33 citations · 84 across the 22 of their papers we have counts for
7 papers · 1 filter
Learning with Conflicts of Interest
Nischal Aryal, Arash Termehchy, Ali Vakilian +1
Financial, social, and political factors often prevent the interests of the owners of ML systems and services and their users from being perfectly aligned. ML systems often produce…
On Socially Fair Low-Rank Approximation and Column Subset Selection
Zhao Song, Ali Vakilian, David P. Woodruff +1
Low-rank approximation and column subset selection are two fundamental and related problems that are applied across a wealth of machine learning applications. In this paper, we stu…
Minimax Group Fairness in Strategic Classification
Emily Diana, Saeed Sharifi-Malvajerdi, Ali Vakilian
In strategic classification, agents manipulate their features, at a cost, to receive a positive classification outcome from the learner's classifier. The goal of the learner in suc…
Bayesian Strategic Classification
Lee Cohen, Saeed Sharifi-Malvajerdi, Kevin Stangl +2
In strategic classification, agents modify their features, at a cost, to ideally obtain a positive classification from the learner's classifier. The typical response of the learner…
Learning the Positions in CountSketch
Yi Li, Honghao Lin, Simin Liu +2
We consider sketching algorithms which first compress data by multiplication with a random sketch matrix, and then apply the sketch to quickly solve an optimization problem, e.g.,…
Learning the Positions in CountSketch
Simin Liu, Tianrui Liu, Ali Vakilian +2
We consider sketching algorithms which first quickly compress data by multiplication with a random sketch matrix, and then apply the sketch to quickly solve an optimization problem…
Learning-Based Low-Rank Approximations
Piotr Indyk, Ali Vakilian, Yang Yuan
We introduce a "learning-based" algorithm for the low-rank decomposition problem: given an matrix , and a parameter , compute a rank- matrix that minimiz…