The Journal of Machine Learning Research (JMLR) stands as a premier international platform dedicated to the dissemination of high-quality, peer-reviewed scholarly articles across the expansive field of machine learning. Established in 2000, JMLR has consistently fostered the growth and evolution of machine learning by providing a completely open-access forum for researchers and academics worldwide. This commitment to open access ensures that groundbreaking research is readily available to anyone, anywhere, furthering the collective knowledge and progress within the machine learning community.
JMLR is renowned for its rigorous yet efficient review process, ensuring both the quality and timely publication of accepted papers. Upon final acceptance, articles are immediately published in electronic format (ISSN 1533-7928), providing researchers with rapid access to the latest advancements in the field. Historically, until the close of 2004, JMLR also produced paper volumes (ISSN 1532-4435) eight times per year in partnership with MIT Press. Currently, paper volumes continue to be published and distributed by Microtome Publishing, catering to institutions and individuals who prefer print editions.
Latest Research Highlights
Stay ahead in the rapidly evolving landscape of machine learning with JMLR’s latest publications. Explore a diverse range of topics and methodologies pushing the boundaries of artificial intelligence and machine learning. Below are some recent examples of the innovative research featured in the Journal of Machine Learning Research:
- Random ReLU Neural Networks as Non-Gaussian Processes: Delve into the theoretical underpinnings of neural networks with this paper exploring the properties of Random ReLU networks.
- Riemannian Bilevel Optimization: Discover new optimization techniques on Riemannian manifolds for bilevel optimization problems.
- Supervised Learning with Evolving Tasks and Performance Guarantees: Investigate adaptive learning strategies in dynamic environments with performance guarantees.
- Error estimation and adaptive tuning for unregularized robust M-estimator: Learn about robust statistical methods for error estimation and adaptive tuning in high-dimensional settings.
- From Sparse to Dense Functional Data in High Dimensions: Revisiting Phase Transitions from a Non-Asymptotic Perspective: Explore the nuances of functional data analysis in high dimensions.
- Locally Private Causal Inference for Randomized Experiments: Examine methods for causal inference while preserving local privacy in randomized experiments.
- Estimating Network-Mediated Causal Effects via Principal Components Network Regression: Understand how network structures influence causal relationships through principal component regression.
- Selective Inference with Distributed Data: Explore techniques for selective inference in distributed data environments.
- Two-Timescale Gradient Descent Ascent Algorithms for Nonconvex Minimax Optimization: Investigate advanced optimization algorithms for nonconvex minimax problems.
- An Axiomatic Definition of Hierarchical Clustering: Gain insights into the theoretical foundations of hierarchical clustering through an axiomatic approach.
- Test-Time Training on Video Streams: Discover innovative methods for adapting models during test time in video stream analysis.
- Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback: Learn about efficient client sampling strategies in federated learning using bandit feedback.
- A Random Matrix Approach to Low-Multilinear-Rank Tensor Approximation: Explore tensor approximation methods using random matrix theory.
- Memory Gym: Towards Endless Tasks to Benchmark Memory Capabilities of Agents: Benchmark memory capabilities of intelligent agents with novel task environments.
- Enhancing Graph Representation Learning with Localized Topological Features: Improve graph representation learning by incorporating localized topological features.
- Deep Out-of-Distribution Uncertainty Quantification via Weight Entropy Maximization: Quantify uncertainty in deep learning models for out-of-distribution data.
- DisC2o-HD: Distributed causal inference with covariates shift for analyzing real-world high-dimensional data: Address challenges in distributed causal inference with covariate shift in high-dimensional data.
- Bayes Meets Bernstein at the Meta Level: an Analysis of Fast Rates in Meta-Learning with PAC-Bayes: Analyze fast learning rates in meta-learning using PAC-Bayes frameworks.
- Efficiently Escaping Saddle Points in Bilevel Optimization: Develop efficient algorithms to escape saddle points in bilevel optimization.
Explore the full list of published papers to delve deeper into the vast repository of machine learning knowledge offered by the Journal of Machine Learning Research.
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