Levent Sagun - RankMe

53 papers A* 9C 1Journal 34Unranked 9

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	A Differentiable Rank-Based Objective For Better Feature Learning. CoRR Krunoslav Lehman Pavasovic, David Lopez-Paz, Giulio Biroli, Levent Sagun
2025	A*	`conf`	A Differentiable Rank-Based Objective for Better Feature Learning. ICLR Krunoslav Lehman Pavasovic, Giulio Biroli, Levent Sagun
2025	A*	`conf`	An Effective Theory of Bias Amplification. ICLR Arjun Subramonian, Samuel J. Bell, Levent Sagun, Elvis Dohmatob
2025	J	`jnl`	Beg to Differ: Understanding Reasoning-Answer Misalignment Across Languages. CoRR Anaelia Ovalle, Candace Ross, Sebastian Ruder, Adina Williams, Karen Ullrich, Mark Ibrahim, Levent Sagun
2025	J	`jnl`	LLM Knowledge is Brittle: Truthfulness Representations Rely on Superficial Resemblance. CoRR Patrick Haller, Mark Ibrahim, Polina Kirichenko, Levent Sagun, Samuel J. Bell
2025	J	`jnl`	Learning the Wrong Lessons: Syntactic-Domain Spurious Correlations in Language Models. CoRR Chantal Shaib, Vinith M. Suriyakumar, Levent Sagun, Byron C. Wallace, Marzyeh Ghassemi
2025	—	`conf`	On the Role of Speech Data in Reducing Toxicity Detection Bias. NAACL (Long Papers) Samuel J. Bell, Mariano Coria Meglioli, Megan Richards, Eduardo Sánchez, Christophe Ropers, Skyler Wang, Adina Williams, Levent Sagun, Marta R. Costa-jussà
2025	—	`conf`	The Root Shapes the Fruit: On the Persistence of Gender-Exclusive Harms in Aligned Language Models. FAccT Anaelia Ovalle, Krunoslav Lehman Pavasovic, Louis Martin, Luke Zettlemoyer, Eric Michael Smith, Kai-Wei Chang, Adina Williams, Levent Sagun
2024	J	`jnl`	An Effective Theory of Bias Amplification. CoRR Arjun Subramonian, Samuel J. Bell, Levent Sagun, Elvis Dohmatob
2024	J	`jnl`	Chained Tuning Leads to Biased Forgetting. CoRR Megan Ung, Alicia Sun, Samuel J. Bell, Bhaktipriya Radharapu, Levent Sagun, Adina Williams
2024	A*	`conf`	Networked Inequality: Preferential Attachment Bias in Graph Neural Network Link Prediction. ICML Arjun Subramonian, Levent Sagun, Yizhou Sun
2024	J	`jnl`	On the Role of Speech Data in Reducing Toxicity Detection Bias. CoRR Samuel J. Bell, Mariano Coria Meglioli, Megan Richards, Eduardo Sánchez, Christophe Ropers, Skyler Wang, Adina Williams, Levent Sagun, Marta R. Costa-jussà
2024	J	`jnl`	Reassessing the Validity of Spurious Correlations Benchmarks. CoRR Samuel J. Bell, Diane Bouchacourt, Levent Sagun
2024	J	`jnl`	The Root Shapes the Fruit: On the Persistence of Gender-Exclusive Harms in Aligned Language Models. CoRR Anaelia Ovalle, Krunoslav Lehman Pavasovic, Louis Martin, Luke Zettlemoyer, Eric Michael Smith, Adina Williams, Levent Sagun
2023	—	`conf`	Confusing Large Models by Confusing Small Models. ICCV (Workshops) Vítor Albiero, Raghav Mehta, Ivan Evtimov, Samuel J. Bell, Levent Sagun, Aram Markosyan
2023	J	`jnl`	Networked Inequality: Preferential Attachment Bias in Graph Neural Network Link Prediction. CoRR Arjun Subramonian, Levent Sagun, Yizhou Sun
2023	—	`conf`	Simplicity Bias Leads to Amplified Performance Disparities. FAccT Samuel James Bell, Levent Sagun
