9 papers
AgentCompass: A Unified Evaluation Infrastructure for Agent Capabilities
Kai Chen, Zichen Ding, Jiaye Ge +20
As Large Language Models (LLMs) evolve into autonomous agents, the need for unified evaluation infrastructure becomes critical. However, current evaluation pipelines remain highly…
OpenCompass: A Universal Evaluation Platform for Large Language Models
Maosong Cao, Kai Chen, Haodong Duan +27
In recent years, the field of artificial intelligence has undergone a paradigm shift from task-specific small-scale models to general-purpose large language models (LLMs). With the…
Hinge Regression Trees and HRT-Boost: Newton-Optimized Oblique Learning for Compact Tabular Models
Hongyi Li, Jun Xu, Hong Yan
Learning high-quality oblique decision trees remains a significant challenge due to the discrete and non-convex nature of split optimization. We present the Hinge Regression Tree (…
Distributed Model Predictive Control for Energy and Comfort Optimization in Large Buildings Using Piecewise Affine Approximation
Hongyi Li, Jun Xu, Jinfeng Liu
The control of large buildings encounters challenges in computational efficiency due to their size and nonlinear components. To address these issues, this paper proposes a Piecewis…
Hinge Regression Tree: A Newton Method for Oblique Regression Tree Splitting
Hongyi Li, Han Lin, Jun Xu
Oblique decision trees combine the transparency of trees with the power of multivariate decision boundaries, but learning high-quality oblique splits is NP-hard, and practical meth…
Rotation-Robust Regression with Convolutional Model Trees
Hongyi Li, William Ward Armstrong, Jun Xu
We study rotation-robust learning for image inputs using Convolutional Model Trees (CMTs) [1], whose split and leaf coefficients can be structured on the image grid and transformed…
LHT: Statistically-Driven Oblique Decision Trees for Interpretable Classification
Hongyi Li, Jun Xu, William Ward Armstrong
We introduce the Learning Hyperplane Tree (LHT), a novel oblique decision tree model designed for expressive and interpretable classification. LHT fundamentally distinguishes itsel…
Learning Hyperplane Tree: A Piecewise Linear and Fully Interpretable Decision-making Framework
Hongyi Li, Jun Xu, William Ward Armstrong
This paper introduces a novel tree-based model, Learning Hyperplane Tree (LHT), which outperforms state-of-the-art (SOTA) tree models for classification tasks on several public dat…
Priority-based Energy Allocation in Buildings through Distributed Model Predictive Control
Hongyi Li, Jun Xu, Qianchuan Zhao
Many countries are facing energy shortage today and most of the global energy is consumed by HVAC systems in buildings. For the scenarios where the energy system is not sufficientl…