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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.

168,742 papers · 148 categories

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48 results for continuous interval maps

We exhibit families of Ricci-flat Kahler metrics on K3 surfaces which collapse to an interval, with Tian-Yau and Taub-NUT metrics occurring as bubbles. There is a corresponding continuous surjective map from the K3 surface to the interval, with regular fibers diffeomorphic to either 3-tori or Heisenberg nilmanifolds.

2018-07-24abs ↗pdf ↗

It is proved that for every fractal continuous mapping F: I\to I^2 of the unit interval onto the unit square there is a pair of points x,y\in I, such that |F(x)-F(y)|^2\ge 5|x-y|.

2004-04-29abs ↗pdf ↗

In topological data analysis, persistent homology is used to study the "shape of data". Persistent homology computations are completely characterized by a set of intervals called a bar code. It is often said that the long intervals represent the "topological signal" and the short intervals represent "noise". We give ev…

2019-05-30abs ↗pdf ↗

CASCADE improves uncertainty communication in Parkinson's disease medication management.

problem Uncertainty in clinical decision-making for Parkinson's disease patients.
method CASCADE uses a novel conformal prediction framework to adaptively scale prediction intervals based on classification uncertainty.
result CASCADE produces more efficient and robust prediction intervals for Parkinson's disease patients.

ICP improves prediction intervals for continuous outcomes at lower computational cost.

problem Systematic bias in point predictions that undermines their use in decision-making.
method Develops Isotonic Conformal Prediction (ICP) framework to decouple calibration from prediction-set construction.
result SICP and TICP procedures match SC-CP coverage at lower computational cost.

MAPS algorithm creates reliable prediction intervals for high-dimensional data.

problem Computing reliable conditional prediction intervals in high-dimensional settings.
method Lifted predictive model (LPM) and MAPS algorithm for distribution-free intervals.
result MAPS algorithm produces valid prediction intervals for any trained model.

Construct pseudo-Anosovs from expanding interval maps, reconciling Thurston's construction.

problem Constructing pseudo-Anosov homeomorphisms from expanding interval maps.
method Classifying circumstances for constructing pseudo-Anosovs from a specific subclass of generalized pseudo-Anosovs.
result Produces pseudo-Anosovs on surfaces of genus gg with algebraically primitive translation structures and Salem dilatations.

In modeling multivariate time series, it is important to allow time-varying smoothness in the mean and covariance process. In particular, there may be certain time intervals exhibiting rapid changes and others in which changes are slow. If such time-varying smoothness is not accounted for, one can obtain misleading inf…

2012-10-07abs ↗pdf ↗

Extends Fisher's Discriminant Analysis for interval-valued data.

problem Classifying entities represented by intervals and histograms.
method Adapts Fisher's Discriminant Analysis using Moore's interval arithmetic and Mallows' distance.
result Discriminant directions for interval-valued data are numerically maximized.

The symmetries of paths in a manifold MM are classified with respect to a given pointwise proper action of a Lie group GG on MM. Here, paths are embeddings of a compact interval into MM. There are at least two types of symmetries: Firstly, paths that are parts of an integral curve of a fundamental vector field on $…

2015-03-21abs ↗pdf ↗

The spectral flow theorem is applied to operators on finite intervals.

problem Operators on finite intervals without boundary conditions are not Fredholm.
method Interpolation theory is used to define boundary conditions making the operators Fredholm. The spectral flow theorem is applied to find the Fredholm index.
result The Fredholm index is given by the spectral flow of the operator path.

Proposes a method for generating prediction intervals in dose-response models using conformal prediction.

problem Uncertainty quantification in continuous treatments for personalized healthcare decisions.
method Causal dose-response problem framed as covariate shift, using weighted conformal prediction with propensity estimation and kernel functions.
result Demonstrates the significance of covariate shift assumptions for robust prediction intervals.

Study bounds for European basket call options in a discrete-time market model with price jumps.

problem Bounding the prices of European basket call options in a market model with price jumps.
method Computed bounds using a binomial model and proved that the lower bound coincides with Jensen's bound.
result The upper bound of the price interval of European basket call options can be computed by restricting to a binomial model.

The paper improves methods for generating prediction intervals in regression.

problem Uncertainty quantification in regression models.
method Formalizes prediction interval generation as an optimization problem, studying generalization and calibration.
result Empirical demonstration of improved testing performances compared to existing methods.

