no code implementations • 11 Mar 2024 • Yuyang Deng, Junyuan Hong, Jiayu Zhou, Mehrdad Mahdavi
Recent advances in unsupervised learning have shown that unsupervised pre-training, followed by fine-tuning, can improve model generalization.
no code implementations • 16 Feb 2024 • Yuyang Deng, Mingda Qiao
We study a variant of Collaborative PAC Learning, in which we aim to learn an accurate classifier for each of the $n$ data distributions, while minimizing the number of samples drawn from them in total.
no code implementations • 10 Dec 2023 • Yuyang Deng, Ni Zhao, Xin Huang
Since its launch, ChatGPT has achieved remarkable success as a versatile conversational AI platform, drawing millions of users worldwide and garnering widespread recognition across academic, industrial, and general communities.
1 code implementation • NeurIPS 2023 • Haobo Zhang, Junyuan Hong, Yuyang Deng, Mehrdad Mahdavi, Jiayu Zhou
Deep Gradient Leakage (DGL) is a highly effective attack that recovers private training images from gradient vectors.
no code implementations • 23 Feb 2023 • Yuyang Deng, Nidham Gazagnadou, Junyuan Hong, Mehrdad Mahdavi, Lingjuan Lyu
Recent studies demonstrated that the adversarially robust learning under $\ell_\infty$ attack is harder to generalize to different domains than standard domain adaptation.
no code implementations • 17 Oct 2022 • Pouria Mahdavinia, Yuyang Deng, Haochuan Li, Mehrdad Mahdavi
Despite the established convergence theory of Optimistic Gradient Descent Ascent (OGDA) and Extragradient (EG) methods for the convex-concave minimax problems, little is known about the theoretical guarantees of these methods in nonconvex settings.
no code implementations • 22 Jul 2021 • Yuyang Deng, Mohammad Mahdi Kamani, Mehrdad Mahdavi
This work is the first to show the convergence of Local SGD on non-smooth functions, and will shed lights on the optimization theory of federated training of deep neural networks.
1 code implementation • NeurIPS 2020 • Yuyang Deng, Mohammad Mahdi Kamani, Mehrdad Mahdavi
To compensate for this, we propose a Distributionally Robust Federated Averaging (DRFA) algorithm that employs a novel snapshotting scheme to approximate the accumulation of history gradients of the mixing parameter.
no code implementations • 25 Feb 2021 • Yuyang Deng, Mehrdad Mahdavi
Local SGD is a promising approach to overcome the communication overhead in distributed learning by reducing the synchronization frequency among worker nodes.
9 code implementations • 30 Mar 2020 • Yuyang Deng, Mohammad Mahdi Kamani, Mehrdad Mahdavi
Investigation of the degree of personalization in federated learning algorithms has shown that only maximizing the performance of the global model will confine the capacity of the local models to personalize.