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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 simple functions

Simple constructions of semi-discrete and discrete surfaces using Jacobi elliptic functions.

problem Constructing semi-discrete and discrete surfaces explicitly.
method Using Jacobi elliptic functions and τ-functions.
result Explicit constructions and periodicities of semi-discrete and discrete surfaces.

Bayesian model uses simple functions to forecast macroeconomic data.

problem Forecasting large datasets in macroeconomics with complex nonlinear relationships.
method Sum of simple two-component location mixtures, logistic function threshold, conjugate priors.
result Accurate point and density forecasts in US macroeconomic aggregates.

qEUBO optimizes decision-making with noisy feedback.

problem Optimizing decision-making with noisy preference feedback.
method Introduces qEUBO as a novel acquisition function for preferential Bayesian optimization.
result qEUBO is one-step Bayes optimal and enjoys an approximation guarantee under noise.

Study Morse functions on projective plane using Reeb graphs.

problem Investigate topological structure of Morse functions on projective plane.
method Use Reeb graphs to describe and prove properties of simple Morse functions on RP2\mathbb{R} P^2.
result Prove that Reeb graphs are a complete topological invariant for simple Morse functions on RP2\mathbb{R} P^2.

Learning automatically the best activation function for the task is an active topic in neural network research. At the moment, despite promising results, it is still difficult to determine a method for learning an activation function that is at the same time theoretically simple and easy to implement. Moreover, most of…

2019-02-08abs ↗pdf ↗

New algorithm solves complex optimization problems efficiently.

problem Minimizing convex upper-level functions over optimal lower-level solutions.
method Reformulates bilevel problems into functionally constrained problems, achieving near-optimal rates.
result Achieves near-optimal rates for both smooth and nonsmooth problems.

Simple neural networks approximate any continuous function with fixed neurons.

problem Approximating arbitrary continuous functions with limited neurons.
method Developed simple feed-forward neural networks with a specific activation function.
result Proven that networks with 36d(2d+1) neurons and depth 11 can approximate any continuous function.

Enhances neural models with simple functions to improve language modeling.

problem Neural models struggle with certain spatial, temporal, or quantitative relationships.
method Integrates simple functions into neural architecture to form a hierarchical NSLM.
result NSLMs significantly reduce perplexity in small-corpus language modeling.

Cylindrical contact homology computed for links of simple singularities.

problem Computing cylindrical contact homology for links of simple singularities.
method Perturbing degenerate contact form on S3/GS^3/G with an invariant Morse function, achieving nondegeneracy up to an action threshold. Recovers cylindrical contact homology via direct limit of action filtered homology groups.
result Ranks of cylindrical contact homology groups are given in terms of extConj(G)| ext{Conj}(G)|, demonstrating a form of the McKay correspondence.

In this note, we observe the behavior of gradient flow and discrete and noisy gradient descent in some simple settings. It is commonly noted that addition of noise to gradient descent can affect the trajectory of gradient descent. Here, we run some computer experiments for gradient descent on some simple functions, and…

2018-08-14abs ↗pdf ↗

A simple formula is derived for the Ricci scalar curvature of any smooth level set ψ(x0,x1,...,xn)=C{ψ(x_0,x_1,...,x_n)=C} embedded in the Euclidean space Rn+1 \mathbb R^{n+1}, in terms of the gradient ψ \nablaψ and the Laplacian Δψ Δψ. Some applications are given to the geometry of low-dimensional pp-harmonic functions and high-dime…

2013-01-10abs ↗pdf ↗

We prove that the expectation value of the index function i(x) over a probability space of injective function f on any finite simple graph G=(V,E) is equal to the curvature K(x) at the vertex x. This result complements and links Gauss-Bonnet sum K(x) = chi(G) and Poincare-Hopf sum i(x) = chi(G) which both hold for arbi…

2012-02-21abs ↗pdf ↗

A theorem for debiasing machine learning with finite sample guarantees.

problem Calculating confidence intervals for machine learning functionals.
method Debiased machine learning based on bias correction and sample splitting.
result Nonasymptotic debiased machine learning theorem with finite sample guarantees.

