1974 papers
This paper introduces the Wasserstein Transform, a general unsupervised framework that enhances features and denoises data by representing points as probability measures and updating distances via Wasserstein metrics, with a specific focus on the computationally efficient Gaussian Transform variant and its applications in tasks like clustering and image segmentation.
This paper addresses the challenge of incentivizing honesty among competing participants in collaborative learning by formulating a game-theoretic framework that identifies rational incentives for manipulation and proposes mechanisms to ensure high-quality learning outcomes comparable to full cooperation.
This paper establishes that finite-time decoupled convergence in nonlinear two-time-scale stochastic approximation is achievable under a nested local linearity assumption with appropriate step sizes, while demonstrating that the nonlinearity of the slow-time-scale update alone can destroy this convergence property.
This paper proposes a novel statistical benchmarking strategy that tests for treatment effect bias in specific subgroups by comparing observational study estimates against randomized trials, ultimately providing an asymptotically valid lower bound on maximum bias strength to ensure reliable clinical decision-making.
This paper introduces a generalized debiased Lasso estimator that leverages a stability principle to provide a computationally efficient, asymptotically accurate update formula for perturbed designs, thereby significantly reducing the cost of resampling-based variable selection methods like the conditional randomization test and local knockoff filter.
This paper proposes a novel, fast-converging greedy algorithm that accurately identifies the optimal number and locations of breakpoints in piecewise polynomial regression, demonstrating superior accuracy and interpretability over existing methods on both synthetic and real-world datasets.
This paper introduces MAHGenTa, an algorithm that leverages information geometry to decompose KL error and select sparse higher-order mode interactions, thereby enabling more data-efficient learning of probability distributions for both generative and discriminative tasks.
This paper introduces a privacy-preserving federated framework using threshold CKKS homomorphic encryption to enable multi-institutional Kaplan-Meier survival analysis that achieves high-fidelity results comparable to centralized data while preventing the reconstruction of sensitive individual records through encrypted aggregation and threshold decryption.
This paper proposes a fully online, batch-free covariance matrix estimator constructed solely from Newton iterates for sketched Newton methods, enabling consistent online statistical inference for model parameters in streaming data scenarios without requiring matrix factorization.
The paper introduces BLADE, a novel Bayesian framework that integrates replica-exchange stochastic gradient Langevin Monte Carlo with active learning to efficiently discover governing equations of complex dynamical systems while providing robust uncertainty quantification and significantly reducing data acquisition costs.
The paper introduces CREAM, a flexible framework for Concept Bottleneck Models that explicitly encodes complex concept relationships and incorporates a regularized side-channel to handle incomplete concept sets, thereby achieving black-box-level performance while maintaining interpretability and enabling efficient interventions.
This paper proposes GAIF, a feedback-enhanced generalized alpha-investing framework that leverages revealed hypothesis outcomes to dynamically adjust thresholds for finite-sample false discovery rate control, and extends this approach to online conformal testing to improve model selection and statistical power.
This paper addresses unsupervised domain adaptation in binary classification where a source subpopulation is unobservable, proposing a distribution matching method with theoretical guarantees to recover target domain predictions and outperform naive benchmarks that ignore this missing data.
This paper introduces PnP-CM, a plug-and-play framework that reinterprets consistency models as proximal operators to solve diverse linear and nonlinear inverse problems with high-quality reconstructions in as few as two to four neural function evaluations, eliminating the need for task-specific training while outperforming existing methods.
This paper introduces a score-based diffusion model that efficiently emulates monthly Earth System Model outputs on a spherical mesh to support rapid climate impact assessment, demonstrating high fidelity to parent models while acknowledging specific limitations in capturing seasonal regime shifts.
The paper introduces GraphBSI, a novel one-shot graph generative model based on Bayesian Sample Inference that iteratively refines distribution parameters in continuous space to achieve state-of-the-art performance in molecular and synthetic graph generation.
This paper establishes the first theoretical correspondence between Partial Information Decomposition components and causal structures in Bayesian networks and hypergraphs, demonstrating that unique information and synergy signatures enable a localist, model-agnostic approach to discovering both pairwise and higher-order causal relationships.
This paper proposes a novel Double Machine Learning framework that utilizes general instrumental variables and uniform regular weighting functions to identify and estimate average dose-response functions for continuous treatments while mitigating bias from unobserved confounders.
This paper introduces a neural path estimation framework based on variational inference and stochastic control to reconstruct and infer stochastic dynamical systems from noisy, nonlinear, and sparse observations by learning a generative model that maps prior path measures to filtering posterior measures.
This paper introduces Discrete Flow Maps, a framework that enables single-step parallel text generation by reconciling trajectory compression with the geometric constraints of discrete data, thereby surpassing previous state-of-the-art results in discrete flow modeling.