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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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14274154 · Jun 202019922001200920172026
48 results for Tippett minimum

Improved OOD detection across various shifts using multi-encoder fusion of RDMs.

problem Out-of-distribution detection across multiple types of distribution shifts.
method Statistical identification of encoder sensitivity, EncMin2L fusion, and Tippett minimum combination.
result Achieves AUROC ≥ 0.94 across four shift types, outperforming state-of-the-art detectors.

For classification of the high frequency trading quantities, waiting times, price increments within and between sessions are referred to as the a-, b-, and c-increments. Statistics of the a-b-c-increments are computed for the Time & Sales records posted by the Chicago Mercantile Exchange Group for the futures traded on…

2013-12-06abs ↗pdf ↗

Minimum attention improves reinforcement learning performance in high-dimensional dynamics.

problem Improving reinforcement learning performance in high-dimensional nonlinear dynamics.
method Applying minimum attention as a regularization technique in reinforcement learning, including model-based and model-free approaches.
result Minimum attention outperforms state-of-the-art algorithms in few-shot adaptation and variance reduction.

The minimum number of colors is a challenging knot invariant since, by definition, its calculation requires taking the minimum over infinitely many minima. In this article we estimate and in some cases calculate the minimum number of colors for the Turk's head knots on three strands.

2010-02-25abs ↗pdf ↗

Minimum braids are a complete invariant of knots and links. This paper defines minimum braids, describes how they can be generated, presents tables for knots up to ten crossings and oriented links up to nine crossings, and uses minimum braids to study graph trees, amphicheirality, unknotting numbers, and periodic table…

2004-01-06abs ↗pdf ↗

The paper finds minimum Dehn colors for knots and defines useful graphs for coloring.

problem Finding the minimum number of colors for Dehn colorings of knots.
method Analyzes Dehn colorings for knots and defines R\R-palette graphs.
result For Dehn pp-colorable knots, the minimum number of colors is at least log2pfloor+2\lfloor \log_2 p floor +2.

Knots are commonly found in molecular chains such as DNA and proteins, and they have been considered to be useful models for structural analysis of these molecules. One interested quantity is the minimum number of monomers necessary to realize a molecular knot. The minimum lattice length $\mbox{Len}(K)$ of a knot KK i…

2014-11-07abs ↗pdf ↗

Study introduces AMVP and AMRR for dynamic portfolio optimization in volatile markets.

problem Optimizing portfolios in volatile and nonstationary financial markets.
method Adaptive Minimum-Variance Portfolio (AMVP) framework with ARFIMA-FIGARCH processes and non-Gaussian innovations.
result Demonstrated superior performance in risk reduction and portfolio stability during market breaks.

This paper studies the geometry of minimum-volume confidence sets for multinomial parameters.

problem Determining if minimum-volume confidence sets for multinomial outcomes are disjoint.
method Enumerating and covering the continuous regions of the exact p-value function to study the geometry of minimum-volume confidence sets.
result The geometry of minimum-volume confidence sets for multinomial parameters is studied, providing insights into their structure and properties.

Building on previous results on the quadratic helicity in magnetohydrodynamics (MHD) we investigate particular minimum helicity states. Those are eigenfunctions of the curl operator and are shown to constitute solutions of the quasi-stationary incompressible ideal MHD equations. We then show that these states have inde…

2018-06-19abs ↗pdf ↗

Inference for normal and Monte Carlo distributions using minimum relative entropy.

problem Inference from partial information on expectations and covariances.
method Minimum relative entropy sub-manifolds, analytical formulas, Monte Carlo simulations.
result Improved numerical implementation for inference from partial information.

ML helps select variables for minimum-variance portfolios, reducing risk and improving performance.

problem Optimizing minimum-variance portfolios with relevant predictors.
method Parameterized minimum-variance portfolio weights using a large pool of firm-level characteristics and their transformations.
result ML-selected predictors lead to lower risk and better performance in minimum-variance portfolios.

