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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.

168,657 papers · 148 categories

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155311466621 · Jun 202019922001200920172026
48 results for distributional equivalence

Local equivalence found between maximally symmetric rolling and flat Cartan distributions.

problem Establishing local equivalence between maximally symmetric rolling and flat Cartan distributions.
method Using complex parametrisation of su(2), a change of coordinates maps the maximally symmetric rolling (2,3,5)(2,3,5)-distribution to the flat Cartan distribution.
result Local equivalence between maximally symmetric rolling and flat Cartan distributions established.

Kernel tests assess equivalence between distributions without assuming specific moments.

problem Traditional goodness-of-fit tests fail to detect meaningful distributional differences.
method Proposes kernel-based tests using kernel Stein discrepancy and Maximum Mean Discrepancy.
result Tests assess the absence of meaningful distributional differences under controlled error rates.

Local equivalence shown between specific distributions and flat Cartan distribution.

problem Establishing local equivalence between specific distributions and flat Cartan distribution.
method Change of coordinates mapping specific distributions to flat Cartan distribution.
result Local equivalence between maximally symmetric (2,3,5)(2,3,5)-distributions and flat Cartan distribution.

We describe financial systems as condensates, similar to Bose-Einstein condensates, and calculate statistical distributions following from the model. The calculated distributions of investments into speculated financial assets are found equivalent to a Pareto distribution, and the calculated distributions of the price …

2003-03-14abs ↗pdf ↗

A new sampler improves the inference of causal structures from observational data.

problem Inferring causal relationships from observational data when DAGs are Markov equivalent.
method Developed a non-reversible Markov chain, Causal Zig-Zag sampler, targeting Markov Equivalence Classes of DAGs.
result The sampler improves mixing and offers efficient algorithms for DAG inference.

Unified framework for fairness, robustness, and distribution shifts.

problem Diverse failure modes of machine learning systems.
method Formalizes biases as violations of conditional independence and proves equivalence conditions.
result Equivalent effects of biases in different failure modes under specific conditions.

New equivalences found between subsampling and ridge regularization methods.

problem Establishing precise structural and risk equivalences between subsampling and ridge regularization.
method Proved structural and risk equivalences between subsample ridge estimators and different ridge regularization levels and subsample aspect ratios.
result Optimally tuned ridge regression exhibits a monotonic prediction risk in the data aspect ratio.

We characterize distributional equivalence in latent-variable models with cycles.

problem Lack of an equivalence characterization for latent-variable causal models with cycles.
method Established graphical criterion for distributional equivalence and developed edge rank constraints.
result First equivalence characterization without structural assumptions for latent-variable models with cycles.

We shrink confidence sets for equivalent discrete distributions using permutation equivalence.

problem Building high-probability confidence sets for equivalent discrete distributions.
method Exploiting permutation-equivalence to refine confidence sets.
result Confidence sets shrink at asymptotic rates of O(1/kKnk)O(1/\sqrt{\sum_{k\in \mathcal K} n_k}) and O(1/maxkKnk)O(1/\max_{k\in K} n_{k}).

We consider a problem of equivalence of generic pairs (X,V)(X,V) on a manifold MM, where VV is a distribution of rank mm and XX is a distribution of rank one. We construct a canonical bundle with a canonical frame. We prove that two pairs are equivalent if and only if the corresponding frames are diffeomorphic. As a p…

2007-12-10abs ↗pdf ↗

We show that for n>2 the following equivalence problems are essentially the same: the equivalence problem for Lagrangians of order n with one dependent and one independent variable considered up to a contact transformation, a multiplication by a nonzero constant, and modulo divergence; the equivalence problem for the s…

2010-04-10abs ↗pdf ↗

New findings on PAC learning and marginal distribution estimation.

problem Understanding how PAC learning relates to marginal distribution estimation under distributional constraints.
method Revisited the connection between PAC learning, uniform convergence, and density estimation, considering a known family of marginal distributions.
result PAC learning is sandwiched between two refined models of density estimation, differing only in whether the learner knows the set of well-estimated events in H.

The Mean-Variance Criterion is equivalent to Second-order Stochastic Dominance under symmetric Elliptical distributions.

problem Determining the equivalence of Mean-Variance Criterion and Stochastic Dominance Criteria.
method Analyzing under symmetric and Skew-Elliptical distributions using Monte Carlo simulations.
result The Mean-Variance Criterion does not coincide with Second-order Stochastic Dominance for some types of risk-averse investors.

An interesting approach to analyzing neural networks that has received renewed attention is to examine the equivalent kernel of the neural network. This is based on the fact that a fully connected feedforward network with one hidden layer, a certain weight distribution, an activation function, and an infinite number of…

2017-11-24abs ↗pdf ↗

Linear properties are either universal or absent across language models.

problem Explaining the prevalence of linear properties in language models.
method Proved identifiability of distribution-equivalent next-token predictors and analyzed various notions of linearity.
result Linear properties either hold in all or none distribution-equivalent next-token predictors.

