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'Recursive Causal Discovery', by Ehsan Mokhtarian, Sepehr Elahi, Sina Akbari, Negar Kiyavash. jmlr.org/papers/v26/24-… #causal #discovery #observational

'Continuously evolving rewards in an open-ended environment', by Richard M. Bailey. jmlr.org/papers/v26/24-… #reward #rewards #adaptive

'Ontolearn---A Framework for Large-scale OWL Class Expression Learning in Python', by Caglar Demir et al. jmlr.org/papers/v26/24-… #owl #ontolearn #sparql

'Estimation of Local Geometric Structure on Manifolds from Noisy Data', by Yariv Aizenbud, Barak Sober. jmlr.org/papers/v26/25-… #manifold #grassmannian #estimation

'Instability, Computational Efficiency and Statistical Accuracy', by Nhat Ho, Koulik Khamaru, Raaz Dwivedi, Martin J. Wainwright, Michael I. Jordan, Bin Yu. jmlr.org/papers/v26/22-… #estimation #estimators #algorithms

'Deletion Robust Non-Monotone Submodular Maximization over Matroids', by Paul Dütting, Federico Fusco, Silvio Lattanzi, Ashkan Norouzi-Fard, Morteza Zadimoghaddam. jmlr.org/papers/v26/23-… #maximization #matroid #approximation

'Fine-Grained Change Point Detection for Topic Modeling with Pitman-Yor Process', by Feifei Wang, Zimeng Zhao, Ruimin Ye, Xiaoge Gu, Xiaoling Lu. jmlr.org/papers/v26/23-… #topics #changes #topic

'Stabilizing Sharpness-Aware Minimization Through A Simple Renormalization Strategy', by Chengli Tan, Jiangshe Zhang, Junmin Liu, Yicheng Wang, Yunda Hao. jmlr.org/papers/v26/24-… #sgd #sharpness #minimization

'Local Linear Recovery Guarantee of Deep Neural Networks at Overparameterization', by Yaoyu Zhang, Leyang Zhang, Zhongwang Zhang, Zhiwei Bai. jmlr.org/papers/v26/24-… #overparameterization #overparameterized #deep

'Sharp Bounds for Sequential Federated Learning on Heterogeneous Data', by Yipeng Li, Xinchen Lyu. jmlr.org/papers/v26/24-… #federated #aggregation #heterogeneous

'Sampling and Estimation on Manifolds using the Langevin Diffusion', by Karthik Bharath, Alexander Lewis, Akash Sharma, Michael V. Tretyakov. jmlr.org/papers/v26/24-… #estimators #estimation #discretization

'Laplace Meets Moreau: Smooth Approximation to Infimal Convolutions Using Laplace's Method', by Ryan J. Tibshirani, Samy Wu Fung, Howard Heaton, Stanley Osher. jmlr.org/papers/v26/24-… #convolutions #laplace #approximations

'Optimization Over a Probability Simplex', by James Chok, Geoffrey M. Vasil. jmlr.org/papers/v26/23-… #optimal #convex #optimize

'On Consistent Bayesian Inference from Synthetic Data', by Ossi Räisä, Joonas Jälkö, Antti Honkela. jmlr.org/papers/v26/23-… #privacy #bayesian #synthetic

'Learning causal graphs via nonlinear sufficient dimension reduction', by Eftychia Solea, Bing Li, Kyongwon Kim. jmlr.org/papers/v26/24-… #nonparametric #sparseness #markov

'Distributed Stochastic Bilevel Optimization: Improved Complexity and Heterogeneity Analysis', by Youcheng Niu, Jinming Xu, Ying Sun, Yan Huang, Li Chai. jmlr.org/papers/v26/24-… #hessian #optimization #convex

'Wasserstein F-tests for Frechet regression on Bures-Wasserstein manifolds', by Haoshu Xu, Hongzhe Li. jmlr.org/papers/v26/24-… #covariates #covariate #genomics

'Derivative-Informed Neural Operator Acceleration of Geometric MCMC for Infinite-Dimensional Bayesian Inverse Problems', by Lianghao Cao, Thomas O'Leary-Roseberry, Omar Ghattas. jmlr.org/papers/v26/24-… #mcmc #hessian #hessians

'Dynamic angular synchronization under smoothness constraints', by Ernesto Araya, Mihai Cucuringu, Hemant Tyagi. jmlr.org/papers/v26/24-… #synchronization #algorithms #estimation

'GraphNeuralNetworks.jl: Deep Learning on Graphs with Julia', by Carlo Lucibello, Aurora Rossi. jmlr.org/papers/v26/24-… #graphneuralnetworks #graphs #graph