arXiv research

Lapse improves parameter servers by dynamically allocating parameters, achieving near-linear scaling.

PALS extends PAL for optimizing stochastic simulators efficiently.

Gaussian Process Factor Analysis (GPFA) has been broadly applied to the problem of identifying smooth, low-dimensional temporal structure underlying large-scale neural recordings. However, spike trains are non-Gaussian, which motivates combining GPFA with discrete observation models for binned spike count data. The dra…

Study predicts lens performance using neural networks.

Algorithm identifies Pareto optimal designs efficiently for noisy, multi-objective functions.

Multi-task learning shares information between related tasks, sometimes reducing the number of parameters required. State-of-the-art results across multiple natural language understanding tasks in the GLUE benchmark have previously used transfer from a single large task: unsupervised pre-training with BERT, where a sep…

Boolean matrix factorization (BMF) is a popular and powerful technique for inferring knowledge from data. The mining result is the Boolean product of two matrices, approximating the input dataset. The Boolean product is a disjunction of rank-1 binary matrices, each describing a feature-relation, called pattern, for a g…

Squint bound improved by removing $\ln \ln T$ term.

New algorithm learns multiclass concepts with finite Littlestone dimension.

Gradient descent optimization improved by circuit perspective.

We study a mean-field version of rank-based models of equity markets such as the Atlas model introduced by Fernholz in the framework of Stochastic Portfolio Theory. We obtain an asymptotic description of the market when the number of companies grows to infinity. Then, we discuss the long-term capital distribution. We r…

New equations reveal how cylinder power in progressive lenses depends on geodesic curvature.

We introduce a new system of stochastic differential equations which models dependence of market beta and unsystematic risk upon size, measured by market capitalization. We fit our model using size deciles data from Kenneth French's data library. This model is somewhat similar to generalized volatility-stabilized model…

In his seminal work \cite{pal:61}, R. Palais extended a substantial part of the theory of compact transformation groups to the case of proper actions of locally compact groups. Here we extend to proper actions some other important results well known for compact group actions. In particular, we prove that if $H$ is a co…

We introduce a formal language IE that is a variant of the language PAL developed in [van Benthem 2011] by adding a belief operator and a common belief operator,specializing to stochastic analysis. A constant symbol in the language denotes a stochastic process so that we can represent several financial events as formul…

Proposes a new method combining Reservoir Computing and Normalizing Flow for predicting stochastic dynamical systems.

The paper studies stochastic optimization on matrices and its limits as dimensions grow.

Study laws of large numbers in online classification, determining optimal regret bounds.

Efficiently estimates optimal transport maps with rigorous guarantees.

Improved mistake bound for group linear separable cases in online multiclass linear classification.

Optimal transport and information geometry both study geometric structures on spaces of probability distributions. Optimal transport characterizes the cost-minimizing movement from one distribution to another, while information geometry originates from coordinate-invariant properties of statistical inference. Their con…

Let $X$ and $Y$ be domains of $\mathbb{R}^n$ equipped with respective probability measures $μ$ and $ν$. We consider the problem of optimal transport from $μ$ to $ν$ with respect to a cost function $c: X \times Y \to \mathbb{R}$. To ensure that the solution to this problem is smooth, it is necessary to make several ass…

Study finds different active learning techniques better for various learning objectives.

Enhanced Bayesian target encoding uses sampling techniques to improve model performance.

In dynamic selection (DS) techniques, only the most competent classifiers, for the classification of a specific test sample are selected to predict the sample's class labels. The more important step in DES techniques is estimating the competence of the base classifiers for the classification of each specific test sampl…

Adapts pivoting technique to circle homeomorphisms for proofs.

This review covers AI in finance, challenges, techniques, and opportunities.

Survey of extreme value modeling techniques for insurance.

This article is written for the Proceedings of the Conference on Current Developments in Mathematics in Harvard University, November 16-17, 2007. It is an exposition of the analytic proof of the finite generation of the canonical ring for a compact complex algebraic manifold of general type. It lists and discusses the …

New displacement technique vanishes bounded cohomology in all degrees.

Models obtained by decision tree induction techniques excel in being interpretable.However, they can be prone to overfitting, which results in a low predictive performance. Ensemble techniques are able to achieve a higher accuracy. However, this comes at a cost of losing interpretability of the resulting model. This ma…

Study harmonic mappings and submanifolds using Bochner technique.

Overview of SML techniques with banking applications.

Reduces survival analysis to common regression tasks.

This study reviews techniques to estimate volatility and price Variance Swaps.

New methods show hyperbolicity of Brunnian links.

This review explores resampling techniques for imbalanced binary classification.

Broad adoption of machine learning techniques has increased privacy concerns for models trained on sensitive data such as medical records. Existing techniques for training differentially private (DP) models give rigorous privacy guarantees, but applying these techniques to neural networks can severely degrade model per…

Paper proposes a black-box technique to generate adversarial samples.

The pursuit of explaining and improving generalization in deep learning has elicited efforts both in regularization techniques as well as visualization techniques of the loss surface geometry. The latter is related to the intuition prevalent in the community that flatter local optima leads to lower generalization error…

Various stochastic models have been proposed to estimate mortality rates. In this paper we illustrate how machine learning techniques allow us to analyze the quality of such mortality models. In addition, we present how these techniques can be used for differentiating the different causes of death in mortality modeling…

New Bochner technique for foliations with non-negative Ricci curvature.

In machine learning, a nonparametric forecasting algorithm for time series data has been proposed, called the kernel spectral hidden Markov model (KSHMM). In this paper, we propose a technique for short-term wind-speed prediction based on KSHMM. We numerically compared the performance of our KSHMM-based forecasting tec…

Vanishing theorem on CR manifolds with non-negative curvature.

This paper compares traditional econometric and contemporary machine/deep learning techniques for forecasting foreign exchange rates.

Abstract notes on generative modeling techniques.

In this article, we propose a novel ensemble technique with a multi-scheme weighting based on a technique called coopetitive soft gating. This technique combines both, ensemble member competition and cooperation, in order to maximize the overall forecasting accuracy of the ensemble. The proposed algorithm combines the …

Screening is an effective technique for speeding up the training process of a sparse learning model by removing the features that are guaranteed to be inactive the process. In this paper, we present a efficient screening technique for sparse support vector machine based on variational inequality. The technique is both …