20 citations · 47 across the 7 of their papers we have counts for
7 papers
Differentially Private Histograms under Continual Observation: Streaming Selection into the Unknown
Adrian Rivera Cardoso, Ryan Rogers
We generalize the continuous observation privacy setting from Dwork et al. '10 and Chan et al. '11 by allowing each event in a stream to be a subset of some (possibly unknown) univ…
A Members First Approach to Enabling LinkedIn's Labor Market Insights at Scale
Ryan Rogers, Adrian Rivera Cardoso, Koray Mancuhan +5
We describe the privatization method used in reporting labor market insights from LinkedIn's Economic Graph, including the differentially private algorithms used to protect member'…
Competing Against Equilibria in Zero-Sum Games with Evolving Payoffs
Adrian Rivera Cardoso, Jacob Abernethy, He Wang +1
We study the problem of repeated play in a zero-sum game in which the payoff matrix may change, in a possibly adversarial fashion, on each round; we call these Online Matrix Games.…
Large Scale Markov Decision Processes with Changing Rewards
Adrian Rivera Cardoso, He Wang, Huan Xu
We consider Markov Decision Processes (MDPs) where the rewards are unknown and may change in an adversarial manner. We provide an algorithm that achieves state-of-the-art regret bo…
Risk-Averse Stochastic Convex Bandit
Adrian Rivera Cardoso, Huan Xu
Motivated by applications in clinical trials and finance, we study the problem of online convex optimization (with bandit feedback) where the decision maker is risk-averse. We prov…
Differentially Private Online Submodular Optimization
Adrian Rivera Cardoso, Rachel Cummings
In this paper we develop the first algorithms for online submodular minimization that preserve differential privacy under full information feedback and bandit feedback. A sequence…
The Online Saddle Point Problem and Online Convex Optimization with Knapsacks
Adrian Rivera, He Wang, Huan Xu
We study the online saddle point problem, an online learning problem where at each iteration a pair of actions need to be chosen without knowledge of the current and future (convex…