1 citations · 1 across the 10 of their papers we have counts for
11 papers
Why Self-Training Helps and Hurts: Denoising vs. Signal Forgetting
Mingqi Wu, Archer Y. Yang, Qiang Sun
Iterative self-training (self-distillation) repeatedly refits a model on pseudo-labels generated by its own predictions. We study this procedure in overparameterized linear regress…
Training-Free Self-Correction for Multimodal Masked Diffusion Models
Yidong Ouyang, Panwen Hu, Zhengyan Wan +7
Masked diffusion models have emerged as a powerful framework for text and multimodal generation. However, their sampling procedure updates multiple tokens simultaneously and treats…
EIR: Enhanced Image Representations for Medical Report Generation
Qiang Sun, Zongcheng Ji, Yinlong Xiao +2
Generating medical reports from chest X-ray images is a critical and time-consuming task for radiologists, especially in emergencies. To alleviate the stress on radiologists and re…
Reward Auditor: Inference on Reward Modeling Suitability in Real-World Perturbed Scenarios
Jianxiang Zang, Yongda Wei, Ruxue Bai +5
Reliable reward models (RMs) are critical for ensuring the safe alignment of large language models (LLMs). However, current RM evaluation methods focus solely on preference percept…
PCA++: How Uniformity Induces Robustness to Background Noise in Contrastive Learning
Mingqi Wu, Qiang Sun, Yi Yang
High-dimensional data often contain low-dimensional signals obscured by structured background noise, which limits the effectiveness of standard PCA. Motivated by contrastive learni…
C3-OWD: A Curriculum Cross-modal Contrastive Learning Framework for Open-World Detection
Siheng Wang, Zhengdao Li, Yanshu Li +12
Object detection has advanced significantly in the closed-set setting, but real-world deployment remains limited by two challenges: poor generalization to unseen categories and ins…
Optimization-Induced Dynamics of Lipschitz Continuity in Neural Networks
Róisín Luo, James McDermott, Christian Gagné +2
Lipschitz continuity characterizes the worst-case sensitivity of neural networks to small input perturbations; yet its dynamics (i.e. temporal evolution) during training remains un…
Generative Market Equilibrium Models with Stable Adversarial Learning via Reinforcement
Anastasis Kratsios, Xiaofei Shi, Qiang Sun +1
We present a general computational framework for solving continuous-time financial market equilibria under minimal modeling assumptions while incorporating realistic financial fric…
On Active Privacy Auditing in Supervised Fine-tuning for White-Box Language Models
Qian Sun, Hanpeng Wu, Xi Sheryl Zhang
The pretraining and fine-tuning approach has become the leading technique for various NLP applications. However, recent studies reveal that fine-tuning data, due to their sensitive…
Cross-Modal Few-Shot Learning: a Generative Transfer Learning Framework
Zhengwei Yang, Yuke Li, Qiang Sun +3
Most existing studies on few-shot learning focus on unimodal settings, where models are trained to generalize to unseen data using a limited amount of labeled examples from a singl…
Ensemble linear interpolators: The role of ensembling
Mingqi Wu, Qiang Sun
Interpolators are unstable. For example, the mininum norm least square interpolator exhibits unbounded test errors when dealing with noisy data. In this paper, we study ho…