docs: Replace OpenReview links with open-access alternatives (#766)

Author: ValerianRey

CHANGELOG.md

@@ -59,7 +59,7 @@ changelog does not include internal changes that do not affect the user.
 ### Added
 
 - 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
+  Impartial Multi-Task Learning](https://www.semanticscholar.org/paper/Towards-Impartial-Multi-task-Learning-Liu-Li/45c0828baec1dd53b81f1b2635788fdf27d0792d) (ICLR 2021), a stateful
   `Scalarizer` that learns a per-task scale `s_i` and combines the values as
   `Σ (exp(s_i) · L_i − s_i)`.
 - Added `UW` (Uncertainty Weighting) from [Multi-Task Learning Using Uncertainty to Weigh Losses
@@ -74,7 +74,7 @@ changelog does not include internal changes that do not affect the user.
 ### Added
 
 - Added `STCH` from [Smooth Tchebycheff Scalarization for Multi-Objective
-  Optimization](https://openreview.net/pdf?id=m4dO5L6eCp), a `Scalarizer` that combines the input
+  Optimization](https://arxiv.org/abs/2402.19078), a `Scalarizer` that combines the input
   tensor of values into a smooth approximation of their (weighted, shifted) maximum.
 - Added `MoDoWeighting` from [Three-Way Trade-Off in Multi-Objective Learning: Optimization, Generalization and Conflict-Avoidance](https://www.jmlr.org/papers/volume25/23-1287/23-1287.pdf) (JMLR 2024). It is a stateful `Weighting` that maintains task weights across calls via a simplex-projected gradient step on a cross-batch matrix `G = J_1 @ J_2.T`, computed from two independent mini-batches using `autojac.jac`.
 - Added `GeometricMean` (also known as GLS) studied in [MultiNet++: Multi-Stream Feature

README.md

@@ -235,7 +235,7 @@ TorchJD provides many existing aggregators from the literature, listed in the fo
 | [FairGrad](https://torchjd.org/stable/docs/aggregation/fairgrad#torchjd.aggregation.FairGrad)              | [FairGradWeighting](https://torchjd.org/stable/docs/aggregation/fairgrad#torchjd.aggregation.FairGradWeighting)        | [Fair Resource Allocation in Multi-Task Learning](https://arxiv.org/pdf/2402.15638)                                                                                  |
 | [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)                                                    |
 | [GradVac](https://torchjd.org/stable/docs/aggregation/gradvac#torchjd.aggregation.GradVac)              | [GradVacWeighting](https://torchjd.org/stable/docs/aggregation/gradvac#torchjd.aggregation.GradVacWeighting)                                                                                                                      | [Gradient Vaccine: Investigating and Improving Multi-task Optimization in Massively Multilingual Models](https://arxiv.org/pdf/2010.05874)                                                    |
-| [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/)                                                                             |
+| [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://www.semanticscholar.org/paper/Towards-Impartial-Multi-task-Learning-Liu-Li/45c0828baec1dd53b81f1b2635788fdf27d0792d)                                                                             |
 | [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://comptes-rendus.academie-sciences.fr/mathematique/articles/10.1016/j.crma.2012.03.014/)                    |

src/torchjd/aggregation/_gradvac.py

@@ -135,7 +135,7 @@ class GradVac(GramianWeightedAggregator, Stateful, _NonDifferentiable):
     :class:`~torchjd.aggregation.GramianWeightedAggregator` implementing the aggregation step of
     Gradient Vaccine (GradVac) from `Gradient Vaccine: Investigating and Improving Multi-task
     Optimization in Massively Multilingual Models (ICLR 2021 Spotlight)
-    <https://openreview.net/forum?id=F1vEjWK-lH_>`_.
+    <https://arxiv.org/abs/2010.05874>`_.
 
     For each task :math:`i`, the order in which other tasks :math:`j` are visited is drawn at
     random. For each pair :math:`(i, j)`, the cosine similarity :math:`\phi_{ij}` between the

src/torchjd/scalarization/_imtl_l.py

@@ -14,7 +14,7 @@ class IMTLL(Scalarizer, Stateful):
     :class:`~torchjd.scalarization.Scalarizer` that combines the input tensor of values using learned
     per-task scales. ``IMTL-L`` is the loss-balancing variant of Impartial
     Multi-Task Learning, proposed in `Towards Impartial Multi-Task Learning
-    <https://openreview.net/pdf?id=IMPnRXEWpvr>`_.
+    <https://www.semanticscholar.org/paper/Towards-Impartial-Multi-task-Learning-Liu-Li/45c0828baec1dd53b81f1b2635788fdf27d0792d>`_.
 
     Each value :math:`L_i` is assigned a learnable scale parameter :math:`s_i`, and the values are
     combined as

src/torchjd/scalarization/_stch.py

@@ -8,7 +8,7 @@ class STCH(Scalarizer):
     r"""
     :class:`~torchjd.scalarization.Scalarizer` that combines the input tensor of values using smooth
     Tchebycheff scalarization, as defined in `Smooth Tchebycheff Scalarization for Multi-Objective
-    Optimization <https://openreview.net/pdf?id=m4dO5L6eCp>`_.
+    Optimization <https://arxiv.org/abs/2402.19078>`_.
 
     It returns