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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Learning Bounds for Risk-sensitive Learning
Authors: Jaeho Lee, Sejun Park, Jinwoo Shin
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we demonstrate the practical implications of the proposed bounds via exploratory experiments on neural networks. |
| Researcher Affiliation | Academia | Jaeho Lee Sejun Park Jinwoo Shin Korea Advanced Institute of Science and Technology (KAIST) School of Electrical Engineering, Graduate School of AI |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. Methods are described in prose and mathematical formulations. |
| Open Source Code | Yes | Code. Available at https://github.com/jaeho-lee/oce. |
| Open Datasets | Yes | In our experiments on CIFAR-10 [29] with Res Net18 [24], we ο¬nd that batch-based SVP indeed outperforms batch-based CVa R minimization (see Section 4). |
| Dataset Splits | No | The paper mentions 'test and train CVa R' but does not explicitly describe a validation set or how it was used in terms of split percentages or counts. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU models, CPU types, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'PyTorch default learning rate' but does not specify a version number for PyTorch or any other software dependency. |
| Experiment Setup | Yes | As a model, we use Res Net18 [24]. As an optimizer, we use Adam with weight decay [34] with a batch size 100 and Py Torch default learning rate. For CVa R, we have experimented with Ξ± = {0.2, 0.4, 0.6, 0.8}. For batch-SVP, we have simply tested over Ξ» = {0.5, 1.0}. |