Search Results for author:
Found 16 papers, 0 papers with code
An Online Learning Theory of Brokerage
If the distribution admits a density bounded by some constant $M$, then, for any time horizon $T$: $\bullet$ If the agents' valuations are revealed after each interaction, we provide an algorithm achieving regret $M \log T$ and show this rate is optimal, up to constant factors.
The Role of Transparency in Repeated First-Price Auctions with Unknown Valuations
We study the problem of regret minimization for a single bidder in a sequence of first-price auctions where the bidder discovers the item's value only if the auction is won.
Repeated Bilateral Trade Against a Smoothed Adversary
We provide a complete characterization of the regret regimes for fixed-price mechanisms under different feedback models in the two cases where the learner can post either the same or different prices to buyers and sellers.
Regret Analysis of Dyadic Search
We analyze the cumulative regret of the Dyadic Search algorithm of Bachoc et al. [2022].
A Near-Optimal Algorithm for Univariate Zeroth-Order Budget Convex Optimization
This paper studies a natural generalization of the problem of minimizing a univariate convex function $f$ by querying its values sequentially.
Online Learning in Supply-Chain Games
We study a repeated game between a supplier and a retailer who want to maximize their respective profits without full knowledge of the problem parameters.
An Application of Online Learning to Spacecraft Memory Dump Optimization
In this paper, we present a real-world application of online learning with expert advice to the field of Space Operations, testing our theory on real-life data coming from the Copernicus Sentinel-6 satellite.
Nonstochastic Bandits with Composite Anonymous Feedback
We investigate a nonstochastic bandit setting in which the loss of an action is not immediately charged to the player, but rather spread over the subsequent rounds in an adversarial way.
Bilateral Trade: A Regret Minimization Perspective
In this paper, we cast the bilateral trade problem in a regret minimization framework over $T$ rounds of seller/buyer interactions, with no prior knowledge on their private valuations.
A Regret Analysis of Bilateral Trade
Despite the simplicity of this problem, a classical result by Myerson and Satterthwaite (1983) affirms the impossibility of designing a mechanism which is simultaneously efficient, incentive compatible, individually rational, and budget balanced.
The sample complexity of level set approximation
We study the problem of approximating the level set of an unknown function by sequentially querying its values.
An Efficient Algorithm for Cooperative Semi-Bandits
Furthermore, we prove that this is only $\sqrt$ k log k-away from the best achievable rate and that Coop-FTPL has a state-of-the-art T 3/2 worst-case computational complexity.
A Nearest Neighbor Characterization of Lebesgue Points in Metric Measure Spaces
The property of almost every point being a Lebesgue point has proven to be crucial for the consistency of several classification algorithms based on nearest neighbors.
Regret analysis of the Piyavskii-Shubert algorithm for global Lipschitz optimization
We consider the problem of maximizing a non-concave Lipschitz multivariate function over a compact domain by sequentially querying its (possibly perturbed) values.
ROI Maximization in Stochastic Online Decision-Making
We introduce a novel theoretical framework for Return On Investment (ROI) maximization in repeated decision-making.
Dynamic Pricing with Finitely Many Unknown Valuations
When $K=2$ in the distribution-dependent case, the hardness of our setting reduces to that of a stochastic $2$-armed bandit: we prove that an upper bound of order $(\log T)/\Delta$ (up to $\log\log$ factors) on the regret can be achieved with no information on the demand curve.
