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What is Learnable in Valiant's Theory of the Learnable? Learning Perturbations to Extrapolate Your LLM Byzantine-Robust Distributed Sparse Learning Revisited The Sample Complexity of Multiple Change Point Identification under Bandit Feedback A proximal gradient algorithm for composite log-concave sampling Model-based Bootstrap of Controlled Markov Chains Approximation of Maximally Monotone Operators : A Graph Convergence Perspective Posterior Contraction Rates for Sparse Kolmogorov-Arnold Networks in Anisotropic Besov Spaces MIST: Reliable Streaming Decision Trees for Online Class-Incremental Learning via McDiarmid Bound A Spectral Framework for Closed-Form Relative Density Estimation Fast Rates for Offline Contextual Bandits with Forward-KL Regularization under Single-Policy Concentrability Higher-Order Equilibrium Tracking for EM-Compressible Online Estimation Scaling Limits of Long-Context Transformers A Note on Non-Negative $L_1$-Approximating Polynomials Susceptibilities and Patterning: A Primer on Linear Response in Bayesian Learning Linear Response Estimators for Singular Statistical Models Statistical inference with belief functions: A survey Robust stochastic first order methods in heavy-tailed noise via medoid mini-batch gradient sampling Every Feedforward Neural Network Definable in an o-Minimal Structure Has Finite Sample Complexity Adaptive auditing of AI systems with anytime-valid guarantees Locally Near Optimal Piecewise Linear Regression in High Dimensions via Difference of Max-Affine Functions Risk-Controlled Post-Processing of Decision Policies Covariate Balancing and Riesz Regression Should Be Guided by the Neyman Orthogonal Score in Debiased Machine Learning A Unified Pair-GRPO Family: From Implicit to Explicit Preference Constraints for Stable and General RL Alignment Time-Inhomogeneous Preconditioned Langevin Dynamics A Fine-Grained Understanding of Uniform Convergence for Halfspaces CITE: Anytime-Valid Statistical Inference in LLM Self-Consistency Ratio-based Loss Functions Optimal Confidence Band for Kernel Gradient Flow Estimator A renormalization-group inspired lattice-based framework for piecewise generalized linear models Direct Estimation of Schrödinger Bridge Time-Series Drifts: Finite-Sample, Asymptotic, and Adaptive Guarantees Information-theoretic Limits of Learning and Estimation Adaptivity Under Realizability Constraints: Comparing In-Context and Agentic Learning Multiscale Euclidean Network Trajectories: Second-Moment Geometry, Attribution, and Change Points Causal discovery under mean independence and linearity Perturbation is All You Need for Extrapolating Language Models Realizable Bayes-Consistency for General Metric Losses Vanishing L2 regularization for the softmax Multi Armed Bandit Imbalanced Classification under Capacity Constraints Intrinsic effective sample size for manifold-valued Markov chain Monte Carlo via kernel discrepancy On the Optimal Sample Complexity of Offline Multi-Armed Bandits with KL Regularization Extrapolation in Statistical Learning with Extreme Value Theory Adaptive Estimation and Inference in Semi-parametric Heterogeneous Clustered Multitask Learning via Neyman Orthogonality Beyond ECE: Calibrated Size Ratio, Risk Assessment, and Confidence-Weighted Metrics Self-Normalized Martingales and Uniform Regret Bounds for Linear Regression Mean Testing under Truncation beyond Gaussian Decoupled Descent: Exact Test Error Tracking Via Approximate Message Passing Hyper Input Convex Neural Networks for Shape Constrained Learning and Optimal Transport Observable Neural ODEs for Identifiable Causal Forecasting in Continuous Time Elite-Driven Support Vector Machines for Classification A Limit Theory of Foundation Models: A Mathematical Approach to Understanding Emergent Intelligence and Scaling Laws Learning Curves and Benign Overfitting of Spectral Algorithms in Large Dimensions Concave Statistical Utility Maximization Bandits via Influence-Function Gradients The Sample Complexity of Multicalibration Cover meets Robbins while Betting on Bounded Data: $\ln n$ Regret and Almost Sure $\ln\ln n$ Regret Achieving the Kesten-Stigum bound in the non-uniform hypergraph stochastic block model On two ways to use determinantal point processes for Monte Carlo integration Recovery Guarantees for Continual Learning of Dependent Tasks: Memory, Data-Dependent Regularization, and Data-Dependent Weights Structural interpretability in SVMs with truncated orthogonal polynomial kernels Cloning is as Hard as Learning for Stabilizer States Ordinary Least Squares is a Special Case of Transformer Identifiability of Potentially Degenerate Gaussian Mixture Models With Piecewise Affine Mixing NetworkNet: A Deep Neural Network Approach for Random Networks with Sparse Nodal Attributes and Complex Nodal Heterogeneity ADD for Multi-Bit Image Watermarking Cost-optimal Sequential Testing via Doubly Robust Q-learning Query Lower Bounds for Diffusion Sampling Tail-Aware Information-Theoretic Generalization for RLHF and SGLD Spatio-temporal