Mirror and Preconditioned Gradient Descent in Wasserstein Space
Authors: Clément Bonet, Théo Uscidda, Adam David, Pierre-Cyril Aubin-Frankowski, Anna Korba
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We illustrate the advantages of adapting the geometry induced by the regularizer on ill-conditioned optimization tasks, and showcase the improvement of choosing different discrepancies and geometries in a computational biology task of aligning single-cells. |
| Researcher Affiliation | Academia | Clément Bonet CREST, ENSAE, IP Paris clement.bonet@ensae.fr Théo Uscidda CREST, ENSAE, IP Paris theo.uscidda@ensae.fr Adam David Institute of Mathematics Technische Universität Berlin david@math.tu-berlin.de Pierre-Cyril Aubin-Frankowski TU Wien pierre-cyril.aubin@tuwien.ac.at Anna Korba CREST, ENSAE, IP Paris anna.korba@ensae.fr |
| Pseudocode | No | The paper describes algorithms using mathematical equations and text, but does not include any clearly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | Yes | The code is available at https://github.com/clbonet/Mirror_and_Preconditioned_Gradient_ Descent_in_Wasserstein_Space. |
| Open Datasets | Yes | We focus on the datasets used in [21], consisting of cell lines analyzed using (i) 4i [58], and (ii) sc RNA sequencing [118]. |
| Dataset Splits | No | The paper mentions '40% of unseen (test) target cells' for evaluation, but no specific training/validation splits (percentages or counts) are detailed for reproduction. |
| Hardware Specification | Yes | These experiments were run on a personal Laptop with a CPU Intel Core i5-9300H. |
| Software Dependencies | No | The paper mentions 'OTT-JAX' but does not specify a version number. No other specific software dependencies with version numbers are listed. |
| Experiment Setup | Yes | We set the step size τ = 1 for all the experiments. Then, we tune the parameter a very simply: for a given metric D and a profiling technology, we pick a random treatment and select a {1.25, 1.5, 1.75} by grid search... |