Search Results for author:
Found 14 papers, 1 papers with code
Tight Bounds for Approximate Carathéodory and Beyond
We give a deterministic nearly-linear time algorithm for approximating any point inside a convex polytope with a sparse convex combination of the polytope's vertices.
Multidimensional Binary Search for Contextual Decision-Making
We consider a multidimensional search problem that is motivated by questions in contextual decision-making, such as dynamic pricing and personalized medicine.
Ego-splitting Framework: from Non-Overlapping to Overlapping Clusters
More precisely, our framework works in two steps: a local ego-net analysis phase, and a global graph partitioning phase .
Ranked #3 on
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Stochastic bandits robust to adversarial corruptions
We introduce a new model of stochastic bandits with adversarial corruptions which aims to capture settings where most of the input follows a stochastic pattern but some fraction of it can be adversarially changed to trick the algorithm, e. g., click fraud, fake reviews and email spam.
Contextual Search via Intrinsic Volumes
We present an algorithm for the contextual search problem for the symmetric loss function $\ell(\theta, p) = |\theta - p|$ that achieves $O_{d}(1)$ total loss.
Learning to Clear the Market
The problem of market clearing is to set a price for an item such that quantity demanded equals quantity supplied.
Optimal Contextual Pricing and Extensions
We provide a generic algorithm with $O(d^2)$ regret where $d$ is the covering dimension of this class.
Bandits with adversarial scaling
We study "adversarial scaling", a multi-armed bandit model where rewards have a stochastic and an adversarial component.
Calibrated Click-Through Auctions: An Information Design Approach
While the auctioneer takes as given the auction rule of the click-through auction, namely the generalized second-price auction, the auctioneer can design the information flow regarding the click-through rates among the bidders.
Learning to Price Against a Moving Target
In the Learning to Price setting, a seller posts prices over time with the goal of maximizing revenue while learning the buyer's valuation.
Contextual Recommendations and Low-Regret Cutting-Plane Algorithms
We design algorithms for this problem which achieve regret $O(d\log T)$ and $\exp(O(d \log d))$.
Corruption-Robust Contextual Search through Density Updates
We study the problem of contextual search in the adversarial noise model.
U-Calibration: Forecasting for an Unknown Agent
We show that sublinear U-calibration error is a necessary and sufficient condition for all agents to achieve sublinear regret guarantees.
Preferences Evolve And So Should Your Bandits: Bandits with Evolving States for Online Platforms
We present online learning algorithms for any possible value of the evolution rate $\lambda$ and we show the robustness of our results to various model misspecifications.
