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.
Defines a new distance for integral current spaces and proves convergence criteria.
In this paper we extend temporal difference policy evaluation algorithms to performance criteria that include the variance of the cumulative reward. Such criteria are useful for risk management, and are important in domains such as finance and process control. We propose both TD(0) and LSTD(lambda) variants with linear…
Managing risk in dynamic decision problems is of cardinal importance in many fields such as finance and process control. The most common approach to defining risk is through various variance related criteria such as the Sharpe Ratio or the standard deviation adjusted reward. It is known that optimizing many of the vari…
New algorithm solves phase retrieval with adaptive stopping criteria.
The study examines Nash equilibria in utility maximization games with multiplicative performance criteria.
The paper establishes criteria for spacetime inextendibility using asymptotic volume-distance-ratio analysis.
Transforms game optimization dynamics into frequency domain for precise hyperparameter analysis.
New methods ensure fairness in noisy protected groups.
Compactness results for Hermitian manifolds help understand Type IIB flow.
This work compares lattice-free and lattice-based training criteria for LVCSR.
We investigate the Kähler-Ricci flow modified by a holomorphic vector field. We find equivalent analytic criteria for the convergence of the flow to a Kähler-Ricci soliton. In addition, we relate the asymptotic behavior of the scalar curvature along the flow to the lower boundedness of the modified Mabuchi energy.
Inference-aware meta-alignment of LLMs reduces computational cost.
FADE framework improves fairness and accuracy in ensemble learning.
By reducing optimization to a sequence of smaller subproblems, working set algorithms achieve fast convergence times for many machine learning problems. Despite such performance, working set implementations often resort to heuristics to determine subproblem size, makeup, and stopping criteria. We propose BlitzWS, a wor…
In many sequential decision-making problems we may want to manage risk by minimizing some measure of variability in rewards in addition to maximizing a standard criterion. Variance related risk measures are among the most common risk-sensitive criteria in finance and operations research. However, optimizing many such c…
Researchers analyze a new neural network training method.
The regret bound of an optimization algorithms is one of the basic criteria for evaluating the performance of the given algorithm. By inspecting the differences between the regret bounds of traditional algorithms and adaptive one, we provide a guide for choosing an optimizer with respect to the given data set and the l…
A very simple heuristic approach to the unfolding problem will be described. An iterative algorithm starts with an empty histogram and every iteration aims to add one entry to this histogram. The entry to be added is selected according to a criteria which includes a test and a regularization. After a relatively s…
The paper studies degenerations of rational maps and their limits as geometrically finite rational maps.
This paper presents a novel signal compression algorithm based on the Blaschke unwinding adaptive Fourier decomposition (AFD). The Blaschke unwinding AFD is a newly developed signal decomposition theory. It utilizes the Nevanlinna factorization and the maximal selection principle in each decomposition step, and achieve…
A one-parameter family of coupled flows depending on a parameter is introduced which reduces when to the coupled flow of a metric with a -form due recently to Y. Li, Y. Yuan, and Y. Zhang. It is shown in particular that, for , estimates for derivatives of all orders would follow from…
We give some general criteria of being a homeomorphism for continuous mappings of topological manifolds, as well as criteria of being a diffeomorphism for smooth mappings of smooth manifolds. As an illustration, we apply these criteria to the problems arising in two- and three-dimensional grid generation.
The study reveals flaws in pruning criteria and proposes a new assumption for better filter selection.
Learning algorithms for implicit generative models can optimize a variety of criteria that measure how the data distribution differs from the implicit model distribution, including the Wasserstein distance, the Energy distance, and the Maximum Mean Discrepancy criterion. A careful look at the geometries induced by thes…
New method finds optimal hyperparameters for multiple tasks and criteria.
We consider flows with normal velocities equal to powers strictly larger than one of the Gauss curvature. Under such flows closed strictly convex surfaces converge to points. In his work on the square of the norm of the second fundamental form, Schnürer proposes criteria for selecting quantities that are suitable for p…
Improved recommendations using latent embeddings from user reviews.
We investigate anomaly detection in an unsupervised framework and introduce Long Short Term Memory (LSTM) neural network based algorithms. In particular, given variable length data sequences, we first pass these sequences through our LSTM based structure and obtain fixed length sequences. We then find a decision functi…
New criteria for Heegaard splittings ensure strong irreducibility and finite Goeritz groups.
Develops scenario theory for multi-criteria decision making.
We consider the problem of identifying patterns in a data set that exhibit anomalous behavior, often referred to as anomaly detection. In most anomaly detection algorithms, the dissimilarity between data samples is calculated by a single criterion, such as Euclidean distance. However, in many cases there may not exist …
The paper evaluates criteria for selecting cryptocurrencies based on historical data.
Study compares different complexity criteria for free boundary minimal surfaces.
The paper analyzes kNN density estimation's convergence rates under different conditions.
We present criteria for establishing a triangulation of a manifold. Given a manifold M, a simplicial complex A, and a map H from the underlying space of A to M, our criteria are presented in local coordinate charts for M, and ensure that H is a homeomorphism. These criteria do not require a differentiable structure, or…
The paper studies how curves evolve under area constraints and converges to a critical point.
When sufficient labeled data are available, classical criteria based on Receiver Operating Characteristic (ROC) or Precision-Recall (PR) curves can be used to compare the performance of un-supervised anomaly detection algorithms. However , in many situations, few or no data are labeled. This calls for alternative crite…
The paper analyzes performance criteria for competing fund managers in Ito-diffusion markets.
Paper defines numerical criteria to test handlebody link irreducibility.
The paper uses quaternions to model quantum learning on devices.
Develops an actor-critic algorithm for risk-sensitive Markov decision processes.
We shall give useful criteria of lips, beaks and swallowtail singularities of smooth map from the plane into the plane. As an application of criteria, we will discuss the singularities of Cauchy problem of single conservation law.
Two recently introduced criteria for estimation of generative models are both based on a reduction to binary classification. Noise-contrastive estimation (NCE) is an estimation procedure in which a generative model is trained to be able to distinguish data samples from noise samples. Generative adversarial networks (GA…
A new method for multi-criteria recommender systems using graph attention networks.
New framework for resilient bi-criteria optimization under noisy feedback.
New algorithms minimize risk in MNL bandits, achieving near-optimal performance.
A game-theoretic approach to multi-criteria ranking from ordinal data.
Recent work on fairness in machine learning has focused on various statistical discrimination criteria and how they trade off. Most of these criteria are observational: They depend only on the joint distribution of predictor, protected attribute, features, and outcome. While convenient to work with, observational crite…