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Emperical Interference

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Modern Magnetic Systems

Perceiving Systems

Physical Intelligence

Robotic Materials

Social Foundations of Computation

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Autonomous Vision

Autonomous Learning

Bioinspired Autonomous Miniature Robots

Dynamic Locomotion

Embodied Vision

Human Aspects of Machine Learning

Intelligent Control Systems

Learning and Dynamical Systems

Locomotion in Biorobotic and Somatic Systems

Micro, Nano, and Molecular Systems

Movement Generation and Control

Neural Capture and Synthesis

Physics for Inference and Optimization

Organizational Leadership and Diversity

Probabilistic Learning Group

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Robot Learning

Conference Paper

2022

Autonomous Learning

Robotics

AI

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Award

Deep Models and Optimization Conference Paper Geometric Inductive Biases of Deep Networks: The Role of Data and Architecture Movahedi, S., Orvieto, A., Moosavi-Dezfooli, S. In The Thirteenth International Conference on Learning Representations, ICLR 2025, The Thirteenth International Conference on Learning Representations, January 2025, Accepted (Accepted) BibTeX
Deep Models and Optimization Conference Paper Adaptive Methods through the Lens of SDEs: Theoretical Insights on the Role of Noise Monzio Compagnoni, E., Liu, T., Islamov, R., Proske, F. N., Orvieto, A., Lucchi, A. In The Thirteenth International Conference on Learning Representations, ICLR 2025, The Thirteenth International Conference on Learning Representations, November 2024, Accepted (Accepted) BibTeX
Deep Models and Optimization Article NIMBA: Towards Robust and Principled Processing of Point Clouds With SSMs Köprücü, N., Okpekpe, D., Orvieto, A. October 2024, In preparation (In preparation) BibTeX
Deep Models and Optimization Article An accelerated Lyapunov function for Polyak’s Heavy-ball on convex quadratics Orvieto, A. Optimization Letters, June 2024 (Published) BibTeX
Deep Models and Optimization Article Gradient Descent on Logistic Regression with Non-Separable Data and Large Step Sizes Yi Meng, S., Orvieto, A., Yiming Cao, D., De Sa, C. June 2024, Submitted (Submitted) BibTeX
Deep Models and Optimization Article An Adaptive Stochastic Gradient Method with Non-negative Gauss-Newton Stepsizes Orvieto, A., Xiao, L. July 2024, Submitted (Submitted) BibTeX
Deep Models and Optimization Conference Paper Loss Landscape Characterization of Neural Networks without Over-Parametrization Islamov, R., Ajroldi, N., Orvieto, A., Lucchi, A. In Proceedings of the Thirty-Eighth Annual Conference on Neural Information Processing Systems, Thirty-Eighth Annual Conference on Neural Information Processing Systems, October 2024 (Published) URL BibTeX
Deep Models and Optimization Conference Paper Recurrent Distance Filtering for Graph Representation Learning Ding, Y., Orvieto, A., He, B., Hofmann, T. In PMLR, ICML, January 2024 (Published) URL BibTeX
Deep Models and Optimization Conference Paper Recurrent neural networks: vanishing and exploding gradients are not the end of the story Zucchet, N., Orvieto, A. In Proceedings of the Thirty-Eighth Annual Conference on Neural Information Processing Systems, Thirty-Eighth Annual Conference on Neural Information Processing Systems, October 2024 (Published) URL BibTeX
Deep Models and Optimization Conference Paper SDEs for Minimax Optimization Monzio Compagnoni, E., Orvieto, A., Kersting, H., Proske, F., Lucchi, A. PMLR, AISTATS, February 2024 (Published) URL BibTeX
Deep Models and Optimization Conference Paper Super Consistency of Neural Network Landscapes and Learning Rate Transfer Noci, L., Meterez, A., Hofmann, T., Orvieto, A. In Proceedings of the Thirty-Eighth Annual Conference on Neural Information Processing Systems, Thirty-Eighth Annual Conference on Neural Information Processing Systems, January 2024 (Published) URL BibTeX
Deep Models and Optimization Conference Paper Theoretical Foundations of Deep Selective State-Space Models Muca Cirone, N., Orvieto, A., Walker, B., Salvi, C., Lyons, T. In Proceedings of the Thirty-Eighth Annual Conference on Neural Information Processing Systems, Thirty-Eighth Annual Conference on Neural Information Processing Systems, October 2024 (Published) URL BibTeX
Deep Models and Optimization Conference Paper Understanding the differences in Foundation Models: Attention, State Space Models, and Recurrent Neural Networks Sieber, J., Amo Alonso, C., Didier, A., Zeilinger, M., Orvieto, A. In Proceedings of the Thirty-Eighth Annual Conference on Neural Information Processing Systems, Thirty-Eighth Annual Conference on Neural Information Processing Systems, October 2024 (Published) URL BibTeX
Deep Models and Optimization Conference Paper Universality of Linear Recurrences Followed by Non-linear Projections: Finite-Width Guarantees and Benefits of Complex Eigenvalues Orvieto, A., De, S., Gulcehre, C., Pascanu, R., Smith, S. L. In Proceedings of Machine Learning Research, Proceedings of the Forty-First International Conference on Machine Learning , Forty-First International Conference on Machine Learning , June 2024 (Published) URL BibTeX
Deep Models and Optimization Conference Paper Resurrecting Recurrent Neural Networks for Long Sequences Orvieto, A., Smith, S. L., Gu, A., Fernando, A., Gulcehre, C., Pascanu, R., De, S. In Proceedings of the Eleventh International Conference on Learning Representations, ICLR, June 2023 (Published) URL BibTeX