Prepare 0.1.0 release (#53)

* Fix CHANGELOG.md
* Add section for version 0.1.0 in CHANGELOG.md

CHANGELOG.md

@@ -7,6 +7,10 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## [Unreleased]
 
+-
+
+## [0.1.0] - 2024-06-22
+
 ### Added
 
 - Basic project structure.
@@ -18,7 +22,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
   - `CAGrad` from [Conflict-Averse Gradient Descent for Multi-task
       Learning](https://arxiv.org/pdf/2110.14048.pdf).
   - `Constant` to aggregate with constant weights.
-  - `DualProjWrapper` adapted from [Gradient Episodic
+  - `DualProj` adapted from [Gradient Episodic
       Memory for Continual Learning](https://proceedings.neurips.cc/paper/2017/file/f87522788a2be2d171666752f97ddebb-Paper.pdf).
   - `GradDrop` from [Just Pick a Sign: Optimizing Deep
       Multitask Models with Gradient Sign Dropout](https://arxiv.org/pdf/2010.06808.pdf).
@@ -28,14 +32,13 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
   - `Mean` to average the rows of the matrix.
   - `MGDA` from [Multiple-gradient descent algorithm (MGDA) for multiobjective optimization](https://www.sciencedirect.com/science/article/pii/S1631073X12000738/pdf?md5=2622857e4abde98b6f7ddc8a13a337e1&pid=1-s2.0-S1631073X12000738-main.pdf>).
   - `NashMTL` from [Multi-Task Learning as a Bargaining Game](https://arxiv.org/pdf/2202.01017.pdf).
-  - `NormalizingWrapper` to normalize the weights obtained by a wrapped `Weigthing`.
   - `PCGrad` from [Gradient Surgery for Multi-Task Learning](https://arxiv.org/pdf/2001.06782.pdf).
   - `Random` from [Reasonable Effectiveness of Random Weighting: A
       Litmus Test for Multi-Task Learning](https://arxiv.org/pdf/2111.10603.pdf).
   - `Sum` to sum the rows of the matrix.
   - `TrimmedMean` from [Byzantine-Robust Distributed Learning: Towards
       Optimal Statistical Rates](https://proceedings.mlr.press/v80/yin18a/yin18a.pdf).
-  - `UPGradWrapper` from [Jacobian Descent for Multi-Objective Optimization](https://arxiv.org/search/?query=jacobian+descent+for+multi-objective+optimization&searchtype=all&source=header).
+  - `UPGrad` from [Jacobian Descent for Multi-Objective Optimization](https://arxiv.org/search/?query=jacobian+descent+for+multi-objective+optimization&searchtype=all&source=header).
 - `backward` function to perform a step of Jacobian descent.
 - Documentation of the public API and of some usage examples.
 - Tests: