71 citations · 180 across the 10 of their papers we have counts for
11 papers
Learning-Augmented Private Algorithms for Multiple Quantile Release
Mikhail Khodak, Kareem Amin, Travis Dick +1
When applying differential privacy to sensitive data, we can often improve performance using external information such as other sensitive data, public data, or human priors. We pro…
Easy Differentially Private Linear Regression
Kareem Amin, Matthew Joseph, Mónica Ribero +1
Linear regression is a fundamental tool for statistical analysis. This has motivated the development of linear regression methods that also satisfy differential privacy and thus gu…
Plume: Differential Privacy at Scale
Kareem Amin, Jennifer Gillenwater, Matthew Joseph +2
Differential privacy has become the standard for private data analysis, and an extensive literature now offers differentially private solutions to a wide variety of problems. Howev…
Learning with User-Level Privacy
Daniel Levy, Ziteng Sun, Kareem Amin +4
We propose and analyze algorithms to solve a range of learning tasks under user-level differential privacy constraints. Rather than guaranteeing only the privacy of individual samp…
Pan-Private Uniformity Testing
Kareem Amin, Matthew Joseph, Jieming Mao
A centrally differentially private algorithm maps raw data to differentially private outputs. In contrast, a locally differentially private algorithm may only access data through p…
Repeated Inverse Reinforcement Learning
Kareem Amin, Nan Jiang, Satinder Singh
We introduce a novel repeated Inverse Reinforcement Learning problem: the agent has to act on behalf of a human in a sequence of tasks and wishes to minimize the number of tasks th…
Towards Resolving Unidentifiability in Inverse Reinforcement Learning
Kareem Amin, Satinder Singh
We consider a setting for Inverse Reinforcement Learning (IRL) where the learner is extended with the ability to actively select multiple environments, observing an agent's behavio…
Online Learning and Profit Maximization from Revealed Preferences
Kareem Amin, Rachel Cummings, Lili Dworkin +2
We consider the problem of learning from revealed preferences in an online setting. In our framework, each period a consumer buys an optimal bundle of goods from a merchant accordi…
Learning Prices for Repeated Auctions with Strategic Buyers
Kareem Amin, Afshin Rostamizadeh, Umar Syed
Inspired by real-time ad exchanges for online display advertising, we consider the problem of inferring a buyer's value distribution for a good when the buyer is repeatedly interac…
Budget Optimization for Sponsored Search: Censored Learning in MDPs
Kareem Amin, Michael Kearns, Peter Key +1
We consider the budget optimization problem faced by an advertiser participating in repeated sponsored search auctions, seeking to maximize the number of clicks attained under that…
Graphical Models for Bandit Problems
Kareem Amin, Michael Kearns, Umar Syed
We introduce a rich class of graphical models for multi-armed bandit problems that permit both the state or context space and the action space to be very large, yet succinctly spec…