Search Results for author:
Found 5 papers, 1 papers with code
How Much Does Each Datapoint Leak Your Privacy? Quantifying the Per-datum Membership Leakage
We study the per-datum Membership Inference Attacks (MIAs), where an attacker aims to infer whether a fixed target datum has been included in the input dataset of an algorithm and thus, violates privacy.
Conservative Exploration for Policy Optimization via Off-Policy Policy Evaluation
A precondition for the deployment of a Reinforcement Learning agent to a real-world system is to provide guarantees on the learning process.
Concentrated Differential Privacy for Bandits
Next, we complement our regret upper bounds with the first minimax lower bounds on the regret of bandits with zCDP.
When Privacy Meets Partial Information: A Refined Analysis of Differentially Private Bandits
First, we prove the minimax and problem-dependent regret lower bounds for stochastic and linear bandits that quantify the hardness of bandits with $\epsilon$-global DP.
Conservative Optimistic Policy Optimization via Multiple Importance Sampling
Reinforcement Learning (RL) has been able to solve hard problems such as playing Atari games or solving the game of Go, with a unified approach.
