5 citations · 9 across the 10 of their papers we have counts for
10 papers
Variational Goal-Oriented Optimal Experimental Design for Mixed-Distribution Quantities of Interest: Application to Ship Roll Safety
Chen Cheng, Xun Huan, Yulin Pan
Goal-oriented optimal experimental design (GO-OED) selects experiments according to the expected information gain (EIG) about a quantity of interest (QoI) rather than the full para…
Is Memorization Helpful or Harmful? Prior Information Sets the Threshold
Chen Cheng, Rina Foygel Barber
We examine the connection between training error and generalization error for arbitrary estimating procedures, working in an overparameterized linear model under general priors in…
Causal-Driven Feature Evaluation for Cross-Domain Image Classification
Chen Cheng, Ang Li
Out-of-distribution (OOD) generalization remains a fundamental challenge in real-world classification, where test distributions often differ substantially from training data. Most…
Concentration Inequalities for Exchangeable Tensors and Matrix-valued Data
Chen Cheng, Rina Foygel Barber
We study concentration inequalities for structured weighted sums of random data, including (i) tensor inner products and (ii) sequential matrix sums. We are interested in tail boun…
Optimal Stopping for Sequential Bayesian Experimental Design
Chen Cheng, Xun Huan
Sequential Bayesian experimental design is often formulated as a fixed-horizon policy optimization problem, in which the number of experiments is specified before data collection b…
Some Robustness Properties of Label Cleaning
Chen Cheng, John Duchi
We demonstrate that learning procedures that rely on aggregated labels, e.g., label information distilled from noisy responses, enjoy robustness properties impossible without data…
State evolution beyond first-order methods I: Rigorous predictions and finite-sample guarantees
Michael Celentano, Chen Cheng, Ashwin Pananjady +1
We develop a toolbox for exact analysis of iterative algorithms on a class of high-dimensional nonconvex optimization problems with random data. While prior work has shown that low…
Exploring the Robustness of In-Context Learning with Noisy Labels
Chen Cheng, Xinzhi Yu, Haodong Wen +4
Recently, the mysterious In-Context Learning (ICL) ability exhibited by Transformer architectures, especially in large language models (LLMs), has sparked significant research inte…
PLReMix: Combating Noisy Labels with Pseudo-Label Relaxed Contrastive Representation Learning
Xiaoyu Liu, Beitong Zhou, Zuogong Yue +1
Recently, the usage of Contrastive Representation Learning (CRL) as a pre-training technique improves the performance of learning with noisy labels (LNL) methods. However, instead…
CUCL: Codebook for Unsupervised Continual Learning
Chen Cheng, Jingkuan Song, Xiaosu Zhu +3
The focus of this study is on Unsupervised Continual Learning (UCL), as it presents an alternative to Supervised Continual Learning which needs high-quality manual labeled data. Th…