Multiclass Transductive Online Learning
Authors: Steve Hanneke, Vinod Raman, Amirreza Shaeiri, Unique Subedi
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, our main contribution is algorithmically answering the following question in the multiclass transductive online learning framework: Given a concept class C YX , what is the minimum expected number of mistakes achievable by a learner against any realizable adversary? To prove this result, we give another combinatorial dimension, termed the Level-constrained Branching dimension, and show that its finiteness characterizes constant minimax expected mistake-bounds. |
| Researcher Affiliation | Academia | Steve Hanneke Department of Computer Science Purdue University West Lafayette, IN 47907 steve.hanneke@gmail.com Vinod Raman Department of Statistics University of Michigan Ann Arbor, MI 48104 vkraman@umich.edu Amirreza Shaeri Department of Computer Science Purdue University West Lafayette, IN 47907 amirreza.shaeiri@gmail.com Unqiue Subedi Department of Statistics University of Michigan Ann Arbor, MI 48104 subedi@umich.edu |
| Pseudocode | No | The paper describes algorithms in paragraph form, such as 'For every t {1, . . . , T}, if we have {c(xt) : c Vt} = {y}, then predict ˆyt = y. Otherwise, define V y t = {c Vt : c(xt) = y} for all y Y, and predict ˆyt = arg maxy Y B(V y t , xt+1:T ).', but it does not include explicitly labeled 'Algorithm' or 'Pseudocode' blocks. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code for the methodology, nor does it provide any links to a code repository. |
| Open Datasets | No | This is a theoretical paper that does not involve empirical studies or the use of datasets for training, validation, or testing. |
| Dataset Splits | No | This is a theoretical paper that does not involve empirical studies or the use of datasets for training, validation, or testing, thus no dataset splits are discussed. |
| Hardware Specification | No | This paper is purely theoretical and does not describe any experiments, therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | This is a theoretical paper without experimental implementation details, and thus no software dependencies with version numbers are mentioned. |
| Experiment Setup | No | This paper is theoretical and does not describe any experimental setup, hyperparameters, or training configurations. |