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 [1].
Robust large-margin learning in hyperbolic space
Authors: Melanie Weber, Manzil Zaheer, Ankit Singh Rawat, Aditya K. Menon, Sanjiv Kumar
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this paper, we present, to our knowledge, the first theoretical guarantees for learning a classifier in hyperbolic rather than Euclidean space. Specifically, we consider the problem of learning a large-margin classifier for data possessing a hierarchical structure. Our first contribution is a hyperbolic perceptron algorithm, which provably converges to a separating hyperplane. We then provide an algorithm to efficiently learn a large-margin hyperplane, relying on the careful injection of adversarial examples. Finally, we prove that for hierarchical data that embeds well into hyperbolic space, the low embedding dimension ensures superior guarantees when learning the classifier directly in hyperbolic space. We now present empirical studies for hyperbolic linear separator learning to corroborate our theory. |
| Researcher Affiliation | Collaboration | Melanie Weber Princeton University EMAIL Manzil Zaheer Google Research EMAIL Ankit Singh Rawat Google Research EMAIL Aditya Menon Google Research EMAIL Sanjiv Kumar Google Research EMAIL |
| Pseudocode | Yes | Algorithm 1 Hyperbolic perceptron, Algorithm 2 Adversarial Training |
| Open Source Code | No | The paper does not provide any explicit statement about releasing source code for the methodology or a link to a code repository. |
| Open Datasets | Yes | We use the Image Net ILSVRC 2012 dataset [26] along with its label hierarchy from wordnet. |
| Dataset Splits | No | The paper describes the datasets used (ImageNet ILSVRC 2012, specific classes, and subtrees with example counts) but does not provide explicit train/validation/test splits (e.g., percentages, counts for each split, or methods like cross-validation) for reproducibility. |
| Hardware Specification | No | The paper does not explicitly describe the hardware (e.g., specific GPU/CPU models, memory amounts) used to run its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., library names with their versions) needed to replicate the experiment. |
| Experiment Setup | Yes | We vary the budget α over {0, 0.25, 0.5, 0.75, 1.0}. In all experiments, we use a constant step-size ηt = 0.01 t. |