On Margins and Generalisation for Voting Classifiers
Authors: Felix Biggs, Valentina Zantedeschi, Benjamin Guedj
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Empirical evaluation In this section we empirically validate our results against existing PAC-Bayesian and margin bounds on several classification datasets from UCI (Dua and Graff, 2017), LIBSVM1 and Zalando (Xiao et al., 2017). Since our main result in Theorem 2 is not associated with any particular algorithm, we use θ outputted from PAC-Bayes-derived algorithms to evaluate this result against other margin bounds (Figure 1) and PAC-Bayes bounds (Figure 2). We then compare optimisation of our secondary result Theorem 3 with optimising those PAC-Bayes bounds directly (Figure 3). |
| Researcher Affiliation | Collaboration | Felix Biggs Department of Computer Science University College London and Inria London contact@felixbiggs.com Valentina Zantedeschi Service Now Research, University College London and Inria London vzantedeschi@gmail.com Benjamin Guedj Department of Computer Science University College London and Inria London b.guedj@ucl.ac.uk |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code for reproducing the results is available at https://github.com/vzantedeschi/dirichlet-margin-bound. |
| Open Datasets | Yes | In this section we empirically validate our results against existing PAC-Bayesian and margin bounds on several classification datasets from UCI (Dua and Graff, 2017), LIBSVM1 and Zalando (Xiao et al., 2017). |
| Dataset Splits | Yes | We reserve 50% of the training data as a training set, and 50% as a validation set. |
| Hardware Specification | No | The paper states, 'The experiments presented in this paper were carried out using the Grid 5000 testbed,' but does not provide specific hardware details such as GPU/CPU models or memory specifications. |
| Software Dependencies | Yes | All experiments were implemented in Python 3.8.10 using PyTorch 1.10.1. |
| Experiment Setup | Yes | Our PAC-Bayes objectives are minimised using stochastic gradient descent (Kingma and Ba, 2015), using the Adam optimizer with a learning rate of 0.001 and weight decay 0.0001 over 200 epochs. |