Journal of Machine Learning Research

Official twitter account for the Journal of Machine Learning Research (JMLR)

[Journal of Machine Learning Research @JmlrOrg]

JMLR has recently launched a Special Issue on ML for addressing problems of climate change! We welcome all submissions which use machine learning to address problems of climate change, including mitigation, adaptation, and climate science. [1/4]

[Journal of Machine Learning Research @JmlrOrg]

'Exogenous Randomness Empowering Random Forests', by Tianxing Mei, Yingying Fan, Jinchi Lv.

https://t.co/vTA6tsjlzD

#forests #trees #tree

[Journal of Machine Learning Research @JmlrOrg]

'Kernel Mean Embedding Deviation Subspace for Unsupervised Learning with Heterogeneous Data', by Luoyao Yu, Lixing Zhu, Ruoqing Zhu, Xuehu Zhu.

https://t.co/dTVykJOG2K

#clustering #dimensional #embedding

[Journal of Machine Learning Research @JmlrOrg]

'Deep Nonparametric Conditional Independence Tests for Images', by Marco Simnacher, Xiangnan Xu, Hani Park, Christoph Lippert, Sonja Greven.

https://t.co/ZRwcR4pljf

#confounders #confounder #embedding

[Journal of Machine Learning Research @JmlrOrg]

'Semi-supervised learning for linear extremile regression', by Rong Jiang, Jiangfeng Wang, Keming Yu.

https://t.co/yY7pDWxQHn

#regression #nonparametric #quantile

[Journal of Machine Learning Research @JmlrOrg]

'Transfer Learning via Regularized Random-effects Linear Discriminant Analysis', by Hongzhe Zhang, Arnab Auddy, Hongzhe Li.

https://t.co/IYByaINJfl

#discriminant #classification #predictors

[Journal of Machine Learning Research @JmlrOrg]

'Cheap Bootstrap for Fast Uncertainty Quantification of Stochastic Gradient Descent', by Henry Lam, Zitong Wang.

https://t.co/PmRiRcXi2F

#sgd #resampling #sgds

[Journal of Machine Learning Research @JmlrOrg]

'A Natural Primal-Dual Hybrid Gradient Method for Adversarial Neural Network Training on Solving Partial Differential Equation', by Shu Liu, Stanley Osher, Wuchen Li.

https://t.co/rQSZL1ySsI

#gradient #optimizer #optimization

[Journal of Machine Learning Research @JmlrOrg]

'Generalized Resubstitution for Regression Error Estimation', by Diego Marcondes, Ulisses Braga-Neto.

https://t.co/O4AA3sLtIf

#estimators #estimator #regression

[Journal of Machine Learning Research @JmlrOrg]

'Transfer Conformal Predictive Inference for Regression', by Ce Zhang, Ting Li, Jinhan Xie, Linglong Kong, Bei Jiang.

https://t.co/WGmiY642H6

#prediction #regression #datasets

[Journal of Machine Learning Research @JmlrOrg]

'Towards Convexity in Anomaly Detection: A New Formulation of SSLM with Unique Optimal Solutions', by Hongying Liu, Hao Wang, Haoran Chu, Yibo Wu.

https://t.co/4Q3UFYI129

#svms #svm #hyperparameters

[Journal of Machine Learning Research @JmlrOrg]

'Node Regression on Latent Position Random Graphs via Local Averaging', by Martin Gjorgjevski, Nicolas Keriven, Simon Barthelme, Yohann De Castro.

https://t.co/un54LRIutq

#nodes #neighboring #node

[Journal of Machine Learning Research @JmlrOrg]

'On the Relevance of Byzantine Robust Optimization Against Data Poisoning', by Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot.

https://t.co/A2hrcHa5jE

#robustness #faults #datasets

[Journal of Machine Learning Research @JmlrOrg]

'Best Arm Identification with Minimal Regret', by Junwen Yang, Vincent Y. F. Tan, Tianyuan Jin.

https://t.co/RCp0hcncIQ

#bandit #regret #optimality

[Journal of Machine Learning Research @JmlrOrg]

'Convergence of Noise-Free Sampling Algorithms with Regularized Wasserstein Proximals', by Fuqun Han, Stanley Osher, Wuchen Li.

https://t.co/lYjSj8JnkB

#langevin #wasserstein #planck

[Journal of Machine Learning Research @JmlrOrg]

'Almost Sure Convergence of Linear Temporal Difference Learning with Arbitrary Features', by Jiuqi Wang, Shangtong Zhang.

https://t.co/ODD2I7gPqM

#reinforcement #prediction #approximation

[Journal of Machine Learning Research @JmlrOrg]

'Asymptotics of Stochastic Gradient Descent with Dropout Regularization in Linear Models', by Jiaqi Li, Johannes Schmidt-Hieber, Wei Biao Wu.

https://t.co/XSs4DeZ6jv

#sgd #dropout #asgd

[Journal of Machine Learning Research @JmlrOrg]

'Beyond Unconstrained Features: Neural Collapse for Shallow Neural Networks with General Data', by Wanli Hong, Shuyang Ling.

https://t.co/RXU921VYHh

#depth #deep #features

[Journal of Machine Learning Research @JmlrOrg]

'Differentially Private Estimation and Inference in High-Dimensional Regression with FDR Control', by Zhanrui Cai, Sai Li, Xintao Xia, Linjun Zhang.

https://t.co/a33olMX0X2

#privacy #private #predictors

[Journal of Machine Learning Research @JmlrOrg]

'Demographic Parity in Regression and Classification Within the Unawareness Framework', by Vincent Divol, Solenne Gaucher.

https://t.co/OhO2NVvLLO

#classifiers #classification #optimal

[Journal of Machine Learning Research @JmlrOrg]

'Enhancing Accuracy in Generative Models via Knowledge Transfer', by Xinyu Tian, Xiaotong Shen.

https://t.co/J9yCVhYcgS

#generative #leveraging #knowledge