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.
Optimizes basis for density-based atomic representations to enhance compactness and accuracy.
We study the problem of dynamically trading a futures contract and its underlying asset under a stochastic basis model. The basis evolution is modeled by a stopped scaled Brownian bridge to account for non-convergence of the basis at maturity. The optimal trading strategies are determined from a utility maximization pr…
Optimizes basis functions for learning dynamical systems from data.
Sharp results link DLN gradient flow to basis pursuit optimization and GHA phase transitions.
BASIS improves LLM reasoning by sharing batchwise rollout info, reducing MSE by 69%.
BP fails to find sparsest solution for structured matrices.
Ordinal Regression (OR) aims to model the ordering information between different data categories, which is a crucial topic in multi-label learning. An important class of approaches to OR models the problem as a linear combination of basis functions that map features to a high dimensional non-linear space. However, most…
Gradient-based training and pruning for radial basis function networks in materials physics.
Proposes a new method to learn entire solution paths without discretization.
This paper addresses a novel data science problem, prescriptive price optimization, which derives the optimal price strategy to maximize future profit/revenue on the basis of massive predictive formulas produced by machine learning. The prescriptive price optimization first builds sales forecast formulas of multiple pr…
A new kernel improves statistical surrogates for stochastic manifolds with diverse data.
OKSVM optimizes RBF kernel hyperparameter for SVMs, improving classification performance.
New framework models complex spatial data with basis functions and graphical vectors.
This paper optimizes PCE for efficient surrogate modeling in engineering.
Study shows Calabi-Yau metrics converge to a specific form under certain conditions.
Radial Basis Functions Neural Networks (RBFNNs) are tools widely used in regression problems. One of their principal drawbacks is that the formulation corresponding to the training with the supervision of both the centers and the weights is a highly non-convex optimization problem, which leads to some fundamentally dif…
A new optimizer for deep learning improves accuracy and reduces training time.
We consider the problem of designing a sparse Gaussian process classifier (SGPC) that generalizes well. Viewing SGPC design as constructing an additive model like in boosting, we present an efficient and effective SGPC design method to perform a stage-wise optimization of a predictive loss function. We introduce new me…
Global optimization problems whose objective function is expensive to evaluate can be solved effectively by recursively fitting a surrogate function to function samples and minimizing an acquisition function to generate new samples. The acquisition step trades off between seeking for a new optimization vector where the…
This paper develops efficient surrogate models for optimization of complex dynamical systems.
For nonconvex optimization in machine learning, this article proves that every local minimum achieves the globally optimal value of the perturbable gradient basis model at any differentiable point. As a result, nonconvex machine learning is theoretically as supported as convex machine learning with a handcrafted basis …
Recently, path norm was proposed as a new capacity measure for neural networks with Rectified Linear Unit (ReLU) activation function, which takes the rescaling-invariant property of ReLU into account. It has been shown that the generalization error bound in terms of the path norm explains the empirical generalization b…
Basis adaptation in Homogeneous Chaos spaces rely on a suitable rotation of the underlying Gaussian germ. Several rotations have been proposed in the literature resulting in adaptations with different convergence properties. In this paper we present a new adaptation mechanism that builds on compressive sensing algorith…
A new method for learning manifolds efficiently using canonical basis functions.
Compressive sensing (CS) has been studied and applied in structural health monitoring for wireless data acquisition and transmission, structural modal identification, and spare damage identification. The key issue in CS is finding the optimal solution for sparse optimization. In the past years, many algorithms have bee…
Recently there has been renewed interest in the mapping-class group of a compact surface of genus and also in its finite order elements. A finite order element of the mapping-class group will be a conformal automorphisms on some Riemann surface of genus . Here we give the details of the proof that there is…
Motivated by the gap between theoretical optimal approximation rates of deep neural networks (DNNs) and the accuracy realized in practice, we seek to improve the training of DNNs. The adoption of an adaptive basis viewpoint of DNNs leads to novel initializations and a hybrid least squares/gradient descent optimizer. We…
Adapts POD basis for parametric ROMs using pGP.
Paper proposes a method to recover point configurations from noisy distance data.
New optimization algorithm for mixed-variable problems improves efficiency.
In this paper, we study the problem of compressed sensing using binary measurement matrices and -norm minimization (basis pursuit) as the recovery algorithm. We derive new upper and lower bounds on the number of measurements to achieve robust sparse recovery with binary matrices. We establish sufficient conditi…
Method learns radial basis function distributions from samples.
Max-norm regularizer has been extensively studied in the last decade as it promotes an effective low-rank estimation for the underlying data. However, such max-norm regularized problems are typically formulated and solved in a batch manner, which prevents it from processing big data due to possible memory budget. In th…
Two new algorithms reduce online kernel regression's computational cost while maintaining optimal regret bounds.
In recent years, spectral clustering has become a standard method for data analysis used in a broad range of applications. In this paper we propose a new class of algorithms for multiway spectral clustering based on optimization of a certain "contrast function" over the unit sphere. These algorithms, partly inspired by…
Improved reinforcement method for optimal control problems.
Dual Bayesian Affine Estimators for Wiener-type state-space models
This paper proposes a new method to adapt ROMs for new parameter settings.
Study on hedging and valuation of basis risk in incomplete markets with partial information.
A core operation in reinforcement learning (RL) is finding an action that is optimal with respect to a learned value function. This operation is often challenging when the learned value function takes continuous actions as input. We introduce deep radial-basis value functions (RBVFs): value functions learned using a de…
CARE method estimates precision matrix for compositional data, achieving optimality in high dimensions.
Finsler space is differentiable manifold for which Minkowski space is the fiber of the tangent bundle. To understand structure of the reference frame in Finsler space, we need to understand the structure of orthonormal basis in Minkowski space. In this paper, we considered the definition of orthonormal basis in Minkows…
A method for optimal Bayesian filtering using progressive particle flow and optimal transport maps.
K-Means and RBF networks are shown to be equivalent under certain conditions.
New neural network models for functional data.
This work studies a stochastic optimal control problem for a pension scheme which provides an income-drawdown policy to its members after their retirement. To manage the scheme efficiently, the manager and members agree to share the investment risk based on a pre-decided risk-sharing rule. The objective is to maximise …
New basis confirms Thurston's conjecture and reveals knot configurations.
Finsler space is differentiable manifold for which Minkowski space is the fiber of the tangent bundle. To understand structure of the reference frame in Finsler space, we need to understand the structure of orthonormal basis in Minkowski space. In this paper, I considered the definition of orthonormal basis in Minkowsk…