlated multiclass labels. Our analys…
Joint Nonparametric Precision Matrix Estimation with Confounding
Sinong Geng, Mladen Kolar, Oluwasanmi Koyejo
We consider the problem of precision matrix estimation where, due to extraneous confounding of the underlying precision matrix, the data are independent but not identically distrib…
Binary Classification with Karmic, Threshold-Quasi-Concave Metrics
Bowei Yan, Oluwasanmi Koyejo, Kai Zhong +1
Complex performance measures, beyond the popular measure of accuracy, are increasingly being used in the context of binary classification. These complex performance measures are ty…
Performance Metric Elicitation from Pairwise Classifier Comparisons
Gaurush Hiranandani, Shant Boodaghians, Ruta Mehta +1
Given a binary prediction problem, which performance metric should the classifier optimize? We address this question by formalizing the problem of Metric Elicitation. The goal of m…
Dependent relevance determination for smooth and structured sparse regression
Anqi Wu, Oluwasanmi Koyejo, Jonathan W. Pillow
In many problem settings, parameter vectors are not merely sparse but dependent in such a way that non-zero coefficients tend to cluster together. We refer to this form of dependen…
Preference Completion from Partial Rankings
Suriya Gunasekar, Oluwasanmi Koyejo, Joydeep Ghosh
We propose a novel and efficient algorithm for the collaborative preference completion problem, which involves jointly estimating individualized rankings for a set of entities over…
Online Classification with Complex Metrics
Bowei Yan, Oluwasanmi Koyejo, Kai Zhong +1
We present a framework and analysis of consistent binary classification for complex and non-decomposable performance metrics such as the F-measure and the Jaccard measure. The prop…
Information Projection and Approximate Inference for Structured Sparse Variables
Rajiv Khanna, Joydeep Ghosh, Russell Poldrack +1
Approximate inference via information projection has been recently introduced as a general-purpose approach for efficient probabilistic inference given sparse variables. This manus…
A simple and provable algorithm for sparse diagonal CCA
Megasthenis Asteris, Anastasios Kyrillidis, Oluwasanmi Koyejo +1
Given two sets of variables, derived from a common set of samples, sparse Canonical Correlation Analysis (CCA) seeks linear combinations of a small number of variables in each set,…
Generalized Linear Models for Aggregated Data
Avradeep Bhowmik, Joydeep Ghosh, Oluwasanmi Koyejo
Databases in domains such as healthcare are routinely released to the public in aggregated form. Unfortunately, naive modeling with aggregated data may significantly diminish the a…
A Constrained Matrix-Variate Gaussian Process for Transposable Data
Oluwasanmi Koyejo, Cheng Lee, Joydeep Ghosh
Transposable data represents interactions among two sets of entities, and are typically represented as a matrix containing the known interaction values. Additional side information…