2023	J	`jnl`	Weisfeiler and Lehman Go Measurement Modeling: Probing the Validity of the WL Test. CoRR Arjun Subramonian, Adina Williams, Maximilian Nickel, Yizhou Sun, Levent Sagun
2022	—	`conf`	Fairness Indicators for Systematic Assessments of Visual Feature Extractors. FAccT Priya Goyal, Adriana Romero-Soriano, Caner Hazirbas, Levent Sagun, Nicolas Usunier
2022	J	`jnl`	Fairness Indicators for Systematic Assessments of Visual Feature Extractors. CoRR Priya Goyal, Adriana Romero-Soriano, Caner Hazirbas, Levent Sagun, Nicolas Usunier
2022	J	`jnl`	Measuring and signing fairness as performance under multiple stakeholder distributions. CoRR David Lopez-Paz, Diane Bouchacourt, Levent Sagun, Nicolas Usunier
2022	J	`jnl`	Simplicity Bias Leads to Amplified Performance Disparities. CoRR Samuel J. Bell, Levent Sagun
2022	J	`jnl`	Understanding out-of-distribution accuracies through quantifying difficulty of test samples. CoRR Berfin Simsek, Melissa Hall, Levent Sagun
2022	J	`jnl`	Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision. CoRR Priya Goyal, Quentin Duval, Isaac Seessel, Mathilde Caron, Ishan Misra, Levent Sagun, Armand Joulin, Piotr Bojanowski
2021	A*	`conf`	ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases. ICML Stéphane d'Ascoli, Hugo Touvron, Matthew L. Leavitt, Ari S. Morcos, Giulio Biroli, Levent Sagun
2021	J	`jnl`	ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases. CoRR Stéphane d'Ascoli, Hugo Touvron, Matthew L. Leavitt, Ari S. Morcos, Giulio Biroli, Levent Sagun
2021	J	`jnl`	More data or more parameters? Investigating the effect of data structure on generalization. CoRR Stéphane d'Ascoli, Marylou Gabrié, Levent Sagun, Giulio Biroli
2021	A*	`conf`	On the interplay between data structure and loss function in classification problems. NeurIPS Stéphane d'Ascoli, Marylou Gabrié, Levent Sagun, Giulio Biroli
2021	J	`jnl`	Transformed CNNs: recasting pre-trained convolutional layers with self-attention. CoRR Stéphane d'Ascoli, Levent Sagun, Giulio Biroli, Ari Morcos
2020	J	`jnl`	Post-Workshop Report on Science meets Engineering in Deep Learning, NeurIPS 2019, Vancouver. CoRR Levent Sagun, Çaglar Gülçehre, Adriana Romero, Negar Rostamzadeh, Stefano Sarao Mannelli
2020	J	`jnl`	Triple descent and the two kinds of overfitting: Where & why do they appear? CoRR Stéphane d'Ascoli, Levent Sagun, Giulio Biroli
2020	A*	`conf`	Triple descent and the two kinds of overfitting: where & why do they appear? NeurIPS Stéphane d'Ascoli, Levent Sagun, Giulio Biroli
2019	A*	`conf`	A Tail-Index Analysis of Stochastic Gradient Noise in Deep Neural Networks. ICML Umut Simsekli, Levent Sagun, Mert Gürbüzbalaban
2019	J	`jnl`	A Tail-Index Analysis of Stochastic Gradient Noise in Deep Neural Networks. CoRR Umut Simsekli, Levent Sagun, Mert Gürbüzbalaban
2019	A*	`conf`	Finding the Needle in the Haystack with Convolutions: on the benefits of architectural bias. NeurIPS Stéphane d'Ascoli, Levent Sagun, Giulio Biroli, Joan Bruna
2019	J	`jnl`	Finding the Needle in the Haystack with Convolutions: on the benefits of architectural bias. CoRR Stéphane d'Ascoli, Levent Sagun, Joan Bruna, Giulio Biroli
