Levent Sagun

According to our database, Levent Sagun authored at least 32 papers between 2015 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

An Effective Theory of Bias Amplification.

Arjun Subramonian, Samuel J. Bell, Levent Sagun, Elvis Dohmatob

CoRR, 2024

Reassessing the Validity of Spurious Correlations Benchmarks.

Samuel J. Bell, Diane Bouchacourt, Levent Sagun

CoRR, 2024

Networked Inequality: Preferential Attachment Bias in Graph Neural Network Link Prediction.

Arjun Subramonian, Levent Sagun, Yizhou Sun

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Weisfeiler and Lehman Go Measurement Modeling: Probing the Validity of the WL Test.

Arjun Subramonian, Adina Williams, Maximilian Nickel, Yizhou Sun, Levent Sagun

CoRR, 2023

Confusing Large Models by Confusing Small Models.

Vítor Albiero, Raghav Mehta, Ivan Evtimov, Samuel J. Bell, Levent Sagun, Aram Markosyan

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

Simplicity Bias Leads to Amplified Performance Disparities.

Samuel James Bell, Levent Sagun

Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, 2023

2022

Measuring and signing fairness as performance under multiple stakeholder distributions.

David Lopez-Paz, Diane Bouchacourt, Levent Sagun, Nicolas Usunier

CoRR, 2022

Understanding out-of-distribution accuracies through quantifying difficulty of test samples.

Berfin Simsek, Melissa Hall, Levent Sagun

CoRR, 2022

Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision.

Priya Goyal, Quentin Duval, Isaac Seessel, Mathilde Caron, Ishan Misra, Levent Sagun, Armand Joulin, Piotr Bojanowski

CoRR, 2022

Fairness Indicators for Systematic Assessments of Visual Feature Extractors.

Priya Goyal, Adriana Romero-Soriano, Caner Hazirbas, Levent Sagun, Nicolas Usunier

Proceedings of the FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency, Seoul, Republic of Korea, June 21, 2022

2021

Transformed CNNs: recasting pre-trained convolutional layers with self-attention.

Stéphane d'Ascoli, Levent Sagun, Giulio Biroli, Ari Morcos

CoRR, 2021

More data or more parameters? Investigating the effect of data structure on generalization.

Stéphane d'Ascoli, Marylou Gabrié, Levent Sagun, Giulio Biroli

CoRR, 2021

On the interplay between data structure and loss function in classification problems.

Stéphane d'Ascoli, Marylou Gabrié, Levent Sagun, Giulio Biroli

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases.

Stéphane d'Ascoli, Hugo Touvron, Matthew L. Leavitt, Ari S. Morcos, Giulio Biroli, Levent Sagun

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Post-Workshop Report on Science meets Engineering in Deep Learning, NeurIPS 2019, Vancouver.

Levent Sagun, Çaglar Gülçehre, Adriana Romero, Negar Rostamzadeh, Stefano Sarao Mannelli

CoRR, 2020

Triple descent and the two kinds of overfitting: where & why do they appear?

Stéphane d'Ascoli, Levent Sagun, Giulio Biroli

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

On the Heavy-Tailed Theory of Stochastic Gradient Descent for Deep Neural Networks.

Umut Simsekli, Mert Gürbüzbalaban, Thanh Huy Nguyen, Gaël Richard, Levent Sagun

CoRR, 2019

Scaling description of generalization with number of parameters in deep learning.

Mario Geiger, Arthur Jacot, Stefano Spigler, Franck Gabriel, Levent Sagun, Stéphane d'Ascoli, Giulio Biroli, Clément Hongler, Matthieu Wyart

CoRR, 2019

Finding the Needle in the Haystack with Convolutions: on the benefits of architectural bias.

Stéphane d'Ascoli, Levent Sagun, Giulio Biroli, Joan Bruna

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

A Tail-Index Analysis of Stochastic Gradient Noise in Deep Neural Networks.

Umut Simsekli, Levent Sagun, Mert Gürbüzbalaban

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

A jamming transition from under- to over-parametrization affects loss landscape and generalization.

Stefano Spigler, Mario Geiger, Stéphane d'Ascoli, Levent Sagun, Giulio Biroli, Matthieu Wyart

CoRR, 2018

The jamming transition as a paradigm to understand the loss landscape of deep neural networks.

Mario Geiger, Stefano Spigler, Stéphane d'Ascoli, Levent Sagun, Marco Baity-Jesi, Giulio Biroli, Matthieu Wyart

CoRR, 2018

Comparing Dynamics: Deep Neural Networks versus Glassy Systems.

Marco Baity-Jesi, Levent Sagun, Mario Geiger, Stefano Spigler, Gérard Ben Arous, Chiara Cammarota, Yann LeCun, Matthieu Wyart, Giulio Biroli

Proceedings of the 35th International Conference on Machine Learning, 2018

Empirical Analysis of the Hessian of Over-Parametrized Neural Networks.

Levent Sagun, Utku Evci, V. Ugur Güney, Yann N. Dauphin, Léon Bottou

Proceedings of the 6th International Conference on Learning Representations, 2018

Easing non-convex optimization with neural networks.

David Lopez-Paz, Levent Sagun

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

SearchQA: A New Q&A Dataset Augmented with Context from a Search Engine.

Matthew Dunn, Levent Sagun, Mike Higgins, V. Ugur Güney, Volkan Cirik, Kyunghyun Cho

CoRR, 2017

Perspective: Energy Landscapes for Machine Learning.

Andrew J. Ballard, Ritankar Das, Stefano Martiniani, Dhagash Mehta, Levent Sagun, Jacob D. Stevenson, David J. Wales

CoRR, 2017

Entropy-SGD: Biasing Gradient Descent Into Wide Valleys.

Pratik Chaudhari, Anna Choromanska, Stefano Soatto, Yann LeCun, Carlo Baldassi, Christian Borgs, Jennifer T. Chayes, Levent Sagun, Riccardo Zecchina

Proceedings of the 5th International Conference on Learning Representations, 2017

Early predictability of asylum court decisions.

Matthew Dunn, Levent Sagun, Hale Sirin, Daniel Chen

Proceedings of the 16th edition of the International Conference on Artificial Intelligence and Law, 2017

2016

Singularity of the Hessian in Deep Learning.

Levent Sagun, Léon Bottou, Yann LeCun

CoRR, 2016

2015

Universality in halting time and its applications in optimization.

Levent Sagun, Thomas Trogdon, Yann LeCun

CoRR, 2015

Explorations on high dimensional landscapes.

Levent Sagun, V. Ugur Güney, Yann LeCun

Proceedings of the 3rd International Conference on Learning Representations, 2015