331 papers
This paper establishes a homology theory for ample groupoids using compactly supported Moore complexes, proving functoriality and Kakutani invariance, deriving Mayer-Vietoris sequences, and demonstrating that a universal coefficient theorem holds for discrete coefficients while identifying specific obstructions for non-discrete ones.
This paper proposes a sample-efficient tensor factorization model that combines cheap automated ratings with a small set of human labels to enable fine-grained, accurate, and confidence-interval-backed evaluation of generative models at the prompt level.
This paper proposes a new paradigm for causal discovery that leverages crowdsourcing, expert elicitation, and LLM-based simulation to aggregate fragmented knowledge from multiple agents into a comprehensive global causal structure unattainable by any single entity.
This paper corrects mathematical errors in the original theoretical foundation of the UMAP algorithm, provides a self-contained derivation of the underlying functors and metric realization, and clarifies the correspondence between the theoretical framework and the practical UMAP implementation.
This paper proposes a "Learning Order Forest" method that employs a joint learning mechanism to iteratively construct tree-based distance structures for qualitative attributes, thereby effectively capturing local order relationships to achieve superior clustering performance on datasets with nominal values.
This paper proposes a novel, implementable adaptive parameter selection strategy for kernel-based gradient descent that integrates bias-variance analysis with the splitting method and empirical effective dimension to achieve optimal generalization error bounds across diverse kernels, target functions, and error metrics.
This paper introduces the Surprisal-Rényi Free Energy (SRFE), a novel log-moment-based functional that bridges forward and reverse Kullback-Leibler divergences by revealing a mean-variance tradeoff and providing a variational characterization that controls large deviations in code-length, thereby clarifying the geometric and statistical structure underlying these distinct learning objectives.
This paper proposes a scalable, contrastive causal discovery model that leverages paired observational and single-regime soft interventional data to construct globally consistent causal structures, theoretically proving its ability to recover identifiable edges and outperform non-contrastive methods in both in-distribution and out-of-distribution scenarios.
This paper proposes a minimax optimal algorithm for tabular reinforcement learning with delayed state observations that combines augmentation and upper confidence bound methods to achieve a regret bound of , which is proven to be optimal up to logarithmic factors.
This paper argues that adversarial examples arise from an exponential misalignment between the high-dimensional perceptual manifolds of neural networks and human concepts, suggesting that achieving robustness requires aligning these dimensions to match human perception.
CausalMix is a variational generative framework that bridges the gap between distributional realism and causal controllability by enabling independent manipulation of overlap, confounding, and treatment effect heterogeneity in synthetic mixed-type tabular data for rigorous causal inference study design and validation.
This paper demonstrates that while the No Free Lunch theorem holds under uniform sampling, algebraic reformulations of permutation-based optimization benchmarks create structured local violations that alter algorithm rankings and performance patterns, necessitating problem-aware algorithm selection.
This paper presents a rigorous framework that extends neural operators to robustly handle out-of-distribution input functions by leveraging kernel approximations and Reproducing Kernel Hilbert Space theory to ensure accurate prediction of both function values and derivatives, validated through solutions of elliptic partial differential equations on manifolds.
This paper establishes the strong convergence with order $1/2$ for a geometric Euler-Maruyama scheme applied to stochastic differential equations on Riemannian manifolds and derives a corresponding Wasserstein bound for sampling via Riemannian Langevin dynamics.
This paper derives a new Stein identity for bounded-support q-Gaussians by extending Bonnet- and Price-type theorems and utilizing escort distributions, resulting in gradient estimators that mirror Gaussian forms while offering reduced variance for applications like Bayesian deep learning.
This paper establishes finite-sample convergence guarantees for score-based diffusion models learning intrinsically low-dimensional distributions, demonstrating that their generalization error scales with the data's intrinsic -Wasserstein dimension rather than the ambient dimension, thereby mitigating the curse of dimensionality without requiring restrictive assumptions like compact support or smooth densities.
This paper proposes a Two-Phase Suffix Imitation framework that enables a reward-free observer to recover optimal policies from a non-stationary learner's actions by discarding initial exploration data, achieving a convergence rate of comparable to fully reward-aware learning.
This paper proposes R-Design, a novel framework that leverages observational data as a prior to focus randomized controlled trials on efficiently estimating bias-correcting residuals, thereby achieving superior convergence rates and information efficiency compared to traditional methods that learn causal effects from scratch.
The paper proposes ISD-linUCB, an algorithm for stochastic non-stationary linear bandits that leverages the decomposition of reward models into stationary and non-stationary components to learn invariances from historical data, thereby reducing problem dimensionality and significantly improving dynamic regret in fast-changing environments.
This paper proposes a hierarchical Bayesian framework that integrates adaptive surrogate models, such as Fourier Neural Operators and parametric PINNs, with ensemble MALA sampling to simultaneously infer individual system parameters and learn shared unknown dynamics via ML-based closure models for ODEs and PDEs.