Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
$\mathttVITS$ : Variational Inference Thompson Sampling for contextual bandits
Authors: Pierre Clavier, Tom Huix, Alain Oliviero Durmus
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we demonstrate experimentally the effectiveness of VITS on both synthetic and real world datasets. |
| Researcher Affiliation | Academia | 1CMAP, CNRS, Ecole Polytechnique, Institut Polytechnique de Paris, 91120 Palaiseau, France. 2Inria Paris, 75015 Paris, France 3Centre de Recherche des Cordeliers, INSERM, Universite de Paris, Sorbonne Universite, 75006 Paris, France . |
| Pseudocode | Yes | Algorithm 1 VITS algorithm; Algorithm 2 VITS-I; Algorithm 3 VITS II / VITS II Hessian-free |
| Open Source Code | No | The paper does not provide a direct statement or link for open-source code for the described methodology. |
| Open Datasets | Yes | Finally, our last contribution is to illustrate the empirical performances of our method on a synthetic and on the real world dataset Movie Lens (Lam & Herlocker). |
| Dataset Splits | No | The paper mentions using synthetic and Movie Lens datasets, but it does not specify explicit training/validation/test splits by percentages or sample counts for reproducibility. |
| Hardware Specification | Yes | In this work, we use GPUs v100-16g or v100-32g for running our code with GPU Nvidia Tesla V100 SXM2 16 Go and CPUs with 192 Go per node. |
| Software Dependencies | No | The paper mentions re-implementing an algorithm in JAX but does not specify a version number for JAX or any other software dependencies. |
| Experiment Setup | Yes | Parameters: step-size ht, number of iterations Kt; Table 1. Lin TS hyperparameter grid-search; Table 2. LMC-TS hyperparameter grid-search; Table 3. VITS hyperparameter grid-search |