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Proceedings of Machine Learning Research

The 39th International Conference on Machine Learning (ICML)

Location: Baltimore , United States

Submission deadline: 1/27/2022

Conference dates: 7/17/2022 - 7/23/2022

Research H-index
106

Ranking & Metrics

Discipline name Position Best Scientists Publications D-Index
Computer Science 9 834 1306 106

Call for Papers

Topics of interest include (but are not limited to):

General Machine Learning (active learning, clustering, online learning, ranking, reinforcement learning, supervised, semi- and self-supervised learning, time series analysis, etc.)

Deep Learning (architectures, generative models, deep reinforcement learning, etc.)

Learning Theory (bandits, game theory, statistical learning theory, etc.)

Optimization (convex and non-convex optimization, matrix/tensor methods, stochastic, online, non-smooth, composite, etc.)

Probabilistic Inference (Bayesian methods, graphical models, Monte Carlo methods, etc.)

Trustworthy Machine Learning (accountability, causality, fairness, privacy, robustness, etc.)

Applications (computational biology, crowdsourcing, healthcare, neuroscience, social good, climate science, etc.)

Overview

This page presents the authoritative ranking of scientific conferences in the field of Computer Science, meticulously compiled by Research.com, a leading platform providing trusted data and analytics on scientific research contributions across all major disciplines since 2014. The ranking is founded on a proprietary bibliometric score developed by Research.com experts, which integrates the estimated h-index and the presence of leading scientists who have contributed to the conference over the past three years.

To ensure the utmost precision and credibility, the ranking process involved a rigorous examination of more than 2,742 conferences, each selected through detailed analysis. Over 148,739 scientific documents published in the last three years were thoroughly reviewed, reflecting the work of 13,184 prominent and widely-recognized scientists in the domain of Computer Science. This extensive effort demonstrates the exceptional depth of research and expert evaluation informing these results.

The Impact Score values provided in the ranking were collected as of 2024-11-27, guaranteeing that the information reflects the most up-to-date academic outputs and influences. The unique and comprehensive methodology used for score computation ensures a fair and objective assessment of conferences, representing a reliable resource for researchers, institutions, and policymakers in the scientific community.

For a detailed explanation of the procedures and criteria involved in calculating these ranking scores, please refer to our Methodology Page.

Papers citation over time

A key indicator for each conference is its effectiveness in reaching other researchers with the papers published at that venue.

The chart below presents the interquartile range (first quartile 25%, median 50% and third quartile 75%) of the number of citations of articles over time.

The top authors publishing at International Conference on Machine Learning (based on the number of publications) are:

  • Michael I. Jordan (65 papers) published 3 papers at the last edition, 6 less than at the previous edition,
  • Masashi Sugiyama (53 papers) published 14 papers at the last edition, 2 more than at the previous edition,
  • Zoubin Ghahramani (51 papers) absent at the last edition,
  • Lawrence Carin (51 papers) absent at the last edition,
  • Shie Mannor (48 papers) published 5 papers at the last edition, 3 more than at the previous edition.

The overall trend for top authors publishing at this conference is outlined below. The chart shows the number of publications at each edition of the conference for top authors.

Only papers with recognized affiliations are considered

The top affiliations publishing at International Conference on Machine Learning (based on the number of publications) are:

  • Google (582 papers) published 129 papers at the last edition, 3 more than at the previous edition,
  • Carnegie Mellon University (485 papers) published 62 papers at the last edition, 11 more than at the previous edition,
  • Stanford University (389 papers) published 70 papers at the last edition, 2 more than at the previous edition,
  • Microsoft (383 papers) published 62 papers at the last edition, 6 more than at the previous edition,
  • University of California, Berkeley (362 papers) published 66 papers at the last edition, 8 less than at the previous edition.

The overall trend for top affiliations publishing at this conference is outlined below. The chart shows the number of publications at each edition of the conference for top affiliations.

Publication chance based on affiliation

The publication chance index shows the ratio of articles published by the best research institutions at the conference edition to all articles published within that conference. The best research institutions were selected based on the largest number of articles published during all editions of the conference.

The chart below presents the percentage ratio of articles from top institutions (based on their ranking of total papers).Top affiliations were grouped by their rank into the following tiers: top 1-10, top 11-20, top 21-50, and top 51+. Only articles with a recognized affiliation are considered.

During the most recent 2021 edition, 3.47% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 34.58% were posted by at least one author from the top 10 institutions publishing at the conference. Another 14.14% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 20.15% of all publications and 31.14% were from other institutions.

Returning Authors Index

A very common phenomenon observed among researchers publishing scientific articles is the intentional selection of conferences they have already attended in the past. In particular, it is worth analyzing the case when the authors participate in the same conference from year to year.

The Returning Authors Index presented below illustrates the ratio of authors who participated in both a given as well as the previous edition of the conference in relation to all participants in a given year.

Returning Institution Index

The graph below shows the Returning Institution Index, illustrating the ratio of institutions that participated in both a given and the previous edition of the conference in relation to all affiliations present in a given year.

The experience to innovation index

Our experience to innovation index was created to show a cross-section of the experience level of authors publishing at a conference. The index includes the authors publishing at the last edition of a conference, grouped by total number of publications throughout their academic career (P) and the total number of citations of these publications ever received (C).

The group intervals were selected empirically to best show the diversity of the authors' experiences, their labels were selected as a convenience, not as judgment. The authors were divided into the following groups:

  • Novice - P < 5 or C < 25 (the number of publications less than 5 or the number of citations less than 25),
  • Competent - P < 10 or C < 100 (the number of publications less than 10 or the number of citations less than 100),
  • Experienced - P < 25 or C < 625 (the number of publications less than 25 or the number of citations less than 625),
  • Master - P < 50 or C < 2500 (the number of publications less than 50 or the number of citations less than 2500),
  • Star - P ≥ 50 and C ≥ 2500 (both the number of publications greater than 50 and the number of citations greater than 2500).

The chart below illustrates experience levels of first authors in cases of publications with multiple authors.

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