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Compositional ADAM: An Adaptive Compositional Solver
v1v2 (latest)

Compositional ADAM: An Adaptive Compositional Solver

10 February 2020
Rasul Tutunov
Minne Li
Alexander I. Cowen-Rivers
Jun Wang
Haitham Bou-Ammar
    ODL
ArXiv (abs)PDFHTML

Papers citing "Compositional ADAM: An Adaptive Compositional Solver"

11 / 11 papers shown
Doubly Robust Instance-Reweighted Adversarial Training
Doubly Robust Instance-Reweighted Adversarial TrainingInternational Conference on Learning Representations (ICLR), 2023
Daouda Sow
Sen-Fon Lin
Zinan Lin
Yitao Liang
AAMLOOD
402
2
0
01 Aug 2023
Stability and Generalization of Stochastic Compositional Gradient
  Descent Algorithms
Stability and Generalization of Stochastic Compositional Gradient Descent AlgorithmsInternational Conference on Machine Learning (ICML), 2023
Minghao Yang
Xiyuan Wei
Tianbao Yang
Yiming Ying
337
4
0
07 Jul 2023
Faster Adaptive Momentum-Based Federated Methods for Distributed
  Composition Optimization
Faster Adaptive Momentum-Based Federated Methods for Distributed Composition Optimization
Feihu Huang
FedML
284
2
0
03 Nov 2022
Compositional federated learning: Applications in distributionally
  robust averaging and meta learning
Compositional federated learning: Applications in distributionally robust averaging and meta learning
Feihu Huang
Junyi Li
FedML
246
17
0
21 Jun 2021
Memory-Based Optimization Methods for Model-Agnostic Meta-Learning and
  Personalized Federated Learning
Memory-Based Optimization Methods for Model-Agnostic Meta-Learning and Personalized Federated LearningJournal of machine learning research (JMLR), 2021
Bokun Wang
Zhuoning Yuan
Yiming Ying
Tianbao Yang
FedML
384
15
0
09 Jun 2021
Efficient Semi-Implicit Variational Inference
Efficient Semi-Implicit Variational Inference
Vincent Moens
Hang Ren
A. Maraval
Rasul Tutunov
Jun Wang
H. Ammar
378
8
0
15 Jan 2021
Are we Forgetting about Compositional Optimisers in Bayesian
  Optimisation?
Are we Forgetting about Compositional Optimisers in Bayesian Optimisation?Journal of machine learning research (JMLR), 2020
Antoine Grosnit
Alexander I. Cowen-Rivers
Rasul Tutunov
Ryan-Rhys Griffiths
Jun Wang
Haitham Bou-Ammar
270
17
0
15 Dec 2020
Solving Stochastic Compositional Optimization is Nearly as Easy as
  Solving Stochastic Optimization
Solving Stochastic Compositional Optimization is Nearly as Easy as Solving Stochastic OptimizationIEEE Transactions on Signal Processing (TSP), 2020
Tianyi Chen
Yuejiao Sun
W. Yin
306
93
0
25 Aug 2020
Descending through a Crowded Valley - Benchmarking Deep Learning
  Optimizers
Descending through a Crowded Valley - Benchmarking Deep Learning Optimizers
Robin M. Schmidt
Frank Schneider
Philipp Hennig
ODL
916
195
0
03 Jul 2020
Gryffin: An algorithm for Bayesian optimization of categorical variables
  informed by expert knowledge
Gryffin: An algorithm for Bayesian optimization of categorical variables informed by expert knowledge
Florian Hase
Matteo Aldeghi
Riley J. Hickman
L. Roch
Alán Aspuru-Guzik
369
132
0
26 Mar 2020
Achieving Robustness to Aleatoric Uncertainty with Heteroscedastic
  Bayesian Optimisation
Achieving Robustness to Aleatoric Uncertainty with Heteroscedastic Bayesian Optimisation
Ryan-Rhys Griffiths
Alexander A. Aldrick
Miguel García-Ortegón
Vidhi R. Lalchand
A. Lee
338
44
0
17 Oct 2019
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