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]

'Approximations and Learning for Continuous State and Action MDPs under Average Cost Criteria', by Ali D. Kara, Serdar Yüksel. jmlr.org/papers/v27/24-… #quantization #quantized #discretization

'Optimal Approximation and Generalization Errors for Deep Convolutional Neural Networks', by Jinxin Wang, Shao-Bo Lin. jmlr.org/papers/v27/24-… #convolutional #deep #depth

'Investigating the Histogram Loss in Regression', by Ehsan Imani, Kai Luedemann, Sam Scholnick-Hughes, Esraa Elelimy, Martha White. jmlr.org/papers/v27/24-… #prediction #classification #hyperparameter

'Bayes-Optimal Fair Classification with Linear Disparity Constraints via Pre-, In-, and Post-processing', by Xianli Zeng, Kevin Jiang, Guang Cheng, Edgar Dobriban. jmlr.org/papers/v27/24-… #classifiers #fairness #classifier

'Why "Classic" Transformers Are Shallow and A Depth-Enabling Technique', by Yueyao Yu, Yin Zhang. jmlr.org/papers/v27/24-… #attention #transformers #deeper

'Kernel-based Distributed Learning', by Heng Lian, Xu Guo. jmlr.org/papers/v27/23-… #loss #losses #convex

'A Convex Framework for Confounding Robust Inference', by Kei Ishikawa, Niao He, Takafumi Kanamori. jmlr.org/papers/v27/23-… #bandits #estimation #confounders

'Global Fr{\'{e}}chet Manifold Learning for Random Objects, With Application to Low-Dimensional Wasserstein Representations of Distributional Data', by Álvaro Gajardo, Hans-Georg Müller. jmlr.org/papers/v27/23-… #distributional #multidimensional #dimensio

'Probabilistic Rainfall Downscaling: Joint Generalized Neural Models with Censored Spatial Gaussian Copula', by David Huk, Rilwan A. Adewoyin, Ritabrata Dutta. jmlr.org/papers/v27/23-… #rainfall #copula #copulas

'Sparse Topic Modeling via Spectral Decomposition and Thresholding', by Huy Tran, Yating Liu, Claire Donnat. jmlr.org/papers/v27/23-… #vocabulary #semantic #vocabularies

'Nonparametric generative modeling for time series via Schr{\"{o}}dinger bridge', by Mohamed Hamdouche, Pierre Henry-Labordère, Huyên Pham. jmlr.org/papers/v27/23-… #autoregressive #schrödinger #brownian

'Do We Need to Penalize Variance of Losses for Learning with Label Noise?', by Yexiong Lin, Yu Yao, Yuxuan Du, Jun Yu, Bo Han, Mingming Gong, Tongliang Liu. jmlr.org/papers/v27/23-… #labels #regularizers #label

'Causal Influences over Social Learning Networks', by Mert Kayaalp, Ali H. Sayed. jmlr.org/papers/v27/23-… #influence #causal #social

'Neural Exploitation and Exploration of Contextual Bandits', by Yikun Ban, Yuchen Yan, Arindam Banerjee, Jingrui He. jmlr.org/papers/v27/23-… #bandits #bandit #exploration

'Knowledge Cascade: Reverse Knowledge Distillation on Nonparametric Multivariate Functional Estimation', by Luyang Fang, Haoran Lu, Yongkai Chen, Wenxuan Zhong, Ping Ma. jmlr.org/papers/v27/23-… #models #nonparametric #knowledge

'Inference with non-differentiable surrogate loss in a general high-dimensional classification framework', by Muxuan Liang, Yang Ning, Maureen A Smith, Ying-Qi Zhao. jmlr.org/papers/v27/23-… #hessian #penalized #classification

'A Functional-Space Mean-Field Theory of Partially-Trained Three-Layer Neural Networks', by Zhengdao Chen, Eric Vanden-Eijnden, Joan Bruna. jmlr.org/papers/v27/22-… #neurons #gradient #rademacher

'The Role of Contextual Information in Best Arm Identification', by Masahiro Kato, Kaito Ariu. jmlr.org/papers/v27/22-… #bandits #contextual #reward

'Transformers Can Overcome the Curse of Dimensionality: A Theoretical Study from an Approximation Perspective', by Yuling Jiao, Yanming Lai, Yang Wang, Bokai Yan. jmlr.org/papers/v27/25-… #softmax #transformer #layers

'Online Bernstein-von Mises theorem', by Jeyong Lee, Junhyeok Choi, Minwoo Chae. jmlr.org/papers/v27/25-… #prior #posterior #incrementally