Operationalizing Contextual Integrity in Privacy-Conscious Assistants.
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CoRR, 2024
Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models.
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CoRR, 2024
ConvNets Match Vision Transformers at Scale.
CoRR, 2023
Unlocking Accuracy and Fairness in Differentially Private Image Classification.
CoRR, 2023
Differentially Private Diffusion Models Generate Useful Synthetic Images.
CoRR, 2023
A Stochastic Bundle Method for Interpolation.
J. Mach. Learn. Res., 2022
Unlocking High-Accuracy Differentially Private Image Classification through Scale.
CoRR, 2022
A Stochastic Bundle Method for Interpolating Networks.
CoRR, 2022
Comment on Stochastic Polyak Step-Size: Performance of ALI-G.
CoRR, 2021
Verifying Probabilistic Specifications with Functional Lagrangians.
CoRR, 2021
Make Sure You're Unsure: A Framework for Verifying Probabilistic Specifications.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021
Training Neural Networks for and by Interpolation.
Proceedings of the 37th International Conference on Machine Learning, 2020
Leveraging structure for optimization in deep learning.
PhD thesis, 2019
Deep Frank-Wolfe For Neural Network Optimization.
Proceedings of the 7th International Conference on Learning Representations, 2019
Smooth Loss Functions for Deep Top-k Classification.
Proceedings of the 6th International Conference on Learning Representations, 2018
Trusting SVM for Piecewise Linear CNNs.
Proceedings of the 5th International Conference on Learning Representations, 2017