6 citations · 16 across the 17 of their papers we have counts for
18 papers
AgentHijack: Benchmarking Computer Use Agent Robustness to Common Environment Corruptions
Jingwei Sun, Jianing Zhu, Yuanyi Li +3
Autonomous computer use agents that powered by multimodal large language models (MLLMs) are emerging as capable assistants for completing complex digital workflows. However, real-w…
Rethinking Model Selection in VLM Through the Lens of Gromov-Wasserstein Distance
Muyang Li, Yucheng Liu, Jianbo Ma +3
Vision-Language Models (VLMs) have enhanced traditional LLMs with visual capabilities through the integration of vision encoders. While recent works have explored various combinati…
Forgetting: A New Mechanism Towards Better Large Language Model Fine-tuning
Ali Taheri, Alireza Taban, Qizhou Wang +4
Supervised fine-tuning (SFT) plays a critical role for pretrained large language models (LLMs), notably enhancing their capacity to acquire domain-specific knowledge while preservi…
When Data-Free Knowledge Distillation Meets Non-Transferable Teacher: Escaping Out-of-Distribution Trap is All You Need
Ziming Hong, Runnan Chen, Zengmao Wang +3
Data-free knowledge distillation (DFKD) transfers knowledge from a teacher to a student without access the real in-distribution (ID) data. Its common solution is to use a generator…
From Debate to Equilibrium: Belief-Driven Multi-Agent LLM Reasoning via Bayesian Nash Equilibrium
Xie Yi, Zhanke Zhou, Chentao Cao +3
Multi-agent frameworks can substantially boost the reasoning power of large language models (LLMs), but they typically incur heavy computational costs and lack convergence guarante…
Instance-dependent Early Stopping
Suqin Yuan, Runqi Lin, Lei Feng +2
In machine learning practice, early stopping has been widely used to regularize models and can save computational costs by halting the training process when the model's performance…
Enhancing Sample Selection Against Label Noise by Cutting Mislabeled Easy Examples
Suqin Yuan, Lei Feng, Bo Han +1
Sample selection is a prevalent approach in learning with noisy labels, aiming to identify confident samples for training. Although existing sample selection methods have achieved…
What If the Input is Expanded in OOD Detection?
Boxuan Zhang, Jianing Zhu, Zengmao Wang +3
Out-of-distribution (OOD) detection aims to identify OOD inputs from unknown classes, which is important for the reliable deployment of machine learning models in the open world. V…
Mind the Gap Between Prototypes and Images in Cross-domain Finetuning
Hongduan Tian, Feng Liu, Zhanke Zhou +3
In cross-domain few-shot classification (CFC), recent works mainly focus on adapting a simple transformation head on top of a frozen pre-trained backbone with few labeled data to p…
Envisioning Outlier Exposure by Large Language Models for Out-of-Distribution Detection
Chentao Cao, Zhun Zhong, Zhanke Zhou +3
Detecting out-of-distribution (OOD) samples is essential when deploying machine learning models in open-world scenarios. Zero-shot OOD detection, requiring no training on in-distri…
Unraveling the Impact of Heterophilic Structures on Graph Positive-Unlabeled Learning
Yuhao Wu, Jiangchao Yao, Bo Han +2
While Positive-Unlabeled (PU) learning is vital in many real-world scenarios, its application to graph data still remains under-explored. We unveil that a critical challenge for PU…
MOKD: Cross-domain Finetuning for Few-shot Classification via Maximizing Optimized Kernel Dependence
Hongduan Tian, Feng Liu, Tongliang Liu +3
In cross-domain few-shot classification, \emph{nearest centroid classifier} (NCC) aims to learn representations to construct a metric space where few-shot classification can be per…
Layer-Aware Analysis of Catastrophic Overfitting: Revealing the Pseudo-Robust Shortcut Dependency
Runqi Lin, Chaojian Yu, Bo Han +2
Catastrophic overfitting (CO) presents a significant challenge in single-step adversarial training (AT), manifesting as highly distorted deep neural networks (DNNs) that are vulner…
InstanT: Semi-supervised Learning with Instance-dependent Thresholds
Muyang Li, Runze Wu, Haoyu Liu +4
Semi-supervised learning (SSL) has been a fundamental challenge in machine learning for decades. The primary family of SSL algorithms, known as pseudo-labeling, involves assigning…
FlatMatch: Bridging Labeled Data and Unlabeled Data with Cross-Sharpness for Semi-Supervised Learning
Zhuo Huang, Li Shen, Jun Yu +2
Semi-Supervised Learning (SSL) has been an effective way to leverage abundant unlabeled data with extremely scarce labeled data. However, most SSL methods are commonly based on ins…
Winning Prize Comes from Losing Tickets: Improve Invariant Learning by Exploring Variant Parameters for Out-of-Distribution Generalization
Zhuo Huang, Muyang Li, Li Shen +4
Out-of-Distribution (OOD) Generalization aims to learn robust models that generalize well to various environments without fitting to distribution-specific features. Recent studies…
On the Over-Memorization During Natural, Robust and Catastrophic Overfitting
Runqi Lin, Chaojian Yu, Bo Han +1
Overfitting negatively impacts the generalization ability of deep neural networks (DNNs) in both natural and adversarial training. Existing methods struggle to consistently address…
Understanding Robust Overfitting from the Feature Generalization Perspective
Chaojian Yu, Xiaolong Shi, Jun Yu +2
Adversarial training (AT) constructs robust neural networks by incorporating adversarial perturbations into natural data. However, it is plagued by the issue of robust overfitting…