Demo Data from EDBT 2026 / reviewers in the wild / expert
Zhuo-Song Zhang
dblp:378/6926
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2024
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
1 paper |
Reinforcement learning · 50% Optimization for machine learning · 25% Probabilistic and Bayesian machine learning · 25% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Probabilistic and Bayesian machine learning › distribution approximation
gaussian approximation |
0.8 | 1 | 2024 | Gaussian Approximation and Multiplier Bootstrap for Polyak-Ruppert Averaged Linear Stochastic Approximation with Applications to TD Learning · NeurIPS 2024 |
Machine learning › Reinforcement learning › function approximation
linear function approximation |
0.8 | 1 | 2024 | Gaussian Approximation and Multiplier Bootstrap for Polyak-Ruppert Averaged Linear Stochastic Approximation with Applications to TD Learning · NeurIPS 2024 |
Machine learning › Optimization for machine learning
stochastic approximation |
0.8 | 1 | 2024 | Gaussian Approximation and Multiplier Bootstrap for Polyak-Ruppert Averaged Linear Stochastic Approximation with Applications to TD Learning · NeurIPS 2024 |
Machine learning › Reinforcement learning
temporal difference learning |
0.8 | 1 | 2024 | Gaussian Approximation and Multiplier Bootstrap for Polyak-Ruppert Averaged Linear Stochastic Approximation with Applications to TD Learning · NeurIPS 2024 |
Methods — techniques the papers use, named apart from their topics
polyak-ruppert averaging · 0.8multiplier bootstrap · 0.8berry-esseen bound · 0.8
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Gaussian Approximation and Multiplier Bootstrap for Polyak-Ruppert Averaged Linear Stochastic Approximation with Applications to TD LearningabstractIn this paper, we obtain the Berry–Esseen bound for multivariate normal approximation for the Polyak-Ruppert averaged iterates of the linear stochastic approximation (LSA) algorithm with decreasing step size. Moreover, we prove the non-asymptotic validity of the confidence intervals for parameter estimation with LSA based on multiplier bootstrap. This procedure updates the LSA estimate together with a set of randomly perturbed LSA estimates upon the arrival of subsequent observations. We illustrate our findings in the setting of temporal difference learning with linear function approximation. Sergey Samsonov, Eric Moulines, Qi-Man Shao, Zhuo-Song Zhang, Alexey Naumov |
NeurIPS | 4 |