Think out of the "Box": Generically-Constrained Asynchronous Composite Optimization and Hedging
Authors: Pooria Joulani, András György, Csaba Szepesvari
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We present two new algorithms, ASYNCADA and HEDGEHOG, for asynchronous sparse online and stochastic optimization. ASYNCADA is, to our knowledge, the first asynchronous stochastic optimization algorithm with finite-time datadependent convergence guarantees for generic convex constraints. |
| Researcher Affiliation | Industry | Pooria Joulani Deep Mind, UK pjoulani@google.com András György Deep Mind, UK agyorgy@google.com Csaba Szepesvári Deep Mind, UK szepi@google.com |
| Pseudocode | Yes | Algorithm 1: ASYNCADA: Asynchronous Composite Adaptive Dual Averaging Algorithm 2: HEDGEHOG!: Asynchronous Stochastic Exponentiated Gradient. |
| Open Source Code | No | The paper does not provide a link to or explicitly state the release of open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments using specific datasets. The mention of 'training data' in Section 1 refers to general machine learning methods, not data used in this paper's research. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments with dataset splits. Therefore, it does not specify training/test/validation splits. |
| Hardware Specification | No | The paper is theoretical and does not describe experimental hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not describe specific software dependencies with version numbers for experimental reproducibility. |
| Experiment Setup | No | The paper is theoretical and does not describe experimental setup details such as hyperparameters or training configurations. |