Spectral Learning from a Single Trajectory under Finite-State Policies
Authors: Borja Balle, Odalric-Ambrym Maillard
ICML 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our approach leverages an efficient SVD-based learning algorithm for weighted automata and provides the first rigorous analysis for learning many important models using dependent data. We state and analyze the algorithm under three increasingly difficult scenarios... Our proofs include novel tools for studying mixing properties of stochastic weighted automata. |
| Researcher Affiliation | Collaboration | 1Amazon Research, Cambridge, UK (work done at Lancaster University) 2Inria Lille Nord Europe, Villeneuve d Ascq, France. |
| Pseudocode | Yes | Algorithm 1: Spectral Learning for WFA |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a link to a code repository. |
| Open Datasets | No | The paper discusses learning from data ('a single trajectory') but does not specify a publicly available dataset by name or provide access information (link, DOI, or formal citation). |
| Dataset Splits | No | The paper focuses on theoretical analysis and algorithm design, and does not provide specific details on dataset splits (e.g., training, validation, test percentages or counts). |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not list any software dependencies with specific version numbers. |
| Experiment Setup | No | The paper focuses on theoretical analysis and algorithm design, and does not provide specific experimental setup details such as hyperparameter values or training configurations. |