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Bing Yan

8 accepted papers

2025

Adjoint Sampling: Highly Scalable Diffusion Samplers via Adjoint Matching

Aaron J Havens, Benjamin Kurt Miller, Bing Yan, Carles Domingo-Enrich, Anuroop Sriram, Daniel S. Levine +7
ICML 2025poster

We introduce Adjoint Sampling, a highly scalable and efficient algorithm for learning diffusion processes that sample from unnormalized densities, or energy functions. It is the first on-policy approach that allows significantly more gradient updates than the number of energy evaluations and model s…

Cited by 0Source ↗PDF ↗code ↗Scholar ↗
2025

PromptSeg: Learning to Segment Medical Image via Visual Prompts

Minfan Zhao, Ziqi Zhu, Jun Shi, Zhaohui Wang, Junshi Chen, Hong An +1
ICASSP 2025accepted

Deep learning has made remarkable medical image segmentation advancements, yet its generalization capability across tasks remains challenging. The variety of task objectives, disease-dependent labeling variations, and multi-center data contribute to the poor generalization capacity of task-specific…

Cited by 0Source ↗Scholar ↗
2024

Near-Optimal Scheduling for IC Packaging Operations Considering Processing-Time Variations and Factory Practices

Tsung-Han Tsai, Bing Yan, Peter B. Luh, Haw Ching Yang, Mikhail A. Bragin, Fan-Tien Cheng
RA-L 2024

Due to the short life cycles of electronic products, trial run lots of new products are crucial in IC packaging for production verification and engineering adjustments. The processing time of trial run lots may differ significantly from production lots due to engineering adjustments and is difficult

Cited by 3Source ↗Scholar ↗
2024

Predictive Accuracy-Based Active Learning for Medical Image Segmentation

Jun Shi, Shulan Ruan, Ziqi Zhu, Minfan Zhao, Hong An, Xudong Xue +1
IJCAI 2024poster

Active learning is considered a viable solution to alleviate the contradiction between the high dependency of deep learning-based segmentation methods on annotated data and the expensive pixel-level annotation cost of medical images. However, most existing methods suffer from unreliable uncertainty…

Cited by 4Source ↗PDF ↗code ↗Scholar ↗
2024

Structured Chemistry Reasoning with Large Language Models

Siru Ouyang, Zhuosheng Zhang, Bing Yan, Xuan Liu, Yejin Choi, Jiawei Han +1
ICML 2024poster

Large Language Models (LLMs) excel in diverse areas, yet struggle with complex scientific reasoning, especially in the field of chemistry. Different from the simple chemistry tasks (e.g., molecule classification) addressed in previous studies, complex chemistry problems require not only vast knowled…

Cited by 25Source ↗PDF ↗code ↗Scholar ↗
2021

A Novel Integer Linear Programming Formulation for Job-Shop Scheduling Problems

Anbang Liu, Peter B. Luh, Bing Yan, Mikhail A. Bragin
RA-L 2021

Job-shop scheduling is an important but difficult problem arising in low-volume high-variety manufacturing. It is usually solved at the beginning of each shift with strict computational time requirements. To obtain near-optimal solutions with quantifiable quality within strict time limits, a directi

Cited by 26Source ↗Scholar ↗
2020

Ordinal-Optimization Concept Enabled Decomposition and Coordination of Mixed-Integer Linear Programming Problems

An-Bang Liu, Peter B. Luh, Mikhail A. Bragin, Bing Yan
RA-L 2020

Many important optimization problems, such as manufacturing scheduling and power system unit commitment, are formulated as Mixed-Integer Linear Programming (MILP) problems. Such problems are generally difficult to solve because of their combinatorial nature, and may subject to strict computation tim

Cited by 18Source ↗Scholar ↗