Reducing Per-Sample Interference in Stochastic Optimization
Modern optimizers combine gradients from the current mini-batch with historical optimization state, such as momentum or adaptive moments. While effective, this standard practice can produce parameter updates that actively increase the loss of individual samples. We term this phenomenon per-sample interference and propose redefining the parameter update as an optimization problem that explicitly minimizes it. Because the exact formulation of the problem is computationally prohibitive, we introduce a highly efficient surrogate. By reducing the problem's dimensionality to the batch size and restricting the optimization to the last linear layer, we overcome memory and speed bottlenecks. This ...
| Authors | Apostolos Avranas |
|---|---|
| Primary category | cs.LG |
| Published | 28 June 2026 |
| Last updated at source | 12 August 2026 |
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