Sebastian Stich
17 papers · 2018–2024 · 3 venues · across top CS/AI venues
Authors are matched by name, so researchers who share a name ("Yang Liu", "Wei Wang") appear as a single profile and their paper counts, venues and achievements are combined. How identity is handled.
Achievements
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πΊοΈ Taxonomy Completionist (19) π Academic Marathon Β· Sprint π Conference Polyglot Β· Bronze π Interdisciplinary Bridge Β· Silver π£ Hot Topic Early Bird
π§
Keyword Pioneer
π€
Dynamic Duo
(12)
π¬
Deep Specialist
(10)
β‘
Prolific Year Β· Productive
π₯
Unstoppable Β· Bronze
β
The Questioner
π
Century Club Β· Bronze
ποΈ
Keyword Collector
(68)
Venues
Top co-authors
Research topics
Machine Learning
(17)
Optimization & Theory Β· Machine Learning
(17)
Mathematics & Optimization
(5)
Optimization
(5)
Artificial Intelligence
(4)
Deep Learning
(4)
Learning Paradigms Β· Artificial Intelligence
(4)
Application Areas
(3)
Learning Paradigms Β· Machine Learning
(2)
Learning Types
(2)
Optimization & Theory Β· Deep Learning
(2)
Techniques
(2)
Computer Science
(1)
Architectures
(1)
Systems
(1)
Keywords
stochastic gradient descent
(6)
federated learning
(5)
convex optimization
(4)
distributed learning
(3)
stochastic optimization
(3)
variance reduction
(3)
distributed optimization
(3)
gradient compression
(3)
communication efficiency
(3)
first-order optimization
(2)
decentralized optimization
(2)
model compression
(2)
gossip algorithm
(2)
local sgd
(2)
lasso regression
(1)
neural network optimization
(1)
deep learning
(1)
neural network training
(1)
gradient computation
(1)
matching pursuit
(1)
Papers
Universality of AdaGrad Stepsizes for Stochastic Optimization: Inexact Oracle, Acceleration and Variance Reduction
NeurIPS 2024
Masked Training of Neural Networks with Partial Gradients
AISTATS 2022
ProgFed: Effective, Communication, and Computation Efficient Federated Learning by Progressive Training
ICML 2022
ProxSkip: Yes! Local Gradient Steps Provably Lead to Communication Acceleration! Finally!
ICML 2022
A Linearly Convergent Algorithm for Decentralized Optimization: Sending Less Bits for Free!
AISTATS 2021
Consensus Control for Decentralized Deep Learning
ICML 2021
Critical Parameters for Scalable Distributed Learning with Large Batches and Asynchronous Updates
AISTATS 2021
LENA: Communication-Efficient Distributed Learning with Self-Triggered Gradient Uploads
AISTATS 2021
Quasi-global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data
ICML 2021
A Unified Theory of Decentralized SGD with Changing Topology and Local Updates
ICML 2020
Extrapolation for Large-batch Training in Deep Learning
ICML 2020
Is Local SGD Better than Minibatch SGD?
ICML 2020
SCAFFOLD: Stochastic Controlled Averaging for Federated Learning
ICML 2020
Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communication
ICML 2019
Error Feedback Fixes SignSGD and other Gradient Compression Schemes
ICML 2019
Adaptive balancing of gradient and update computation times using global geometry and approximate subproblems
AISTATS 2018
On Matching Pursuit and Coordinate Descent
ICML 2018