2 citations · 2 across the 9 of their papers we have counts for
12 papers
Does Compression Imply Generalization? A Minimum Description Length Perspective
Lincen Yang, Jiayang Shi, Nan Pu +4
A central question in modern learning theory is what notion of compression, if any, explains why over-parameterized neural networks generalize. Existing answers measure complexity…
Modularized Reinforcement Learning on LLMs: From MDP Creation to Exploration and Learning
Zhao Yang, Yuxuan Jiang, Ting-Chih Chen +18
Reinforcement learning (RL) has become central to LLM post-training, yet the methods that dominate current pipelines, PPO and GRPO, represent only a narrow slice of what RL offers.…
MM-OptBench: A Solver-Grounded Benchmark for Multimodal Optimization Modeling
Zhong Li, Qi Huang, Yuxuan Zhu +6
Optimization modeling translates real decision-making problems into mathematical optimization models and solver-executable implementations. Although language models are increasingl…
Efficient Flow Matching for Sparse-View CT Reconstruction
Jiayang Shi, Lincen Yang, Zhong Li +3
Generative models, particularly Diffusion Models (DM), have shown strong potential for Computed Tomography (CT) reconstruction serving as expressive priors for solving ill-posed in…
Learning Subgroups with Maximum Treatment Effects without Causal Heuristics
Lincen Yang, Zhong Li, Matthijs van Leeuwen +1
Discovering subgroups with the maximum average treatment effect is crucial for targeted decision making in domains such as precision medicine, public policy, and education. While m…
Scalable, Explainable and Provably Robust Anomaly Detection with One-Step Flow Matching
Zhong Li, Qi Huang, Yuxuan Zhu +4
We introduce Time-Conditioned Contraction Matching (TCCM), a novel method for semi-supervised anomaly detection in tabular data. TCCM is inspired by flow matching, a recent generat…
Diffusion and Flow Matching Models for Tabular Data: A Survey
Zhong Li, Qi Huang, Lincen Yang +5
Deep generative models have made rapid progress in image, text, audio, and video generation, and are increasingly being applied to structured records. For tabular data, however, ge…
Conditional Density Estimation with Histogram Trees
Lincen Yang, Matthijs van Leeuwen
Conditional density estimation (CDE) goes beyond regression by modeling the full conditional distribution, providing a richer understanding of the data than just the conditional me…
Probabilistic Truly Unordered Rule Sets
Lincen Yang, Matthijs van Leeuwen
Rule set learning has recently been frequently revisited because of its interpretability. Existing methods have several shortcomings though. First, most existing methods impose ord…
Truly Unordered Probabilistic Rule Sets for Multi-class Classification
Lincen Yang, Matthijs van Leeuwen
Rule set learning has long been studied and has recently been frequently revisited due to the need for interpretable models. Still, existing methods have several shortcomings: 1) m…
Estimating Conditional Mutual Information for Discrete-Continuous Mixtures using Multi-Dimensional Adaptive Histograms
Alexander Marx, Lincen Yang, Matthijs van Leeuwen
Estimating conditional mutual information (CMI) is an essential yet challenging step in many machine learning and data mining tasks. Estimating CMI from data that contains both dis…
Unsupervised Discretization by Two-dimensional MDL-based Histogram
Lincen Yang, Mitra Baratchi, Matthijs van Leeuwen
Unsupervised discretization is a crucial step in many knowledge discovery tasks. The state-of-the-art method for one-dimensional data infers locally adaptive histograms using the m…