Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Neural Active Learning with Performance Guarantees
Authors: Zhilei Wang, Pranjal Awasthi, Christoph Dann, Ayush Sekhari, Claudio Gentile
NeurIPS 2021 | Venue PDF | 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 EMAIL Pranjal Awasthi Google Research New York, NY 10011 EMAIL Christoph Dann Google Research New York, NY 10011 EMAIL Ayush Sekhari Cornell University Ithaca, NY 14850 EMAIL Claudio Gentile Google Research New York, NY 10011 EMAIL |
| 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. |