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  1. Score-based Causal Representation Learning: Linear and General Transformations

    Published in JMLR (2026)
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  2. On the Statistical Properties of Generative Adversarial Models for Low Intrinsic Data Dimension

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  3. Prominent Roles of Conditionally Invariant Components in Domain Adaptation: Theory and Algorithms

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  4. Near-Optimal Nonconvex-Strongly-Convex Bilevel Optimization with Fully First-Order Oracles

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  5. Adaptive Distributed Kernel Ridge Regression: A Feasible Distributed Learning Scheme for Data Silos

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  6. On Global and Local Convergence of Iterative Linear Quadratic Optimization Algorithms for Discrete Time Nonlinear Control

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  7. A Decentralized Proximal Gradient Tracking Algorithm for Composite Optimization on Riemannian Manifolds

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  8. Learning conditional distributions on continuous spaces

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  9. A Unified Analysis of Nonstochastic Delayed Feedback for Combinatorial Semi-Bandits, Linear Bandits, and MDPs

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  10. Error bounds for particle gradient descent, and extensions of the log-Sobolev and Talagrand inequalities

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  11. Linear Hypothesis Testing in High-Dimensional Expected Shortfall Regression with Heavy-Tailed Errors

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  12. Efficient Numerical Integration in Reproducing Kernel Hilbert Spaces via Leverage Scores Sampling

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  13. Distribution Free Tests for Model Selection Based on Maximum Mean Discrepancy with Estimated Parameters

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  14. Statistical field theory for Markov decision processes under uncertainty

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  15. Bayesian Data Sketching for Varying Coefficient Regression Models

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  16. Bagged k-Distance for Mode-Based Clustering Using the Probability of Localized Level Sets

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  17. Linear cost and exponentially convergent approximation of Gaussian Matérn processes on intervals

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  18. Invariant Subspace Decomposition

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  19. Posterior Concentrations of Fully-Connected Bayesian Neural Networks with General Priors on the Weights

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  20. Outlier Robust and Sparse Estimation of Linear Regression Coefficients

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