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Found 9 papers, 1 papers with code
Robust Auction Design with Support Information
With "high" support information, SPAs are strictly suboptimal, and an optimal mechanism belongs to a class of mechanisms we introduce, which we call pooling auctions (POOL); whenever the highest value is above a threshold, the mechanism still allocates to the highest bidder, but otherwise the mechanism allocates to a uniformly random buyer, i. e., pools low types.
From Contextual Data to Newsvendor Decisions: On the Actual Performance of Data-Driven Algorithms
This class includes classical policies such as ERM, k-Nearest Neighbors and kernel-based policies.
Beyond IID: data-driven decision-making in heterogeneous environments
We then leverage this connection to quantify the performance that is achievable by Sample Average Approximation (SAA) as a function of the radius of the heterogeneity ball: for any integral probability metric, we derive bounds depending on the approximation parameter, a notion which quantifies how the interplay between the heterogeneity and the problem structure impacts the performance of SAA.
On the Robustness of Second-Price Auctions in Prior-Independent Mechanism Design
We study the design of prior-independent mechanisms that relax this assumption: the seller is selling an indivisible item to $n$ buyers such that the buyers' valuations are drawn from a joint distribution that is unknown to both the buyers and the seller; buyers do not need to form beliefs about competitors, and the seller assumes the distribution is adversarially chosen from a specified class.
Contextual Inverse Optimization: Offline and Online Learning
In the online setting, we leverage this geometric characterization to optimize the cumulative regret.
Optimal Pricing with a Single Point
For settings where the seller knows the exact probability of sale associated with one historical price or only a confidence interval for it, we fully characterize optimal performance and near-optimal pricing algorithms that adjust to the information at hand.
Computer Science and Game Theory Information Theory Information Theory
Mechanism Design under Approximate Incentive Compatibility
We establish that the gains that can be garnered depend on the local curvature of the seller's revenue function around the optimal posted price when the buyer is a perfect optimizer.
Stochastic Multi-Armed-Bandit Problem with Non-stationary Rewards
In a multi-armed bandit (MAB) problem a gambler needs to choose at each round of play one of K arms, each characterized by an unknown reward distribution.
Optimal Exploration-Exploitation in a Multi-Armed-Bandit Problem with Non-stationary Rewards
In a multi-armed bandit (MAB) problem a gambler needs to choose at each round of play one of K arms, each characterized by an unknown reward distribution.
