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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,695 papers · 148 categories

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306191121 · May 202619922001200920172026
48 results for unbounded covariates

Paper tackles unbounded density ratio estimation for covariate shift adaptation.

problem Understudied challenge in statistical learning: unbounded density ratios.
method Three-step estimation method: relative density ratio, truncation, and transformation.
result Established rigorous convergence guarantees for density ratio and regression estimators.

New algorithm estimates Gaussian means and covariances efficiently and privately.

problem Estimating Gaussian parameters privately and efficiently.
method Differentially private preconditioner to transform arbitrary Gaussian samples.
result First polynomial-time, sample-efficient estimator for arbitrary Gaussian distributions.

Paper develops estimators for unbounded density ratios with applications in error control.

problem Estimating density ratios with unbounded domains and ranges.
method Least squares and logistic regression loss functions for density ratio estimation.
result Established upper bounds on estimation errors with optimal rates for unbounded density ratios.

Spectral algorithms improve under covariate shift with novel weighted techniques.

problem Improving spectral algorithms' performance under covariate shift.
method Analysis of spectral algorithms in non-parametric regression over RKHS, proposing a weighted spectral algorithm with clipped weights.
result Normalized weighted spectral algorithm achieves optimal capacity-independent convergence rates, and clipped weights can approach optimal capacity-dependent rates.

LLA shows strong performance in Bayesian optimization but has unbounded search space issues.

problem Applying LLA in unbounded search spaces for Bayesian optimization.
method Linearized-Laplace approximation applied to Bayesian optimization problems.
result LLA demonstrates strong performance but also presents unbounded search space challenges.

Efficient method for learning continuous exponential families beyond Gaussian.

problem Learning continuous exponential families with unbounded support.
method Interaction Screening approach for scalable learning of continuous graphical models.
result Our estimator maintains similar accuracy and sample complexity scalings compared to alternative approaches, while improving run-time.

This paper rethinks confidence calibration under covariate shifts.

problem Calibration methods struggle with covariate shifts and unstable importance weighting.
method Derives Expectation consistency condition and proposes Expectation consistency loss (ECL).
result ECL loss is compatible with various types of calibration and has the same sample complexity as ECE.

Optimally tackles covariate shift in RKHS-based nonparametric regression.

problem Covariate shift in nonparametric regression over RKHS.
method Two families of covariate shift problems defined using likelihood ratios. Minimax rate-optimal estimators for KRR and reweighted KRR.
result KRR is minimax rate-optimal and strictly sub-optimal compared to naive estimator under covariate shift.

Generalizes Ricci flow starting from small curvature concentration with a Morrey-type condition.

problem Ricci flow starting from manifolds with unbounded curvature.
method Replaces bounded curvature with a Morrey-type condition on the gradient of the metric relative to a complete bounded curvature metric.
result Long-time existence of Ricci flow with curvature decay estimates and diffeomorphic manifold.

Study projective representations of infinite-dimensional Hilbert-Lie groups.

problem Characterize and classify representations of Hilbert-Lie groups.
method Use covariance with respect to one-parameter groups of automorphisms and implement perturbation theory.
result Explicit determination of central extensions for projective representations.

Paper analyzes spectral algorithms under covariate shift, providing convergence rates.

problem Addressing distributional mismatch in regression models.
method Incorporates importance weights into spectral algorithms in RKHS.
result Establishes minimax-optimal convergence rates for misspecified cases.

Two new algorithms improve performance in adversarial bandits with unbounded losses.

problem Adversarial Multi-Armed Bandits with unbounded losses.
method Developed UMAB-NN and UMAB-G for non-negative and general unbounded losses respectively.
result UMAB-NN achieves the first adaptive and scale-free regret bound for non-negative unbounded losses.

This memoir presents a systematic study of the utility maximization problem of an investor in a constrained and unbounded financial market. Building upon the work of Hu et al. (2005) [Ann. Appl. Probab., 15, 1691--1712] in a bounded framework, we extend our analysis to the more challenging unbounded case. Our methodolo…

2017-07-01abs ↗pdf ↗

It is shown that the compactly supported identity component of the diffeomorphism group of the 2-dimensional punctured torus Tp2\mathbb T^2_p is an unbounded group. It follows that the fragmentation norm of Tp2\mathbb T^2_p is unbounded.

2011-03-18abs ↗pdf ↗

New inequalities for unbounded functions improve denoising score matching.

problem Statistical error bounds for denoising score matching with unbounded objective functions.
method Derive new concentration inequalities using McDiarmid's inequality and Rademacher complexity bounds.
result Improved statistical error bounds for denoising score matching.

