On Margins and Generalisation for Voting Classifiers
Authors: Felix Biggs, Valentina Zantedeschi, Benjamin Guedj
NeurIPS 2022 | Venue 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 EMAIL Valentina Zantedeschi Service Now Research, University College London and Inria London EMAIL Benjamin Guedj Department of Computer Science University College London and Inria London EMAIL |
| 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. |