Active Nearest-Neighbor Learning in Metric Spaces
Authors: Aryeh Kontorovich, Sivan Sabato, Ruth Urner
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We propose a pool-based non-parametric active learning algorithm for general metric spaces, called MArgin Regularized Metric Active Nearest Neighbor (MARMANN), which outputs a nearest-neighbor classifier. We give prediction error guarantees that depend on the noisy-margin properties of the input sample, and are competitive with those obtained by previously proposed passive learners. We prove that the label complexity of MARMANN is significantly lower than that of any passive learner with similar error guarantees. |
| Researcher Affiliation | Academia | Aryeh Kontorovich Department of Computer Science Ben-Gurion University of the Negev Beer Sheva 8499000, Israel Sivan Sabato Department of Computer Science Ben-Gurion University of the Negev Beer Sheva 8499000, Israel Ruth Urner Max Planck Institute for Intelligent Systems Department for Empirical Inference Tübingen 72076, Germany |
| Pseudocode | Yes | Algorithm 1 Generate NNSet(t, I, δ) |
| Open Source Code | No | The paper does not contain any statement or link indicating that the source code for MARMANN is open-sourced or available. |
| Open Datasets | No | The paper describes a theoretical framework and does not use or provide access information for any specific real-world or simulated dataset for empirical training. It refers to 'Sin Dm' as a theoretical sample. |
| Dataset Splits | No | The paper is theoretical and does not conduct empirical experiments with validation datasets or specific splits. |
| Hardware Specification | No | The paper does not mention any specific hardware used, as it focuses on theoretical analysis rather than empirical experimentation. |
| Software Dependencies | No | The paper does not mention any specific software dependencies or versions, as it presents theoretical work without empirical implementation details. |
| Experiment Setup | No | The paper does not describe any experimental setup details such as hyperparameters or training configurations, as it is a theoretical paper. |