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.
Paper extends FOFC algorithm to work with mixed data types.
In this report we describe a tool for comparing the performance of graphical causal structure learning algorithms implemented in the TETRAD freeware suite of causal analysis methods. Currently the tool is available as package in the TETRAD source code (written in Java). Simulations can be done varying the number of run…
Study examines how Lorentz transformations affect foliations in spacetime.
The main result of the paper is a new representation for the Weyl Lagrangian (massless Dirac Lagrangian). As the dynamical variable we use the coframe, i.e. an orthonormal tetrad of covector fields. We write down a simple Lagrangian - wedge product of axial torsion with a lightlike element of the coframe - and show tha…
The main result of the paper is a new representation for the Weyl Lagrangian (massless Dirac Lagrangian). As the dynamical variable we use the coframe, i.e. an orthonormal tetrad of covector fields. We write down a simple Lagrangian - wedge product of axial torsion with a lightlike element of the coframe - and show tha…
The main result of the paper is a new representation of the Weyl Lagrangian (massless Dirac Lagrangian). As the dynamical variable we use the coframe, i.e. an orthonormal tetrad of covector fields. We write down a simple Lagrangian - wedge product of axial torsion with a lightlike element of the coframe - and show that…
An integrated approach to Lie derivatives of spinors, spinor connections and the gravitational field is presented, in the context of a previously proposed, partly original formulation of a theory of Einstein-Carta-Maxwell-Dirac fields based on "minimal geometric data": all the needed underlying structure is geometrical…
In this paper, the second in a series of eight we continue our development of the basic tools of the multivector and extensor calculus which are used in our formulation of the differential geometry of smooth manifolds of arbitrary topology . We introduce metric and gauge extensors, pseudo-orthogonal metric extensors, g…
The work focuses upon the relativistic and geometric properties of the space--time endowed tentatively with the metric function of the Berwald--Moor type. The zero curvature of indicatrix is a remarkable property of the approach. We demonstrate how the associated geodesic equations can be solved in a transparent way, t…
Three RFF-based methods for nonlinear causal discovery in mixed data.
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…
Study null curves and their motion in 3D flat space-time, leading to integrable hierarchies.
We present a family of four-dimensional Lorentzian manifolds whose invariant classification requires the seventh covariant derivative of the curvature tensor. The spacetimes in questions are null radiation, type N solutions on an anti-de Sitter background. The large order of the bound is due to the fact that these spac…
We consider generic static spacetimes with Killing horizons and study properties of curvature tensors in the horizon limit. It is determined that the Weyl, Ricci, Riemann and Einstein tensors are algebraically special and mutually aligned on the horizon. It is also pointed out that results obtained in the tetrad adjust…
Benchpress streamlines benchmarking structure learning algorithms for probabilistic models.
These are notes for a short course and some talks gave at Departament of Mathematics and at Departament of Physics of Federal University of Minas Gerais, based on the author's paper arXiv:1808.09249. Some new information and results are also presented. Unlike the original work, here we try to give a more physical empha…
A new algorithm tackles submodular bandit problems with multiple constraints.
This work proposes an online learning approach to tighten constraints in stochastic control problems.
We study constrained clustering, where constraints guide the clustering process. In existing works, two categories of constraints have been widely explored, namely pairwise and cardinality constraints. Pairwise constraints enforce the cluster labels of two instances to be the same (must-link constraints) or different (…
Simplifies neural network constraints with computationally efficient method.
Reduces Lie (bi-)algebroids and Dirac manifolds using constraint vector bundles.
Optimistic algorithm reduces regret and constraint violations in online convex optimization with adversarial constraints.
Holistic GLMs add constraints for better model quality.
A new ML method teaches constraints directly to models.
Paper tackles constrained bandit problems with a new learning framework.
In the present paper, the minimal investment risk for a portfolio optimization problem with imposed budget and investment concentration constraints is considered using replica analysis. Since the minimal investment risk is influenced by the investment concentration constraint (as well as the budget constraint), it is i…
Survey of Gaussian process constraints for modeling expensive data.
This paper considers online convex optimization over a complicated constraint set, which typically consists of multiple functional constraints and a set constraint. The conventional online projection algorithm (Zinkevich, 2003) can be difficult to implement due to the potentially high computation complexity of the proj…
We provide a dynamic programming principle for stochastic optimal control problems with expectation constraints. A weak formulation, using test functions and a probabilistic relaxation of the constraint, avoids restrictions related to a measurable selection but still implies the Hamilton-Jacobi-Bellman equation in the …
Iterative method learns unknown constraints for MPC control.
We reformulate data-dependent constraints to ensure they are always met with high probability.
New algorithm reduces regret and constraint violation in online convex optimization with complex constraints.
Algorithm ensures privacy while strictly adhering to constraints.
Proposes NUV priors for half-space and box constraints.
Meta-gradient D4PG optimizes performance and constraint adherence in RL.
Geometrically characterizes virtual nonlinear nonholonomic constraints using symplectic methods.
This work is a further study on the Generalized Constraint Neural Network (GCNN) model [1], [2]. Two challenges are encountered in the study, that is, to embed any type of prior information and to select its imposing schemes. The work focuses on the second challenge and studies a new constraint imposing scheme for equa…
The paper improves Gaussian processes by adding sum constraints, enhancing prediction accuracy.
Develops a new method for optimizing with uncertain data.
Efficient algorithms decide algebraic constraints of causal graphs.
The paper introduces MU for NMF with -divergences and disjoint constraints.
FISAR uses neural networks to optimize safe reinforcement learning with forward-invariant constraints.
Solves Einstein constraint equations on compact manifolds with specified boundaries.
We propose a general method for deformation quantization of any second-class constrained system on a symplectic manifold. The constraints determining an arbitrary constraint surface are in general defined only locally and can be components of a section of a non-trivial vector bundle over the phase-space manifold. The c…
Algorithm finds real line mapping from points under ordinal constraints.
HardCoRe-NAS finds fitting neural networks adhering to hard resource constraints.
Unified approach adjusts classifiers to meet system-level constraints.
Study optimizes portfolio allocation policies using off-policy data and constraints.