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chore: Move DWA to [Unreleased] in changelog #741

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ValerianRey merged 1 commit into from
Jun 15, 2026
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chore: Move DWA to [Unreleased] in changelog #741

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10 changes: 5 additions & 5 deletions CHANGELOG.md
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Expand Up @@ -10,6 +10,11 @@ changelog does not include internal changes that do not affect the user.

### Added

- Added `DWA` (Dynamic Weight Average) from [End-to-End Multi-Task Learning with
Attention](https://openaccess.thecvf.com/content_CVPR_2019/papers/Liu_End-To-End_Multi-Task_Learning_With_Attention_CVPR_2019_paper.pdf)
(CVPR 2019), a stateful `Scalarizer` that weights each value by the relative rate at which its
loss decreased over the two previous epochs. It has no learnable parameters; call its `step()`
method once per epoch to roll the loss history.
- Added `FAMO` (Fast Adaptive Multitask Optimization) from [FAMO: Fast Adaptive Multitask
Optimization](https://proceedings.neurips.cc/paper_files/paper/2023/file/b2fe1ee8d936ac08dd26f2ff58986c8f-Paper-Conference.pdf)
(NeurIPS 2023), a stateful `Scalarizer` that decreases all task losses at an approximately equal
Expand All @@ -20,11 +25,6 @@ changelog does not include internal changes that do not affect the user.

### Added

- Added `DWA` (Dynamic Weight Average) from [End-to-End Multi-Task Learning with
Attention](https://openaccess.thecvf.com/content_CVPR_2019/papers/Liu_End-To-End_Multi-Task_Learning_With_Attention_CVPR_2019_paper.pdf)
(CVPR 2019), a stateful `Scalarizer` that weights each value by the relative rate at which its
loss decreased over the two previous epochs. It has no learnable parameters; call its `step()`
method once per epoch to roll the loss history.
- Added `SDMGradWeighting` from [Direction-oriented Multi-objective Learning: Simple and Provable Stochastic Algorithms](https://arxiv.org/pdf/2305.18409) (NeurIPS 2023). It is a stateful `Weighting` that solves for task weights via a simplex-projected inner loop on a cross-batch matrix `A = J_1 @ J_2.T` (computed from two independent mini-batches using `autojac.jac`), with a direction-oriented regularizer pulling the descent direction toward a preference direction.
- Added `IMTL-L` (the loss-balancing variant of Impartial Multi-Task Learning) from [Towards
Impartial Multi-Task Learning](https://openreview.net/pdf?id=IMPnRXEWpvr) (ICLR 2021), a stateful
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