A Non-generative Framework and Convex Relaxations for Unsupervised Learning
Authors: Elad Hazan, Tengyu Ma
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We give a novel formal theoretical framework for unsupervised learning with two distinctive characteristics. First, it does not assume any generative model and based on a worst-case performance metric. Second, it is comparative, namely performance is measured with respect to a given hypothesis class. |
| Researcher Affiliation | Academia | Elad Hazan Princeton University 35 Olden Street 08540 ehazan@cs.princeton.edu. Tengyu Ma Princeton University 35 Olden Street, NJ 08540 tengyu@cs.princeton.edu. |
| Pseudocode | Yes | Algorithm 1 group encoding/decoding for improper dictionary learning |
| Open Source Code | No | The paper does not contain any statements or links indicating that open-source code for the described methodology is provided. |
| Open Datasets | No | The paper is theoretical and does not describe empirical experiments on specific, publicly available datasets. |
| Dataset Splits | No | The paper is theoretical and does not discuss dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide any specific hardware details used for running experiments. |
| Software Dependencies | No | The paper does not list any specific software components with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe specific experimental setup details such as hyperparameters or training configurations. |