Explorations on high dimensional landscapes. - Levent Sagun; V. Ugur Güney; Yann LeCun (ICLR (Workshop), 2015)

Universality in halting time and its applications in optimization. - Levent Sagun; Thomas Trogdon; Yann LeCun (CoRR, 2015)

Singularity of the Hessian in Deep Learning. - Levent Sagun; Léon Bottou; Yann LeCun (CoRR, 2016)

Early predictability of asylum court decisions. - Matthew Dunn; Levent Sagun; Hale Sirin; Daniel Chen (ICAIL, 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 (ICLR (Poster), 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)

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)

Easing non-convex optimization with neural networks. - David Lopez-Paz; Levent Sagun (ICLR (Workshop), 2018)

Empirical Analysis of the Hessian of Over-Parametrized Neural Networks. - Levent Sagun; Utku Evci; V. Ugur Güney; Yann N. Dauphin; Léon Bottou (ICLR (Workshop), 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 (ICML, 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)

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)

A Tail-Index Analysis of Stochastic Gradient Noise in Deep Neural Networks. - Umut Simsekli; Levent Sagun; Mert Gürbüzbalaban (ICML, 2019)

Finding the Needle in the Haystack with Convolutions: on the benefits of architectural bias. - Stéphane d'Ascoli; Levent Sagun; Giulio Biroli; Joan Bruna (NeurIPS, 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)

On the Heavy-Tailed Theory of Stochastic Gradient Descent for Deep Neural Networks. - Umut Simsekli; Mert Gürbüzbalaban; Thanh Huy Nguyen 001; Gaël Richard; Levent Sagun (CoRR, 2019)

Triple descent and the two kinds of overfitting: where & why do they appear? - Stéphane d'Ascoli; Levent Sagun; Giulio Biroli (NeurIPS, 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)

ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases. - Stéphane d'Ascoli; Hugo Touvron; Matthew L. Leavitt; Ari S. Morcos; Giulio Biroli; Levent Sagun (ICML, 2021)

On the interplay between data structure and loss function in classification problems. - Stéphane d'Ascoli; Marylou Gabrié; Levent Sagun; Giulio Biroli (NeurIPS, 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)

Transformed CNNs: recasting pre-trained convolutional layers with self-attention. - Stéphane d'Ascoli; Levent Sagun; Giulio Biroli; Ari Morcos (CoRR, 2021)

Fairness Indicators for Systematic Assessments of Visual Feature Extractors. - Priya Goyal; Adriana Romero-Soriano; Caner Hazirbas; Levent Sagun; Nicolas Usunier (FAccT, 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)

Understanding out-of-distribution accuracies through quantifying difficulty of test samples. - Berfin Simsek; Melissa Hall; Levent Sagun (CoRR, 2022)

Measuring and signing fairness as performance under multiple stakeholder distributions. - David Lopez-Paz; Diane Bouchacourt; Levent Sagun; Nicolas Usunier (CoRR, 2022)

Simplicity Bias Leads to Amplified Performance Disparities. - Samuel James Bell; Levent Sagun (FAccT, 2023)

Confusing Large Models by Confusing Small Models. - Vítor Albiero; Raghav Mehta; Ivan Evtimov; Samuel J. Bell; Levent Sagun; Aram Markosyan (ICCV (Workshops), 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)

Networked Inequality: Preferential Attachment Bias in Graph Neural Network Link Prediction. - Arjun Subramonian; Levent Sagun; Yizhou Sun (ICML, 2024)

Reassessing the Validity of Spurious Correlations Benchmarks. - Samuel J. Bell; Diane Bouchacourt; Levent Sagun (CoRR, 2024)

The Root Shapes the Fruit: On the Persistence of Gender-Exclusive Harms in Aligned Language Models. - Anaelia Ovalle; Krunoslav Lehman Pavasovic; Louis Martin; Luke Zettlemoyer; Eric Michael Smith; Adina Williams; Levent Sagun (CoRR, 2024)

Chained Tuning Leads to Biased Forgetting. - Megan Ung; Alicia Sun; Samuel J. Bell; Bhaktipriya Radharapu; Levent Sagun; Adina Williams (CoRR, 2024)

The Root Shapes the Fruit: On the Persistence of Gender-Exclusive Harms in Aligned Language Models. - Anaelia Ovalle; Krunoslav Lehman Pavasovic; Louis Martin; Luke Zettlemoyer; Eric Michael Smith; Kai-Wei Chang; Adina Williams; Levent Sagun (FAccT, 2025)

A Differentiable Rank-Based Objective for Better Feature Learning. - Krunoslav Lehman Pavasovic; Giulio Biroli; Levent Sagun (ICLR, 2025)

An Effective Theory of Bias Amplification. - Arjun Subramonian; Samuel J. Bell; Levent Sagun; Elvis Dohmatob (ICLR, 2025)

On the Role of Speech Data in Reducing Toxicity Detection Bias. - Samuel J. Bell; Mariano Coria Meglioli; Megan Richards; Eduardo Sánchez; Christophe Ropers; Skyler Wang; Adina Williams; Levent Sagun; Marta R. Costa-jussà (NAACL (Long Papers), 2025)

A Differentiable Rank-Based Objective For Better Feature Learning. - Krunoslav Lehman Pavasovic; David Lopez-Paz; Giulio Biroli; Levent Sagun (CoRR, 2025)