Author name cluster

Xia Yin

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

2 author rows

AAAI Conference 2026 Conference Paper

Minimum-Cost Network Flow with Dual Predictions

Zhiyang Chen
Hailong Yao
Xia Yin

Recent work has shown that machine-learned predictions can provably improve the performance of classic algorithms. In this work, we propose the first minimum-cost network flow algorithm augmented with a dual prediction. Our method is based on a classic minimum-cost flow algorithm, namely ε-relaxation. We provide time complexity bounds in terms of the infinity norm prediction error, which is both consistent and robust. We also prove sample complexity bounds for PAC-learning the prediction. We empirically validate our theoretical results on two applications of minimum-cost flow, i.e., traffic networks and chip escape routing, in which we learn a fixed prediction, and a feature-based neural network model to infer the prediction, respectively. Experimental results illustrate 12.74× and 1.64× average speedup on two applications.

PDF Details DOI

NeurIPS Conference 2025 Conference Paper

Generalization Bounds for Model-based Algorithm Configuration

Zhiyang Chen
Hailong Yao
Xia Yin

Algorithm configuration, which involves selecting algorithm parameters based on sampled problem instances, is a crucial step in applying modern algorithms such as SAT solvers. Although prior work has attempted to understand the theoretical foundations of algorithm configuration, we still lack a comprehensive understanding of why practical algorithm configurators exhibit strong generalization performances in real-world scenarios. In this paper, through the lens of machine learning theory, we provide an algorithm-dependent generalization bound for the widely used model-based algorithm configurators under mild assumptions. Our approach is based on the algorithmic stability framework for generalization bounds. To the best of our knowledge, this is the first generalization bound that applies to a model closely approximating practical model-based algorithm configurators.

PDF Details

ICML Conference 2025 Conference Paper

Learning Configurations for Data-Driven Multi-Objective Optimization

Zhiyang Chen 0006
Hailong Yao 0002
Xia Yin

Multi-objective optimization problems arise widely in various fields. In practice, multi-objective optimization is generally solved by heuristics with tunable parameters that are highly application-specific. Tuning parameters based on real-world instances (a. k. a. algorithm configuration) are generally empirical without theoretical guarantees. In this work, we establish the theoretical foundation of data-driven multi-objective optimization through the lens of machine learning theory. We provide generalization guarantees on selecting parameters for multi-objective optimization algorithms based on sampled problem instances. Moreover, if the performance metric of the algorithm is the Pareto volume, we can PAC-learn the approximately optimal configuration in polynomial time. We apply our framework to various algorithms, including approximation algorithms, local search, and linear programming. Experiments on multiple problems verify our theoretical findings.

Details