1 citations · 1 across the 15 of their papers we have counts for
17 papers
Latent World Models Should Plan with Distances Along the Latent Manifold
Zunzhi You, Linwei Tao, Siyu Xu +2
Latent world models such as LeWM plan by searching for an action sequence whose predicted terminal latent is closest to the goal latent under an ambient norm, such as the Euclidean…
Revealing Epistemic Uncertainty in MLLMs via Causal-Invariant Masking
Haoyang Luo, Linwei Tao, Jie Gui +4
Multimodal Large Language Models (MLLMs) suffer from hallucinations, creating a critical need for Uncertainty Quantification (UQ) to ensure reliable deployment. However, existing a…
Retrieval-Augmented Linguistic Calibration
Yi-Fan Yeh, Linwei Tao, Minjing Dong +4
Linguistic cues such as "I believe" and "probably" offer an intuitive interface for communicating confidence, yet a generalisable, principled calibration framework for linguistic c…
Confidence Calibration under Ambiguous Ground Truth
Linwei Tao, Haoyang Luo, Minjing Dong +1
Confidence calibration assumes a unique ground-truth label per input, yet this assumption fails wherever annotators genuinely disagree. Post-hoc calibrators fitted on majority-vote…
Can Large Language Models Express Uncertainty Like Human?
Linwei Tao, Yi-Fan Yeh, Bo Kai +6
Large language models (LLMs) are increasingly used in high-stakes settings, where overconfident responses can mislead users. Reliable confidence estimation has been shown to enhanc…
WATS: Calibrating Graph Neural Networks with Wavelet-Aware Temperature Scaling
Xiaoyang Li, Linwei Tao, Haohui Lu +3
Graph Neural Networks (GNNs) have demonstrated strong predictive performance on relational data; however, their confidence estimates often misalign with actual predictive correctne…
Sample Margin-Aware Recalibration of Temperature Scaling
Haolan Guo, Linwei Tao, Haoyang Luo +2
Recent advances in deep learning have significantly improved predictive accuracy. However, modern neural networks remain systematically overconfident, posing risks for deployment i…
Revisiting Uncertainty Estimation and Calibration of Large Language Models
Linwei Tao, Yi-Fan Yeh, Minjing Dong +3
As large language models (LLMs) are increasingly deployed in high-stakes applications, robust uncertainty estimation is essential for ensuring the safe and trustworthy deployment o…
Beyond One-Hot Labels: Semantic Mixing for Model Calibration
Haoyang Luo, Linwei Tao, Minjing Dong +1
Model calibration seeks to ensure that models produce confidence scores that accurately reflect the true likelihood of their predictions being correct. However, existing calibratio…
Uncertainty Weighted Gradients for Model Calibration
Jinxu Lin, Linwei Tao, Minjing Dong +1
Model calibration is essential for ensuring that the predictions of deep neural networks accurately reflect true probabilities in real-world classification tasks. However, deep net…
Mitigating Object Hallucinations in Large Vision-Language Models via Attention Calibration
Younan Zhu, Linwei Tao, Minjing Dong +1
Large Vision-Language Models (LVLMs) exhibit impressive multimodal reasoning capabilities but remain highly susceptible to object hallucination, where models generate responses tha…
Feature Clipping for Uncertainty Calibration
Linwei Tao, Minjing Dong, Chang Xu
Deep neural networks (DNNs) have achieved significant success across various tasks, but ensuring reliable uncertainty estimates, known as model calibration, is crucial for their sa…
Consistency Calibration: Improving Uncertainty Calibration via Consistency among Perturbed Neighbors
Linwei Tao, Haolan Guo, Minjing Dong +1
Calibration is crucial in deep learning applications, especially in fields like healthcare and autonomous driving, where accurate confidence estimates are vital for decision-making…
Diffusion Attribution Score: Evaluating Training Data Influence in Diffusion Models
Jinxu Lin, Linwei Tao, Minjing Dong +1
As diffusion models become increasingly popular, the misuse of copyrighted and private images has emerged as a major concern. One promising solution to mitigate this issue is ident…
Visual Imitation Learning with Calibrated Contrastive Representation
Yunke Wang, Linwei Tao, Bo Du +2
Adversarial Imitation Learning (AIL) allows the agent to reproduce expert behavior with low-dimensional states and actions. However, challenges arise in handling visual states due…
Aligning Language Models with Offline Learning from Human Feedback
Jian Hu, Li Tao, June Yang +1
Learning from human preferences is crucial for language models (LMs) to effectively cater to human needs and societal values. Previous research has made notable progress by leverag…
A Benchmark Study on Calibration
Linwei Tao, Younan Zhu, Haolan Guo +2
Deep neural networks are increasingly utilized in various machine learning tasks. However, as these models grow in complexity, they often face calibration issues, despite enhanced…