Wojciech Szpankowski

Wojciech Szpankowski

Purdue University

H-index: 52

North America-United States

About Wojciech Szpankowski

Wojciech Szpankowski, With an exceptional h-index of 52 and a recent h-index of 19 (since 2020), a distinguished researcher at Purdue University, specializes in the field of analysis of algorithms, information theory.

His recent articles reflect a diverse array of research interests and contributions to the field:

Oracle-Efficient Hybrid Online Learning with Unknown Distribution

Precise Regularized Minimax Regret with Unbounded Weights

Efficient Gradient Estimation of Variational Quantum Circuits with Lie Algebraic Symmetries

On the concentration of the maximum degree in the duplication-divergence models

Online Distribution Learning with Local Private Constraints

Towards a Unification of Logic and Information Theory

Online learning in dynamically changing environments

Learning functional distributions with private labels

Wojciech Szpankowski Information

University

Position

___

Citations(all)

9430

Citations(since 2020)

1431

Cited By

8770

hIndex(all)

52

hIndex(since 2020)

19

i10Index(all)

167

i10Index(since 2020)

40

Email

University Profile Page

Purdue University

Google Scholar

View Google Scholar Profile

Wojciech Szpankowski Skills & Research Interests

analysis of algorithms

information theory

Top articles of Wojciech Szpankowski

Title

Journal

Author(s)

Publication Date

Oracle-Efficient Hybrid Online Learning with Unknown Distribution

arXiv preprint arXiv:2401.15520

Changlong Wu

Jin Sima

Wojciech Szpankowski

2024/1/27

Precise Regularized Minimax Regret with Unbounded Weights

Proceedings of Machine Learning Research vol

Michael Drmota

Philippe Jacquet

Changlong Wu

Wojciech Szpankowski

2024

Efficient Gradient Estimation of Variational Quantum Circuits with Lie Algebraic Symmetries

arXiv preprint arXiv:2404.05108

Mohsen Heidari

Masih Mozakka

Wojciech Szpankowski

2024/4/7

On the concentration of the maximum degree in the duplication-divergence models

SIAM Journal on Discrete Mathematics

Alan M Frieze

Krzysztof Turowski

Wojciech Szpankowski

2024/3/31

Online Distribution Learning with Local Private Constraints

International Conference on Artificial Intelligence and Statistics (AISTATS)

Jin Sima

Changlong Wu

Olgica Milenkovic

Wojciech Szpankowski

2024/2/1

Towards a Unification of Logic and Information Theory

arXiv preprint arXiv:2301.10414

Luis A Lastras

Barry Trager

Jonathan Lenchner

Wojtek Szpankowski

Chai Wah Wu

...

2023/1/25

Online learning in dynamically changing environments

Changlong Wu

Ananth Grama

Wojciech Szpankowski

2023/1/31

Learning functional distributions with private labels

Changlong Wu

Yifan Wang

Ananth Grama

Wojciech Szpankowski

2023

Regret Bounds for Log-loss via Bayesian Algorithms

IEEE Transactions on Information Theory

Changlong Wu

Mohsen Heidari

Ananth Grama

Wojciech Szpankowski

2023/5

Quantum Shadow Gradient Descent for Quantum Learning

arXiv preprint arXiv:2310.06935

Mohsen Heidari

Mobasshir A Naved

Wenbo Xie

Arjun Jacob Grama

Wojciech Szpankowski

2023/10/10

Agnostic PAC Learning of -juntas Using -Polynomial Regression

Mohsen Heidari

Wojciech Szpankowski

2023/4/11

Analytic Information Theory: From Compression to Learning

Michael Drmota

Wojciech Szpankowski

2023/9/7

Learning k-qubit Quantum Operators via Pauli Decomposition

Mohsen Heidari

Wojciech Szpankowski

2023/4/11

Robust Online Classification: From Estimation to Denoising

arXiv preprint arXiv:2309.01698

Changlong Wu

Ananth Grama

Wojciech Szpankowski

2023/9/4

Precise minimax regret for logistic regression

Philippe Jacquet

Gil I Shamir

Wojciech Szpankowski

2022/6/26

Regret for Online Regression with General Log-Type Losses

Mohsen Heidari

Philippe Jacquet

Wojciech Szpankowski

2022

Statistical and computational thresholds for the planted k-densest sub-hypergraph problem

Luca Corinzia

Paolo Penna

Wojciech Szpankowski

Joachim Buhmann

2022/5/3

Data-derived weak universal consistency

Journal of Machine Learning Research

Narayana Santhanam

Venkatachalam Anantharam

Wojciech Szpankowski

2022

Precise regret bounds for log-loss via a truncated bayesian algorithm

Changlong Wu

Mohsen Heidari

Ananth Grama

Wojciech Szpankowski

2022/5/7

Expected worst case regret via stochastic sequential covering

Transactions on Machine Learning Research (TMLR)

Changlong Wu

Mohsen Heidari

Ananth Grama

Wojciech Szpankowski

2022/9/9

See List of Professors in Wojciech Szpankowski University(Purdue University)