4 citations · 7 across the 6 of their papers we have counts for
9 papers
Exact Endpoint Asymptotics and Almost-Sure Limits for Uniform Empirical Measures in Two Dimensions
Weitao Huang, Yichen Wu, Renzhen Wang +1
For the empirical measure of the first points of one infinite sequence of independent uniform samples in the unit square, we identify the leading constant in the bounded-Lipsch…
MAC-Splat: Multi-Attribute Consistency for High-Fidelity Sparse-View Reconstruction
Jinqian Yang, Yichen Wu, Wanhua Li +4
Reconstructing high-fidelity 3D scenes from sparse-views remains a central problem in generalizable neural rendering. Existing generalizable 3D Gaussian Splatting (3DGS) methods of…
Semi-Supervised Regression with Heteroscedastic Pseudo-Labels
Xueqing Sun, Renzhen Wang, Quanziang Wang +3
Pseudo-labeling is a commonly used paradigm in semi-supervised learning, yet its application to semi-supervised regression (SSR) remains relatively under-explored. Unlike classific…
Data-Distill-Net: A Data Distillation Approach Tailored for Reply-based Continual Learning
Wenyang Liao, Quanziang Wang, Yichen Wu +2
Replay-based continual learning (CL) methods assume that models trained on a small subset can also effectively minimize the empirical risk of the complete dataset. These methods ma…
Dual-CBA: Improving Online Continual Learning via Dual Continual Bias Adaptors from a Bi-level Optimization Perspective
Quanziang Wang, Renzhen Wang, Yichen Wu +3
In online continual learning (CL), models trained on changing distributions easily forget previously learned knowledge and bias toward newly received tasks. To address this issue,…
CBA: Improving Online Continual Learning via Continual Bias Adaptor
Quanziang Wang, Renzhen Wang, Yichen Wu +2
Online continual learning (CL) aims to learn new knowledge and consolidate previously learned knowledge from non-stationary data streams. Due to the time-varying training setting,…
Imbalanced Semi-supervised Learning with Bias Adaptive Classifier
Renzhen Wang, Xixi Jia, Quanziang Wang +2
Pseudo-labeling has proven to be a promising semi-supervised learning (SSL) paradigm. Existing pseudo-labeling methods commonly assume that the class distributions of training data…
Learning to generate imaginary tasks for improving generalization in meta-learning
Yichen Wu, Long-Kai Huang, Ying Wei
The success of meta-learning on existing benchmarks is predicated on the assumption that the distribution of meta-training tasks covers meta-testing tasks. Frequent violation of th…
Learning to Purify Noisy Labels via Meta Soft Label Corrector
Yichen Wu, Jun Shu, Qi Xie +2
Recent deep neural networks (DNNs) can easily overfit to biased training data with noisy labels. Label correction strategy is commonly used to alleviate this issue by designing a m…