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Journal Photo for Proceedings of Machine Learning Research
Peer reviewed only Open Access

Proceedings of Machine Learning Research (PMLR)

Machine Learning
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e-ISSN 2640-3498
p-ISSN 1938-7228
Issue Frequency Monthly
Est. Year 2017
Country United States
Language English
APC YES
Impact Factor Assignee Google Scholar
Email proceedings@mlr.press

Journal Descriptions

Proceedings of Machine Learning Research (PMLR) is :-

  • International, Peer-Reviewed, Open Access, Refereed, Machine Learning , Online or Print , Monthly Journal

  • UGC Approved, ISSN Approved: P-ISSN P-ISSN: 1938-7228, E-ISSN: 2640-3498, Established: 2017,
  • Does Not Provide Crossref DOI

  • Indexed in: PubMed

  • Not indexed in Scopus, WoS, DOAJ, UGC CARE

Indexing

Publications of PMLR

DBGDGM: Dynamic Brain Graph Deep Generative Model
Pietro Liò December, 2023
Graphs are a natural representation of brain activity derived from functional magnetic imaging (fMRI) data. It is well known that clusters of anatomical brain regions, known as functional co...
Bridging Equational Properties and Patterns on Graphs: an AI-Based Approach
Pietro Liò July, 2023
AI-assisted solutions have recently proven successful when applied to Mathematics and have opened new possibilities for exploring unsolved problems that have eluded traditional approaches fo...
Graph classification Gaussian processes via spectral features
Pietro Liò September, 2023
Graph classification aims to categorise graphs based on their structure and node attributes. In this work, we propose to tackle this task using tools from graph signal processing by deriving...
APPROXIMATE EQUIVARIANCE SO(3) NEEDLET CONVOLUTION
Pietro Liò November, 2022
This paper develops a rotation-invariant needlet convolution for rotation group SO(3) to distill multiscale information of spherical signals. The spherical needlet transform is generalized f...
Sheaf Neural Networks with Connection Laplacians
Pietro Liò November, 2022
A Sheaf Neural Network (SNN) is a type of Graph Neural Network (GNN) that operates on a sheaf, an object that equips a graph with vector spaces over its nodes and edges and linear maps betwe...
DBGSL: Dynamic Brain Graph Structure Learning
Pietro Liò August, 2023
Recently, graph neural networks (GNNs) have shown success at learning representations of brain graphs derived from functional magnetic resonance imaging (fMRI) data. The majority of existing...
Attentional Meta-learners for Few-shot Polythetic Classification
Pietro Liò July, 2022
Polythetic classifications, based on shared patterns of features that need neither be universal nor constant among members of a class, are common in the natural world and greatly outnumber m...
Bayesian Link Prediction with Deep Graph Convolutional Gaussian Processes
Pietro Liò July, 2022
Link prediction aims to reveal missing edges in a graph. We introduce a deep graph convolutional Gaussian process model for this task, which addresses recent challenges in graph machine lear...
Early Exit Ensembles for Uncertainty Quantification
Pietro Liò December, 2021
Deep learning is increasingly used for decision-making in health applications. However, commonly used deep learning models are deterministic and are unable to provide any estimate of predict...