39th Conference on Uncertainty in Artificial Intelligence

Top Research Topics at Uncertainty in Artificial Intelligence?

• Artificial intelligence (36.99%)
• Machine learning (20.90%)
• Algorithm (19.10%)

The discussions in the conference mainly cover the fields of Artificial intelligence, Machine learning, Algorithm, Mathematical optimization and Inference. Topics in Artificial intelligence were tackled in line with various other fields like Data mining and Pattern recognition. The research on Algorithm featured in it combines topics in other fields like Graphical model, Set (abstract data type) and Markov chain.

The Mathematical optimization study tackled is a key component of adjacent topics in the area of Markov decision process. Most of the works presented in the event deals with Inference but it intersects with the subject of Theoretical computer science. The study on Bayesian network presented is investigated in conjunction with research in Variable-order Bayesian network.

What are the most cited papers published at the conference?

• Empirical analysis of predictive algorithms for collaborative filtering (4440 citations)
• BPR: Bayesian personalized ranking from implicit feedback (2760 citations)
• Estimating continuous distributions in Bayesian classifiers (2509 citations)

Research areas of the most cited articles at Uncertainty in Artificial Intelligence:

The published papers explore disciplines such as Artificial intelligence, Machine learning, Bayesian network, Algorithm and Mathematical optimization. The studies on Artificial intelligence discussed at the conference articles can also contribute to research in the domains of Data mining and Pattern recognition. While Machine learning is the focus of the published papers, it also provides insights into the studies of Structure (mathematical logic), Representation (mathematics) and Bayes' theorem.

What topics the last edition of the conference is best known for?

• Artificial intelligence
• Statistics
• Machine learning

The previous edition focused in particular on these issues:

The scientific interests tackled in the conference are Artificial intelligence, Algorithm, Machine learning, Inference and Artificial neural network. The study on Artificial intelligence presented in the conference intersects with the topics under Pattern recognition. Uncertainty in Artificial Intelligence facilitates discussions on Algorithm that incorporate concepts from other fields like Particle filter, Estimator, State space and Generalization.

The conference explores topics in Machine learning which can be helpful for research in disciplines like Domain (software engineering), Representation (mathematics), Task (project management), Bayesian probability and Benchmark (computing). In addition to Inference research, Uncertainty in Artificial Intelligence aims to explore topics under Gaussian process and Bayesian inference. Issues in Reinforcement learning were discussed, taking into consideration concepts from other disciplines like Context (language use) and Mathematical optimization.

The most cited articles from the last conference are:

• Variational Refinement for Importance Sampling Using the Forward Kullback-Leibler Divergence (5 citations)
• Distribution-free uncertainty quantification for classification under label shift (3 citations)
• Robust Reinforcement Learning Under Minimax Regret for Green Security (3 citations)

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.

Top authors and change over time

The top authors publishing at Uncertainty in Artificial Intelligence (based on the number of publications) are:

• David Heckerman (57 papers) absent at the last edition,
• Judea Pearl (43 papers) absent at the last edition,
• Eric Horvitz (34 papers) absent at the last edition,
• Daphne Koller (34 papers) absent at the last edition,
• Ross D. Shachter (32 papers) absent at the last 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.

Top affiliations and change over time

Only papers with recognized affiliations are considered

The top affiliations publishing at Uncertainty in Artificial Intelligence (based on the number of publications) are:

• Stanford University (180 papers) published 4 papers at the last edition the same number as at the previous edition,
• Carnegie Mellon University (153 papers) published 10 papers at the last edition, 6 more than at the previous edition,
• Microsoft (126 papers) published 9 papers at the last edition, 7 more than at the previous edition,
• University of California, Los Angeles (89 papers) published 3 papers at the last edition the same number as at the previous edition,
• University of California, Berkeley (87 papers) published 6 papers at the last edition, 3 more 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, 2.44% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 23.00% were posted by at least one author from the top 10 institutions publishing at the conference. Another 13.00% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 16.50% of all publications and 47.50% 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.