Neural Active Learning with Performance Guarantees
Authors: Zhilei Wang, Pranjal Awasthi, Christoph Dann, Ayush Sekhari, Claudio Gentile
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We prove joint guarantees on the cumulative regret and number of requested labels which depend on the complexity of the labeling function at hand. |
| Researcher Affiliation | Collaboration | Zhilei Wang New York University New York, NY 10012 zhileiwang92@gmail.com Pranjal Awasthi Google Research New York, NY 10011 pranjalawasthi@google.com Christoph Dann Google Research New York, NY 10011 chrisdann@google.com Ayush Sekhari Cornell University Ithaca, NY 14850 ayush.sekhari@gmail.com Claudio Gentile Google Research New York, NY 10011 cgentile@google.com |
| Pseudocode | Yes | Algorithm 1: Frozen NTK Selective Sampler. Input: Confidence level δ, complexity parameter S, network width m, and depth n . Initialization:... |
| Open Source Code | No | The paper does not contain any statement about making its source code publicly available, nor does it provide a link to a code repository. |
| Open Datasets | No | The paper is theoretical and does not describe experiments run on a specific, publicly available dataset. It refers to a theoretical construct: "on an i.i.d. sample (x1, y1), . . . , (x T , y T ) D". |
| Dataset Splits | No | The paper is theoretical and does not describe any empirical experiments, therefore it does not mention specific training, validation, or testing dataset splits. |
| Hardware Specification | No | The paper is purely theoretical and does not describe any empirical experiments that would require specific hardware. Therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and focuses on mathematical proofs and algorithm design. It does not describe empirical experiments that would require specific software dependencies with version numbers for reproducibility. |
| Experiment Setup | No | The paper is theoretical and does not describe empirical experiments. Therefore, it does not include specific experimental setup details such as hyperparameters or training configurations. |