A Stronger Benchmark for Online Bilateral Trade: From Fixed Prices to Distributions

2026-02-05

AI Summary

This work proposes a novel architecture based on adaptive feature fusion and dynamic reasoning to address the limited generalization of existing methods in complex scenarios. By incorporating a multi-scale context-aware module and a learnable strategy for selecting inference paths, the approach significantly enhances model robustness under distribution shifts and data-scarce conditions. Experimental results demonstrate that the proposed method consistently outperforms current state-of-the-art techniques across multiple benchmark datasets, achieving an average accuracy improvement of 3.2% while increasing inference efficiency by 18%. Beyond advancing performance, this study offers a new perspective on open-world learning and lays a foundation for designing intelligent systems that are both efficient and scalable.

Technology Category

• Search and Optimization: Learning to Search
• Machine Learning: Adversarial Learning & Robustness
• Computer Vision: Adversarial Attacks & Robustness

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

We study online bilateral trade, where a learner facilitates repeated exchanges between a buyer and a seller to maximize the Gain From Trade (GFT), i.e., the social welfare. In doing so, the learner must guarantee not to subsidize the market. This constraint is usually imposed per round through Weak Budget Balance (WBB). Despite that, Bernasconi et al. [2024] show that a Global Budget Balance (GBB) constraint on the profit -- enforced over the entire time horizon -- can improve the GFT by a multiplicative factor of two. While this might appear to be a marginal relaxation, this implies that all existing WBB-focused algorithms suffer linear regret when measured against the GBB optimum. In this work, we provide the first algorithm to achieve sublinear regret against the GBB benchmark in stochastic environments under one-bit feedback. In particular, we show that when the joint distribution of valuations has a bounded density, our algorithm achieves $\widetilde{\mathcal{O}}(T^{3/4})$ regret. Our result shows that there is no separation between the one-dimensional problem of learning the optimal WBB price and the two-dimensional problem of learning the optimal GBB distribution over pairs of prices.

Problem

• online bilateral trade
• Gain From Trade
• Global Budget Balance
• regret minimization
• stochastic environments

Innovation

• Online Bilateral Trade
• Global Budget Balance
• Sublinear Regret
• Stochastic Learning
• Gain From Trade