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cs.LG2026
Tula: Optimizing Time, Cost, and Generalization in Distributed Large-Batch Training
Sahil Tyagi, Feiyi Wang
Distributed training increases the number of batches processed per iteration either by scaling-out (adding more nodes) or scaling-up (increasing the batch-size). However, the large…
cs.CV2026
Accelerating Large-Scale Dataset Distillation via Exploration-Exploitation Optimization
Muhammad J. Alahmadi, Peng Gao, Feiyi Wang +1
Dataset distillation compresses the original data into compact synthetic datasets, reducing training time and storage while retaining model performance, enabling deployment under l…
cs.LG2026
Topology-Aware Revival for Efficient Sparse Training
Meiling Jin, Fei Wang, Xiaoyun Yuan +2
Static sparse training is a promising route to efficient learning by committing to a fixed mask pattern, yet the constrained structure reduces robustness. Early pruning decisions c…