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Asynchronous Stochastic Optimization Robust to Arbitrary Delays
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use Picky SGD instead of SGD | ResNet56 on CIFAR-10, delay schedule A · CIFAR-10, delay schedule A | epochs to 0.99 accuracy: 350 -> 344 | Helped |
| Use Picky SGD instead of SGD | ResNet56 on CIFAR-10, delay schedule B · CIFAR-10, delay schedule B | epochs to 0.99 accuracy: 451 -> 333 | Helped |
| Use Picky SGD instead of SGD | ResNet56 on CIFAR-10, delay schedule C · CIFAR-10, delay schedule C | epochs to 0.99 accuracy: 438 -> 337 | Helped |
| Use Picky SGD instead of SGD | ResNet56 on CIFAR-10, delay schedule D · CIFAR-10, delay schedule D | epochs to 0.99 accuracy: 466 -> 288 | Helped |
| Use Picky SGD instead of SGD | ResNet56 on CIFAR-10, delay schedule A · CIFAR-10, delay schedule A | test accuracy: 92.56 -> 92.98 | Helped |
| Use Picky SGD instead of SGD | ResNet56 on CIFAR-10, delay schedule B · CIFAR-10, delay schedule B | test accuracy: 89.64 -> 91.82 | Helped |
| Use Picky SGD instead of SGD | ResNet56 on CIFAR-10, delay schedule C · CIFAR-10, delay schedule C | test accuracy: 90.09 -> 92.12 | Helped |
| Use Picky SGD instead of SGD | ResNet56 on CIFAR-10, delay schedule D · CIFAR-10, delay schedule D | test accuracy: 90.15 -> 91.80 | Helped |
Closest papers by meaning
- Faster Stochastic Optimization with Arbitrary Delays via Adaptive Asynchronous Mini-Batching (ICML 2025 · similarity 0.92)
- On the Optimal Time Complexities in Decentralized Stochastic Asynchronous Optimization (NeurIPS 2024 · similarity 0.92)
- Asynchronous Distributed Learning : Adapting to Gradient Delays without Prior Knowledge (ICML 2021 · similarity 0.92)
- Do We Need Asynchronous SGD? On the Near-Optimality of Synchronous Methods (NeurIPS 2026 · similarity 0.89)
- Tight Time Complexities in Parallel Stochastic Optimization with Arbitrary Computation Dynamics (ICLR 2025 · similarity 0.89)
- Rescaled Asynchronous SGD: Optimal Distributed Optimization under Data and System Heterogeneity (NeurIPS 2026 · similarity 0.88)
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