315 papers
This paper proposes SPPCSO, an adaptive penalized estimation method that integrates single-parametric principal component regression with regularization to achieve stable variable selection and robust coefficient estimation in high-dimensional, highly correlated, and noisy datasets, outperforming traditional approaches in both theoretical bounds and practical applications such as gene discovery.
This paper introduces Synthetic Monitoring Environments (SMEs), a configurable suite of continuous control tasks with known optimal policies and exact regret metrics, designed to enable rigorous, white-box diagnostics and systematic analysis of reinforcement learning algorithms' performance under varying conditions.
This paper proposes the BAEN-SVM, a robust binary classification model utilizing a novel bounded asymmetric elastic net loss function that effectively handles noise and geometric irregularities through theoretical guarantees of bounded influence and Fisher consistency, solved via an efficient half-quadratic algorithm.
This paper presents a framework for the certified and accurate computation of deep neural network function space norms (including , , and ) by combining interval arithmetic, adaptive refinement, and quadrature to derive guaranteed global bounds from local certificates, thereby enabling reliable error control for PDE applications like PINNs.
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 introduces a semantics-aware caching approach that significantly reduces the runtime of concept learning by efficiently managing instance retrieval across diverse symbolic and neuro-symbolic reasoners, achieving speedups of up to an order of magnitude.
This paper introduces Onflow, a model-free, online reinforcement learning algorithm that optimizes portfolio allocation via gradient flows to maximize expected log returns, demonstrating robustness to high transaction costs and superior performance compared to existing dynamic strategies without requiring assumptions about asset return distributions.
The paper introduces Finance-Informed Neural Networks (FINN), a self-supervised machine learning framework that learns option pricing and hedging by minimizing dynamic replication error rather than fitting market prices, thereby ensuring economic consistency, recovering arbitrage-free prices, and enabling robust pricing for assets with no listed options.
This paper introduces a partition-based functional ridge regression framework that decomposes coefficient functions into dominant and weaker components to apply differential penalization, thereby improving numerical stability, interpretability, and predictive performance in high-dimensional functional linear models without relying on explicit variable selection.
Co-Diffusion is a novel two-stage latent diffusion framework that enhances generalizable drug-target affinity prediction by aligning embeddings in an affinity-steered manifold and employing modality-specific diffusion as a regularizer, thereby achieving superior zero-shot performance on unseen molecular scaffolds and protein families compared to state-of-the-art methods.
This paper introduces a three-dimensional height-augmented ReLU network architecture that achieves significantly more efficient exponential approximation rates for analytic functions and provides the first quantitative, non-asymptotic high-order approximation for general functions, thereby advancing the theoretical foundation for designing parameter-efficient neural networks.
This paper proposes and analyzes non-penalized and sparse-penalized deep neural network estimators based on the minimum error entropy principle for nonparametric regression with strongly mixing dependent observations, establishing that they achieve minimax optimal convergence rates up to a logarithmic factor.
This paper formally defines algorithmic capture as the ability of infinite-width transformers to generalize to arbitrary problem sizes and demonstrates that, despite their universal expressivity, their inductive bias restricts them to learning only low-complexity algorithms within the Efficient Polynomial Time Heuristic Scheme (EPTHS) class.
This paper introduces diagnostic transport maps as a method to recalibrate potentially misspecified predictive distributions for high-dimensional inputs, enabling real-time local diagnostics and improved accuracy for rare events, as demonstrated in tropical cyclone intensity forecasting.
This paper proposes RIE-Greedy, a contextual bandit algorithm that leverages the inherent stochasticity of cross-validation-based regularization in black-box models to induce effective exploration, theoretically matching Thompson Sampling in simple cases and empirically outperforming existing methods in large-scale applications.
This paper introduces the "outrigger" local polynomial estimator, a distributionally adaptive method that achieves minimax optimality across various conditional error distributions without requiring structural assumptions like independence or symmetry, while guaranteeing performance at least as good as standard estimators under Gaussian errors.
This paper introduces a unified framework called wcPCA for developing worst-case optimal low-rank approximations across heterogeneous domains, extending the approach to new estimators and matrix completion while proving theoretical guarantees and demonstrating improved robustness in real-world applications with minimal loss to average performance.
This paper demonstrates that Langevin dynamics is fundamentally non-robust to small (or ) errors in the estimated score function, producing distributions far from the target even in polynomial time, thereby providing a theoretical justification for preferring diffusion models over Langevin dynamics when scores are learned from data.
This paper proposes a doubly robust estimator combined with a novel jackknife spatial HAC variance correction to achieve valid asymptotic inference when using machine learning predictions for missing-at-random labels in the presence of spatial dependence, addressing the instability caused by cross-fitting-induced correlations.
This paper introduces a formal framework decomposing observational multiplicity in decision trees into leaf and structural regret, demonstrating that structural instability is the primary driver of prediction variability and that leveraging these measures for selective prediction significantly enhances model safety and recall.