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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.

169,051 papers · 148 categories

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48 results for mean action

Study shows mean action of periodic orbits in annuli is bounded by their Calabi invariant.

problem Understanding the average distortion of periodic orbits in area-preserving annuli.
method Analyzes action functions and Calabi invariants of diffeomorphisms near annulus boundaries.
result Infimum of mean action of periodic orbits is bounded by their Calabi invariant.

The paper studies mean curvature flows on specific orbits of Hermann actions.

problem Analyzing mean curvature flows on principal orbits of Hermann actions.
method Using Mathematica to illustrate and calculate the flows and orbits.
result The minimal principal orbit's position is calculated and illustrated.

We consider the reduced Allen-Cahn action functional, which appears as the sharp interface limit of the Allen-Cahn action functional and can be understood as a formal action functional for a stochastically perturbed mean curvature flow. For suitable evolutions of generalized hypersurfaces this functional consists of th…

2013-04-07abs ↗pdf ↗

Develops a dynamic mean field theory for reinforcement learning.

problem Finite state and action Bayesian reinforcement learning in large state spaces.
method Analogies with statistical physics, interpreting probabilities as couplings and values as spins, solving mean field equations.
result State-action values are statistically independent in the asymptotic state space limit, with exact or approximate equations for computation.

We propose a new algorithm, Mean Actor-Critic (MAC), for discrete-action continuous-state reinforcement learning. MAC is a policy gradient algorithm that uses the agent's explicit representation of all action values to estimate the gradient of the policy, rather than using only the actions that were actually executed. …

2017-09-01abs ↗pdf ↗

Investors with asymmetric information play a game to optimize their portfolios.

problem Two investors with different information levels compete in portfolio selection.
method Modelled as a Stackelberg game with entropy-regularized mean-variance objectives.
result Equilibria exist where follower's strategy depends on leader's actions.

We consider a simple instance of action up to homotopy. More precisely, we consider strict actions of DGLAs in degrees -1 and 0 on degree 1 NQ-manifolds. In a more conventional language this means: strict actions of Lie algebra crossed modules on Lie algebroids. When the action is strict, we show that it integrates to …

2010-12-02abs ↗pdf ↗

The study classifies and investigates translators invariant under hyperpolar actions on symmetric spaces.

problem Understanding translators invariant under hyperpolar actions on symmetric spaces.
method Classification and investigation of translators given by functions invariant under hyperpolar actions.
result Classification and investigation of translators in symmetric spaces under hyperpolar actions.

Develops a new reinforcement learning framework for complex control problems.

problem Continuous-time extended mean field control with deterministic policies.
method Model-free sensitivity formula, deterministic policy gradient, local value and advantage-rate representations.
result Demonstrates efficiency, stability, and robustness in solving complex control problems.

CAEL-MIPS learns embeddings to improve MIPS for better OPE in contextual bandits.

problem High variance in IPS weighting for OPE in large action spaces.
method Context-Action Embedding Learning (CAEL) for MIPS to minimize MSE.
result CAEL-MIPS outperforms baselines in MSE for OPE in contextual bandits.

By a Cantor group we mean a topological group homeomorphic to the Cantor set. We show that a compact metric space of rational cohomological dimension nn can be obtained as the orbit space of a Cantor group action on a metric compact space of covering dimension nn. Moreover, the action can be assumed to be free if $n=…

2013-09-28abs ↗pdf ↗

New algorithm reduces regret in stochastic linear bandits with heteroscedastic noise.

problem Optimizing performance in stochastic linear bandits with varying noise levels.
method Variance-adaptive algorithm VAEE with active exploration strategy.
result Achieves simple regret with a nearly harmonic-mean dependent rate.

The study finds conditions for solutions to a mean field equation on a Riemannian surface with group action.

problem Existence of solutions to a mean field equation on a Riemannian surface with group action.
method Analyzes the mean field equation with a sufficient condition for existence of solutions.
result Provides conditions for the existence of solutions to the mean field equation.

