Transductive Active Learning: Theory and Applications
Authors: Jonas Hübotter, Bhavya , Lenart Treven, Yarden As, Andreas Krause
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate their strong sample efficiency in two key applications: active fine-tuning of large neural networks and safe Bayesian optimization, where they achieve state-of-the-art performance. |
| Researcher Affiliation | Academia | Jonas Hübotter Department of Computer Science ETH Zürich, Switzerland Bhavya Sukhija Department of Computer Science ETH Zürich, Switzerland Lenart Treven Department of Computer Science ETH Zürich, Switzerland Yarden As Department of Computer Science ETH Zürich, Switzerland Andreas Krause Department of Computer Science ETH Zürich, Switzerland |
| Pseudocode | Yes | Algorithm 1 Active Fine-Tuning of NNs |
| Open Source Code | Yes | Question: Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material? Answer: [Yes] Justification: |
| Open Datasets | Yes | We use the MNIST (Le Cun et al., 1998) and CIFAR-100 (Krizhevsky et al., 2009) datasets as testbeds. |
| Dataset Splits | No | The paper mentions training until "convergence on oracle validation accuracy" to stabilize learning curves, but does not provide specific details on the validation split or how it was formed (e.g., percentages, counts, or reference to a standard split). |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, memory specifications, or types of compute workers used for running the experiments. It only vaguely mentions "small compute resources" in the NeurIPS checklist. |
| Software Dependencies | No | The paper mentions software like "ADAM optimizer", "Efficient Net-B0", and "quadcopter simulation was adapted from Chandra (2023)", but it does not specify version numbers for any of these software components, libraries, or programming languages used. |
| Experiment Setup | Yes | Table 1: Hyperparameter summary of NN experiments. (*) we train until convergence on oracle validation accuracy. MNIST CIFAR-100 ρ 0.01 1 M 30 100 m 3 10 k 1 000 1 000 batch size b 1 10 # of epochs (*) 5 learning rate 0.001 0.001 |