Publications associées à How data structures affect generalization in Kernel Methods and Deep Learning

Publications associées (14)

Adaptive Joint Distribution Learning

We develop a new framework for estimating joint probability distributions using tensor product reproducing kernel Hilbert spaces (RKHS). Our framework accommodates a low-dimensional, normalized, and positive model of a Radon-Nikodym derivative, which we es ...

SIAM PUBLICATIONS2025

Joint Compression of Multi-model Systems for Edge Devices

Mathieu Salzmann, João Luís Prado Vieira

Deploying deep learning models for computer vision (CV) often requires a careful trade-off between performance, efficiency, and cost. In the context of edge inference of machine learning systems, this trade-off translates into a challenge, as computing and ...

Springer Science and Business Media Deutschland GmbH2025

RAFT: Regularized Adversarial Fine-Tuning to Enhance Deep Reinforcement Learning for Self-Parking

Andrea Cavallaro

Deep reinforcement learning (DRL) is a powerful method for local motion planning in automated driving. However, training of DRL agents is difficult and subject to instability. We propose Regularized Adversarial Fine-Tuning (RAFT), an adversarial DRL traini ...

2025

Adaptive energy reference for machine-learning models of the electronic density of states

Michele Ceriotti, Federico Grasselli, Kevin Kazuki Huguenin-Dumittan, Sanggyu Chong, Wei Bin How

The electronic density of states (DOS) provides information regarding the distribution of electronic energy levels in a material, and can be used to approximate its optical and electronic properties and therefore guide computational materials design. Given ...

2025

Low-Resource Speech Recognition and Understanding for Challenging Applications

Juan Pablo Zuluaga Gomez

Automatic speech recognition (ASR) and spoken language understanding (SLU) is the core component of current voice-powered AI assistants such as Siri and Alexa. It involves speech transcription with ASR and its comprehension with natural language understand ...

EPFL2024

FedSSA: Semantic Similarity-based Aggregation for Efficient Model-Heterogeneous Personalized Federated Learning

Zhuan Shi, Gang Wang

Federated learning (FL) is a privacy-preserving collaboratively machine learning paradigm. Traditional FL requires all data owners (a.k.a. FL clients) to train the same local model. This design is not well-suited for scenarios involving data and/or system ...

IJCAI2024

Revealing the Structure of Land Ownership through the Automatic Vectorisation of Swiss Cadastral Plans

Isabella Di Lenardo, Rémi Guillaume Petitpierre, Lucas Arnaud André Rappo

This paper explores how the dynamics and structure of land ownership can be studied by automating the vectorisation of historical cadastral sources. Our analysis focuses mainly on three cadastres of Lausanne (1722, 1831, 1883). The plans are first georefer ...

2024

Development of Machine Learning methods for the kinetic modelling of metabolic networks for biotechnological and biomedical applications

Subham Choudhury

Research in biological sciences is in the midst of a true revolution as a result of the emergence and growing impact of a series of new '-omics' disciplines and tools. These include genomics, transcriptomics, proteomics and metabolomics devoted respectivel ...

EPFL2024

Co-optimising Design and Operation of Energy Systems using Reinforcement Learning

Marine Dominique Cauz

The energy transition is ongoing, in Switzerland and abroad, leading to an ever-increasing development of renewable energy sources. These sources are decentralised, heterogeneous, come with a high dependance on weather conditions, and are therefore hard t ...

EPFL2024

Exploring Memristive Biosensing Dynamics: A COMSOL Multiphysics Approach

Sandro Carrara

This paper presents a novel methodology for mod-eling memristive biosensing within COMSOL Multiphysics, fo-cusing on critical performance metrics such as antigen-antibody binding concentration and output resistive states. By studying the impact of increasi ...

Institute of Electrical and Electronics Engineers Inc.2024

A Zero Auxiliary Knowledge Membership Inference Attack on Aggregate Location Data

Ana-Maria Cretu

Location data is frequently collected from populations and shared in aggregate form to guide policy and decision making. However, the prevalence of aggregated data also raises the privacy concern of membership inference attacks (MIAs). MIAs infer whether a ...

Privacy Enhancing Technologies Symposium Advisory Board2024

Scalable PAC-Bayesian Meta-Learning via the PAC-Optimal Hyper-Posterior: From Theory to Practice

Martin Josifoski

Meta-Learning aims to speed up the learning process on new tasks by acquiring useful inductive biases from datasets of related learning tasks. While, in practice, the number of related tasks available is often small, most of the existing approaches assume ...

2023

Multi-agent Learning with Privacy Guarantees

Elsa Rizk

A multi-agent system consists of a collection of decision-making or learning agents subjected to streaming observations from some real-world phenomenon. The goal of the system is to solve some global learning or optimization problem in a distributed or dec ...

EPFL2023

Critical analysis of decision variables for high-throughput experimentation (HTE) with perovskite solar cells

Mohammad Khaja Nazeeruddin, François Maréchal, Naveen Bhati

Perovskite solar cells are among the most dominant emerging PV technologies which have shown unparallel improvement in power conversion efficiency in just over a decade. Long charge carrier diffusion lengths and lifetimes, high absorption coefficients, ban ...

PERGAMON-ELSEVIER SCIENCE LTD2023