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Charles Elkan
PeerJ Editor
1,240 Points

Contributions by role

Editor 1,240

Contributions by subject area

Data Mining and Machine Learning
Optimization Theory and Computation
Scientific Computing and Simulation
Artificial Intelligence
Data Science
Bioinformatics
Graphics
Algorithms and Analysis of Algorithms
Neural Networks
Natural Language and Speech
Text Mining
Sentiment Analysis
Computational Biology

Charles P. Elkan

PeerJ Editor

Summary

Professor of computer science at the University of California, San Diego, and also Amazon Fellow.

Artificial Intelligence Data Mining & Machine Learning Data Science Network Science & Online Social Networks World Wide Web & Web Science

Editorial Board Member

PeerJ Computer Science

Past or current institution affiliations

UC San Diego

Work details

Professor

University of California, San Diego
Computer Science and Engineering

Websites

  • Google Scholar
  • UCSD

PeerJ Contributions

  • Edited 8

Academic Editor on

January 30, 2025
Fast binary logistic regression
Nurdan Ayse Saran, Fatih Nar
https://doi.org/10.7717/peerj-cs.2579
October 18, 2024
Ability of clinical data to predict readmission in Child and Adolescent Mental Health Services
Kaban Koochakpour, Dipendra Pant, Odd Sverre Westbye, Thomas Brox Røst, Bennett Leventhal, Roman Koposov, Carolyn Clausen, Norbert Skokauskas, Øystein Nytrø
https://doi.org/10.7717/peerj-cs.2367
January 2, 2024
Disentangled self-attention neural network based on information sharing for click-through rate prediction
Yingqi Wang, Huiqin Ji, Xin He, Junyang Yu, Hongyu Han, Rui Zhai, Longge Wang
https://doi.org/10.7717/peerj-cs.1764
June 2, 2021
Model independent feature attributions: Shapley values that uncover non-linear dependencies
Daniel Vidali Fryer, Inga Strumke, Hien Nguyen
https://doi.org/10.7717/peerj-cs.582
May 18, 2020
Adaptive divergence for rapid adversarial optimization
Maxim Borisyak, Tatiana Gaintseva, Andrey Ustyuzhanin
https://doi.org/10.7717/peerj-cs.274
January 21, 2019
Analysis of cause-effect inference by comparing regression errors
Patrick Blöbaum, Dominik Janzing, Takashi Washio, Shohei Shimizu, Bernhard Schölkopf
https://doi.org/10.7717/peerj-cs.169
July 24, 2017
Accelerating the XGBoost algorithm using GPU computing
Rory Mitchell, Eibe Frank
https://doi.org/10.7717/peerj-cs.127
April 6, 2016
Probabilistic programming in Python using PyMC3
John Salvatier, Thomas V. Wiecki, Christopher Fonnesbeck
https://doi.org/10.7717/peerj-cs.55