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 [1].
$\mathttVITS$ : 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 |