302 papers
This paper proposes novel kernel-based equivalence tests using kernel Stein discrepancy and Maximum Mean Discrepancy to rigorously assess the absence of statistically meaningful differences between distributions, addressing the limitations of traditional goodness-of-fit tests and existing parametric methods through asymptotic and bootstrap critical value approximations.
ReTabSyn is a reinforcement learning-based pipeline for realistic tabular data synthesis that prioritizes learning conditional distributions to better preserve feature correlations and improve downstream model utility in low-data, imbalanced, and shifted settings.
This paper introduces TS_Adam, a lightweight variant of the Adam optimizer that removes second-order bias correction to enhance responsiveness to distributional drift in non-stationary time series forecasting, achieving significant performance improvements across various tasks without requiring additional hyperparameters.
This paper establishes the convergence of Dual Space Preconditioned Gradient Descent to an interpolating solution for overparameterized linear models using novel Bregman divergence techniques, while characterizing its implicit bias to show that isotropic preconditioners recover standard gradient descent solutions and general preconditioners yield solutions within a constant factor of the standard solution.
This paper introduces "Brenier isotonic regression," a novel multi-output regression framework that extends classical isotonic regression by enforcing cyclic monotonicity through optimal transport principles, demonstrating superior performance in probability calibration and generalized linear models.
This paper proposes a trust-region interior-point stochastic sequential quadratic programming (TR-IP-SSQP) method that utilizes adaptive stochastic oracles to solve optimization problems with stochastic objectives and deterministic nonlinear constraints, proving its global almost-sure convergence to first-order stationary points and demonstrating practical performance on benchmark and logistic regression problems.
This paper proposes Weighted improved Greedy Sampling (WiGS), a reinforcement learning-based active learning framework that dynamically adapts the balance between feature-space diversity and output-space uncertainty to outperform static multiplicative methods, particularly in domains with irregular data density.
This paper reformulates continuous-time optimal stopping as a regularized singular stochastic control problem using randomized stopping times and cumulative residual entropy to encourage exploration, enabling the derivation of unique optimal strategies and the development of scalable, policy-improving reinforcement learning algorithms.
The paper introduces Panda, a pretrained foundation model for chaotic dynamics that, despite being trained exclusively on synthetic low-dimensional ordinary differential equations, demonstrates emergent zero-shot forecasting capabilities for unseen chaotic systems, real-world experimental data, and even partial differential equations.
This paper addresses the fixed-budget best-arm identification problem in non-stationary linear bandits by establishing a tighter, arm-set-dependent lower bound on error probability and proposing the algorithm, which utilizes an Adjacent-optimal design to achieve minimax-optimal performance that fully leverages the geometric structure of the arm set.
The paper introduces MultiwayPAM, a novel tensor clustering method designed to simultaneously estimate cluster memberships and medoids for questions, answerers, and evaluators in LLM-as-a-Judge score tensors, thereby addressing computational costs and revealing inherent evaluator biases.
This paper demonstrates that when the prior given hyperparameters in a Bayesian hierarchical model is a canonical maximum entropy distribution, the resulting dependent marginal prior also possesses a maximum entropy property, but with respect to a constraint on the marginal distribution of a function of the unknown parameters, thereby clarifying the implicit information assumptions of such models.
This paper establishes that a continuous-time diffusion approximation of policy gradient for stochastic bandits achieves logarithmic regret with a learning rate of , while demonstrating that a significantly smaller learning rate of is necessary to avoid linear regret in specific instances.
This paper establishes a general stability criterion for stochastic mirror descent algorithms to enable valid statistical inference in adaptive bandit settings, introducing regularized-EXP3 variants that simultaneously achieve minimax-optimal regret, nominal confidence interval coverage, and robustness to adversarial corruptions.
The paper introduces SDSR, a scalable spectral divide-and-conquer algorithm for species tree reconstruction that achieves up to 10-fold faster runtimes compared to standard methods while maintaining comparable accuracy under the multispecies coalescent model.
This paper introduces DistBART, a scalable Bayesian nonparametric method for distribution regression that models the regression function using a linear functional with a BART prior, offering both theoretical guarantees on posterior concentration and practical efficiency through random-feature approximations.
This paper proposes a sequential "Clusters-of-Centres" algorithm that utilizes multivariate Cochran-type tests on summary statistics to identify and merge homogeneous groups in multi-centre studies, establishing asymptotic distributions and proving that a multi-round bootstrap variant can recover the true centre partition with high probability.
This book serves as a pedagogical resource that unifies scattered research on conformal prediction and distribution-free inference by curating key results and presenting their proofs in a coherent framework to help readers understand the fundamental technical arguments behind these uncertainty quantification methods.
This paper establishes the first large deviation principle for convolutional neural networks in the infinite-channel regime, characterizing the asymptotic behavior of conditional covariance matrices and posterior distributions under Gaussian priors while also providing streamlined proofs for their Gaussian equivalence and concentration.
The paper introduces DAISI, a scalable data assimilation algorithm that leverages flow-based generative models with a novel inverse-sampling step to integrate forecast information and guide conditional sampling, enabling accurate filtering in complex, nonlinear systems where traditional Gaussian-based methods fail.