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Zizhen Zhang

7 accepted papers

2026

UCPO: A Universal Constrained Combinatorial Optimization Method via Preference Optimization

AAAI 2026technical

Neural solvers have demonstrated remarkable success in combinatorial optimization, often surpassing traditional heuristics in speed, solution quality, and generalization. However, their efficacy deteriorates significantly when confronted with complex constraints that cannot be effectively managed th

Cited by 0SourcePDFScholar
2025

BOPO: Neural Combinatorial Optimization via Best-anchored and Objective-guided Preference Optimization

ICML 2025poster

Neural Combinatorial Optimization (NCO) has emerged as a promising approach for NP-hard problems. However, prevailing RL-based methods suffer from low sample efficiency due to sparse rewards and underused solutions. We propose *Best-anchored and Objective-guided Preference Optimization (BOPO)*, a tr…

Cited by 0SourcePDFScholar
2025

Rethinking Neural Multi-Objective Combinatorial Optimization via Neat Weight Embedding

ICLR 2025poster

Recent decomposition-based neural multi-objective combinatorial optimization (MOCO) methods struggle to achieve desirable performance. Even equipped with complex learning techniques, they often suffer from significant optimality gaps in weight-specific subproblems. To address this challenge, we prop…

Cited by 0SourcePDFScholar
2024

Neural Combinatorial Optimization for Robust Routing Problem with Uncertain Travel Times

NeurIPS 2024poster

We consider the robust routing problem with uncertain travel times under the min-max regret criterion, which represents an extended and robust version of the classic traveling salesman problem (TSP) and vehicle routing problem (VRP). The general budget uncertainty set is employed to capture the unce…

Cited by 2SourcePDFScholar
2023

Efficient Meta Neural Heuristic for Multi-Objective Combinatorial Optimization

NeurIPS 2023poster

Recently, neural heuristics based on deep reinforcement learning have exhibited promise in solving multi-objective combinatorial optimization problems (MOCOPs). However, they are still struggling to achieve high learning efficiency and solution quality. To tackle this issue, we propose an efficient…

2023

Neural Multi-Objective Combinatorial Optimization with Diversity Enhancement

NeurIPS 2023poster

Most of existing neural methods for multi-objective combinatorial optimization (MOCO) problems solely rely on decomposition, which often leads to repetitive solutions for the respective subproblems, thus a limited Pareto set. Beyond decomposition, we propose a novel neural heuristic with diversity e…

2022

UECA-Prompt: Universal Prompt for Emotion Cause Analysis

COLING 2022main

Emotion cause analysis (ECA) aims to extract emotion clauses and find the corresponding cause of the emotion. Existing methods adopt fine-tuning paradigm to solve certain types of ECA tasks. These task-specific methods have a deficiency of universality. And the relations among multiple objectives in…