Dmitry Yarotsky

Orcid: 0000-0002-5432-7143

According to our database1, Dmitry Yarotsky authored at least 37 papers between 2013 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Tight Convergence Rate Bounds for Optimization Under Power Law Spectral Conditions.
J. Mach. Learn. Res., 2024

Learning high-dimensional targets by two-parameter models and gradient flow.
CoRR, 2024

Generalization error of spectral algorithms.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023
Training FFT to Select Beams in Massive MIMO.
IEEE Wirel. Commun. Lett., 2023

Beamspace Selection in Multi-User Massive MIMO.
IEEE Access, 2023

Structure of universal formulas.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

A view of mini-batch SGD via generating functions: conditions of convergence, phase transitions, benefit from negative momenta.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022
Embedded Ensembles: infinite width limit and operating regimes.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021
Lower Performance Bound for Beamspace Channel Estimation in Massive MIMO.
IEEE Wirel. Commun. Lett., 2021

Machine Learning-Assisted PAPR Reduction in Massive MIMO.
IEEE Wirel. Commun. Lett., 2021

Efficient Performance Bound for Channel Estimation in Massive MIMO Receiver.
IEEE Trans. Wirel. Commun., 2021

Universal scaling laws in the gradient descent training of neural networks.
CoRR, 2021

Machine Learning-Assisted Channel Estimation in Massive MIMO Receiver.
Proceedings of the 93rd IEEE Vehicular Technology Conference, 2021

Adaptive Channel Interpolation in High-Speed Massive MIMO.
Proceedings of the 93rd IEEE Vehicular Technology Conference, 2021

Spatial Denoising for Sparse Channel Estimation in Coherent Massive MIMO.
Proceedings of the 94th IEEE Vehicular Technology Conference, 2021

Data-Driven Beams Selection for Beamspace Channel Estimation in Massive MIMO.
Proceedings of the 93rd IEEE Vehicular Technology Conference, 2021

Explicit loss asymptotics in the gradient descent training of neural networks.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Elementary superexpressive activations.
Proceedings of the 38th International Conference on Machine Learning, 2021

2020
Bayesian Approach to Channel Interpolation in Massive MIMO Receiver.
IEEE Commun. Lett., 2020

Data-Aided LS Channel Estimation in Massive MIMO Turbo-Receiver.
Proceedings of the 91st IEEE Vehicular Technology Conference, 2020

High Performance Interference Suppression in Multi-User Massive MIMO Detector.
Proceedings of the 91st IEEE Vehicular Technology Conference, 2020

The phase diagram of approximation rates for deep neural networks.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Low-loss connection of weight vectors: distribution-based approaches.
Proceedings of the 37th International Conference on Machine Learning, 2020

Theoretical Performance Bound of Uplink Channel Estimation Accuracy in Massive MIMO.
Proceedings of the 2020 IEEE International Conference on Acoustics, 2020

2019
Iterative Nonlinear Detection and Decoding in Multi-User Massive MIMO.
Proceedings of the 15th International Wireless Communications & Mobile Computing Conference, 2019

2018
Collective evolution of weights in wide neural networks.
CoRR, 2018

Universal approximations of invariant maps by neural networks.
CoRR, 2018

Smart Sorting in Massive MIMO Detection.
Proceedings of the 14th International Conference on Wireless and Mobile Computing, 2018

Unused Beam Reservation for PAPR Reduction in Massive MIMO System.
Proceedings of the 87th IEEE Vehicular Technology Conference, 2018

Optimal approximation of continuous functions by very deep ReLU networks.
Proceedings of the Conference On Learning Theory, 2018

2017
Error bounds for approximations with deep ReLU networks.
Neural Networks, 2017

Quantified advantage of discontinuous weight selection in approximations with deep neural networks.
CoRR, 2017

Geometric features for voxel-based surface recognition.
CoRR, 2017

Railway Incident Ranking with Machine Learning.
Proceedings of the 16th IEEE International Conference on Machine Learning and Applications, 2017

2016
GTApprox: Surrogate modeling for industrial design.
Adv. Eng. Softw., 2016

2013
Examples of inconsistency in optimization by expected improvement.
J. Glob. Optim., 2013

Univariate interpolation by exponential functions and Gaussian RBFs for generic sets of nodes.
J. Approx. Theory, 2013


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