Mixture weights optimisation for Alpha-Divergence Variational Inference
Authors: Kamélia Daudel, randal douc
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Lastly, we compare numerically the behavior of the unbiased Power Descent and of the biased Rényi Descent and we discuss the potential advantages of one algorithm over the other. ... Finally, we run some numerical experiments in Section 5 to compare the behavior of the Power Descent and of the Rényi Descent altogether, before discussing the potential benefits of one approach over the other. ... 5 Simulation study |
| Researcher Affiliation | Academia | 1: LTCI, Télécom Paris, Institut Polytechnique de Paris, France 2: Department of Statistics, University of Oxford, United Kingdom 3: SAMOVAR, Télécom Sud Paris, Institut Polytechnique de Paris, France |
| Pseudocode | Yes | Algorithm 1: Power descent one-step transition (Γ(v) = [(α 1)v + 1]η/(1 α)) ... Algorithm 2: Complete Exploitation-Exploration Algorithm |
| Open Source Code | Yes | The code for all the subsequent numerical experiments is available at https://github.com/ kdaudel/Mixture Weights Alpha VI. |
| Open Datasets | No | The paper uses a synthetic target distribution defined as "p(y) = c [0.5N(y; sud, Id) + 0.5N(y; sud, Id)]" for its simulation study, rather than a pre-existing, publicly available dataset that would require a public link or citation. Therefore, there is no information about publicly available training data. |
| Dataset Splits | No | The paper does not describe specific training, validation, or test dataset splits. The experiments are conducted on a synthetically defined target density, and the performance is assessed through metrics like the Variational Rényi bound, not through standard dataset splits. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments (e.g., CPU/GPU models, memory, or cloud computing specifications). It only mentions running numerical experiments. |
| Software Dependencies | No | The paper mentions that the code is available on GitHub and written in Python, but it does not specify any particular software libraries, frameworks, or their version numbers (e.g., PyTorch 1.9, TensorFlow 2.x, or specific Python library versions) that would be necessary to replicate the experiments. |
| Experiment Setup | Yes | We take J = 100, M {100, 1000, 2000}, α = 0.5, κ = 0, η0 = 0.3 and q0 is a centered normal distribution with covariance matrix 5Id. We let T = 10, N = 20 and we replicate the experiment 100 times independently for each algorithm. |