docs: Fix MGDA links

CHANGELOG.md

@@ -439,7 +439,7 @@ Informed Neural Networks](https://arxiv.org/pdf/2408.11104).
   - `Krum` from [Machine Learning with Adversaries: Byzantine
       Tolerant Gradient Descent](https://proceedings.neurips.cc/paper/2017/file/f4b9ec30ad9f68f89b29639786cb62ef-Paper.pdf).
   - `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>).
+  - `MGDA` from [Multiple-gradient descent algorithm (MGDA) for multiobjective optimization](https://comptes-rendus.academie-sciences.fr/mathematique/articles/10.1016/j.crma.2012.03.014/).
   - `NashMTL` from [Multi-Task Learning as a Bargaining Game](https://arxiv.org/pdf/2202.01017.pdf).
   - `PCGrad` from [Gradient Surgery for Multi-Task Learning](https://arxiv.org/pdf/2001.06782.pdf).
   - `Random` from [Reasonable Effectiveness of Random Weighting: A

README.md

@@ -287,7 +287,7 @@ TorchJD provides many existing aggregators from the literature, listed in the fo
 | [IMTLG](https://torchjd.org/stable/docs/aggregation/imtl_g#torchjd.aggregation.IMTLG)                      | [IMTLGWeighting](https://torchjd.org/stable/docs/aggregation/imtl_g#torchjd.aggregation.IMTLGWeighting)                | [Towards Impartial Multi-task Learning](https://discovery.ucl.ac.uk/id/eprint/10120667/)                                                                             |
 | [Krum](https://torchjd.org/stable/docs/aggregation/krum#torchjd.aggregation.Krum)                          | [KrumWeighting](https://torchjd.org/stable/docs/aggregation/krum#torchjd.aggregation.KrumWeighting)                    | [Machine Learning with Adversaries: Byzantine Tolerant Gradient Descent](https://proceedings.neurips.cc/paper/2017/file/f4b9ec30ad9f68f89b29639786cb62ef-Paper.pdf)  |
 | [Mean](https://torchjd.org/stable/docs/aggregation/mean#torchjd.aggregation.Mean)                          | [MeanWeighting](https://torchjd.org/stable/docs/aggregation/mean#torchjd.aggregation.MeanWeighting)                    | -                                                                                                                                                                    |
-| [MGDA](https://torchjd.org/stable/docs/aggregation/mgda#torchjd.aggregation.MGDA)                          | [MGDAWeighting](https://torchjd.org/stable/docs/aggregation/mgda#torchjd.aggregation.MGDAWeighting)                    | [Multiple-gradient descent algorithm (MGDA) for multiobjective optimization](https://www.sciencedirect.com/science/article/pii/S1631073X12000738)                    |
+| [MGDA](https://torchjd.org/stable/docs/aggregation/mgda#torchjd.aggregation.MGDA)                          | [MGDAWeighting](https://torchjd.org/stable/docs/aggregation/mgda#torchjd.aggregation.MGDAWeighting)                    | [Multiple-gradient descent algorithm (MGDA) for multiobjective optimization](https://comptes-rendus.academie-sciences.fr/mathematique/articles/10.1016/j.crma.2012.03.014/)                    |
 | [NashMTL](https://torchjd.org/stable/docs/aggregation/nash_mtl#torchjd.aggregation.NashMTL)                | -                                                                                                                      | [Multi-Task Learning as a Bargaining Game](https://arxiv.org/pdf/2202.01017)                                                                                         |
 | [PCGrad](https://torchjd.org/stable/docs/aggregation/pcgrad#torchjd.aggregation.PCGrad)                    | [PCGradWeighting](https://torchjd.org/stable/docs/aggregation/pcgrad#torchjd.aggregation.PCGradWeighting)              | [Gradient Surgery for Multi-Task Learning](https://arxiv.org/pdf/2001.06782)                                                                                         |
 | [Random](https://torchjd.org/stable/docs/aggregation/random#torchjd.aggregation.Random)                    | [RandomWeighting](https://torchjd.org/stable/docs/aggregation/random#torchjd.aggregation.RandomWeighting)              | [Reasonable Effectiveness of Random Weighting: A Litmus Test for Multi-Task Learning](https://arxiv.org/pdf/2111.10603)                                              |