2 citations · 3 across the 28 of their papers we have counts for
12 papers · 1 filter
SymboLLM-FE: LLM-Accelerated Symbolic Regression for Automated Feature Engineering on Tabular Data
Zi-Jian Cheng, Zi-Yi Jia, Zhi Zhou +2
Tabular data, as a core data format in machine learning, often lacks the discriminative power needed for high-performance modeling due to insufficient feature informativeness. Auto…
On the Learnability of Test-Time Adaptation: A Recovery Complexity Perspective
Zhi Zhou, Ming Yang, Shi-Yu Tian +3
Test-time adaptation (TTA) aims to adapt models to maintain reliable performance on non-stationary test streams without requiring labeled data. Despite its empirical success, the l…
MapTab: A Diagnostic Benchmark for Long-Horizon Multi-Criteria Multimodal Reasoning on Heterogeneous Topological Graphs
Ziqiao Shang, Ling-Yue Ge, Zian Xu +10
Systematically evaluating Multimodal Large Language Models (MLLMs) is essential for advancing Artificial General Intelligence (AGI). Yet existing benchmarks remain inadequate for r…
A Theoretical Study on Bridging Internal Probability and Self-Consistency for LLM Reasoning
Zhi Zhou, Yuhao Tan, Zenan Li +4
Test-time scaling seeks to improve the reasoning performance of large language models (LLMs) by adding computational resources. A prevalent approach within the field is sampling-ba…
Data Selection for LLM Alignment Using Fine-Grained Preferences
Jia Zhang, Yao Liu, Chen-Xi Zhang +4
Large language models (LLMs) alignment aims to ensure that the behavior of LLMs meets human preferences. While collecting data from multiple fine-grained, aspect-specific preferenc…
Quantitative Estimation of Target Task Performance from Unsupervised Pretext Task in Semi/Self-Supervised Learning
Lin-Han Jia, Si-Yu Han, Wen-Chao Hu +5
The effectiveness of unlabeled data in Semi/Self-Supervised Learning (SSL) depends on appropriate assumptions for specific scenarios, thereby enabling the selection of beneficial u…
Realistic Evaluation of TabPFN v2 in Open Environments
Zi-Jian Cheng, Zi-Yi Jia, Zhi Zhou +2
Tabular data, owing to its ubiquitous presence in real-world domains, has garnered significant attention in machine learning research. While tree-based models have long dominated t…
A Smooth Transition Between Induction and Deduction: Fast Abductive Learning Based on Probabilistic Symbol Perception
Lin-Han Jia, Si-Yu Han, Lan-Zhe Guo +4
Abductive learning (ABL) that integrates strengths of machine learning and logical reasoning to improve the learning generalization, has been recently shown effective. However, its…
Bridging Internal Probability and Self-Consistency for Effective and Efficient LLM Reasoning
Zhi Zhou, Tan Yuhao, Zenan Li +4
Recent advancements in large language models (LLMs) have demonstrated remarkable reasoning capabilities. However, single-shot inference often yields unreliable results for complex…
BMIP: Bi-directional Modality Interaction Prompt Learning for VLM
Song-Lin Lv, Yu-Yang Chen, Zhi Zhou +2
Vision-language models (VLMs) have exhibited remarkable generalization capabilities, and prompt learning for VLMs has attracted great attention for the ability to adapt pre-trained…
TabFSBench: Tabular Benchmark for Feature Shifts in Open Environments
Zi-Jian Cheng, Zi-Yi Jia, Zhi Zhou +2
Tabular data is widely utilized in various machine learning tasks. Current tabular learning research predominantly focuses on closed environments, while in real-world applications,…
Vision-Language Model Selection and Reuse for Downstream Adaptation
Hao-Zhe Tan, Zhi Zhou, Yu-Feng Li +1
Pre-trained Vision-Language Models (VLMs) are becoming increasingly popular across various visual tasks, and several open-sourced VLM variants have been released. However, selectin…