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..
Split-kl and PAC-Bayes-split-kl Inequalities for Ternary Random Variables
Authors: Yi-Shan Wu, Yevgeny Seldin
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present an extensive set of experiments, where we first compare the kl, Empirical Bernstein, Unexpected Bernstein, and split-kl inequalities applied to (individual) sums of independent random variables in simulated data, and then compare the PAC-Bayes-kl, PAC-Bayes-Unexpected-Bersnstein, PAC-Bayes-split-kl, and, in some of the setups, PAC-Bayes-Empirical-Bennett, for several prediction models on several UCI datasets. |
| Researcher Affiliation | Academia | Yi-Shan Wu University of Copenhagen EMAIL Yevgeny Seldin University of Copenhagen EMAIL |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | New code in the supplementary. |
| Open Datasets | Yes | We evaluate the performance of the PAC-Bayes-split-kl inequality in linear classification and in weighted majority vote using several data sets from UCI and Lib SVM repositories [Dua and Graff, 2019, Chang and Lin, 2011]. |
| Dataset Splits | Yes | If we split S into two equal parts, S = S1 S2, we can use S1 to train both a reference prediction rule h S1 and a prior πS1, and then learn a PAC-Bayes posterior on S2, and the other way around. By combining the 'forward' and 'backward' approaches we can write Eρ[L(h)] = 1 2Eρ[ L(h, h S1)] + 1 2Eρ[ L(h, h S2)] + 1 2 (L(h S1) + L(h S2)) |
| Hardware Specification | Yes | All experiments were performed on a local server equipped with an Intel Core i9-9900K CPU and an NVIDIA GeForce RTX 2080 Ti GPU. |
| Software Dependencies | No | The paper mentions software like TensorFlow and optimization algorithms like Adam and Rprop, but does not provide specific version numbers for these software dependencies (e.g., 'TensorFlow 2.x' or 'Rprop vX.Y.Z'). |
| Experiment Setup | Yes | In the experiments we take δ = 0.05, and truncate the bounds at 1. For the Unexpected Bernstein bound we take a grid of γ {1/(2b), , 1/(2kb)} for k = log2( p n/ ln(1/δ)/2) and a union bound over the grid, as proposed by Mhammedi et al. [2019]. For the split-kl bound we take µ to be the middle value, 0, of the ternary random variable. |