Journal of Machine Learning Research

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]

'Reparameterized Complex-valued Neurons Can Efficiently Learn More than Real-valued Neurons via Gradient Descent', by Jin-Hui Wu, Shao-Qun Zhang, Yuan Jiang, Zhi-Hua Zhou. jmlr.org/papers/v27/25-… #neuron #complex #learns

'Hierarchical Causal Models', by Eli N. Weinstein, David M. Blei. jmlr.org/papers/v27/25-… #causal #hierarchical #inference

'Optimizing Attention with Mirror Descent: Generalized Max-Margin Token Selection', by Addison Kristanto Julistiono, Davoud Ataee Tarzanagh, Navid Azizan. jmlr.org/papers/v27/25-… #softmax #attention #svm

'Adaptive Forward Stepwise: A Method for High Sparsity Regression', by Ivy Zhang, Robert Tibshirani. jmlr.org/papers/v27/25-… #lasso #sparser #boosting

'Optimization and Generalization of Gradient Descent for Shallow ReLU Networks with Minimal Width', by Yunwen Lei, Puyu Wang, Yiming Ying, Ding-Xuan Zhou. jmlr.org/papers/v27/24-… #rademacher #optimal #generalization

'Finite Neural Networks as Mixtures of Gaussian Processes: From Provable Error Bounds to Prior Selection', by Steven Adams, Andrea Patanè, Morteza Lahijanian, Luca Laurenti. jmlr.org/papers/v27/24-… #gaussian #approximation #approximate

'CHANI: Correlation-based Hawkes Aggregation of Neurons with bio-Inspiration', by Sophie Jaffard, Samuel Vaiter, Patricia Reynaud-Bouret. jmlr.org/papers/v27/24-… #neuron #neurons #neuronal

'Persistence Diagrams Estimation of Multivariate Piecewise H{\"o}lder-continuous Signals', by Hugo Henneuse. jmlr.org/papers/v27/24-… #persistence #stability #estimation

'Exploring Novel Uncertainty Quantification through Forward Intensity Function Modeling', by Yudong Wang, Zhi-Sheng Ye, Cheng Yong Tang. jmlr.org/papers/v27/23-… #predicting #stochastic #predictions

'Generative Bayesian Inference with GANs', by Yuexi Wang, Veronika Rockova. jmlr.org/papers/v27/23-… #generative #gans #gan

'Communication-efficient Distributed Statistical Inference for Massive Data with Heterogeneous Auxiliary Information', by Miaomiao Yu, Zhongfeng Jiang, Jiaxuan Li, Yong Zhou. jmlr.org/papers/v27/23-… #heterogeneous #data #privacy

'Decorrelated Local Linear Estimator: Inference for Non-linear Effects in High-dimensional Additive Models', by Zijian Guo, Wei Yuan, Cunhui Zhang. jmlr.org/papers/v27/22-… #additive #regression #models

'Refined Risk Bounds for Unbounded Losses via Transductive Priors', by Jian Qian, Alexander Rakhlin, Nikita Zhivotovskiy. jmlr.org/papers/v27/25-… #optimal #priors #classification

'A Common Interface for Automatic Differentiation', by Guillaume Dalle, Adrian Hill. jmlr.org/papers/v27/25-… #differentiationinterface #differentiation #julia

'LazyDINO: Fast, Scalable, and Efficiently Amortized Bayesian Inversion via Structure-Exploiting and Surrogate-Driven Measure Transport', by Lianghao Cao, Joshua Chen, Michael Brennan, Thomas O'Leary-Roseberry, Youssef Marzouk, Omar Ghattas. jmlr.org/papers/v27/25-….

'The Distribution of Ridgeless Least Squares Interpolators', by Qiyang Han, Xiaocong Xu. jmlr.org/papers/v27/25-… #regularization #ridgeless #ridge

'Nonparametric Estimation of a Factorizable Density using Diffusion Models', by Hyeok Kyu Kwon, Dongha Kim, Ilsang Ohn, Minwoo Chae. jmlr.org/papers/v27/25-… #sparse #generative #diffusion

'Learning Bayesian Network Classifiers to Minimize Class Variable Parameters', by Shouta Sugahara, Koya Kato, James Cussens, Maomi Ueno. jmlr.org/papers/v27/24-… #classifier #bayesian #class

'Simulation-based Calibration of Uncertainty Intervals under Approximate Bayesian Estimation', by Terrance D. Savitsky, Julie Gershunskaya. jmlr.org/papers/v27/24-… #estimation #estimates #estimated

'An Anytime Algorithm for Good Arm Identification', by Marc Jourdan, Andrée Delahaye-Duriez, Clémence Réda. jmlr.org/papers/v27/24-… #bandits #sampling #adaptive