{"total":30,"rows":[{"id":"2206.04030","title":"High-dimensional limit theorems for SGD: Effective dynamics and critical scaling","created":"2022-06-08","cited_by_count":70,"area":null},{"id":"2102.10346","title":"Convergence Rates of Stochastic Gradient Descent under Infinite Noise Variance","created":"2021-02-20","cited_by_count":35,"area":null},{"id":"2106.03156","title":"Fast and Robust Online Inference with Stochastic Gradient Descent via Random Scaling","created":"2021-06-06","cited_by_count":28,"area":null},{"id":"2405.16644","title":"Gaussian Approximation and Multiplier Bootstrap for Polyak-Ruppert Averaged Linear Stochastic Approximation with Applications to TD Learning","created":"2024-05-26","cited_by_count":19,"area":null},{"id":"2401.15719","title":"Rates of Convergence in the Central Limit Theorem for Markov Chains, with an Application to TD Learning","created":"2024-01-28","cited_by_count":17,"area":null},{"id":"2401.09346","title":"High Confidence Level Inference is Almost Free using Parallel Stochastic Optimization","created":"2024-01-17","cited_by_count":10,"area":null},{"id":"2502.06719","title":"Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent","created":"2025-02-10","cited_by_count":10,"area":null},{"id":"2307.06915","title":"Weighted Averaged Stochastic Gradient Descent: Asymptotic Normality and Optimality","created":"2023-07-13","cited_by_count":8,"area":null},{"id":"2308.01481","title":"Online covariance estimation for stochastic gradient descent under Markovian sampling","created":"2023-08-03","cited_by_count":7,"area":null},{"id":"2102.05198","title":"Statistical Inference for Polyak-Ruppert Averaged Zeroth-order Stochastic Gradient Algorithm","created":"2021-02-10","cited_by_count":6,"area":null},{"id":"2304.00707","title":"High-dimensional scaling limits and fluctuations of online least-squares SGD with smooth covariance","created":"2023-04-03","cited_by_count":6,"area":null},{"id":"2502.09884","title":"Nonasymptotic CLT and Error Bounds for Linear Two-Time-Scale Stochastic Approximation","created":"2025-02-14","cited_by_count":6,"area":null},{"id":"2405.19463","title":"Stochastic Optimization Algorithms for Instrumental Variable Regression with Streaming Data","created":"2024-05-29","cited_by_count":4,"area":null},{"id":"2502.05305","title":"Online Covariance Estimation in Nonsmooth Stochastic Approximation","created":"2025-02-07","cited_by_count":4,"area":null},{"id":"2509.18964","title":"Central Limit Theorems for Asynchronous Averaged Q-Learning","created":"2025-09-23","cited_by_count":4,"area":null},{"id":"2510.12375","title":"Improved Central Limit Theorem and Bootstrap Approximations for Linear Stochastic Approximation","created":"2025-10-14","cited_by_count":4,"area":null},{"id":"2308.13564","title":"SGMM: Stochastic Approximation to Generalized Method of Moments","created":"2023-08-25","cited_by_count":3,"area":null},{"id":"2109.01326","title":"Statistical Estimation and Inference via Local SGD in Federated Learning","created":"2021-09-03","cited_by_count":2,"area":null},{"id":"2502.07584","title":"Generalization Bounds for Markov Algorithms through Entropy Flow Computations","created":"2025-02-11","cited_by_count":2,"area":null},{"id":"2505.17300","title":"Statistical Inference for Online Algorithms","created":"2025-05-22","cited_by_count":2,"area":null},{"id":"2509.14039","title":"On the Rate of Gaussian Approximation for Linear Regression Problems","created":"2025-09-17","cited_by_count":2,"area":null},{"id":"2106.02713","title":"Learning Curves for SGD on Structured Features","created":"2021-06-04","cited_by_count":1,"area":null},{"id":"2302.09727","title":"Statistical Inference for Linear Functionals of Online SGD in High-dimensional Linear Regression","created":"2023-02-20","cited_by_count":1,"area":null},{"id":"2505.08125","title":"Sharp Gaussian approximations for Decentralized Federated Learning","created":"2025-05-12","cited_by_count":1,"area":null},{"id":"2510.19734","title":"Statistical Inference for Linear Functionals of Online Least-squares SGD when $t \\gtrsim d^{1+\\delta}$","created":"2025-10-22","cited_by_count":1,"area":null},{"id":"2511.02258","title":"Limit Theorems for Stochastic Gradient Descent in High-Dimensional Single-Layer Networks","created":"2025-11-04","cited_by_count":1,"area":null},{"id":"2609.09480","title":"Gaussian Approximation in Wasserstein-p: Markovian Martingale Differences and Stochastic Approximation","created":"2026-09-08","cited_by_count":1,"area":null},{"id":"2310.11065","title":"Cheap Bootstrap for Fast Uncertainty Quantification of Stochastic Gradient Descent","created":"2023-10-17","cited_by_count":0,"area":null},{"id":"2312.09639","title":"Multiple Instance Learning for Uplift Modeling","created":"2023-12-15","cited_by_count":0,"area":null},{"id":"2606.00309","title":"Large-scale Uncertainty Quantification for Latent Variable Models Using Subsampling Markov Chain Monte Carlo","created":"2026-05-29","cited_by_count":0,"area":null}],"next_cursor":null}