arXiv subjects

Zeng Fu

Publications and source records attributed to Zeng Fu.

2 recordsLinked to original sources

arXiv2026

Online Bidding for Contextual First-Price Auctions with Budgets under One-Sided Information Feedback

In this paper, we study the problem of learning to bid in repeated first-price auctions with budget constraints. In each period, the decision maker needs to submit a bid to win the auction and maximize the total collected reward, subject to a budget constraint throughout the horizon. We focus on the setting with one-sided information feedback where only the winning bid is revealed to the decision maker at each period. Different from previous papers that assume homogeneous competitors' bids, we assume that the highest bid of other bidders depends on the context of the impression, which is initially unknown and needs to be learned over time. To tackle the learning difficulty, we propose a novel robust regression method based on conditional quantile invariance to learn the contextual parameter. Further combined with a dual update procedure, we develop a new bidding algorithm and prove that our algorithm achieves $\widetilde{O}(\sqrt{T})$ regret, which is order-optimal. We further extend our approach to the multi-dimensional setting and demonstrate the practical efficiency of our algorithm through numerical experiments.

math.OC↗

arXiv2018

LoAdaBoost: loss-based AdaBoost federated machine learning with reduced computational complexity on IID and non-IID intensive care data

Intensive care data are valuable for improvement of health care, policy making and many other purposes. Vast amount of such data are stored in different locations, on many different devices and in different data silos. Sharing data among different sources is a big challenge due to regulatory, operational and security reasons. One potential solution is federated machine learning, which is a method that sends machine learning algorithms simultaneously to all data sources, trains models in each source and aggregates the learned models. This strategy allows utilization of valuable data without moving them. One challenge in applying federated machine learning is the possibly different distributions of data from diverse sources. To tackle this problem, we proposed an adaptive boosting method named LoAdaBoost that increases the efficiency of federated machine learning. Using intensive care unit data from hospitals, we investigated the performance of learning in IID and non-IID data distribution scenarios, and showed that the proposed LoAdaBoost method achieved higher predictive accuracy with lower computational complexity than the baseline method.

cs.LG↗