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- Spectral Indistinguishability and Learning-Curve Identifiability in Quadratic Networks
- Follow the Perturbed Leader for Linear Bandits: √T Regret over General Action Sets
- How Well Do Prior-Data Fitted Networks Amortize Bayesian Inference?
- Leaner Transformers Can Easily Learn to Cluster
- Identifying compromise solutions across multiple treatment benefit-harm profiles in multivariate network meta-analysis
- On the Trade-off Between Information Loss and Generalization in Sparse Attention
- FAER: Auditable Utility-Aligned Trajectory Replay for Language Model Post-Training
- CVaR anchor regression protects against rare shifts
- The kinematics of global warming: Semiparametric analysis of warming level, rate, and acceleration
- Online Inference in Distributional Temporal-Difference Learning
- Online Inference for Quantile Distributional Reinforcement Learning
- Bayesian inference on beta diversity via feature allocation models
- Marginal generalized raking with parametric working models
- Human-Centered Learning Mechanics: Observable Entropy Control under Protected-Risk Constraints
- Exploration and Experimentation with a Misspecified Simulator
- Empowering Bayes with sources of uncertainty: from confidence modelling to sparse estimation
- Measuring Differences between Conditional Distributions using Kernel Embeddings
- 2D Stability Selection: Design Jittering to Exclude Correlated Irrelevant Features
- Identifiability from Moments in Independent Component Analysis Mixture Models
- Canonical Layer Inference for Deep Gaussian Processes via Symmetry-Breaking Augmentation
- Parametric Resonance in Primal-Dual Learning for Slowly Non-Stationary Constrained MDPs
- Feasible closed form moment matching for scenario generation through covariance rotations
- Robust Local Fréchet Regression: Nonasymptotic Theory and Minimax Optimality
- Exact Tail Laws for Nonlinear SGD with Infinite-Variance Gradients
- A general recipe for data-adaptive anytime-valid envelopes for iterative algorithms