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RATQ: A Universal Fixed-Length Quantizer for Stochastic Optimization
IEEE Transactions on Information Theory (IEEE Trans. Inf. Theory), 2019
22 August 2019
Prathamesh Mayekar
Himanshu Tyagi
MQ
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Papers citing
"RATQ: A Universal Fixed-Length Quantizer for Stochastic Optimization"
26 / 26 papers shown
Compressed Federated Reinforcement Learning with a Generative Model
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Correlated Quantization for Faster Nonconvex Distributed Optimization
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Yury Demidovich
Ahmad Rammal
Peter Richtárik
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319
4
0
10 Jan 2024
Matrix Compression via Randomized Low Rank and Low Precision Factorization
Neural Information Processing Systems (NeurIPS), 2023
R. Saha
Varun Srivastava
Mert Pilanci
272
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0
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Fundamental Limits of Distributed Optimization over Multiple Access Channel
Information Theory Workshop (ITW), 2023
Shubham K. Jha
228
1
0
05 Oct 2023
Optimal Compression of Unit Norm Vectors in the High Distortion Regime
International Symposium on Information Theory (ISIT), 2023
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Avishek Ghosh
A. Mazumdar
264
3
0
16 Jul 2023
Unbiased Compression Saves Communication in Distributed Optimization: When and How Much?
Neural Information Processing Systems (NeurIPS), 2023
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Xinmeng Huang
Kun Yuan
367
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0
25 May 2023
Communication-Constrained Bandits under Additive Gaussian Noise
International Conference on Machine Learning (ICML), 2023
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Jonathan Scarlett
Vincent Y. F. Tan
348
4
0
25 Apr 2023
Temporal Difference Learning with Compressed Updates: Error-Feedback meets Reinforcement Learning
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George J. Pappas
Hamed Hassani
257
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03 Jan 2023
Learning in Distributed Contextual Linear Bandits Without Sharing the Context
Osama A. Hanna
Lin F. Yang
Christina Fragouli
FedML
208
1
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08 Jun 2022
EF-BV: A Unified Theory of Error Feedback and Variance Reduction Mechanisms for Biased and Unbiased Compression in Distributed Optimization
Neural Information Processing Systems (NeurIPS), 2022
Laurent Condat
Kai Yi
Peter Richtárik
425
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0
09 May 2022
Correlated quantization for distributed mean estimation and optimization
International Conference on Machine Learning (ICML), 2022
A. Suresh
Ziteng Sun
Jae Hun Ro
Felix X. Yu
360
17
0
09 Mar 2022
Linear Stochastic Bandits over a Bit-Constrained Channel
Conference on Learning for Dynamics & Control (L4DC), 2022
A. Mitra
Hamed Hassani
George J. Pappas
242
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0
02 Mar 2022
Wyner-Ziv Gradient Compression for Federated Learning
Kai Liang
Huiru Zhong
Haoning Chen
Youlong Wu
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138
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0
16 Nov 2021
Solving Multi-Arm Bandit Using a Few Bits of Communication
International Conference on Artificial Intelligence and Statistics (AISTATS), 2021
Osama A. Hanna
Lin F. Yang
Christina Fragouli
233
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0
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Leveraging Spatial and Temporal Correlations in Sparsified Mean Estimation
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Ankur Mallick
Advait Gadhikar
S. Kadhe
Gauri Joshi
198
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0
14 Oct 2021
Unbiased Single-scale and Multi-scale Quantizers for Distributed Optimization
S. Vineeth
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174
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26 Sep 2021
Fundamental limits of over-the-air optimization: Are analog schemes optimal?
Shubham K. Jha
Prathamesh Mayekar
Himanshu Tyagi
225
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0
11 Sep 2021
Efficient Randomized Subspace Embeddings for Distributed Optimization under a Communication Budget
IEEE Journal on Selected Areas in Information Theory (JSAIT), 2021
R. Saha
Mert Pilanci
Andrea J. Goldsmith
285
6
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13 Mar 2021
Preserved central model for faster bidirectional compression in distributed settings
Neural Information Processing Systems (NeurIPS), 2021
Constantin Philippenko
Hadrien Hendrikx
204
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24 Feb 2021
Distributed Online Learning for Joint Regret with Communication Constraints
International Conference on Algorithmic Learning Theory (ALT), 2021
Dirk van der Hoeven
Hédi Hadiji
T. Erven
266
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0
15 Feb 2021
Quantizing data for distributed learning
IEEE Journal on Selected Areas in Information Theory (JSAIT), 2020
Osama A. Hanna
Yahya H. Ezzeldin
Christina Fragouli
Suhas Diggavi
FedML
431
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0
14 Dec 2020
Wyner-Ziv Estimators for Distributed Mean Estimation with Side Information and Optimization
IEEE Transactions on Information Theory (IEEE Trans. Inf. Theory), 2020
Prathamesh Mayekar
Shubham K. Jha
A. Suresh
Himanshu Tyagi
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325
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0
24 Nov 2020
Bidirectional compression in heterogeneous settings for distributed or federated learning with partial participation: tight convergence guarantees
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446
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0
25 Jun 2020
Differentially Quantized Gradient Methods
IEEE Transactions on Information Theory (IEEE Trans. Inf. Theory), 2020
Chung-Yi Lin
V. Kostina
B. Hassibi
MQ
391
8
0
06 Feb 2020
vqSGD: Vector Quantized Stochastic Gradient Descent
V. Gandikota
Daniel Kane
R. Maity
A. Mazumdar
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311
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0
18 Nov 2019
On-Device Machine Learning: An Algorithms and Learning Theory Perspective
Sauptik Dhar
Junyao Guo
Jiayi Liu
S. Tripathi
Unmesh Kurup
Mohak Shah
539
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02 Nov 2019
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