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Found 7 papers, 0 papers with code
Observation-Free Attacks on Stochastic Bandits
To the best of our knowledge, we develop the first data corruption attack on stochastic multi arm bandit algorithms which works without observing the algorithm's realized behavior.
Learning Auctions with Robust Incentive Guarantees
We study the problem of learning Bayesian-optimal revenue-maximizing auctions.
On Frank-Wolfe and Equilibrium Computation
We consider the Frank-Wolfe (FW) method for constrained convex optimization, and we show that this classical technique can be interpreted from a different perspective: FW emerges as the computation of an equilibrium (saddle point) of a special convex-concave zero sum game.
Threshold Bandits, With and Without Censored Feedback
The learner selects one of $K$ actions (arms), this action generates a random sample from a fixed distribution, and the action then receives a unit payoff in the event that this sample exceeds the threshold value.
A Market Framework for Eliciting Private Data
We propose a mechanism for purchasing information from a sequence of participants. The participants may simply hold data points they wish to sell, or may have more sophisticated information; either way, they are incentivized to participate as long as they believe their data points are representative or their information will improve the mechanism's future prediction on a test set. The mechanism, which draws on the principles of prediction markets, has a bounded budget and minimizes generalization error for Bregman divergence loss functions. We then show how to modify this mechanism to preserve the privacy of participants' information: At any given time, the current prices and predictions of the mechanism reveal almost no information about any one participant, yet in total over all participants, information is accurately aggregated.
A Collaborative Mechanism for Crowdsourcing Prediction Problems
Machine Learning competitions such as the Netflix Prize have proven reasonably successful as a method of “crowdsourcing” prediction tasks.
Repeated Games against Budgeted Adversaries
We study repeated zero-sum games against an adversary on a budget.
