output
20042026
most citedAdvanced capabilities for materials modelling with Quantum ESPRESSO

7.7k citations

Showing cs.LGShow all

16 papers · 1 filter

cs.LG2026

Exact Algebraic Computation of Learning Coefficients for Two-Dimensional Singular Models

Grégoire Sergeant-Perthuis, Elias Tsigaridas, Jules Tsukahara

Classical information criteria such as the Bayesian Information Criterion (BIC) rely on regularity assumptions that break down for singular models, leading to incorrect model selec…

cs.LG2026

Benchmarking Generalization in Financial Statement Fraud Detection: robust evaluation and novel tasks

Guy Stephane Waffo Dzuyo, Gaël Guibon, Christophe Cerisara +1

Financial statement fraud detection (FSFD) is crucial for market integrity but faces challenges from increasingly sophisticated schemes and under-utilized textual data in financial…

cs.LG2024★ 3 cited

A Survey of Features Used for Representing Black-box Single-objective Continuous Optimization

Gjorgjina Cenikj, Ana Nikolikj, Gašper Petelin +3

This survey examines key advancements in designing features to represent optimization problem instances, algorithm instances, and their interactions within the context of single-ob…

cs.LG2023★ 13 cited

Prediction of Transportation Index for Urban Patterns in Small and Medium-sized Indian Cities using Hybrid RidgeGAN Model

Rahisha Thottolil, Uttam Kumar, Tanujit Chakraborty

The rapid urbanization trend in most developing countries including India is creating a plethora of civic concerns such as loss of green space, degradation of environmental health,…

cs.LG2023

New methods for new data? An overview and illustration of quantitative inductive methods for HRM research

Alain LACROUX

"Data is the new oil", in short, data would be the essential source of the ongoing fourth industrial revolution, which has led some commentators to assimilate too quickly the quant…

cs.LG2023★ 1 cited

From Noisy Fixed-Point Iterations to Private ADMM for Centralized and Federated Learning

Edwige Cyffers, Aurélien Bellet, Debabrota Basu

We study differentially private (DP) machine learning algorithms as instances of noisy fixed-point iterations, in order to derive privacy and utility results from this well-studied…