Paper proves edge-connectivity equals minimum degree for graphs with non-negative curvature.

problem Edge-connectivity vs. minimum degree in graphs with non-negative curvature.
method Analyzes finite connected graphs with non-negative Lin-Lu-Yau curvature.
result Edge-connectivity equals minimum degree for graphs with non-negative curvature.

New approach finds minimum width for deep, narrow MLPs.

problem Finding the minimum width for deep, narrow MLPs to approximate continuous functions.
method Proposes a framework to simplify finding minimum width into determining a geometrical function w(dx,dy)w(d_x, d_y) based on input and output dimensions.
result Proves that w(dx,dy)w(d_x, d_y) equals the optimal minimum width for deep, narrow MLPs to achieve universality.

We consider the relations between different measures of complexity for free homotopy classes of curves on a surface ΣΣ, including the minimum number of self-intersections, the minimum length of the words representing them in a geometric presentation of π1(Σ)π_1(Σ), and the minimum degree of the coverings of ΣΣ to which …

2017-12-18abs ↗pdf ↗

Unified framework for geometric computation of minimum-area homotopy.

problem Computing the minimum homotopy area of a closed curve.
method Unified combinatorial word approach combining geometric and algebraic methods.
result Unified geometric proof and constructive algorithm for minimum area homotopy.

The study tightens bounds on binomial probabilities and minimums using KL-divergence.

problem Tightening bounds on binomial probabilities and minimums of i.i.d. Binomials.
method Applied Sanov's theorem to derive upper and lower bounds on binomial tail probabilities and minimums, expressed in terms of KL-divergence.
result High probability upper and lower bounds on the minimum of i.i.d. Binomial random variables, finite sample, asymptotically tight.

Originally, the SW-equations discovered by Seiberg-Witten are 1st-order PDE, which solutions (A,φ), with φ\ne 0, are known as SW-monopoles. It is known that the solutions of these 1st-order eq correspond to the minimum of SW-functional. However, it is not true, that for all spin^{c} class α, the minimum is always attai…

2002-05-06abs ↗pdf ↗

Investigates the long-only minimum variance portfolio in factor models.

problem Understanding the long-only minimum variance portfolio in factor models.
method Investigates the long-only global minimum variance portfolio in a factor model of returns, providing explicit and geometric descriptions for different factor models.
result Provides rigorous and explicit descriptions of the long-only solution in terms of covariance matrix parameters and geometric descriptions for multiple factors.

Defines MER for Bayesian learning, a gap between achievable and optimal performance.

problem Analyzing the best performance of Bayesian learning under generative models.
method Two methods for deriving upper bounds for MER: conditional mutual information and minimum estimation error.
result Quantifies the rate at which MER decays to zero with more data and relates it to model richness.

Consider the problem of estimating the minimum entropy of pseudo-Anosov maps on a surface of genus gg with nn punctures. We determine the behaviour of this minimum number for a certain large subset of the (g,n)(g,n) plane, up to a multiplicative constant. In particular it has been shown that for fixed nn, this minimum …

2018-01-05abs ↗pdf ↗

New framework for DNN training guarantees convergence to global minimum.

problem Training deep neural networks to converge to global minimum.
method Reformulated minimization problem with recursive algorithmic framework, using bounded style assumptions.
result Convergence to an ε-(global) minimum with O(1/ε^3) gradient computations.

Exponential models of distributions are widely used in machine learning for classiffication and modelling. It is well known that they can be interpreted as maximum entropy models under empirical expectation constraints. In this work, we argue that for classiffication tasks, mutual information is a more suitable informa…

2012-07-11abs ↗pdf ↗

A wide range of fundamental machine learning tasks that are addressed by the maximum a posteriori estimation can be reduced to a general minimum conical hull problem. The best-known solution to tackle general minimum conical hull problems is the divide-and-conquer anchoring learning scheme (DCA), whose runtime complexi…

2019-07-16abs ↗pdf ↗