The Bayesian Learning Rule
Authors: Mohammad Emtiyaz Khan, Håvard Rue
JMLR 2023 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We show that many machine-learning algorithms are specific instances of a single algorithm called the Bayesian learning rule. The rule, derived from Bayesian principles, yields a wide-range of algorithms from fields such as optimization, deep learning, and graphical models. This includes classical algorithms such as ridge regression, Newton s method, and Kalman filter, as well as modern deep-learning algorithms such as stochastic-gradient descent, RMSprop, and Dropout. ... Our work not only unifies, generalizes, and improves existing algorithms, but also helps us design new ones. ... A full list of the learning algorithms derived in this paper is given in Table 1. We not only unify and generalize existing algorithms but also derive new ones. These includes: (i) a new multimodal optimization algorithm, (ii) new uncertainty estimation algorithms (OGN and VOGN), (iii) the Bayes Bi NN algorithm for binary neural networks, and (iv) non-conjugate variational inference algorithms. |
| Researcher Affiliation | Academia | Mohammad Emtiyaz Khan EMAIL RIKEN Center for Advanced Intelligence Project 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; H avard Rue EMAIL CEMSE Division King Abdullah University of Science and Technology Thuwal 23955-6900, Saudi Arabia |
| Pseudocode | No | The paper describes algorithms using mathematical equations and textual descriptions of updates (e.g., Eq. 3, Eq. 6, Eq. 7, Eq. 8, Eq. 12) rather than structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statements regarding the release of source code for the described methodology, nor does it provide links to any code repositories. |
| Open Datasets | No | The paper primarily focuses on theoretical derivations and algorithmic design. While it mentions 'Image Net data set' in Section 4.4, this is in the context of discussing challenges for Bayesian methods in deep learning generally, not as a dataset used for empirical evaluation within this work. No specific datasets are identified or provided with access information for experiments conducted in this paper. |
| Dataset Splits | No | As the paper is theoretical and focuses on algorithm derivation and unification rather than empirical evaluation, it does not describe any dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper focuses on theoretical derivations of algorithms and does not report on experimental results that would require specific hardware. Therefore, no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical, deriving algorithms from Bayesian principles. It does not mention any specific software dependencies or versions used for implementation or experimentation. |
| Experiment Setup | No | The paper is theoretical, presenting derivations and unifying various algorithms. It does not include an experimental setup section with hyperparameters, training configurations, or system-level settings. |