Comments (1)
The COntinuous COin Betting (COCOB) algorithm does actually have a tensorflow implementation here:
https://www.tensorflow.org/addons/api_docs/python/tfa/optimizers/COCOB
Unfortunately it is implemented in the soon-to-be deprecated TensorFlow Addons, but this might provide a starting point for an updated implementation.
Also, a minor correction to your attribution. Coin betting as an optimisation method was introduced by Orabona and Pal in [1], and extended by Orabona and Tommasi for training deep neural nets in [2]. Our innovation in [3] was to adapt this approach for sampling problems.
[1] https://proceedings.neurips.cc//paper/by-source-2016-320
[2] https://proceedings.neurips.cc/paper_files/paper/2017/hash/7c82fab8c8f89124e2ce92984e04fb40-Abstract.html
[3] https://proceedings.mlr.press/v202/sharrock23a
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Related Issues (20)
- Change Support / Acknowledgements HOT 1
- Publish as conda installable package
- Parallelize Test Workflows HOT 2
- `test_time_series_transformer` occasionally fails
- Make heavier use of `pytest.fixture`
- Diagnostic plots do not do so well with simple (one-parameter) models HOT 2
- Remove code duplication from diagnostics module HOT 1
- Add tests for model comparison
- Links in the table of contents of the example notebooks do not work
- Dependency problems HOT 1
- Backport dependency fixes to releases/master HOT 1
- pip install v1.1.5 fails on Mac (M1) HOT 1
- OOM after ~ 50 epochs HOT 10
- bayesflow breaks existing tensorflow installation HOT 5
- Affine coupling flows underperforming with current settings on streamlined-backend
- OfflineDataset should not require both batch_size and batches_per_epoch
- Loss not shown in keras output HOT 4
- streamlined-backend DeepSet
- Implement LSTMNet for time series embedding
- InvertibleNetwork error (Input 0 of layer "dense" is incompatible with the layer) HOT 1
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