7 papers
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…
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…
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…
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…
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…
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…