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Found 14 papers, 1 papers with code
Learning in Repeated Multi-Unit Pay-As-Bid Auctions
In each of these auctions, a large number of (identical) items are to be allocated to the largest submitted bids, where the price of each of the winning bids is equal to the bid itself.
Online Resource Allocation with Convex-set Machine-Learned Advice
Specifically, in a C-Pareto optimal setting, we maximize the robust ratio while ensuring that the consistent ratio is at least C. Our proposed C-Pareto optimal algorithm is an adaptive protection level algorithm, which extends the classical fixed protection level algorithm introduced in Littlewood (2005) and Ball and Queyranne (2009).
Multi-Platform Budget Management in Ad Markets with Non-IC Auctions
Our strategy maximizes the expected total utility across auctions while satisfying the advertiser's budget constraints in expectation.
Interpolating Item and User Fairness in Multi-Sided Recommendations
Motivated by this, we formulate a novel fair recommendation framework, called Problem (FAIR), that not only maximizes the platform's revenue, but also accommodates varying fairness considerations from the perspectives of items and users.
Learning and Collusion in Multi-unit Auctions
We analyze the properties of this auction in both the offline and online settings.
Multi-channel Autobidding with Budget and ROI Constraints
In light of this finding, under a bandit feedback setting that mimics real-world scenarios where advertisers have limited information on ad auctions in each channels and how channels procure ads, we present an efficient learning algorithm that produces per-channel budgets whose resulting conversion approximates that of the global optimal problem.
Non-Stationary Bandits with Auto-Regressive Temporal Dependency
Traditional multi-armed bandit (MAB) frameworks, predominantly examined under stochastic or adversarial settings, often overlook the temporal dynamics inherent in many real-world applications such as recommendation systems and online advertising.
Active Learning for Non-Parametric Choice Models
To overcome the identifiability problem, we introduce a directed acyclic graph (DAG) representation of the choice model.
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.
Learning Product Rankings Robust to Fake Users
We first show that existing learning algorithms---that are optimal in the absence of fake users---may converge to highly sub-optimal rankings under manipulation by fake users.
Optimal Learning for Structured Bandits
We propose a novel learning algorithm that we call "DUSA" whose regret matches the information-theoretic regret lower bound up to a constant factor and can handle a wide range of structural information.
Dynamic Incentive-aware Learning: Robust Pricing in Contextual Auctions
Motivated by pricing in ad exchange markets, we consider the problem of robust learning of reserve prices against strategic buyers in repeated contextual second-price auctions.
Incentive-aware Contextual Pricing with Non-parametric Market Noise
We show that this design allows the seller to control the number of periods in which buyers significantly corrupt their bids.
Contextual Bandits with Cross-learning
We consider the variant of this problem where in addition to receiving the reward $r_{i, t}(c)$, the learner also learns the values of $r_{i, t}(c')$ for some other contexts $c'$ in set $\mathcal{O}_i(c)$; i. e., the rewards that would have been achieved by performing that action under different contexts $c'\in \mathcal{O}_i(c)$.
