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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 / No. 8: AAAI-22 Technical Tracks 8

AdaLoss: A Computationally-Efficient and Provably Convergent Adaptive Gradient Method

February 1, 2023

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Authors

Xiaoxia Wu

Microsoft


Yuege Xie

The University of Texas at Austin


Simon Shaolei Du

University of Washington


Rachel Ward

The University of Texas at Austin


DOI:

10.1609/aaai.v36i8.20848


Abstract:

We propose a computationally-friendly adaptive learning rate schedule, ``AdaLoss", which directly uses the information of the loss function to adjust the stepsize in gradient descent methods. We prove that this schedule enjoys linear convergence in linear regression. Moreover, we extend the to the non-convex regime, in the context of two-layer over-parameterized neural networks. If the width is sufficiently large (polynomially), then AdaLoss converges robustly to the global minimum in polynomial time. We numerically verify the theoretical results and extend the scope of the numerical experiments by considering applications in LSTM models for text clarification and policy gradients for control problems.

Topics: AAAI

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HOW TO CITE:

Xiaoxia Wu||Yuege Xie||Simon Shaolei Du||Rachel Ward AdaLoss: A Computationally-Efficient and Provably Convergent Adaptive Gradient Method Proceedings of the AAAI Conference on Artificial Intelligence (2022) 8691-8699.

Xiaoxia Wu||Yuege Xie||Simon Shaolei Du||Rachel Ward AdaLoss: A Computationally-Efficient and Provably Convergent Adaptive Gradient Method AAAI 2022, 8691-8699.

Xiaoxia Wu||Yuege Xie||Simon Shaolei Du||Rachel Ward (2022). AdaLoss: A Computationally-Efficient and Provably Convergent Adaptive Gradient Method. Proceedings of the AAAI Conference on Artificial Intelligence, 8691-8699.

Xiaoxia Wu||Yuege Xie||Simon Shaolei Du||Rachel Ward. AdaLoss: A Computationally-Efficient and Provably Convergent Adaptive Gradient Method. Proceedings of the AAAI Conference on Artificial Intelligence 2022 p.8691-8699.

Xiaoxia Wu||Yuege Xie||Simon Shaolei Du||Rachel Ward. 2022. AdaLoss: A Computationally-Efficient and Provably Convergent Adaptive Gradient Method. "Proceedings of the AAAI Conference on Artificial Intelligence". 8691-8699.

Xiaoxia Wu||Yuege Xie||Simon Shaolei Du||Rachel Ward. (2022) "AdaLoss: A Computationally-Efficient and Provably Convergent Adaptive Gradient Method", Proceedings of the AAAI Conference on Artificial Intelligence, p.8691-8699

Xiaoxia Wu||Yuege Xie||Simon Shaolei Du||Rachel Ward, "AdaLoss: A Computationally-Efficient and Provably Convergent Adaptive Gradient Method", AAAI, p.8691-8699, 2022.

Xiaoxia Wu||Yuege Xie||Simon Shaolei Du||Rachel Ward. "AdaLoss: A Computationally-Efficient and Provably Convergent Adaptive Gradient Method". Proceedings of the AAAI Conference on Artificial Intelligence, 2022, p.8691-8699.

Xiaoxia Wu||Yuege Xie||Simon Shaolei Du||Rachel Ward. "AdaLoss: A Computationally-Efficient and Provably Convergent Adaptive Gradient Method". Proceedings of the AAAI Conference on Artificial Intelligence, (2022): 8691-8699.

Xiaoxia Wu||Yuege Xie||Simon Shaolei Du||Rachel Ward. AdaLoss: A Computationally-Efficient and Provably Convergent Adaptive Gradient Method. AAAI[Internet]. 2022[cited 2023]; 8691-8699.


ISSN: 2374-3468


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