2019	J	`jnl`	On the Heavy-Tailed Theory of Stochastic Gradient Descent for Deep Neural Networks. CoRR Umut Simsekli, Mert Gürbüzbalaban, Thanh Huy Nguyen, Gaël Richard, Levent Sagun
2019	J	`jnl`	Scaling description of generalization with number of parameters in deep learning. CoRR Mario Geiger, Arthur Jacot, Stefano Spigler, Franck Gabriel, Levent Sagun, Stéphane d'Ascoli, Giulio Biroli, Clément Hongler, Matthieu Wyart
2018	J	`jnl`	A jamming transition from under- to over-parametrization affects loss landscape and generalization. CoRR Stefano Spigler, Mario Geiger, Stéphane d'Ascoli, Levent Sagun, Giulio Biroli, Matthieu Wyart
2018	A*	`conf`	Comparing Dynamics: Deep Neural Networks versus Glassy Systems. ICML Marco Baity-Jesi, Levent Sagun, Mario Geiger, Stefano Spigler, Gérard Ben Arous, Chiara Cammarota, Yann LeCun, Matthieu Wyart, Giulio Biroli
2018	J	`jnl`	Comparing Dynamics: Deep Neural Networks versus Glassy Systems. CoRR Marco Baity-Jesi, Levent Sagun, Mario Geiger, Stefano Spigler, Gérard Ben Arous, Chiara Cammarota, Yann LeCun, Matthieu Wyart, Giulio Biroli
2018	—	`conf`	Easing non-convex optimization with neural networks. ICLR (Workshop) David Lopez-Paz, Levent Sagun
2018	—	`conf`	Empirical Analysis of the Hessian of Over-Parametrized Neural Networks. ICLR (Workshop) Levent Sagun, Utku Evci, V. Ugur Güney, Yann N. Dauphin, Léon Bottou
2018	J	`jnl`	The jamming transition as a paradigm to understand the loss landscape of deep neural networks. CoRR Mario Geiger, Stefano Spigler, Stéphane d'Ascoli, Levent Sagun, Marco Baity-Jesi, Giulio Biroli, Matthieu Wyart
2017	C	`conf`	Early predictability of asylum court decisions. ICAIL Matthew Dunn, Levent Sagun, Hale Sirin, Daniel Chen
2017	J	`jnl`	Empirical Analysis of the Hessian of Over-Parametrized Neural Networks. CoRR Levent Sagun, Utku Evci, V. Ugur Güney, Yann N. Dauphin, Léon Bottou
2017	—	`conf`	Entropy-SGD: Biasing Gradient Descent Into Wide Valleys. ICLR (Poster) Pratik Chaudhari, Anna Choromanska, Stefano Soatto, Yann LeCun, Carlo Baldassi, Christian Borgs, Jennifer T. Chayes, Levent Sagun, Riccardo Zecchina
2017	J	`jnl`	Perspective: Energy Landscapes for Machine Learning. CoRR Andrew J. Ballard, Ritankar Das, Stefano Martiniani, Dhagash Mehta, Levent Sagun, Jacob D. Stevenson, David J. Wales
2017	J	`jnl`	SearchQA: A New Q&A Dataset Augmented with Context from a Search Engine. CoRR Matthew Dunn, Levent Sagun, Mike Higgins, V. Ugur Güney, Volkan Cirik, Kyunghyun Cho
2016	J	`jnl`	Entropy-SGD: Biasing Gradient Descent Into Wide Valleys. CoRR Pratik Chaudhari, Anna Choromanska, Stefano Soatto, Yann LeCun, Carlo Baldassi, Christian Borgs, Jennifer T. Chayes, Levent Sagun, Riccardo Zecchina
2016	J	`jnl`	Singularity of the Hessian in Deep Learning. CoRR Levent Sagun, Léon Bottou, Yann LeCun
2015	—	`conf`	Explorations on high dimensional landscapes. ICLR (Workshop) Levent Sagun, V. Ugur Güney, Yann LeCun
2015	J	`jnl`	Universality in halting time and its applications in optimization. CoRR Levent Sagun, Thomas Trogdon, Yann LeCun