Tangle machines are topologically inspired diagrammatic models. Their novel feature is their natural notion of equivalence. Equivalent tangle machines may differ locally, but globally they are considered to share the same information content. The goal of tangle machine equivalence is to provide a context-independent me…

2014-04-10abs ↗pdf ↗

Consider a smooth manifold MM equipped with a bracket generating distribution DD. Two sub-Riemannian metrics on (M,D)(M,D) are said to be projectively (resp. affinely) equivalent if they have the same geodesics up to reparameterization (resp. up to affine reparameterization). A sub-Riemannian metric gg is called rigid …

2018-01-12abs ↗pdf ↗

The paper extends distributions by singular curves, revealing structural equivalences.

problem Extending (3,6)(3, 6)-distributions using singular curves.
method Using data from singular curves, the paper extends (3,6)(3, 6)-distributions to higher rank distributions.
result The equivalence of classification problems for four extended distribution classes.

Study shows deterministic equivalent for neural network kernel convergence.

problem Understanding convergence of neural network kernels.
method Analyzes empirical spectral distribution of Conjugate Kernel, proving convergence to a deterministic limit.
result Obtains a deterministic equivalent for the Stieltjes transform and resolvent of the Conjugate Kernel.

Study shows solutions of differential inclusions are homotopy equivalent in W1,pW^{1,p}-topology.

problem Homotopy properties of solutions in differential inclusions.
method Analyzes differential inclusion with specific assumptions on corank one distribution.
result Solutions are homotopy equivalent to loop spaces in W1,pW^{1,p}-topology.

We construct a canonical frame for an arbitrary Gl(2)-structure thus solving the equivalence problem for Gl(2)-structures. Our treatment includes also a problem of contact equivalence of ordinary differential equations and applies to certain classes of vector distributions. Additionally we characterise Gl(2)-structures…

2010-12-03abs ↗pdf ↗

A Riemannian symmetric space is a Riemannian manifold in which it is possible to reflect all geodesics through a point by an isometry of the space. On such spaces, we introduce the notion of a distributional lattice, generalizing the notion of lattice. Distributional lattices exist in any Riemannian symmetric space: th…

2017-07-02abs ↗pdf ↗

Study reveals an equivalence principle for the spectrum of random inner-product kernel matrices in polynomial scaling.

problem Understanding the spectrum of random kernel matrices in polynomial scaling regimes.
method Investigates random matrices with nonlinear kernel functions applied to inner products of uniformly distributed vectors.
result The spectrum of the random kernel matrix is asymptotically equivalent to a simpler matrix model through free additive convolution.

The paper develops robust risk measures for uncertain loss positions.

problem Risk assessment for loss positions with uncertain distributions.
method Robust optimized certainty equivalents and generalized quantiles are proposed and analyzed.
result Robust expectiles with specific penalization functions are coherent risk measures.

The paper explores arbitrage opportunities in derivative markets under specific conditions.

problem Arbitrage opportunities in derivative markets under different conditions.
method Analyzes the relationship between pricing kernel monotonicity and stochastic arbitrage opportunities.
result Pricing kernel nonmonotonicity is equivalent to stochastic arbitrage opportunities under adequacy.

Characterizes causal structure dominance for latent variables.

problem Determining dominance relations between causal structures with latent variables.
method Complete characterization for three visible variables, partial for four; uses nontrivial inequality constraints.
result Equivalence classes with nontrivial inequality constraints become ubiquitous as the number of visible variables increases.

Epsilon-machines are minimal, unifilar presentations of stationary stochastic processes. They were originally defined in the history machine sense, as hidden Markov models whose states are the equivalence classes of infinite pasts with the same probability distribution over futures. In analyzing synchronization, though…

2011-11-18abs ↗pdf ↗

The study shows that generative models can be effectively used to understand neural network performance.

problem Understanding the impact of data structure on neural network performance.
method Gaussian equivalence to model training data from generative models.
result The performance of neural networks can be fully captured by an appropriately chosen Gaussian model.

This work analyzes self-attention matrices using random matrix theory.

problem Understanding the theoretical behavior of self-attention layers in neural networks.
method Asymptotic spectral analysis of the attention matrix, Gaussian equivalence, and linearization.
result The singular value distribution of the attention matrix is asymptotically characterized by a linear model.

We present a new family of models that is based on graphs that may have undirected, directed and bidirected edges. We name these new models marginal AMP (MAMP) chain graphs because each of them is Markov equivalent to some AMP chain graph under marginalization of some of its nodes. However, MAMP chain graphs do not onl…

2013-05-03abs ↗pdf ↗

Proposes new rule for ranking investment prospects over long horizons.

problem Ranking investment prospects over long horizons considering bounded risk aversion.
method Introduces asymptotic fractional-order stochastic dominance with bounded relative risk aversion.
result Establishes equivalent conditions for the new rule under lognormal returns without mean non-negativity constraint.

Leveraging an equivalence property in the state-space of a Markov Decision Process (MDP) has been investigated in several studies. This paper studies equivalence structure in the reinforcement learning (RL) setup, where transition distributions are no longer assumed to be known. We present a notion of similarity betwee…

2019-10-09abs ↗pdf ↗

For (2+2)-dimensional nonholonomic distributions, the physical information contained into a spacetime (pseudo) Riemannian metric can be encoded equivalently into new types of geometric structures and linear connections constructed as nonholonomic deformations of the Levi-Civita connection. Such deformations and induced…

2009-02-05abs ↗pdf ↗

Classifies multiply-transitive (2,3,5)-distributions using modern Cartan geometry.

problem Classifying multiply-transitive (2,3,5)(2,3,5)-distributions.
method Modern Cartan-geometric approach, incorporating G2G_2 structure theory.
result Complete classifications in both complex and real settings, with full curvature and infinitesimal holonomy.