13 citations · 26 across the 17 of their papers we have counts for
17 papers
Interpreting Neural Combinatorial Optimization via Evolving Programmatic Bottlenecks
Haocheng Duan, Yuxin Guo, Jieyi Bi +4
Neural Combinatorial Optimization (NCO) achieves strong performance, yet its black-box nature remains a key roadblock to deployment and scientific diagnosis. Standard interpretabil…
FrontierOR: Benchmarking LLMs' Capacity for Efficient Algorithm Design in Large-Scale Optimization
Minwei Kong, Chonghe Jiang, Ao Qu +24
Large language models (LLMs) are increasingly used for optimization modeling and solver-code generation, yet practical operations research and optimization problems often require a…
CORAL: Towards Autonomous Multi-Agent Evolution for Open-Ended Discovery
Ao Qu, Han Zheng, Zijian Zhou +14
Large language model (LLM)-based evolution is a promising approach for open-ended discovery, where progress requires sustained search and knowledge accumulation. Existing methods s…
Probability-Aware Parking Selection
Cameron Hickert, Sirui Li, Zhengbing He +1
Current navigation systems conflate time-to-drive with the true time-to-arrive by ignoring parking search duration and the final walking leg. Such underestimation can significantly…
Multi-residual Mixture of Experts Learning for Cooperative Control in Multi-vehicle Systems
Vindula Jayawardana, Sirui Li, Yashar Farid +1
Autonomous vehicles (AVs) are becoming increasingly popular, with their applications now extending beyond just a mode of transportation to serving as mobile actuators of a traffic…
Learning to Segment for Vehicle Routing Problems
Wenbin Ouyang, Sirui Li, Yining Ma +1
Iterative heuristics are widely recognized as state-of-the-art for Vehicle Routing Problems (VRPs). In this work, we exploit a critical observation: a large portion of the solution…
Learning-Guided Rolling Horizon Optimization for Long-Horizon Flexible Job-Shop Scheduling
Sirui Li, Wenbin Ouyang, Yining Ma +1
Long-horizon combinatorial optimization problems (COPs), such as the Flexible Job-Shop Scheduling Problem (FJSP), often involve complex, interdependent decisions over extended time…
Towards Foundation Models for Mixed Integer Linear Programming
Sirui Li, Janardhan Kulkarni, Ishai Menache +2
Mixed Integer Linear Programming (MILP) is essential for modeling complex decision-making problems but faces challenges in computational tractability and requires expert formulatio…
Model-Based Transfer Learning for Contextual Reinforcement Learning
Jung-Hoon Cho, Vindula Jayawardana, Sirui Li +1
Deep reinforcement learning (RL) is a powerful approach to complex decision making. However, one issue that limits its practical application is its brittleness, sometimes failing t…
Generalizing Cooperative Eco-driving via Multi-residual Task Learning
Vindula Jayawardana, Sirui Li, Cathy Wu +2
Conventional control, such as model-based control, is commonly utilized in autonomous driving due to its efficiency and reliability. However, real-world autonomous driving contends…
Temporal Transfer Learning for Traffic Optimization with Coarse-grained Advisory Autonomy
Jung-Hoon Cho, Sirui Li, Jeongyun Kim +1
The recent development of connected and automated vehicle (CAV) technologies has spurred investigations to optimize dense urban traffic to maximize vehicle speed and throughput. Th…
Learning to Configure Separators in Branch-and-Cut
Sirui Li, Wenbin Ouyang, Max B. Paulus +1
Cutting planes are crucial in solving mixed integer linear programs (MILP) as they facilitate bound improvements on the optimal solution. Modern MILP solvers rely on a variety of s…
Hybrid System Stability Analysis of Multi-Lane Mixed-Autonomy Traffic
Sirui Li, Roy Dong, Cathy Wu
Autonomous vehicles (AVs) hold vast potential to enhance transportation systems by reducing congestion, improving safety, and lowering emissions. AV controls lead to emergent traff…
Integrated Analysis of Coarse-Grained Guidance for Traffic Flow Stability
Sirui Li, Roy Dong, Cathy Wu
Autonomous vehicles (AVs) enable more efficient and sustainable transportation systems. Ample studies have shown that controlling a small fraction of AVs can smooth traffic flow an…
The Impact of Task Underspecification in Evaluating Deep Reinforcement Learning
Vindula Jayawardana, Catherine Tang, Sirui Li +2
Evaluations of Deep Reinforcement Learning (DRL) methods are an integral part of scientific progress of the field. Beyond designing DRL methods for general intelligence, designing…
Cooperation for Scalable Supervision of Autonomy in Mixed Traffic
Cameron Hickert, Sirui Li, Cathy Wu
Advances in autonomy offer the potential for dramatic positive outcomes in a number of domains, yet enabling their safe deployment remains an open problem. This work's motivating q…
Learning to Delegate for Large-scale Vehicle Routing
Sirui Li, Zhongxia Yan, Cathy Wu
Vehicle routing problems (VRPs) form a class of combinatorial problems with wide practical applications. While previous heuristic or learning-based works achieve decent solutions o…