289 papers
This paper reports the discovery of "murmurations," a new arithmetic phenomenon identified through AI-assisted analysis of large datasets that encodes subtle information about Frobenius traces and connects to the Birch and Swinnerton-Dyer conjecture and random matrix theory.
This paper proposes a framework and develops several estimators with varying degrees of robustness for estimating and conducting inference on heterogeneous principal causal effects under principal ignorability, demonstrating their theoretical properties and practical application through the Camden Coalition hotspotting randomized trial.
This paper establishes a geometric framework demonstrating that the leading-order bias of posterior functionals in variational inference is determined by the component of the functional orthogonal to the variational tangent space, thereby explaining why structured mean-field approximations systematically distort cross-block dependencies due to omitted interaction directions.
This paper establishes that history-dependent policies with sublinear expected regret are robust-optimal for non-rectangular average-reward robust MDPs without requiring rectangularity, and introduces a transient-value framework with an epoch-based policy that achieves constant-order finite-time performance by combining worst-case optimality with online learning.
This paper proposes a robust, data-driven framework for assortment optimization that maximizes worst-case expected revenue under potential customer preference shifts, establishing computational tractability and deriving statistically optimal algorithms with tight sample complexity bounds to ensure reliable generalization.
This paper introduces AffPCL, a personalized collaborative learning framework that utilizes affinity-based variance reduction to automatically adapt to unknown heterogeneity among agents, achieving sample complexity improvements that seamlessly interpolate between independent learning and linear speedup without requiring prior knowledge of system similarity.
This paper investigates repulsive Monte Carlo methods for computing integrals on the unit sphere, specifically for the sliced Wasserstein distance, by benchmarking determinantal and repelled point processes while analyzing the UnifOrtho estimator to recommend randomized quasi-Monte Carlo for low dimensions and UnifOrtho for high dimensions.
This paper establishes the first global linear convergence guarantees for a dynamic smoothing variant of Iteratively Reweighted Least Squares (IRLS) in robust subspace and affine subspace recovery, extending these theoretical results to nonconvex optimization on Riemannian manifolds and demonstrating their practical utility in low-dimensional neural network training.
This paper introduces the Mixture of Interacting Cascades (MIC), a joint user-topic interaction model based on marked multidimensional Hawkes processes that outperforms existing methods in modeling information spread and provides insightful visualizations of social network activity.
This paper introduces two novel model-free algorithms, Q-EarlySettled-LowCost and FedQ-EarlySettled-LowCost, for single-agent and federated reinforcement learning that simultaneously achieve near-optimal regret, linear burn-in costs in state and action spaces, and logarithmic policy switching or communication costs, while also providing improved gap-dependent theoretical guarantees.
This paper proposes an unsupervised prognostics framework that utilizes unlabeled run-to-failure data to simultaneously identify latent failure modes and select informative sensors, thereby enabling accurate remaining useful life prediction for autonomous deep-space habitats under multiple unknown failure conditions.
This paper introduces Feature Importance Rescaling (FIR), a theoretically grounded method that improves the reliability of cluster validity indices in noisy, high-dimensional Gaussian mixtures by attenuating irrelevant features, thereby strengthening the correlation between unsupervised evaluation metrics and ground truth.
This paper introduces a-TMFG, a scalable algorithm that overcomes the memory and computational limitations of traditional Triangulated Maximally Filtered Graphs by leveraging k-Nearest Neighbors and on-the-fly correlation estimation to construct sparse graphs from massive datasets.
This paper introduces BRACE, a parameter-free algorithm for multi-armed bandits with noncompliance that simultaneously optimizes recommendation welfare and treatment learning by performing certified instrumental variable inversion only when identification is strong, otherwise providing honest structural intervals to navigate the trade-offs between mediated and direct-control regimes.
This paper advances the theoretical understanding of Gaussian process Thompson sampling (GP-TS) in Bayesian optimization by establishing a regret lower bound, deriving improved upper bounds for cumulative regret and the second moment of regret, and providing expected lenient regret bounds that address gaps in existing analyses compared to GP-UCB.
This paper proposes a unified, target-gradient-free generative sampling framework that enforces time-reversibility constraints via Maximum Mean Discrepancy minimization between forward and backward Markov trajectories, enabling efficient sampling from complex continuous, discrete, and hybrid distributions using only energy evaluations.
This paper verifies that persistent observers in causally invariant hypergraph substrates satisfy the Conant-Ashby Good Regulator Theorem, thereby necessitating internal models that lead to natural gradient descent as the unique learning rule and yielding a model-dependent closed-form formula for Vanchurin's regime parameter with a quantum-classical threshold at .
This book reinterprets generative AI, specifically through flow matching, as a statistical framework for nonparametric distribution learning that enables principled inference for tasks like missing-data imputation and causal analysis by integrating generative models with double/debiased machine learning techniques to ensure inferential validity.
This paper proposes a data-driven robust Markov decision process framework for Borel spaces with unknown disturbance distributions, utilizing ambiguity sets defined by distance functions to establish finite-sample performance guarantees, probabilistic convergence rates, and out-of-distribution bounds that empirical MDPs fail to provide.
This thesis addresses the problem of overconfident conclusions in simulation-based inference by introducing a "balancing" regularization technique and a novel Bayesian neural network prior to ensure more reliable and calibrated statistical approximations.