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].

'DRM Revisited: A Complete Error Analysis', by Yuling Jiao, Ruoxuan Li, Peiying Wu, Jerry Zhijian Yang, Pingwen Zhang. . #overparameterization #overparameterized #deep.

What do you want to know?.

'Principled Penalty-based Methods for Bilevel Reinforcement Learning and RLHF', by Han Shen, Zhuoran Yang, Tianyi Chen. . #reinforcement #incentive #optimization.

'Precise High-Dimensional Asymptotics for Quantifying Heterogeneous Transfers', by Fan Yang, Hongyang R. Zhang, Sen Wu, Christopher Re, Weijie J. Su. . #tasks #task #bias.

'Score-based Causal Representation Learning: Linear and General Transformations', by Burak Var{{\i}}c{{\i}}, Emre Acartürk, Karthikeyan Shanmugam, Abhishek Kumar, Ali Tajer. . #causal #interventions #interventional.

'On the Statistical Properties of Generative Adversarial Models for Low Intrinsic Data Dimension', by Saptarshi Chakraborty, Peter L. Bartlett. . #gans #generative #adversarial.

'Prominent Roles of Conditionally Invariant Components in Domain Adaptation: Theory and Algorithms', by Keru Wu, Yuansi Chen, Wooseok Ha, Bin Yu. . #adaptation #prediction #risk.

'Near-Optimal Nonconvex-Strongly-Convex Bilevel Optimization with Fully First-Order Oracles', by Lesi Chen, Yaohua Ma, Jingzhao Zhang. . #optimization #optimal #hessian.

'Adaptive Distributed Kernel Ridge Regression: A Feasible Distributed Learning Scheme for Data Silos', by Shao-Bo Lin, Xiaotong Liu, Di Wang, Hai Zhang, Ding-Xuan Zhou. . #privacy #adadkrr #distributed.

'On Global and Local Convergence of Iterative Linear Quadratic Optimization Algorithms for Discrete Time Nonlinear Control', by Vincent Roulet, Siddhartha Srinivasa, Maryam Fazel, Zaid Harchaoui. . #discretization #linearized #linear.

'A Decentralized Proximal Gradient Tracking Algorithm for Composite Optimization on Riemannian Manifolds', by Lei Wang, Le Bao, Xin Liu. . #regularization #subgradient #optimization.

'Learning conditional distributions on continuous spaces', by Cyril Benezet, Ziteng Cheng, Sebastian Jaimungal. . #clustering #space #nearest.

'A Unified Analysis of Nonstochastic Delayed Feedback for Combinatorial Semi-Bandits, Linear Bandits, and MDPs', by Lukas Zierahn, Dirk van der Hoeven, Tal Lancewicki, Aviv Rosenberg, Nicolò Cesa-Bianchi. . #bandit #bandits #delay.

'Error bounds for particle gradient descent, and extensions of the log-Sobolev and Talagrand inequalities', by Rocco Caprio, Juan Kuntz, Samuel Power, Adam M. Johansen. . #minimizers #likelihoods #estimation.

'Linear Hypothesis Testing in High-Dimensional Expected Shortfall Regression with Heavy-Tailed Errors', by Gaoyu Wu, Jelena Bradic, Kean Ming Tan, Wen-Xin Zhou. . #shortfall #estimators #quantile.

'Efficient Numerical Integration in Reproducing Kernel Hilbert Spaces via Leverage Scores Sampling', by Antoine Chatalic, Nicolas Schreuder, Ernesto De Vito, Lorenzo Rosasco. . #sampling #approximating #optimal.

'Distribution Free Tests for Model Selection Based on Maximum Mean Discrepancy with Estimated Parameters', by Florian Brück, Jean-David Fermanian, Aleksey Min. . #testing #specification #models.

'Statistical field theory for Markov decision processes under uncertainty', by George Stamatescu. . #markov #bayesian #estimators.

'Bayesian Data Sketching for Varying Coefficient Regression Models', by Rajarshi Guhaniyogi, Laura Baracaldo, Sudipto Banerjee. . #models #predicting #regression.

'Bagged k-Distance for Mode-Based Clustering Using the Probability of Localized Level Sets', by Hanyuan Hang. . #clusters #ensemble #clustering.

'Linear cost and exponentially convergent approximation of Gaussian Matérn processes on intervals', by David Bolin, Vaibhav Mehandiratta, Alexandre B. Simas. . #gaussian #prediction #computational.