How to iron out rough landscapes and get optimal performances: Averaged Gradient Descent and its application to tensor PCA

29 May 2019

Papers citing "How to iron out rough landscapes and get optimal performances: Averaged Gradient Descent and its application to tensor PCA"

5 / 5 papers shown

Title
Sharp Analysis of Power Iteration for Tensor PCA Yuchen Wu Kangjie Zhou 31 0 0 02 Jan 2024
Smoothing the Landscape Boosts the Signal for SGD: Optimal Sample Complexity for Learning Single Index Models Alexandru Damian Eshaan Nichani Rong Ge Jason D. Lee MLT 36 33 0 18 May 2023
Selective Multiple Power Iteration: from Tensor PCA to gradient-based exploration of landscapes M. Ouerfelli M. Tamaazousti V. Rivasseau 33 7 0 23 Dec 2021
Some Remarks on Replicated Simulated Annealing Vicent Gripon Matthias Löwe Franck Vermet 14 2 0 30 Sep 2020
Estimating Rank-One Spikes from Heavy-Tailed Noise via Self-Avoiding Walks Jingqiu Ding Samuel B. Hopkins David Steurer 21 10 0 31 Aug 2020