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Found 12 papers, 0 papers with code
Selling Joint Ads: A Regret Minimization Perspective
In the stochastic setting, we design an efficient learning algorithm achieving a regret bound of $O(T^{3/4})$.
Learning from Aggregate responses: Instance Level versus Bag Level Loss Functions
In this paper, we study two natural loss functions for learning from aggregate responses: bag-level loss and the instance-level loss.
Optimal Unbiased Randomizers for Regression with Label Differential Privacy
We propose a new family of label randomizers for training regression models under the constraint of label differential privacy (DP).
Incrementality Bidding via Reinforcement Learning under Mixed and Delayed Rewards
Incrementality, which is used to measure the causal effect of showing an ad to a potential customer (e. g. a user in an internet platform) versus not, is a central object for advertisers in online advertising platforms.
Learning to Bid in Contextual First Price Auctions
For binary feedback, when the noise distribution $\mathcal{F}$ is known, we propose a bidding algorithm, by using maximum likelihood estimation (MLE) method to achieve at most $\widetilde{O}(\sqrt{\log(d) T})$ regret.
Online Learning via Offline Greedy Algorithms: Applications in Market Design and Optimization
We leverage this notion to transform greedy robust offline algorithms into a $O(T^{2/3})$ (approximate) regret in the bandit setting.
Handling many conversions per click in modeling delayed feedback
Predicting the expected value or number of post-click conversions (purchases or other events) is a key task in performance-based digital advertising.
Submodular Maximization Through Barrier Functions
In this paper, we introduce a novel technique for constrained submodular maximization, inspired by barrier functions in continuous optimization.
Distributed Submodular Cover: Succinctly Summarizing Massive Data
In this paper, we formalize this challenge as a submodular cover problem.
Lazier Than Lazy Greedy
Is it possible to maximize a monotone submodular function faster than the widely used lazy greedy algorithm (also known as accelerated greedy), both in theory and practice?
Resourceful Contextual Bandits
We study contextual bandits with ancillary constraints on resources, which are common in real-world applications such as choosing ads or dynamic pricing of items.
Bandits with Knapsacks
As one example of a concrete application, we consider the problem of dynamic posted pricing with limited supply and obtain the first algorithm whose regret, with respect to the optimal dynamic policy, is sublinear in the supply.