Study focal surfaces of wave fronts with unbounded curvatures.

problem Characterizing singularities of focal surfaces near non-degenerate singular points.
method Characterizations based on types of singularities and geometrical properties of initial fronts.
result Investigation of Gaussian curvature behavior of focal surfaces.

This paper solves the normalizability crisis in sequential inference by introducing bounded information geometry.

problem Structural failure in standard sequential inference architectures when dealing with extreme outliers.
method Non-parametric field actions and bounded information geometry to truncate infinite tails of spatial distributions.
result Empirical benchmarks across three domains show robust estimation without infinite-tailed distributional assumptions.

Study shows unbounded Pontryagin numbers on curved manifolds.

problem Understanding unbounded Pontryagin numbers on curved manifolds.
method Analyzing rational linear combinations of Pontryagin numbers and their relation to the universal elliptic genus.
result Proves existence of unbounded Pontryagin numbers on nonnegatively curved spin manifolds.

Paper tackles online control of linear systems with unbounded noise.

problem Online control of linear systems under unbounded noise with unknown convex cost functions.
method Developed an algorithm achieving ildeO(T) ilde{O}(\sqrt{T}) high-probability regret under unbounded noise, and established O(mpoly(logT)) O({ m poly} (\log T)) regret bound for strongly convex costs and sub-Gaussian noise.
result Achieved ildeO(T) ilde{O}(\sqrt{T}) high-probability regret under unbounded noise, and O(mpoly(logT)) O({ m poly} (\log T)) regret bound for specific noise and cost conditions.

New approach finds solutions to games with unbounded controls.

problem Existence of equilibrium in mean-field games with unbounded controls.
method Weak formulation and new existence/stability results for quadratic-growth generalized McKean-Vlasov BSDEs.
result Existence of equilibrium result for non-Markovian mean-field games with unbounded control space.

Novel SVM approach for extreme quantile regression with heavy tailed inputs.

problem Learning from extreme values in quantile regression.
method Support Vector Machine framework for handling high-dimensional and nonlinear settings.
result Established finite-sample learning guarantees under mild regularity assumptions.

The paper provides gradient estimates for Neumann semigroups on manifolds with boundary under unbounded curvature conditions.

problem Gradient estimates for Neumann semigroups on manifolds with boundary under unbounded curvature conditions.
method Establishes Bismut-type formulas and gradient estimates for Feynman--Kac semigroups on Riemannian manifolds with boundary, under geometric conditions formulated in terms of Ricci curvature and second fundamental form.
result Derives pointwise gradient estimates for the Neumann semigroup under variable, possibly unbounded, lower curvature bounds.

Improved sampling from Gaussian distributions with privacy constraints.

problem Sampling from unbounded Gaussian distributions with differential privacy.
method First $\widetilde{\mathcal{O}}\left(d ight)$-sample algorithm for unbounded Gaussians under $\left(\varepsilon, δ ight)$-differential privacy.
result A quadratic improvement over previous results, settling an open question.

In this paper, we derive Li-Yau inequality for unbounded Laplacian on complete weighted graphs with the assumption of the curvature-dimension inequality CDE(n,K)CDE'(n,K), which can be regarded as a notion of curvature on graphs. Furthermore, we obtain some applications of Li-Yau inequality, including Harnack inequality, hea…

2018-01-18abs ↗pdf ↗

Study unbounded sl3\mathfrak{sl}_3-laminations around punctures.

problem Classify and understand structures of sl3\mathfrak{sl}_3-laminations at punctures.
method Relate to root data, classify signed webs, describe tropicalization, clarify relationships with other approaches.
result Clarify the relationship between sl3\mathfrak{sl}_3-laminations and other approaches.

We consider the problem of minimizing the relative perimeter under a volume constraint in an unbounded convex body CRn+1C\subset \mathbb{R}^{n+1}, without assuming any further regularity on the boundary of CC. Motivated by an example of an unbounded convex body with null isoperimetric profile, we introduce the concept of…

2016-06-13abs ↗pdf ↗

Solves open problem on universally consistent online learning with unbounded losses.

problem Open problem on universally consistent online learning with unbounded losses.
method Constructs random measurable partitions of the instance space.
result Simple memorization rule is optimistically universal for any unbounded loss.

Suppose kk centers are fit to mm points by heuristically minimizing the kk-means cost; what is the corresponding fit over the source distribution? This question is resolved here for distributions with p4p\geq 4 bounded moments; in particular, the difference between the sample cost and distribution cost decays with $…

2013-11-08abs ↗pdf ↗

New method tackles endogeneity in online learning with improved regret bounds.

problem Endogeneity in real data due to omitted variables, strategic behaviors, etc.
method O2SLS (Online Two-Stage Least Squares) for Instrumental Variable (IV) regression.
result O2SLS achieves identification and oracle regret bounds for stochastic online learning.