Towards Minimax Online Learning with Unknown Time Horizon
Authors: Haipeng Luo, Robert Schapire
ICML 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that our algorithm outperforms many other existing algorithms in an online linear optimization setting. |
| Researcher Affiliation | Academia | Haipeng Luo HAIPENGL@CS.PRINCETON.EDU Department of Computer Science, Princeton University, Princeton, NJ 08540 Robert E. Schapire SCHAPIRE@CS.PRINCETON.EDU Department of Computer Science, Princeton University, Princeton, NJ 08540 |
| Pseudocode | No | The paper describes algorithms verbally and mathematically, but it does not include a clearly labeled 'Pseudocode' or 'Algorithm' block/figure. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository for the methodology described. |
| Open Datasets | No | The paper describes experiments conducted in a simulated online linear optimization setting rather than using a named, publicly available dataset. It states, 'we conduct an experiment that compares the regrets of four algorithms at any time step within 1000 rounds against an adversary that chooses points in S uniformly at random (N = 10).' |
| Dataset Splits | No | The paper describes an online learning experiment setup where data is generated sequentially, rather than using traditional train/validation/test splits on a static dataset. No specific dataset split percentages or sample counts for validation are provided. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., GPU/CPU models, memory, cloud resources) used to run the experiments. |
| Software Dependencies | No | The paper describes the algorithms and their theoretical properties but does not list specific software libraries or their version numbers used for implementation or experimentation. |
| Experiment Setup | Yes | In Section 6.1, the paper states: 'we conduct an experiment that compares the regrets of four algorithms at any time step within 1000 rounds against an adversary that chooses points in S uniformly at random (N = 10).' It also describes specific algorithmic parameters, such as 'OGD, with parameter = 2/t' and for the proposed algorithm 'continuous random variable with probability density f(T) / 1/T^2'. |