Journal of Machine Learning Research : Jmlr
Overview
The Journal of Machine Learning Research (JMLR) is a leading open-access journal that publishes high-quality research papers in the field of machine learning. It covers a wide range of topics including algorithms, theory, applications, and methodologies. JMLR aims to foster the exchange of knowledge and ideas among researchers, practitioners, and students, making it a valuable resource for anyone interested in the advancements and innovations in machine learning.
Details
Details
Abbr.
J Mach Learn Res
Publisher
MIT Press
Start
2001
End
Continuing
Frequency
Eight times per year (Jan.1, Feb.15, Apr.1, May 15, July 1, Aug.15, Oct.1, Nov.15)
p-ISSN
1532-4435
e-ISSN
1533-7928
Country
United States
Language
English
Metrics
Metrics
h-index / Ranks: 312
261
SJR / Ranks: 690
2796
CiteScore / Ranks: 1607
9.50
JIF / Ranks: 1121
6.0
Recent Articles
1.
Wang M, Shen X, Pan W
J Mach Learn Res
. 2025 Jan;
25.
PMID: 39758585
This article presents a novel method for causal discovery with generalized structural equation models suited for analyzing diverse types of outcomes, including discrete, continuous, and mixed data. Causal discovery often...
2.
Chakrabarti A, Ni Y, Morris E, Salinas M, Chapkin R, Mallick B
J Mach Learn Res
. 2024 Dec;
25.
PMID: 39691937
We consider the problem of clustering grouped data with possibly non-exchangeable groups whose dependencies can be characterized by a known directed acyclic graph. To allow the sharing of clusters among...
3.
Kundu S, Lukemire J
J Mach Learn Res
. 2024 Dec;
25.
PMID: 39679282
Bayesian non-parametric methods based on Dirichlet process mixtures have seen tremendous success in various domains and are appealing in being able to borrow information by clustering samples that share identical...
4.
Li X, Sung A, Xie J
J Mach Learn Res
. 2024 Dec;
24.
PMID: 39669222
Multiple testing is a commonly used tool in modern data science. Sometimes, the hypotheses are embedded in a space; the distances between the hypotheses reflect their co-null/co-alternative patterns. Properly incorporating...
5.
Li C, Rudin C, McCormick T
J Mach Learn Res
. 2024 Nov;
23.
PMID: 39507403
Instrumental variables (IV) are widely used in the social and health sciences in situations where a researcher would like to measure a causal effect but cannot perform an experiment. For...
6.
Xu S, Strohmer T
J Mach Learn Res
. 2024 Oct;
24.
PMID: 39440315
As machine learning powered decision-making becomes increasingly important in our daily lives, it is imperative to strive for fairness in the underlying data processing. We propose a pre-processing algorithm for...
7.
Li X, Yu S, Wang Y, Wang G, Wang L, Lai M
J Mach Learn Res
. 2024 Oct;
25.
PMID: 39391298
In recent years, there has been an exponentially increased amount of point clouds collected with irregular shapes in various areas. Motivated by the importance of solid modeling for point clouds,...
8.
Chen S, Zheng Q, Long Q, Su W
J Mach Learn Res
. 2024 Aug;
24.
PMID: 39105110
A widely recognized difficulty in federated learning arises from the statistical heterogeneity among clients: local datasets often originate from distinct yet not entirely unrelated probability distributions, and personalization is, therefore,...
9.
Saengkyongam S, Pfister N, Klasnja P, Murphy S, Peters J
J Mach Learn Res
. 2024 Jul;
25.
PMID: 39082006
Policy learning is an important component of many real-world learning systems. A major challenge in policy learning is how to adapt efficiently to unseen environments or tasks. Recently, it has...
10.
Kucukyavuz S, Shojaie A, Manzour H, Wei L, Wu H
J Mach Learn Res
. 2024 Jul;
24.
PMID: 39027423
Bayesian Networks (BNs) represent conditional probability relations among a set of random variables (nodes) in the form of a directed acyclic graph (DAG), and have found diverse applications in knowledge...