arXiv research

Paper explores MM strategies that can refuse to quote or provide single-sided quotes.

MM-DREX adapts LLM experts for financial trading via dynamic routing.

IMM uses imitation learning and predictive representation learning to improve market making strategies.

Introduces screening rules for non-convex Lasso problems.

We model the behavior of three agent classes acting dynamically in a limit order book of a financial asset. Namely, we consider market makers (MM), high-frequency trading (HFT) firms, and institutional brokers (IB). Given a prior dynamic of the order book, similar to the one considered in the Queue-Reactive models [14,…

ARL and Hawkes processes improve market-making strategies with variable volatility.

Deep-learning improves 6x6-mm OCTA angiograms by reducing noise and artifacts.

Non-convex optimization is ubiquitous in machine learning. Majorization-Minimization (MM) is a powerful iterative procedure for optimizing non-convex functions that works by optimizing a sequence of bounds on the function. In MM, the bound at each iteration is required to \emph{touch} the objective function at the opti…

This paper uses deep RL to optimize market quotes from LOB data.

MM (majorization--minimization) algorithms are an increasingly popular tool for solving optimization problems in machine learning and statistical estimation. This article introduces the MM algorithm framework in general and via three popular example applications: Gaussian mixture regressions, multinomial logistic regre…

Unified approach for federated learning using MM optimization.

NS-GAN mode collapse due to sample weighting inversion, solved with MM-nsat.

The MM algorithm improves robust penalized estimation for outlier-contaminated data.

Proposes MM-DUST for efficient generalized lasso solution paths.

Develops RL for optimal market-making in non-Markov processes.

The purpose of this paper is the study of the roots in the mapping class groups. Let $Σ$ be a compact oriented surface, possibly with boundary, let $\PP$ be a finite set of punctures in the interior of $Σ$, and let $\MM (Σ, \PP)$ denote the mapping class group of $(Σ, \PP)$. We prove that, if $Σ$ is of genus 0, then ea…

Let $(\MM ,{\tilde g})$ be an $N$-dimensional smooth compact Riemannian manifold. We consider the singularly perturbed Allen-Cahn equation $$ ε^2Δ_{ {\tilde g}} {u}\,+\, (1 - {u}^2)u \,=\,0\quad \mbox{in } \MM, $$ where $ε$ is a small parameter. Let $\KK\subset \MM$ be an $(N-1)$-dimensional smooth minimal submanifold …

The paper classifies Poincaré complexes as topological manifolds.

New algorithms reduce bias in federated learning for non-i.i.d. data.

This paper proposes MM-DAGs for analyzing traffic congestion, learning multiple DAGs jointly.

In this paper, we study a popular method for inference of the Bradley-Terry model parameters, namely the MM algorithm, for maximum likelihood estimation and maximum a posteriori probability estimation. This class of models includes the Bradley-Terry model of paired comparisons, the Rao-Kupper model of paired comparison…

Self-attention optimizers converge to optimal weights, revealing bias patterns.

A novel MM algorithm optimizes DCOV for SDR and SVS.

New approach for distributed learning of Gaussian mixtures.

Malignant Pleural Mesothelioma (MPM) or malignant mesothelioma (MM) is an atypical, aggressive tumor that matures into cancer in the pleura, a stratum of tissue bordering the lungs. Diagnosis of MPM is difficult and it accounts for about seventy-five percent of all mesothelioma diagnosed yearly in the United States of …

Proposes MM-KTD for efficient RL learning with reduced sample size.

Support vector machines (SVMs) are an important tool in modern data analysis. Traditionally, support vector machines have been fitted via quadratic programming, either using purpose-built or off-the-shelf algorithms. We present an alternative approach to SVM fitting via the majorization--minimization (MM) paradigm. Alg…

A new method accelerates deep neural network training using minimal margin score.

The measure concentration property of an mm-space $X$ is roughly described as that any 1-Lipschitz map on $X$ to a metric space $Y$ is almost close to a constant map. The target space $Y$ is called the screen. The case of $Y=\mathbb{R}$ is widely studied in many literature (see \cite{gromov}, \cite{ledoux}, \cite{mil2}…

We describe a new method to automatically discriminate between patients with Alzheimer's disease (AD) or mild cognitive impairment (MCI) and elderly controls, based on multidimensional classification of hippocampal shape features. This approach uses spherical harmonics (SPHARM) coefficients to model the shape of the hi…

Unified algorithm for tensor decomposition supports multiple loss functions and models.

Let $E_f$ be the energy of some knot $τ$ for any $f$ from certain class of functions. The problem is to find knots with extremal values of energy. We discuss the notion of the locally perturbed knot. The knot circle minimizes some energies $E_f$ and maximizes some others. So, is there any energy such that the circle ne…

DGMM improves Gaussian mixture modeling efficiency and stability.

New methods for parameter estimation in mechanistic models using data-consistent inversion.

In this paper we introduce some new copulas emerging from shock models. It was shown earlier that reflected maxmin copulas (RMM for short) are not just some specific singular copulas; they contain many important absolutely continuous copulas including the negative quadrant dependent part of the Eyraud-Farlie-Gumbel-Mor…

A new method trains physics-constrained neural networks more efficiently.

Proposes a new method for estimating sparse precision matrices in GMRF-MM models.

New algorithm speeds up NMF with $β$-divergence.

This paper presents a novel model for multimodal learning based on gated neural networks. The Gated Multimodal Unit (GMU) model is intended to be used as an internal unit in a neural network architecture whose purpose is to find an intermediate representation based on a combination of data from different modalities. Th…

We consider Markov models of stochastic processes where the next-step conditional distribution is defined by a kernel density estimator (KDE), similar to Markov forecast densities and certain time-series bootstrap schemes. The KDE Markov models (KDE-MMs) we discuss are nonlinear, nonparametric, fully probabilistic repr…

Proposes a new metric for comparing shapes in different spaces.

Study models illiquid stock prices and finds low correlation due to constant prices.

Bayesian optimization for function-valued responses, addressing worst case deviations.

Some aspects of the multidimensional soliton geometry are considered. The relation between soliton equations in 2+1 dimensions and the Self-Dual Yang-Mills and Bogomolny equations are discussed.

Deformation estimation of elastic object assuming an internal organ is important for the computer navigation of surgery. The aim of this study is to estimate the deformation of an entire three-dimensional elastic object using displacement information of very few observation points. A learning approach with a neural net…

New method uses subtractive mixture models for approximate inference.

New algorithm improves on EM for streaming data, outperforming existing methods.

New lower bounds improve logistic log-likelihood optimization and inference.