Papers
Adversarial Robustness is at Odds with Lazy Training
Yunjuan Wang, Enayat Ullah, Poorya Mianjy et al.
Adversarial Style Augmentation for Domain Generalized Urban-Scene Segmentation
Zhun Zhong, Yuyang Zhao, Gim Hee Lee et al.
Adversarial Task Up-sampling for Meta-learning
Yichen WU, Long-Kai Huang, Ying Wei
Adversarial training for high-stakes reliability
Daniel Ziegler, Seraphina Nix, Lawrence Chan et al.
Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks
Jianan Zhou, Jianing Zhu, Jingfeng ZHANG et al.
Adversarial Unlearning: Reducing Confidence Along Adversarial Directions
Amrith Setlur, Benjamin Eysenbach, Virginia Smith et al.
A Fast Post-Training Pruning Framework for Transformers
Woosuk Kwon, Sehoon Kim, Michael W. Mahoney et al.
A Fast Scale-Invariant Algorithm for Non-negative Least Squares with Non-negative Data
Jelena Diakonikolas, Chenghui Li, Swati Padmanabhan et al.
A Few Expert Queries Suffices for Sample-Efficient RL with Resets and Linear Value Approximation
Philip Amortila, Nan Jiang, Dhruv Madeka et al.
A Fourier Approach to Mixture Learning
Mingda Qiao, Guru Guruganesh, Ankit Rawat et al.
A framework for bilevel optimization that enables stochastic and global variance reduction algorithms
Mathieu Dagréou, Pierre Ablin, Samuel Vaiter et al.
A general approximation lower bound in $L^p$ norm, with applications to feed-forward neural networks
El Mehdi Achour, Armand Foucault, Sébastien Gerchinovitz et al.
A General Framework for Auditing Differentially Private Machine Learning
Fred Lu, Joseph Munoz, Maya Fuchs et al.
A Geometric Perspective on Variational Autoencoders
Clément Chadebec, Stephanie Allassonniere
A gradient estimator via L1-randomization for online zero-order optimization with two point feedback
Arya Akhavan, Evgenii Chzhen, Massimiliano Pontil et al.
A gradient sampling method with complexity guarantees for Lipschitz functions in high and low dimensions
Damek Davis, Dmitriy Drusvyatskiy, Yin Tat Lee et al.
AgraSSt: Approximate Graph Stein Statistics for Interpretable Assessment of Implicit Graph Generators
Wenkai Xu, Gesine D Reinert
A Greek Parliament Proceedings Dataset for Computational Linguistics and Political Analysis
Konstantina Dritsa, Aikaterini Thoma, Ioannis Pavlopoulos et al.
Agreement-on-the-line: Predicting the Performance of Neural Networks under Distribution Shift
Christina Baek, Yiding Jiang, Aditi Raghunathan et al.
AirfRANS: High Fidelity Computational Fluid Dynamics Dataset for Approximating Reynolds-Averaged Navier–Stokes Solutions
Florent Bonnet, Jocelyn Mazari, Paola Cinnella et al.
A Kernelised Stein Statistic for Assessing Implicit Generative Models
Wenkai Xu, Gesine D Reinert
A Lagrangian Duality Approach to Active Learning
Juan Elenter, Navid Naderializadeh, Alejandro Ribeiro
A Large Scale Search Dataset for Unbiased Learning to Rank
Lixin Zou, Haitao Mao, Xiaokai Chu et al.
Algorithms that Approximate Data Removal: New Results and Limitations
Vinith Suriyakumar, Ashia C Wilson