Poster Session 1 (gather.town)
in
Workshop: OPT 2021: Optimization for Machine Learning
Abstract
Please join us in gather.town (see link above). To see the abstracts of the posters presented in this session, please see below the schedule.
Authors/papers presenting posters in gather.town for this session:
Gaussian Graphical Models as an Ensemble Method for Distributed Gaussian Processes, Hamed Jalali
DAdaQuant: Doubly-adaptive quantization for communication-efficient Federated Learning, Robert Hönig
Using a one dimensional parabolic model of the full-batch loss to estimate learning rates during training, Maximus Mutschler
COCO Denoiser: Using Co-Coercivity for Variance Reduction in Stochastic Convex Optimization, Manuel Madeira
Decentralized Personalized Federated Learning: Lower Bounds and Optimal Algorithm for All Personalization Modes, Abdurakhmon Sadiev
Shifted Compression Framework: Generalizations and Improvements, Egor Shulgin
Faking Interpolation Until You Make It, Alasdair J Paren
Towards Modeling and Resolving Singular Parameter Spaces using Stratifolds, Pascal M Esser
Spherical Perspective on Learning with Normalization Layers, Simon W Roburin
Adaptive Optimization with Examplewise Gradients, Julius Kunze
On the Relation between Distributionally Robust Optimization and Data Curation, Agnieszka Słowik
Fast, Exact Subsampled Natural Gradients and First-Order KFAC, Frederik Benzing
Understanding Memorization from the Perspective of Optimization via Efficient Influence Estimation, Futong Liu
Community-based Layerwise Distributed Training of Graph Convolutional Networks, Hongyi Li
A New Scheme for Boosting with an Avarage Margin Distribution Oracle, Ryotaro Mitsuboshi
Better Linear Rates for SGD with Data Shuffling, Grigory Malinovsky
Structured Low-Rank Tensor Learning, Jayadev Naram
ANITA: An Optimal Loopless Accelerated Variance-Reduced Gradient Method, Zhize Li
EF21 with Bells & Whistles: Practical Algorithmic Extensions of Modern Error Feedback, Igor Sokolov
On Server-Side Stepsizes in Federated Optimization: Theory Explaining the Heuristics, Grigory Malinovsky