Peter Richtarik

0:02:53

Peter Richtarik - external mentor

0:30:59

Fellow Short Talks: Dr Peter Richtarik, Edinburgh University

1:29:16

Peter Richtarik. Accelerated, Parallel and Proximal Coordinate Descent. 12.02.2014

1:07:36

Machine Learning NeEDS Mathematical Optimization with Prof Peter Richtarik

1:04:35

Peter Richtarik - The Resolution of a Question Related to Local Training in Federated Learning

0:38:31

Peter Richtarik -- Variance Reduction for Gradient Compression

1:12:22

Peter Richtarik - On Second Order Methods and Randomness

1:07:36

Peter Richtarik: 'Permutation compressors for provably faster distributed nonconvex optimization'

1:09:12

Peter Richtarik talk ProxSkip: Yes! Local Gradient Steps Provably Lead to Communication Acceleration

0:58:50

Peter Richtarik, KAUST - On Second Order Methods and Randomness

0:29:13

Peter Richtarik - The First Optimal Distributed SGD in the Presence of Data, Compute...

4:58:58

P. Richtarik mini-course: 'A Guided Walk Through the ZOO of Stochastic Gradient Descent Methods'

1:02:43

Peter Richtarik 'Stochastic primal-dual hybrid gradient algorithm with arbitrary sampling'

1:20:07

FLOW Seminar #40: Peter Richtárik (KAUST) Beyond Local and Gradient Methods for Federated Learning

2:16:18

FLOW Seminar #105: Peter Richtárik (KAUST) On the 5th Generation of Local Training Methods in FL

1:02:37

FLOW Seminar #45: Peter Richtárik (KAUST) Faster Error Feedback

1:11:22

FLOW Seminar #71: Peter Richtárik (KAUST) Local Gradient Steps Provably Lead to Comm. Acceleration

1:18:02

FLOW Seminar #65: Peter Richtárik (KAUST) Permutation Compressor for Faster Distributed Optimization

1:43:17

FLOW Seminar #125: Peter Richtarik (KAUST) The First Optimal Parallel SGD

0:32:36

Stochastic Quasi-Gradient Methods: Variance Reduction via Jacobian Sketching

1:02:11

Session #3

2:04:17

Data Science Seminar: Federated Learning: The future of Edge Intelligence is now!

1:02:11

Day 1 Lightning Talks: Federated Optimization and Analytics

0:08:23

99% of Worker-Master Communication in Distributed Optimization Is Not Needed