Fully Unconstrained Online Learning
Authors: Ashok Cutkosky, Zak Mhammedi
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper provides new algorithms for online learning, which is a standard framework for the design and analysis of iterative first-order optimization algorithms used throughout machine learning. Specifically, we consider a variant of online learning often called online convex optimization [1, 2]. Formally, an online learning algorithm is designed to play a kind of game between the learning algorithm and the environment, which we can describe using the following protocol: Protocol 1. Online Learning/Online Convex Optimization. Input: Convex domain W Rd, number of rounds T: |
| Researcher Affiliation | Collaboration | Ashok Cutkosky Boston University ashok@cutkosky.com Zakaria Mhammedi Google Research mhammedi@google.com |
| Pseudocode | Yes | Algorithm 1 Reduction From General W to R Algorithm 2 Algorithm for Protocol 2 (REG) Algorithm 3 1-Dimensional Learner for Protocol 4 (BASE) Algorithm 4 Fully Unconstrained Learning in One Dimension Algorithm 5 Fully Unconstrained Learning Algorithm 6 Regularized 1-dimensional learner (REG) for Protocol 2 |
| Open Source Code | No | This paper has only mathematical congtent. There are no experiments in this paper. |
| Open Datasets | No | This paper has only mathematical congtent. There are no experiments in this paper. |
| Dataset Splits | No | This paper has only mathematical congtent. There are no experiments in this paper. |
| Hardware Specification | No | This paper has only mathematical congtent. There are no experiments in this paper. |
| Software Dependencies | No | This paper has only mathematical congtent. There are no experiments in this paper. |
| Experiment Setup | No | This paper has only mathematical congtent. There are no experiments in this paper. |