Stochastic thermodynamics of learning. - Sebastian Goldt (2018)

Dynamics of stochastic gradient descent for two-layer neural networks in the teacher-student setup. - Sebastian Goldt; Madhu Advani; Andrew M. Saxe; Florent Krzakala; Lenka Zdeborová (NeurIPS, 2019)

Generalisation dynamics of online learning in over-parameterised neural networks. - Sebastian Goldt; Madhu S. Advani; Andrew M. Saxe; Florent Krzakala; Lenka Zdeborová (CoRR, 2019)

Modelling the influence of data structure on learning in neural networks. - Sebastian Goldt; Marc Mézard; Florent Krzakala; Lenka Zdeborová (CoRR, 2019)

The Gaussian equivalence of generative models for learning with two-layer neural networks. - Sebastian Goldt; Galen Reeves; Marc Mézard; Florent Krzakala; Lenka Zdeborová (CoRR, 2020)

The dynamics of learning with feedback alignment. - Maria Refinetti; Stéphane d'Ascoli; Ruben Ohana; Sebastian Goldt (CoRR, 2020)

Continual Learning in the Teacher-Student Setup: Impact of Task Similarity. - Sebastian Lee; Sebastian Goldt; Andrew M. Saxe (ICML, 2021)

Classifying high-dimensional Gaussian mixtures: Where kernel methods fail and neural networks succeed. - Maria Refinetti; Sebastian Goldt; Florent Krzakala; Lenka Zdeborová (ICML, 2021)

Align, then memorise: the dynamics of learning with feedback alignment. - Maria Refinetti; Stéphane d'Ascoli; Ruben Ohana; Sebastian Goldt (ICML, 2021)

The Gaussian equivalence of generative models for learning with shallow neural networks. - Sebastian Goldt; Bruno Loureiro; Galen Reeves; Florent Krzakala; Marc Mézard; Lenka Zdeborová (MSML, 2021)

Learning curves of generic features maps for realistic datasets with a teacher-student model. - Bruno Loureiro; Cédric Gerbelot; Hugo Cui; Sebastian Goldt; Florent Krzakala; Marc Mézard; Lenka Zdeborová (NeurIPS, 2021)

Capturing the learning curves of generic features maps for realistic data sets with a teacher-student model. - Bruno Loureiro; Cédric Gerbelot; Hugo Cui; Sebastian Goldt; Florent Krzakala; Marc Mézard; Lenka Zdeborová (CoRR, 2021)

Representation mitosis in wide neural networks. - Diego Doimo; Aldo Glielmo; Sebastian Goldt; Alessandro Laio (CoRR, 2021)

Maslow's Hammer in Catastrophic Forgetting: Node Re-Use vs. Node Activation. - Sebastian Lee; Stefano Sarao Mannelli; Claudia Clopath; Sebastian Goldt; Andrew M. Saxe (ICML, 2022)

The dynamics of representation learning in shallow, non-linear autoencoders. - Maria Refinetti; Sebastian Goldt (ICML, 2022)

Redundant representations help generalization in wide neural networks. - Diego Doimo; Aldo Glielmo; Sebastian Goldt; Alessandro Laio (NeurIPS, 2022)

The impact of memory on learning sequence-to-sequence tasks. - Alireza Seif; Sarah A. M. Loos; Gennaro Tucci; Édgar Roldán; Sebastian Goldt (CoRR, 2022)

Neural networks trained with SGD learn distributions of increasing complexity. - Maria Refinetti; Alessandro Ingrosso; Sebastian Goldt (ICML, 2023)

Attacks on Online Learners: a Teacher-Student Analysis. - Riccardo Giuseppe Margiotta; Sebastian Goldt; Guido Sanguinetti (NeurIPS, 2023)

Quantifying lottery tickets under label noise: accuracy, calibration, and complexity. - Viplove Arora; Daniele Irto; Sebastian Goldt; Guido Sanguinetti (UAI, 2023)

Optimal inference of a generalised Potts model by single-layer transformers with factored attention. - Riccardo Rende; Federica Gerace; Alessandro Laio; Sebastian Goldt (CoRR, 2023)

The RL Perceptron: Generalisation Dynamics of Policy Learning in High Dimensions. - Nishil Patel; Sebastian Lee; Stefano Sarao Mannelli; Sebastian Goldt; Andrew M. Saxe (CoRR, 2023)

Learning from higher-order statistics, efficiently: hypothesis tests, random features, and neural networks. - Eszter Székely; Lorenzo Bardone; Federica Gerace; Sebastian Goldt (CoRR, 2023)

Bayesian reconstruction of memories stored in neural networks from their connectivity. - Sebastian Goldt; Florent Krzakala; Lenka Zdeborová; Nicolas Brunel (PLoS Comput. Biol., 2023)

Sliding Down the Stairs: How Correlated Latent Variables Accelerate Learning with Neural Networks. - Lorenzo Bardone; Sebastian Goldt (ICML, 2024)

A distributional simplicity bias in the learning dynamics of transformers. - Riccardo Rende; Federica Gerace; Alessandro Laio; Sebastian Goldt (NeurIPS, 2024)

Learning from higher-order correlations, efficiently: hypothesis tests, random features, and neural networks. - Eszter Székely; Lorenzo Bardone; Federica Gerace; Sebastian Goldt (NeurIPS, 2024)

How Iterative Magnitude Pruning Discovers Local Receptive Fields in Fully Connected Neural Networks. - William T. Redman; Zhangyang Wang; Alessandro Ingrosso; Sebastian Goldt (CPAL, 2025)

Exploring the potential of Direct Feedback Alignment for Continual Learning. - Sara Folchini; Viplove Arora; Sebastian Goldt (Trans. Mach. Learn. Res., 2025)

Feature learning from non-Gaussian inputs: the case of Independent Component Analysis in high dimensions. - Fabiola Ricci; Lorenzo Bardone; Sebastian Goldt (CoRR, 2025)

Two failure modes of deep transformers and how to avoid them: a unified theory of signal propagation at initialisation. - Alessio Giorlandino; Sebastian Goldt (CoRR, 2025)

Generalization Dynamics of Linear Diffusion Models. - Claudia Merger; Sebastian Goldt (CoRR, 2025)