Sample and Computationally Efficient Learning Algorithms under S-Concave Distributions
Authors: Maria-Florina F. Balcan, Hongyang Zhang
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We provide new results for noise-tolerant and sample-efficient learning algorithms under s-concave distributions. ... In this work, we introduce new convex geometry tools to study the properties of s-concave distributions and use these properties to provide bounds on quantities of interest to learning including the probability of disagreement between two halfspaces, disagreement outside a band, and the disagreement coefficient. ... To prove Theorem 1, we introduce multiple new techniques... Our analysis of s-concave distributions bridges these algorithms to the strong guarantees of noise-tolerant and sample-efficient learning algorithms. |
| Researcher Affiliation | Academia | Maria-Florina Balcan Machine Learning Department Carnegie Mellon University, USA ninamf@cs.cmu.edu Hongyang Zhang Machine Learning Department Carnegie Mellon University, USA hongyanz@cs.cmu.edu |
| Pseudocode | Yes | Algorithm 1 Margin Based Active Learning under S-Concave Distributions |
| Open Source Code | No | The paper does not include any explicit statement about releasing source code for the described methodology or a link to a code repository. |
| Open Datasets | No | The paper is theoretical and does not describe empirical experiments that would involve training on a specific dataset. It discusses theoretical distributions rather than practical datasets. |
| Dataset Splits | No | The paper is theoretical and does not describe empirical experiments involving datasets, thus it does not provide details on training, validation, or test splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not describe specific software dependencies with version numbers, as it does not report empirical experiments requiring such details. |
| Experiment Setup | No | The paper is theoretical and focuses on mathematical proofs and algorithms. It does not describe an empirical experimental setup with specific hyperparameters or system-level training settings. |