Publications associées à Parsimonious Online Learning with Kernels and Random Features with Applications to Stochastic Optimal Control

Publications associées (17)

Variational Quantum Simulation: A Case Study for Understanding Warm Starts

Marc Sanz Drudis, Zoë Holmes, Thanasilp Supanut, Ricard Puig I Valls

The barren plateau phenomenon, characterized by loss gradients that vanish exponentially with system size, poses a challenge to scaling variational quantum algorithms. Here we explore the potential of warm starts, whereby one initializes closer to a soluti ...

2025

Interactive symbolic regression with co-design mechanism through offline reinforcement learning

Wenqing Zhou, Olga Fink, Michele Viscione

Symbolic Regression holds great potential for uncovering underlying mathematical and physical relationships from observed data. However, the vast combinatorial space of possible expressions poses significant challenges for previous online search methods an ...

Springer Science and Business Media LLC2025

High-Resolution Predictions of the Fuel and Cladding Temperatures for the 3D PWR Core with Artificial Neural Networks trained on CTF

Marianna Papadionysiou

High resolution multi-physics core solvers predict safety parameters on a local scale with high accuracy while simulating local heterogeneities and their consequences. To achieve this, large computational resources are needed which renders the extensive us ...

American Nuclear Society2025

Distributed Optimization with Byzantine Robustness Guarantees

Lie He

As modern machine learning continues to achieve unprecedented benchmarks, the resource demands to train these advanced models grow drastically. This has led to a paradigm shift towards distributed training. However, the presence of adversariesâ whether ma ...

EPFL2023

Learning from the past: A short term forecast method for the COVID-19 incidence curve

Jean-David Horacio Morel

Author summaryForecasting the short time evolution of the COVID-19 daily incidence is a key issue in the epidemic decision making policy. We propose a machine learning method which forecasts the future values of the daily incidence trend based on the evolu ...

PUBLIC LIBRARY SCIENCE2023

Efficient approximation of cardiac mechanics through reduced-order modeling with deep learning-based operator approximation

Alfio Quarteroni, Andrea Manzoni

Reducing the computational time required by high-fidelity, full-order models (FOMs) for the solution of problems in cardiac mechanics is crucial to allow the translation of patient-specific simulations into clinical practice. Indeed, while FOMs, such as th ...

Wiley2023

Byzantine-Resilient Learning Beyond Gradients: Distributing Evolutionary Search

Rachid Guerraoui, Matteo Monti, Andrei Kucharavy

Modern machine learning (ML) models are capable of impressive performances. However, their prowess is not due only to the improvements in their architecture and training algorithms but also to a drastic increase in computational power used to train them.|S ...

Assoc Computing Machinery2023

Bayesian reconstruction of memories stored in neural networks from their connectivity

Lenka Zdeborová, Florent Gérard Krzakala

The advent of comprehensive synaptic wiring diagrams of large neural circuits has created the field of connectomics and given rise to a number of open research questions. One such question is whether it is possible to reconstruct the information stored in ...

2023

Drift Estimation of Multiscale Diffusions Based on Filtered Data

Assyr Abdulle, Andrea Zanoni, Giacomo Garegnani

We study the problem of drift estimation for two-scale continuous time series. We set ourselves in the framework of overdamped Langevin equations, for which a single-scale surrogate homogenized equation exists. In this setting, estimating the drift coeffic ...

2023

Robust Classification Using Contractive Hamiltonian Neural ODEs

Giancarlo Ferrari Trecate, Muhammad Zakwan, Liang Xu

Deep neural networks can be fragile and sensitive to small input perturbations that might cause a significant change in the output. In this letter, we employ contraction theory to improve the robustness of neural ODEs (NODEs). A dynamical system is contrac ...

2022

NeuralTree: A 256-Channel 0.227-mu J/Class Versatile Neural Activity Classification and Closed-Loop Neuromodulation SoC

Stéphanie Lacour, Mahsa Shoaran, Bingzhao Zhu, Uisub Shin, Alix Esther Anne Trouillet, Cong Ding, Emilie Cornelia Maria Revol, Yashwanth Sai Reddy Vyza

Closed-loop neural interfaces with on-chip machine learning can detect and suppress disease symptoms in neurological disorders or restore lost functions in paralyzed patients. While high-density neural recording can provide rich neural activity information ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

What Makes for Automatic Reconstruction of Pulmonary Segments

Li Zhang, Jiancheng Yang

3D reconstruction of pulmonary segments plays an important role in surgical treatment planning of lung cancer, which facilitates preservation of pulmonary function and helps ensure low recurrence rates. However, automatic reconstruction of pulmonary segmen ...

SPRINGER INTERNATIONAL PUBLISHING AG2022

VABO: Violation-Aware Bayesian Optimization for Closed-Loop Control Performance Optimization with Unmodeled Constraints

Colin Neil Jones, Wenjie Xu

We study the problem of performance optimization of closed-loop control systems with unmodeled dynamics. Bayesian optimization (BO) has been demonstrated effective for improving closed-loop performance by automatically tuning controller gains or reference ...

IEEE2022

Mismatched Estimation of Non-Symmetric Rank-One Matrices Under Gaussian Noise

Nicolas Macris, Farzad Pourkamali

We consider the estimation of a nxm matrix uv(T) observed through an additive Gaussian noise channel, a problem that frequently arises in statistics and machine learning. We investigate a scenario involving mismatched Bayesian inference in which the stat ...

IEEE2022

Hyperaccurate bounds in discrete-state Markovian systems

Daniel Maria Busiello

Generalized empirical currents represent a vast class of thermodynamic observables of mesoscopic systems. Their fluctuations satisfy the thermodynamic uncertainty relations (TURs), as they can be bounded by the average entropy production. Here, we derive a ...

2022

Semantics-Aware Spatial-Temporal Binaries for Cross-Modal Video Retrieval

Jie Luo, Mengshi Qi

With the current exponential growth of video-based social networks, video retrieval using natural language is receiving ever-increasing attention. Most existing approaches tackle this task by extracting individual frame-level spatial features to represent ...

2021

Fidelity Estimation Improves Noisy-Image Classification with Pretrained Networks

Sabine Süsstrunk, Majed El Helou, Deblina Bhattacharjee

Image classification has significantly improved using deep learning. This is mainly due to convolutional neural networks (CNNs) that are capable of learning rich feature extractors from large datasets. However, most deep learning classification methods are ...

IEEE Institute of Electrical and Electronics Engineers2021