Publications associées à Fluctuations, Bias, Variance & Ensemble of Learners: Exact Asymptotics for Convex Losses in High-Dimension

Publications associées (19)

Adversarial Training For Defense Against Label Poisoning Attacks

As machine learning models grow in complexity and increasingly rely on publicly sourced data, such as the human-annotated labels used in training large language models, they become more vulnerable to label poisoning attacks. These attacks, in which adversa ...

2025

Sim-to-Real Causal Transfer: A Metric Learning Approach to Causally-Aware Interaction Representations

Alexandre Massoud Alahi, Yuejiang Liu, Po-Chien Luan, Ahmad Rahimi

Modeling spatial-temporal interactions among neighboring agents is at the heart of multi-agent problems such as motion forecasting and crowd navigation. Despite notable progress, it remains unclear to which extent modern representations can capture the cau ...

2025

Do You Trust Your Model? Emerging Malware Threats in the Deep Learning Ecosystem

Fernando Perez Cruz

Training high-quality deep learning models is a challenging task due to computational and technical requirements. A growing number of individuals, institutions, and companies increasingly rely on pre-trained, third-party models made available in public rep ...

2025

Uncertainty-aware diabetic retinopathy detection using deep learning enhanced by Bayesian approaches

Zaffar Ahmed Shaikh

Deep learning-based medical image analysis has shown strong potential in disease categorization, segmentation, detection, and even prediction. However, in high-stakes and complex domains like healthcare, the opaque nature of these models makes it challengi ...

2025

Social-Pose: Enhancing Trajectory Prediction with Human Body Pose

Alexandre Massoud Alahi, Saeed Saadatnejad, Yang Gao

Accurate human trajectory prediction is one of the most crucial tasks for autonomous driving, ensuring its safety. Yet, existing models often fail to fully leverage the visual cues that humans subconsciously communicate when navigating the space. In this w ...

IEEE Institute of Electrical and Electronics Engineers2025

Controlled Training Data Generation with Diffusion Models

Aleksandr Alekseev, Shuqing Teresa Yeo, Andrei Atanov

We present a method to control a text-to-image generative model to produce training data useful for supervised learning. Unlike previous works that employ an open-loop approach via pre-defined prompts to generate new data using either a language model or h ...

2025

Machine learning Hubbard parameters with equivariant neural networks

Nicola Marzari, Iurii Timrov, Martin Uhrin, Luca Binci, Austin Jacob Zadoks

Density-functional theory with extended Hubbard functionals (DFT + U + V) provides a robust framework to accurately describe complex materials containing transition-metal or rare-earth elements. It does so by mitigating self-interaction errors inherent to ...

NATURE PORTFOLIO2025

Joint Multiband Deconvolution for Euclid and Vera C. Rubin Images

Frédéric Courbin, Pascale Jablonka, Utsav Akhaury

With the advent of surveys like Euclid and Vera C. Rubin, astrophysicists will have access to both deep, high-resolution images and multiband images. However, these two types are not simultaneously available in any single dataset. It is therefore vital to ...

EDP Sciences SA2025

A prediction rigidity formalism for low-cost uncertainties in trained neural networks

Michele Ceriotti, Federico Grasselli, Sanggyu Chong, Filippo Bigi

Quantifying the uncertainty of regression models is essential to ensure their reliability, particularly since their application often extends beyond their training domain. Based on the solution of a constrained optimization problem, this work proposes ‘pre ...

2024

Chemical education in digital chemistry

Philippe Schwaller

In this digital age where machine learning has won the Nobel Prizes in both Physics and Chemistry, it is ever more important to give chemistry students an educational advantage that will enable them to use the tools of artificial intelligence and machine l ...

CELL PRESS2024

Enhancing Peer Review with AI-Powered Suggestion Generation Assistance: Investigating the Design Dynamics

Xiaotian Su, Seyed Parsa Neshaei

While writing peer reviews resembles an important task in science, education, and large organizations, providing fruitful suggestions to peers is not a straightforward task, as different user interaction designs of text suggestion interfaces can have diver ...

Association for Computing Machinery2024

Reaction-Agnostic Featurization of Bidentate Ligands for Bayesian Ridge Regression of Enantioselectivity

Jérôme Waser, Anne-Clémence Corminboeuf, Matthew Wodrich, Rubén Laplaza Solanas, Alexandre Alain Schöpfer

Chiral ligands are important components in asymmetric homogeneous catalysis, but their synthesis and screening can be both time-consuming and resource-intensive. Data-driven approaches, in contrast to screening procedures based on intuition, have the poten ...

Amer Chemical Soc2024

Unravelling the components of diffuse scattering using deep learning

Lucas Sebastian Powell-Rudden

Many technologically important material properties are underpinned by disorder and short-range structural correlations; therefore, elucidating structure-property relationships in functional materials requires understanding both the average and the local st ...

Int Union Crystallography2024

Stripping the Swiss discount curve using kernel ridge regression

Nicolas Etienne Camenzind, Damir Filipovic

We analyze and implement the kernel ridge regression (KR) method developed in Filipovic et al. (Stripping the discount curve-a robust machine learning approach. Swiss Finance Institute Research Paper No. 22-24. SSRN. https://ssrn.com/abstract=4058150, 2022 ...

Springer Heidelberg2024

A two-level resolution neural network with enhanced interpretability for freeway traffic forecasting

Nikolaos Geroliminis, Semin Kwak, Danya Li

© The Author(s) 2024.Deep learning models are widely used for traffic forecasting on freeways due to their ability to learn complex temporal and spatial relationships. In particular, graph neural networks, which integrate graph theory into deep learning, h ...

Nature Research2024

PARAMETRIC SHAPE HOLOMORPHY OF BOUNDARY INTEGRAL OPERATORS WITH APPLICATIONS

Fernando José Henriquez Barraza

We consider a family of boundary integral operators supported on a collection of parametrically defined bounded Lipschitz boundaries. Consequently, the boundary integral operators themselves also depend on the parametric variables, thus leading to a parame ...

2024

Qualify-as-you-go: sensor fusion of optical and acoustic signatures with contrastive deep learning for multi-material composition monitoring in laser powder bed fusion process

Christian Leinenbach, Antonios Nikolaos Baganis

Growing demand for multi-material Laser Powder Bed Fusion (LPBF) faces process control and quality monitoring challenges, particularly in ensuring precise material composition. This study explores optical and acoustic emission signals during LPBF processes ...

Taylor & Francis Ltd2024

Morse theoretic signal compression and reconstruction on chain complexes

Kelly Spry Maggs, Celia Camille Hacker, Stefania Ebli

At the intersection of Topological Data Analysis (TDA) and machine learning, the field of cellular signal processing has advanced rapidly in recent years. In this context, each signal on the cells of a complex is processed using the combinatorial Laplacian ...

2024

Point-process-based Representation Learning for Electronic Health Records

David Atienza Alonso, Anisoara Ionescu, Hojjat Karami

Irregular sampling of time series in electronic health records (EHRs) presents a challenge for the development of machine learning models. Additionally, the pattern of missing data in certain clinical variables is not random, but depends on the decisions o ...

IEEE2023