Welcome

Gong show presentation of participants

Learning new physics from a machine

• Andrea Wulzer (Padova University)
• Gaia Grosso (CERN)

Efficient large scale kernel methods for high energy physics

• Marco Letizia (University of Genova)

Non-parametric analysis of the Hubble Diagram with Neural Networks

• Matilde Signorini (University of Firenze)
• Lorenzo Giambagli (University of Firenze)

Deep Learning techniques for Genomics

• Lorenzo Buffoni (University of Lisbon)

Welcome

Interpretable graphical models for collider studies

• Manuel Szewc (Jozef Stefan Institute, Ljubljana)

Gong show presentation of participants

Machine learning for HEP

• Jesse Thaler (MIT)

Exploring EFT with ML at the LHC

• Alessandra Cappati (LLR, Ecole Polytechnique, Paris)
• Robert Schöfbeck (HEPHY Vienna)

Discussion: ML for jets and reinforcement learning for BSM

Recurrent NNs for fast signal discovery in pulsar timing arrays

• Marat Freytsis (Rutgers University)

Discussion: EFT

Learning of Phases of Matter: What’s Next?

• Eliska Greplova (TU Delft)

DIscussion: New physics as an anomaly

Welcome

Introduction to normalizing flows and some applications to LHC and Gaia

• David Shih

The Cosmological Analysis of Large-Scale-Structure Data

• Guido D'Amico

Machine Learning and Physics: A Faustian Bargain?

• Jeff Byers (University of Padova)

Discussion session on Generative Models

Deterministic Langevin and Hamiltonian methods for sampling

• Uros Seljak

Spectral Learning for Neural Network

• Lorenzo Giambagli

Welcome

Spectral Learning

• Lorenzo Chicchi

Large-scale imaging and feature extraction using advanced high-resolution microscopy techniques

• Giacomo Mazzamuto

Generalization in Similarity Learning

• Nayara Fonseca
• Veronica Guidetti

ML applied to Identification/characterisation of X-ray sources and open problems

• Mara Salvato

Visit to Galileo's Villa il Gioiello

PDF Determination as Machine Learning

• Stefano Forte

Welcome

TBA

• Mario Krenn

On the Dynamics of Inference and Learning

• David Berman

Universal mean-field upper bound for the generalization gap of deep neural network

• Sebastiano Ariosto

Model independent measurements of Standard Model cross sections with Domain Adaptation

• Benedetta Camaiani

TBA

• Paolo Nesi