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Found 8 papers, 2 papers with code
Addressing Budget Allocation and Revenue Allocation in Data Market Environments Using an Adaptive Sampling Algorithm
Data markets help with identifying valuable training data: model consumers pay to train a model, the market uses that budget to identify data and train the model (the budget allocation problem), and finally the market compensates data providers according to their data contribution (revenue allocation problem).
Pairwise Ranking Losses of Click-Through Rates Prediction for Welfare Maximization in Ad Auctions
We study the design of loss functions for click-through rates (CTR) to optimize (social) welfare in advertising auctions.
A Reinforcement Learning Approach in Multi-Phase Second-Price Auction Design
First, from the seller's perspective, we need to efficiently explore the environment in the presence of potentially nontruthful bidders who aim to manipulates seller's policy.
One Policy is Enough: Parallel Exploration with a Single Policy is Near-Optimal for Reward-Free Reinforcement Learning
Although parallelism has been extensively used in reinforcement learning (RL), the quantitative effects of parallel exploration are not well understood theoretically.
Pessimism meets VCG: Learning Dynamic Mechanism Design via Offline Reinforcement Learning
In the setting where the function approximation is employed to handle large state spaces, with only mild assumptions on the expressiveness of the function class, we are able to design a dynamic mechanism using offline reinforcement learning algorithms.
Personalized Federated Learning with Multiple Known Clusters
We consider the problem of personalized federated learning when there are known cluster structures within users.
Learning Dynamic Mechanisms in Unknown Environments: A Reinforcement Learning Approach
Dynamic mechanism design studies how mechanism designers should allocate resources among agents in a time-varying environment.
L-SVRG and L-Katyusha with Adaptive Sampling
Stochastic gradient-based optimization methods, such as L-SVRG and its accelerated variant L-Katyusha (Kovalev et al., 2020), are widely used to train machine learning models. The theoretical and empirical performance of L-SVRG and L-Katyusha can be improved by sampling observations from a non-uniform distribution (Qian et al., 2021).
