7 citations · 14 across the 10 of their papers we have counts for
12 papers
From Objectives to What Models Learn: A Landau Theory of Invariant Learning
Pinli Wang, Yue He, Peng Cui
Invariant-learning objectives pursue similar goals yet produce qualitatively different regularization paths, leaving unclear when shortcuts can be suppressed without damaging stabl…
Closed-Form Spectral Regularization for Multi-Task Model Merging
Yongxian Wei, Runxi Cheng, Xingxuan Zhang +4
Model merging combines several independently fine-tuned experts into a single multi-task model without any training data, reducing the storage, serving, and decentralized-developme…
LimiX-2M: Mitigating Low-Rank Collapse and Attention Bottlenecks in Tabular Foundation Models
Yuanrui Wang, Xingxuan Zhang, Han Yu +7
Tabular foundation models (TFMs) increasingly rival tree ensembles, but their performance is often compute-inefficient: with standard affine scalar tokenization, each feature injec…
Generating Risky Samples with Conformity Constraints via Diffusion Models
Han Yu, Hao Zou, Xingxuan Zhang +4
Although neural networks achieve promising performance in many tasks, they may still fail when encountering some examples and bring about risks to applications. To discover risky s…
ODP-Bench: Benchmarking Out-of-Distribution Performance Prediction
Han Yu, Kehan Li, Dongbai Li +3
Recently, there has been gradually more attention paid to Out-of-Distribution (OOD) performance prediction, whose goal is to predict the performance of trained models on unlabeled…
COUNTS: Benchmarking Object Detectors and Multimodal Large Language Models under Distribution Shifts
Jiansheng Li, Xingxuan Zhang, Hao Zou +6
Current object detectors often suffer significant perfor-mance degradation in real-world applications when encountering distributional shifts. Consequently, the out-of-distribution…
Understanding the Generalization of In-Context Learning in Transformers: An Empirical Study
Xingxuan Zhang, Haoran Wang, Jiansheng Li +6
Large language models (LLMs) like GPT-4 and LLaMA-3 utilize the powerful in-context learning (ICL) capability of Transformer architecture to learn on the fly from limited examples.…
Sample Weight Averaging for Stable Prediction
Han Yu, Yue He, Renzhe Xu +4
The challenge of Out-of-Distribution (OOD) generalization poses a foundational concern for the application of machine learning algorithms to risk-sensitive areas. Inspired by tradi…
Error Slice Discovery via Manifold Compactness
Han Yu, Hao Zou, Jiashuo Liu +4
Despite the great performance of deep learning models in many areas, they still make mistakes and underperform on certain subsets of data, i.e. error slices. Given a trained model,…
A Survey on Evaluation of Out-of-Distribution Generalization
Han Yu, Jiashuo Liu, Xingxuan Zhang +2
Machine learning models, while progressively advanced, rely heavily on the IID assumption, which is often unfulfilled in practice due to inevitable distribution shifts. This render…
PPA-Game: Characterizing and Learning Competitive Dynamics Among Online Content Creators
Renzhe Xu, Haotian Wang, Xingxuan Zhang +2
In this paper, we present the Proportional Payoff Allocation Game (PPA-Game), which characterizes situations where agents compete for divisible resources. In the PPA-game, agents s…
On the Out-Of-Distribution Generalization of Multimodal Large Language Models
Xingxuan Zhang, Jiansheng Li, Wenjing Chu +6
We investigate the generalization boundaries of current Multimodal Large Language Models (MLLMs) via comprehensive evaluation under out-of-distribution scenarios and domain-specifi…