Fix table formatting in README

Author: ValerianRey

README.md

@@ -160,24 +160,24 @@ More usage examples can be found [here](https://torchjd.org/stable/examples/).
 TorchJD provides many existing aggregators from the literature, listed in the following table.
 
 <!-- recommended aggregators first, then alphabetical order -->
-| Aggregator                                                                                                 | Weighting                                                                                                               | Publication                                                                                                                                                          |
-|------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| [UPGrad](https://torchjd.org/stable/docs/aggregation/upgrad.html#torchjd.aggregation.UPGrad) (recommended) | [UPGradWeighting](https://torchjd.org/stable/docs/aggregation/upgrad#torchjd.aggregation.UPGradWeighting) | [Jacobian Descent For Multi-Objective Optimization](https://arxiv.org/pdf/2406.16232)                                                                                |
-| [AlignedMTL](https://torchjd.org/stable/docs/aggregation/aligned_mtl#torchjd.aggregation.AlignedMTL)       | [AlignedMTLWeighting](https://torchjd.org/stable/docs/aggregation/aligned_mtl#torchjd.aggregation.AlignedMTLWeighting)  | [Independent Component Alignment for Multi-Task Learning](https://arxiv.org/pdf/2305.19000)                                                                          |
-| [CAGrad](https://torchjd.org/stable/docs/aggregation/cagrad#torchjd.aggregation.CAGrad)                    | [CAGradWeighting](https://torchjd.org/stable/docs/aggregation/cagrad#torchjd.aggregation.CAGradWeighting)               | [Conflict-Averse Gradient Descent for Multi-task Learning](https://arxiv.org/pdf/2110.14048)                                                                         |
-| [ConFIG](https://torchjd.org/stable/docs/aggregation/config#torchjd.aggregation.ConFIG)                    | -                                                                                                                       | [ConFIG: Towards Conflict-free Training of Physics Informed Neural Networks](https://arxiv.org/pdf/2408.11104)                                                       |
-| [Constant](https://torchjd.org/stable/docs/aggregation/constant#torchjd.aggregation.Constant)              | [ConstantWeighting](https://torchjd.org/stable/docs/aggregation/constant#torchjd.aggregation.ConstantWeighting)         | -                                                                                                                                                                    |
-| [DualProj](https://torchjd.org/stable/docs/aggregation/dualproj#torchjd.aggregation.DualProj)              | [DualProjWeighting](https://torchjd.org/stable/docs/aggregation/dualproj#torchjd.aggregation.DualProjWeighting)         | [Gradient Episodic Memory for Continual Learning](https://arxiv.org/pdf/1706.08840)                                                                                  |
-| [GradDrop](https://torchjd.org/stable/docs/aggregation/graddrop#torchjd.aggregation.GradDrop)              | -                                                                                                                       | [Just Pick a Sign: Optimizing Deep Multitask Models with Gradient Sign Dropout](https://arxiv.org/pdf/2010.06808)                                                    |
-| [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)                    |
-| [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)                                              |
-| [Sum](https://torchjd.org/stable/docs/aggregation/sum#torchjd.aggregation.Sum)                             | [SumWeighting](https://torchjd.org/stable/docs/aggregation/sum#torchjd.aggregation.SumWeighting)                        | -                                                                                                                                                                    |
-| [Trimmed Mean](https://torchjd.org/stable/docs/aggregation/trimmed_mean#torchjd.aggregation.TrimmedMean)   | -                                                                                                                       | [Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates](https://proceedings.mlr.press/v80/yin18a/yin18a.pdf)                                      |
+| Aggregator                                                                                                 | Weighting                                                                                                              | Publication                                                                                                                                                          |
+|------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| [UPGrad](https://torchjd.org/stable/docs/aggregation/upgrad.html#torchjd.aggregation.UPGrad) (recommended) | [UPGradWeighting](https://torchjd.org/stable/docs/aggregation/upgrad#torchjd.aggregation.UPGradWeighting)              | [Jacobian Descent For Multi-Objective Optimization](https://arxiv.org/pdf/2406.16232)                                                                                |
+| [AlignedMTL](https://torchjd.org/stable/docs/aggregation/aligned_mtl#torchjd.aggregation.AlignedMTL)       | [AlignedMTLWeighting](https://torchjd.org/stable/docs/aggregation/aligned_mtl#torchjd.aggregation.AlignedMTLWeighting) | [Independent Component Alignment for Multi-Task Learning](https://arxiv.org/pdf/2305.19000)                                                                          |
+| [CAGrad](https://torchjd.org/stable/docs/aggregation/cagrad#torchjd.aggregation.CAGrad)                    | [CAGradWeighting](https://torchjd.org/stable/docs/aggregation/cagrad#torchjd.aggregation.CAGradWeighting)              | [Conflict-Averse Gradient Descent for Multi-task Learning](https://arxiv.org/pdf/2110.14048)                                                                         |
+| [ConFIG](https://torchjd.org/stable/docs/aggregation/config#torchjd.aggregation.ConFIG)                    | -                                                                                                                      | [ConFIG: Towards Conflict-free Training of Physics Informed Neural Networks](https://arxiv.org/pdf/2408.11104)                                                       |
+| [Constant](https://torchjd.org/stable/docs/aggregation/constant#torchjd.aggregation.Constant)              | [ConstantWeighting](https://torchjd.org/stable/docs/aggregation/constant#torchjd.aggregation.ConstantWeighting)        | -                                                                                                                                                                    |
+| [DualProj](https://torchjd.org/stable/docs/aggregation/dualproj#torchjd.aggregation.DualProj)              | [DualProjWeighting](https://torchjd.org/stable/docs/aggregation/dualproj#torchjd.aggregation.DualProjWeighting)        | [Gradient Episodic Memory for Continual Learning](https://arxiv.org/pdf/1706.08840)                                                                                  |
+| [GradDrop](https://torchjd.org/stable/docs/aggregation/graddrop#torchjd.aggregation.GradDrop)              | -                                                                                                                      | [Just Pick a Sign: Optimizing Deep Multitask Models with Gradient Sign Dropout](https://arxiv.org/pdf/2010.06808)                                                    |
+| [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)                    |
+| [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)                                              |
+| [Sum](https://torchjd.org/stable/docs/aggregation/sum#torchjd.aggregation.Sum)                             | [SumWeighting](https://torchjd.org/stable/docs/aggregation/sum#torchjd.aggregation.SumWeighting)                       | -                                                                                                                                                                    |
+| [Trimmed Mean](https://torchjd.org/stable/docs/aggregation/trimmed_mean#torchjd.aggregation.TrimmedMean)   | -                                                                                                                      | [Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates](https://proceedings.mlr.press/v80/yin18a/yin18a.pdf)                                      |
 
 ## Contribution
 Please read the [Contribution page](CONTRIBUTING.md).