In this paper we study iterative procedures for stationary equilibria in games with large number of players. Most of learning algorithms for games with continuous action spaces are limited to strict contraction best reply maps in which the Banach-Picard iteration converges with geometrical convergence rate. When the be…

2012-10-17abs ↗pdf ↗

In this paper we give a natural condition for when a volumorphism on a Riemannian manifold (M,g)(M,g) is actually an isometry with respect to some other, optimal, Riemannian metric hh. We consider the natural action of volumorphisms on the space $\M_μ^s$ of all Riemannian metrics of Sobolev class HsH^s, s>n/2s>n/2, with a f…

2012-06-02abs ↗pdf ↗

In this paper, we construct various examples of Lagrangian mean curvature flows in Calabi-Yau manifolds, using moment maps for actions of abelian Lie groups on them. The examples include Lagrangian self-shrinkers and translating solitons in the Euclidean spaces. Moreover, our method can be applied to construct examples…

2017-02-28abs ↗pdf ↗

We consider a sequential learning problem with Gaussian payoffs and side information: after selecting an action ii, the learner receives information about the payoff of every action jj in the form of Gaussian observations whose mean is the same as the mean payoff, but the variance depends on the pair (i,j)(i,j) (and may…

2015-10-27abs ↗pdf ↗

In this note we consider the relationship between the dressing action and the holonomy representation in the context of constant mean curvature surfaces. We characterize dressing elements that preserve the topology of a surface and discuss dressing by simple factors as a means of adding bubbles to a class of non finite…

2004-04-27abs ↗pdf ↗

Optimal strategy proposed for maximizing cumulative reward in continuum-armed bandits.

problem Maximizing cumulative reward in a scenario with limited resources and unknown stochastic rewards.
method Proposed an optimal strategy for a nonparametric setting with side information on actions.
result Optimal regret scales as \(O(T^{1/3})\) up to poly-logarithmic factors when \(T\) is proportional to \(N\).

New OPE estimator improves offline policy evaluation for large action spaces.

problem Existing OPE estimators fail with large action spaces, leading to extreme bias and variance.
method Proposes a new estimator using marginalized importance weights and action embeddings.
result Empirical performance improvement enables reliable OPE even with many actions.

We classify minimal hypersurfaces in Rn×SmR^n \times S^m, n,m2n,m \geq 2, which are invariant by the canonical action of O(n)×O(m)O(n) \times O(m). We also construct compact and noncompact examples of invariant hypersurfaces of constant mean curvature. We show that the minimal hypersurfaces and the noncompact constant mean curvatu…

2014-05-15abs ↗pdf ↗

In this paper, we prove that the two well-known natural normalizations of Hamiltonian functions on the symplectic manifold (M,ω)(M,ω) canonically relates the action spectra of different normalized Hamiltonians on {\it arbitrary} symplectic manifolds (M,ω)(M,ω). The natural class of normalized Hamiltonians consists of those w…

2002-06-10abs ↗pdf ↗

A new method estimates multi-dimensional value distributions using Hilbert space embeddings.

problem Estimating value distributions in complex, multi-dimensional reinforcement learning settings.
method Hilbert space mappings and kernel mean embeddings to estimate the kernel mean embedding of multi-dimensional value distributions.
result Uniform convergence guarantees and robust off-policy evaluation demonstrated in simulations.

We introduce a natural generalisation of holomorphic curves to morphisms of supermanifolds, referred to as holomorphic supercurves. More precisely, supercurves are morphisms from a Riemann surface, endowed with the structure of a supermanifold which is induced by a holomorphic line bundle, to an ordinary almost complex…

2011-02-24abs ↗pdf ↗

Study uses Dynnikov coordinates to analyze actions of Dehn twists on a thrice-punctured disc.

problem Analyzing actions of Dehn twists in geometric group theory.
method Application of Dynnikov coordinates to describe orbits and dynamics of Dehn twists in a thrice-punctured disc.
result The action of Dehn twists has a geometric meaning as a piecewise linear Z2\mathbb{Z}^{2}-automorphism.

Given a singular Riemannian foliation on a compact Riemannian manifold, we study the mean curvature flow equation with a regular leaf as initial datum. We prove that if the leaves are compact and the mean curvature vector field is basic, then any finite time singularity is a singular leaf, and the singularity is of typ…

2014-08-22abs ↗pdf ↗

We propose here a new discretization method for a class continuum gauge theories which action functionnals are polynomials of the curvature. Based on the notion of holonomy, this discretization procedure appears gauge-invariant for discretized analogs of Yang-Mills theories, and hence gauge-fixing is fully rigorous for…

2017-06-29abs ↗pdf ↗

New connections found on zero-mean multivariate normal distributions.

problem Characterizing statistical connections on zero-mean multivariate normal distributions.
method Investigating invariant conjugate symmetric statistical connections on the submanifold of zero-mean multivariate normal distributions.
result Invariant connections on zero-mean multivariate normal distributions are not uniquely characterized by invariance under the general linear group action.

Extending work of Bielawski-Dancer and Konno, we develop a theory of toric hyperkahler varieties, which involves toric geometry, matroid theory and convex polyhedra. The framework is a detailed study of semi-projective toric varieties, meaning GIT quotients of affine spaces by torus actions, and specifically, of Lawren…

2002-03-11abs ↗pdf ↗