We show a very simple and general total second variation formula for Perelman's W\mathcal{W}-functional at arbitrary points in the space of Riemannian metrics. Moreover we perform a study of the properties of the variations of Kähler structures. We deduce a quite simple and general total second variation formula for P…

2012-01-04abs ↗pdf ↗

A very simple R3\mathbb R^3 realization of the Möbius strip, significantly simpler than the common one, is given. For any, however large width/length ratio of the strip, it is shown that this realization, in contrast with the common one, is the union of a vertical segment and the graph of a simple rational function on …

2018-08-12abs ↗pdf ↗

Paper proves Łojasiewicz inequalities near simple bubble trees on surfaces.

problem Proving Łojasiewicz inequalities for critical points on surfaces.
method Deriving sufficient conditions for Łojasiewicz inequalities near almost-critical points in a Hilbert space.
result Sequences of almost critical points satisfy Łojasiewicz inequalities as they approach the first non-trivial bubble tree.

Ensemble techniques are powerful approaches that combine several weak learners to build a stronger one. As a meta-learning framework, ensemble techniques can easily be applied to many machine learning methods. Inspired by ensemble techniques, in this paper we propose an ensemble loss functions applied to a simple regre…

2018-10-25abs ↗pdf ↗

Generic metrics on manifolds yield simple Steklov eigenvalues and Morse boundary functions.

problem Understanding the properties of Steklov eigenfunctions under generic metrics.
method Analyzing smooth compact manifolds with smooth boundaries and generic metrics of CkC^k type.
result Nonzero Steklov eigenvalues are simple and non-constant eigenfunctions are Morse functions on the boundary.

Simple rules ensure gradient descent adapts to local geometry, converging for convex and nonconvex problems.

problem Minimizing convex and nonconvex functions efficiently.
method Two rules: don't increase stepsize too fast and don't overstep local curvature.
result Method converges for convex and nonconvex problems, even with infinite global smoothness.

The purpose of this note is to give a simple description of a (complete) family of functions in involution on certain hermitian symmetric spaces. This family, obtained via bi-hamiltonian approach using the Bruhat Poisson structure, is especially simple for the projective spaces, where the formulas in terms of the momen…

2001-02-26abs ↗pdf ↗

The main aim of this work is to construct several new families of proper biharmonic functions defined on open subsets of the classical compact simple Lie groups $\SU n$, $\SO n$ and $\Sp n$. We work in a geometric setting which connects our study with the theory of submersive harmonic morphisms. We develop a general du…

2016-08-30abs ↗pdf ↗

Study finds simple model-agreement scores perform well in various error estimation scenarios.

problem Evaluating model performance on unseen distributions using disparate scoring functions.
method Rigorously studied popular scoring functions (confidence, local manifold smoothness, model agreement) independently of mechanism choice.
result Simple model-agreement scores outperform confidence- and smoothness-based scores in realistic settings with compromised training data.

Paper optimizes multi-fidelity function with fast learning rates.

problem Optimizing a locally smooth function with limited budget and varying fidelity approximations.
method Kometo algorithm that achieves simple regret rates without knowing function smoothness or fidelity assumptions.
result Kometo algorithm outperforms previous methods empirically.

While conformal transformations of the plane preserve Laplace's equation, Lorentz-conformal mappings preserve the wave equation. We discover how simple geometric objects, such as quadrilaterals and pairs of crossing curves, are transformed under nonlinear Lorentz-conformal mappings. Squares are transformed into curvili…

2013-06-05abs ↗pdf ↗

In this note, we give a generalization of the inversion formulas of Pestov-Uhlmann for the geodesic ray transform of functions and vector fields on simple 2-dimensional manifolds of constant curvature. The inversion formulas given here hold for 2-dimensional simple manifolds whose curvatures close to a constant.

2007-12-08abs ↗pdf ↗

The paper characterizes graph manifolds using fold maps and embeddability of polyhedra.

problem Understanding the global topologies of graph manifolds.
method Using fold maps into the plane and embeddability of polyhedra in 3-manifolds.
result Characterizes graph manifolds via fold maps and polyhedra embeddability.

Study proper sampling for X-ray transforms on simple surfaces.

problem Proper discretizing and sampling issues related to geodesic X-ray transforms on simple surfaces.
method Provide minimal sampling rates for faithful reconstruction, quantify sampling quality, and predict artifacts.
result Minimal sampling rates and artifact prediction for geodesic X-ray transforms on simple surfaces.