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Methodology
← Optimization & Theory
Machine Learning
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Optimization & Theory
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Distributed Learning
1100 directly classified papers
Papers per year
2006: 1
2007: 3
2008: 3
2009: 5
2010: 6
2011: 4
2012: 9
2013: 20
2014: 27
2015: 18
2016: 44
2017: 49
2018: 70
2019: 92
2020: 108
2021: 125
2022: 127
2023: 145
2024: 125
2025: 89
2026: 30
Papers
Projection-free Distributed Online Convex Optimization with $O(\sqrtT)$ Communication Complexity
ICML 2020
KungFu: Making Training in Distributed Machine Learning Adaptive
OSDI 2020
AntMan: Dynamic Scaling on GPU Clusters for Deep Learning
OSDI 2020
A Decentralized Parallel Algorithm for Training Generative Adversarial Nets
NIPS 2020
Empirical Studies of Institutional Federated Learning For Natural Language Processing
EMNLP 2020
Decentralized Langevin Dynamics for Bayesian Learning
NIPS 2020
ScaleCom: Scalable Sparsified Gradient Compression for Communication-Efficient Distributed Training
NIPS 2020
Coded Sequential Matrix Multiplication For Straggler Mitigation
NIPS 2020
FEARLESS STEPS Challenge (FS-2): Supervised Learning with Massive Naturalistic Apollo Data
INTERSPEECH 2020
Knowledge Base Embedding By Cooperative Knowledge Distillation
COLING 2020
Distributed Newton Can Communicate Less and Resist Byzantine Workers
NIPS 2020
Optimal and Practical Algorithms for Smooth and Strongly Convex Decentralized Optimization
NIPS 2020
Distributed Distillation for On-Device Learning
NIPS 2020
Byzantine Ordered Consensus without Byzantine Oligarchy
OSDI 2020
AutoSync: Learning to Synchronize for Data-Parallel Distributed Deep Learning
NIPS 2020
Assisted Learning: A Framework for Multi-Organization Learning
NIPS 2020
Byzantine Resilient Distributed Multi-Task Learning
NIPS 2020
Delay-Adaptive Distributed Stochastic Optimization
AAAI 2020
Distributed Training with Heterogeneous Data: Bridging Median- and Mean-Based Algorithms
NIPS 2020
Second Order Optimality in Decentralized Non-Convex Optimization via Perturbed Gradient Tracking
NIPS 2020
Linearly Converging Error Compensated SGD
NIPS 2020
Lower Bounds and Optimal Algorithms for Personalized Federated Learning
NIPS 2020
Coresets for Regressions with Panel Data
NIPS 2020
A Unified Architecture for Accelerating Distributed DNN Training in Heterogeneous GPU/CPU Clusters
OSDI 2020
On Communication Complexity of Classification Problems
COLT 2019
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