381 citations · 1.4k across the 100 of their papers we have counts for
10 papers · 1 filter
Random Quadratic Forms with Dependence: Applications to Restricted Isometry and Beyond
Arindam Banerjee, Qilong Gu, Vidyashankar Sivakumar +1
Several important families of computational and statistical results in machine learning and randomized algorithms rely on uniform bounds on quadratic forms of random vectors or mat…
Keeping Designers in the Loop: Communicating Inherent Algorithmic Trade-offs Across Multiple Objectives
Bowen Yu, Ye Yuan, Loren Terveen +3
Artificial intelligence algorithms have been used to enhance a wide variety of products and services, including assisting human decision making in high-stakes contexts. However, th…
Oracle Efficient Private Non-Convex Optimization
Seth Neel, Aaron Roth, Giuseppe Vietri +1
One of the most effective algorithms for differentially private learning and optimization is objective perturbation. This technique augments a given optimization problem (e.g. deri…
Distributed Training with Heterogeneous Data: Bridging Median- and Mean-Based Algorithms
Xiangyi Chen, Tiancong Chen, Haoran Sun +2
Recently, there is a growing interest in the study of median-based algorithms for distributed non-convex optimization. Two prominent such algorithms include signSGD with majority v…
Fair Regression: Quantitative Definitions and Reduction-based Algorithms
Alekh Agarwal, Miroslav Dudík, Zhiwei Steven Wu
In this paper, we study the prediction of a real-valued target, such as a risk score or recidivism rate, while guaranteeing a quantitative notion of fairness with respect to a prot…
Private Hypothesis Selection
Mark Bun, Gautam Kamath, Thomas Steinke +1
We provide a differentially private algorithm for hypothesis selection. Given samples from an unknown probability distribution and a set of probability distributions $\math…
An Algorithmic Framework for Fairness Elicitation
Christopher Jung, Michael Kearns, Seth Neel +3
We consider settings in which the right notion of fairness is not captured by simple mathematical definitions (such as equality of error rates across groups), but might be more com…
Bayesian Exploration with Heterogeneous Agents
Nicole Immorlica, Jieming Mao, Aleksandrs Slivkins +1
It is common in recommendation systems that users both consume and produce information as they make strategic choices under uncertainty. While a social planner would balance "explo…
The Perils of Exploration under Competition: A Computational Modeling Approach
Guy Aridor, Kevin Liu, Aleksandrs Slivkins +1
We empirically study the interplay between exploration and competition. Systems that learn from interactions with users often engage in exploration: making potentially suboptimal d…
Equal Opportunity in Online Classification with Partial Feedback
Yahav Bechavod, Katrina Ligett, Aaron Roth +2
We study an online classification problem with partial feedback in which individuals arrive one at a time from a fixed but unknown distribution, and must be classified as positive…