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  1. Recursive Causal Discovery

    Published in JMLR (2026)
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  2. Evaluation of Active Feature Acquisition Methods for Time-varying Feature Settings

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  3. On Adaptive Stochastic Optimization for Streaming Data: A Newton's Method with O(dN) Operations

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  4. Determine the Number of States in Hidden Markov Models via Marginal Likelihood

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  5. Variance-Aware Estimation of Kernel Mean Embedding

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  6. Scaling ResNets in the Large-depth Regime

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  7. A Comparative Evaluation of Quantification Methods

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  8. Lightning UQ Box: Uncertainty Quantification for Neural Networks

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  9. Scaling Data-Constrained Language Models

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  10. Curvature-based Clustering on Graphs

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  11. Composite Goodness-of-fit Tests with Kernels

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  12. PFLlib: A Beginner-Friendly and Comprehensive Personalized Federated Learning Library and Benchmark

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  13. The Effect of SGD Batch Size on Autoencoder Learning: Sparsity, Sharpness, and Feature Learning

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  14. Efficient and Robust Transfer Learning of Optimal Individualized Treatment Regimes with Right-Censored Survival Data

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  15. DAGs as Minimal I-maps for the Induced Models of Causal Bayesian Networks under Conditioning

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  16. Adjusted Expected Improvement for Cumulative Regret Minimization in Noisy Bayesian Optimization

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  17. Manifold Fitting under Unbounded Noise

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  18. Learning Global Nash Equilibrium in Team Competitive Games with Generalized Fictitious Cross-Play

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  19. Wasserstein Convergence Guarantees for a General Class of Score-Based Generative Models

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  20. Extremal graphical modeling with latent variables via convex optimization

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