1 citations · 1 across the 7 of their papers we have counts for
7 papers
Admission Without Answers: Label-Free Certification and Experience Learning for LLM-Based Optimization Modeling
Junbo Jacob Lian, Huiling Chen, Hanzhang Qin +1
Agents that learn from experience improve at optimization modeling by storing solved trajectories and reusing them as skills. A wrong trajectory that enters the library can be retr…
ReLoop: Structured Modeling and Behavioral Verification for Reliable LLM-Based Optimization
Junbo Jacob Lian, Yujun Sun, Huiling Chen +3
Large language models (LLMs) can translate natural-language problem descriptions into optimization code, but the code is prone to silent failures: it executes and returns a solver-…
TwistNet-2D: Learning Second-Order Channel Interactions via Spiral Twisting for Texture Recognition
Junbo Jacob Lian, Feng Xiong, Yujun Sun +7
Second-order feature statistics are central to texture recognition, yet existing mechanisms exhibit a structural tension: bilinear pooling and Gram matrices capture global channel…
Wasserstein Evolution : Evolutionary Optimization as Phase Transition
Kaichen Ouyang, Mingyang Yu, Zong Ke +5
Evolutionary algorithms (EAs) serve as powerful black-box optimizers inspired by biological evolution. However, most existing EAs predominantly focus on heuristic operators such as…
Time-Fair Benchmarking for Metaheuristics: A Restart-Fair Protocol for Fixed-Time Comparisons
Junbo Jacob Lian
Numerous purportedly improved metaheuristics claim superior performance based on equivalent function evaluations (FEs), yet often conceal additional computational burdens in more i…
Twisted Convolutional Networks (TCNs): Enhancing Feature Interactions for Non-Spatial Data Classification
Junbo Jacob Lian, Haoran Chen, Kaichen Ouyang +3
Twisted Convolutional Networks (TCNs) are proposed as a novel deep learning architecture for classifying one-dimensional data with arbitrary feature order and minimal spatial relat…
Support Vector Boosting Machine (SVBM): Enhancing Classification Performance with AdaBoost and Residual Connections
Junbo Jacob Lian
In traditional boosting algorithms, the focus on misclassified training samples emphasizes their importance based on difficulty during the learning process. While using a standard…