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  1. Generative Adversarial Networks: Dynamics

    Published in JMLR (2026)
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  2. "What is Different Between These Datasets?" A Framework for Explaining Data Distribution Shifts

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  3. Assumption-lean and data-adaptive post-prediction inference

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  4. Bagged Regularized k-Distances for Anomaly Detection

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  5. Four Axiomatic Characterizations of the Integrated Gradients Attribution Method

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  6. Fast Algorithm for Constrained Linear Inverse Problems

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  7. High-Rank Irreducible Cartesian Tensor Decomposition and Bases of Equivariant Spaces

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  8. Best Linear Unbiased Estimate from Privatized Contingency Tables

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  9. Interpretable Global Minima of Deep ReLU Neural Networks on Sequentially Separable Data

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  10. Enhanced Feature Learning via Regularisation: Integrating Neural Networks and Kernel Methods

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  11. Data-Driven Performance Guarantees for Classical and Learned Optimizers

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  12. Contextual Bandits with Stage-wise Constraints

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  13. Boosting Causal Additive Models

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  14. Frequentist Guarantees of Distributed (Non)-Bayesian Inference

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  15. Asymptotic Inference for Multi-Stage Stationary Treatment Policy with Variable Selection

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  16. EMaP: Explainable AI with Manifold-based Perturbations

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  17. Autoencoders in Function Space

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  18. Nonparametric Regression on Random Geometric Graphs Sampled from Submanifolds

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  19. System Neural Diversity: Measuring Behavioral Heterogeneity in Multi-Agent Learning

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  20. Distribution Estimation under the Infinity Norm

    Published in JMLR (2026)
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