Learning Bounds for Risk-sensitive Learning
Authors: Jaeho Lee, Sejun Park, Jinwoo Shin
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | 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 find 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 α = {0.2, 0.4, 0.6, 0.8}. For batch-SVP, we have simply tested over λ = {0.5, 1.0}. |