probabilistic forecast using MMAF-guided learning The Geometry of Knowing: From Possibilistic Ignorance to Probabilistic Certainty -- A Measure-Theoretic Framework for Epistemic Convergence Generalization Properties of Score-matching Diffusion Models for Intrinsically Low-dimensional Data Conformal Policy Control Continuous-time reinforcement learning: ellipticity enables model-free value function approximation High-accuracy sampling for diffusion models and log-concave distributions Analyzing Shapley Additive Explanations to Understand Anomaly Detection Algorithm Behaviors and Their Complementarity Optimal Lower Bounds for Online Multicalibration Understanding Overparametrization in Survival Models through Interpolation Eventually LIL Regret: Almost Sure $\ln\ln T$ Regret for a sub-Gaussian Mixture on Unbounded Data Limit Theorems for Stochastic Gradient Descent in High-Dimensional Single-Layer Networks Optimal In-context Adaptivity and Distributional Robustness of Transformers Don't Pass@k: A Bayesian Framework for Large Language Model Evaluation The Good, the Bad, and the Sampled: a No-Regret Approach to Safe Online Classification GOSPA and T-GOSPA quasi-metrics for evaluation of multi-object tracking algorithms A note on the unique properties of the Kullback--Leibler divergence for sampling via gradient flows Multi-Armed Bandits With Machine Learning-Generated Surrogate Rewards Efficient compression of neural networks and datasets Out-of-Distribution Generalization of In-Context Learning: A Low-Dimensional Subspace Perspective Super-fast Rates of Convergence for Neural Network Classifiers under the Hard Margin Condition Sharp Gaussian approximations for Decentralized Federated Learning Learning Operators by Regularized Stochastic Gradient Descent with Operator-valued Kernels Smoothed Analysis of Learning from Positive Samples Statistical Impossibility and Possibility of Aligning LLMs with Human Preferences: From Condorcet Paradox to Nash Equilibrium Sharp Risk Bounds for Early-Stopping in Gaussian Linear Regression Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent Copula-enhanced Vision Transformer for high myopia diagnosis through OU UWF fundus images General Frameworks for Conditional Two-Sample Testing Improved Hardness Results for Learning Intersections of Halfspaces Consistency of Lloyd's Algorithm Under Perturbations Convergence Rates for Non-Log-Concave Sampling and Log-Partition Estimation Distribution-Free Stochastic Analysis and Robust Multilevel Vector Field Anomaly Detection Efficient Parameter Estimation of Truncated Boolean Product Distributions
A Conversation with Yuan Shih Chow
Zhiliang Ying, Cun-Hui Zhang · 2006-06-22 · via math.ST updates on arXiv.org

Yuan Shih Chow was born in Hubei province in China, on September 1, 1924. The eldest child of a local militia and political leader, he grew up in war and turmoil. His hometown was on the front line during most of the Japanese invasion and occupation of China. When he was 16, Y. S. Chow journeyed, mostly on foot, to Chongqing (Chung-King), the wartime Chinese capital, to finish his high school education. When the Communist party gained power in China, Y. S. Chow had already followed his university job to Taiwan. In Taiwan, he taught mathematics as an assistant at National Taiwan University until he came to the United States in 1954. At the University of Illinois, he studied under J. L. Doob and received his Ph.D. in 1958. He served as a staff mathematician and adjunct faculty at the IBM Watson Research Laboratory and Columbia University from 1959 to 1962. He was a member of the Statistics Department at Purdue University from 1962 to 1968. From 1968 until his retirement in 1993, Y. S. Chow served as Professor of Mathematical Statistics at Columbia University. At different times, he was a visiting professor at the University of California at Berkeley, University of Heidelberg (Germany) and the National Central University, Taiwan. He served as Director of the Institute of Mathematics of Academia Sinica, Taiwan, and Director of the Center of Applied Statistics at Nankai University, Tianjin, China. He was instrumental in establishing the Institute of Statistics of Academia Sinica in Taiwan. He is currently Professor Emeritus at Columbia University. Y. S. Chow is a fellow of the Institute of Mathematical Statistics, a member of the International Statistical Institute and a member of Taiwan's Academia Sinica. He has numerous publications, including Great Expectations: The Theory of Optimal Stopping (1971), in collaboration with Herbert Robbins and David Siegmund, and Probability Theory (1978), in collaboration with Henry Teicher. Y. S. Chow has a strong interest in mathematics education. He taught high school mathematics for one year in 1947 and wrote a book on high school algebra in collaboration with J. H. Teng and M. L. Chu. In 1992, Y. S. Chow, together with I. S. Chang and W. C. Ho, established the Chinese Institute of Probability and Statistics in Taiwan. This conversation took place in the fall of 2003 in Dobbs Ferry, New York.