In [Mas82] and [Vee78] it was proved independently that almost every interval exchange transformation is uniquely ergodic. The Birkhoff ergodic theorem implies that these maps mainly have uniformly distributed orbits. This raises the question under which conditions the orbits yield low-discrepancy sequences. The case o…

2017-11-20abs ↗pdf ↗

CRUs model irregular time series with continuous hidden states.

problem Handling irregular time intervals in sequential data.
method Continuous Recurrent Units (CRUs) that integrate hidden states via a linear stochastic differential equation.
result CRUs outperform methods based on neural ordinary differential equations in irregular time series interpolation.

Develops conformalized prediction intervals for bounded continuous outcomes.

problem Predicting continuous outcomes within bounded ranges, especially when models are misspecified.
method Conformal prediction intervals based on transformation regression models, accounting for heteroscedasticity and asymmetry.
result Valid finite-sample coverage confirmed in simulations and real data applications.

We present proofs of basic results, including those developed by Harold Bell, for the plane fixed point problem: does every map of a non-separating plane continuum have a fixed point? Some of these results had been announced much earlier by Bell but without accessible proofs. We define the concept of the variation of a…

2010-04-01abs ↗pdf ↗

This paper analyzes optimal stopping regions for American options with Poisson exercise opportunities.

problem Analyzing the optimal stopping regions for American options with Poisson exercise opportunities.
method Computing identities related to the first Poisson arrival time to an interval and applying them to the computation of the optimal strategies.
result Explicit expressions of the stopping and continuation regions and the value function are obtained.

A natural generalization of interval exchange maps are linear involutions, first introduced by Danthony and Nogueira. Recurrent train tracks with a single switch provide a subclass of linear involutions. We call such linear involutions non-classical interval exchanges. They are related to measured foliations on orienta…

2009-06-14abs ↗pdf ↗

We prove that a continuum XX is tree-like (resp. circle-like, chainable) if and only if for each open cover $\U_4=\{U_1,U_2,U_3,U_4\}$ of XX there is a $\U_4$-map f:XYf:X\to Y onto a tree (resp. onto the circle, onto the interval). A continuum XX is an acyclic curve if and only if for each open cover $\U_3=\{U_1,U_2,U…

2010-03-28abs ↗pdf ↗

Stochastic gradient Langevin dynamics (SGLD) is a computationally efficient sampler for Bayesian posterior inference given a large scale dataset. Although SGLD is designed for unbounded random variables, many practical models incorporate variables with boundaries such as non-negative ones or those in a finite interval.…

2019-03-07abs ↗pdf ↗

We study the J-flow on the toric manifolds, through study the transition map between the moment maps induced by two Kähler metrics, which is a diffeomorphism between polytopes. This is similar to the work of Fang-Lai, under the assumption of Calabi symmetry, they study the monotone map between two intervals. We get a p…

2014-07-04abs ↗pdf ↗

Neural Laplace Control tackles offline RL for continuous-time delayed systems with irregular observations.

problem Offline reinforcement learning problems involving continuous-time environments with delays and irregular observations.
method Combines a Neural Laplace dynamics model with a model predictive control (MPC) planner.
result Achieves near expert policy performance on continuous-time delayed environments.

Proves the existence of accurate, certifiably robust neural networks.

problem Training neural networks to be robust against adversarial attacks.
method Proves the existence of networks that approximate continuous functions and ensure robustness through interval-bound propagation.
result Proves the existence of accurate, interval-certified ReLU networks.

We develop a model to cluster time-series data with interval censoring, improving disease phenotyping.

problem Noise and interval censoring hinder clustering in disease phenotyping.
method Deep generative, continuous-time model that clusters time-series data while correcting for censorship.
result Our model corrects for interval censoring and recovers known clinical subtypes.

Develops analysis of Hölder continuous mappings on Heisenberg groups.

problem Analyzing Hölder continuous mappings on Heisenberg groups.
method Theory of distributional Jacobians and pullbacks of differential forms.
result Simple proof of a generalization of the Gromov non-embedding theorem and new results about Hölder homotopy groups.

This paper develops sparse alternatives to continuous distributions, including new types of Gaussians and attention mechanisms.

problem Creating flexible continuous distributions with varying support for machine learning applications.
method Defining ΩΩ-regularized prediction maps and Fenchel-Young losses for arbitrary domains, and deriving new types of Gaussians and attention mechanisms.
result Sparse alternatives to continuous distributions, including deformed exponential families and ββ-Gaussians, are introduced.

This work uses statistical bootstrapping to provide accurate confidence intervals for policy value in reinforcement learning.

problem Bias in estimating policy value using empirical transitions and rewards.
method Statistical bootstrapping to produce calibrated confidence intervals for the true policy value.
result Statistical bootstrapping can yield correct confidence intervals under certain conditions, and mechanisms are proposed to mitigate these conditions.