arXiv research

Cooperation benefits entities by reducing fluctuation effects, leading to higher growth rates.

We pursue an early stopping technique that helps Gaussian Restricted Boltzmann Machines (GRBMs) to gain good natural image representations in terms of overcompleteness and data fitting. GRBMs are widely considered as an unsuitable model for natural images because they gain non-overcomplete representations which include…

New method improves causal structure discovery with Prior-Fitted Networks.

Investment horizon approach has been used to analyze indexes of Polish stock market.Optimal time horizon for each return value is evaluated by fitting appropriate function form of the distribution. Strong asymmetry of gain-loss curves is observed for WIG index, whereas gain and loss curves look similar for WIG20 and fo…

LayerNorm improves training and generalization by re-centering and re-scaling backward gradients.

Study improves understanding of network degree distributions using non-linear ERGs.

We explore a simple lattice field model intended to describe statistical properties of high frequency financial markets. The model is relevant in the cross-disciplinary area of econophysics. Its signature feature is the emergence of a self-organized critical state. This implies scale invariance of the model, without tu…

Optimistic estimate predicts best fitting performance of nonlinear models.

This paper explores machine learning landscapes using molecular energy analogy.

Kernel-embedding tests can be suboptimal, but a simple modification improves their performance.

This paper defines resource-constrained classifier performance and its impact on algorithm choice.

Study examines how body segments respond to random vibrations.

Improved estimators for causal inference using cross-fitting and undersmoothing.

Study semi-supervised learning with noisy proxy covariates, deriving bounds and showing gains.

Proposes using prior variable importance information in high-dimensional regression.

Accumulated stock returns exhibit tempered skew t-distribution.

Improved kernel Stein discrepancy for large-scale data.

Enhanced detection of sneutrinos at the LHC using machine learning.

New insights into overfitting peaks in generalization error for $l_2$ and $l_1$ penalized interpolation.

Enhanced visual feature attribution via adaptive baseline weighting.

Faster method detects bias in lending, hiring, and policing decisions.

Paper develops MOSP for online resource allocation with sub-linear regret and fit.

New method improves Gaussian Mixture Model fitting speed.

Study of historic stock returns distributions, highlighting asymmetry and outliers.

Algorithm estimates parameters over time-varying graphs without special assumptions.

Optimal feature learning strength improves generalization in deep networks.

ABC uses GPs to speed up estimating bacterial gene transfers.

A major problem in the study of complex socioeconomic systems is represented by privacy issues$-$that can put severe limitations on the amount of accessible information, forcing to build models on the basis of incomplete knowledge. In this paper we investigate a novel method to reconstruct global topological properties…

How can we model networks with a mathematically tractable model that allows for rigorous analysis of network properties? Networks exhibit a long list of surprising properties: heavy tails for the degree distribution; small diameters; and densification and shrinking diameters over time. Most present network models eithe…

Machine learning reduces variance in online experiment results.

Optimistic search speeds up change point detection in large datasets.

Monte Carlo (MC) techniques are often used to estimate integrals of a multivariate function using randomly generated samples of the function. In light of the increasing interest in uncertainty quantification and robust design applications in aerospace engineering, the calculation of expected values of such functions (e…

The study analyzes how neural reward models learn features for policy optimization in a Gaussian single-index model.

FQE with deep neural networks achieves asymptotic normality and finite-sample bounds.

This paper presents an improvement to model learning when using multi-class LogitBoost for classification. Motivated by the statistical view, LogitBoost can be seen as additive tree regression. Two important factors in this setting are: 1) coupled classifier output due to a sum-to-zero constraint, and 2) the dense Hess…

A new method calibrates value predictions in offline RL to improve reliability.

A simplified Bayesian approach for online sports rating.

The stochastic block model (SBM) is a popular tool for community detection in networks, but fitting it by maximum likelihood (MLE) involves a computationally infeasible optimization problem. We propose a new semidefinite programming (SDP) solution to the problem of fitting the SBM, derived as a relaxation of the MLE. W…

Reducing volatility proxy improves apparent market correlation dynamics.

Paper proposes player roles from match data to help clubs.

Paper proposes robust reinforcement learning method for better real-world application.

SGD learns a simple network for multi-class classification from mixtures of well-separated distributions.

Study tests how U.S. equity prices align with global asset frequencies using financial variables.

Faster deep neural networks converge and generalize better.

New architecture improves decision-making in dense traffic.

L1-orthogonal regularization improves decision tree explainability of deep neural networks.

Neural networks can memorize random labels just as well as true labels.

Earth observation embeddings can convert discrete biome maps into continuous representations that better capture ecological variation.