NEU: A Meta-Algorithm for Universal UAP-Invariant Feature Representation
Authors: Anastasis Kratsios, Cody Hyndman
JMLR 2021 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Section 4 Numerical Evaluation of NEU-OLS and NEU-PCA: Next, we evaluate the performance of NEU across various learning tasks. First, we investigate the performance of NEU in the chaotic environment provided by real-world financial data. Then, we stress test NEU s behaviour within the controlled environment provided by simulation studies. Our implementations focus on financial data analysis. |
| Researcher Affiliation | Academia | Anastasis Kratsios EMAIL Department of Mathematics Eidgen ossische Technische Hochschule Z urich (ETH) R amistrasse 101, 8092 Z urich, ZH, Switzerland Cody Hyndman EMAIL Department of Mathematics and Statistics Concordia University 1455 boulevard de Maisonneuve Ouest, Montr eal, Qu ebec, H3G 1M8, Canada |
| Pseudocode | Yes | Meta-Algorithm 1: Non-Euclidean Upgrading (NEU) input : Hypothesis class F, loss-function L, penalty function P, Training Data {xn}n N Feature map s depth J Robustness Hyper-parameter λ > 0 output: NEU-model f NEU ˆf ˆφˆI. 1 ˆφ argmin φ Φ :d P n N w ,λ n L (f(xn), Aφ(xn) + b, xn) + P(Aφ + b) ; Get Feature Map 2 ˆf argmin ˆf F P n N w ,λ n L f(xn), ˆf ˆφ(xn) + b, xn + P(ˆf ˆφ) ; Get NEU-Model |
| Open Source Code | No | The Tensorflow (v.2.4.1) code and data-sets for our implementations is available online at ?. |
| Open Datasets | No | The Tensorflow (v.2.4.1) code and data-sets for our implementations is available online at ?. |
| Dataset Splits | Yes | The models are trained on the first 75% of the data and the remaining 25% is used to evaluate the out-of-sample predictive performance of the trained models, and is illustrated in Figure 2. |
| Hardware Specification | No | The paper does not provide specific hardware details used for running its experiments. |
| Software Dependencies | Yes | The TensorFlow (v.2.4.1) code and data-sets for our implementations is available online at ?. |
| Experiment Setup | No | Each of the hyper-parameters is selected by cross-validations and randomized search from a large grid, hyper-parameters include the choice of kernel. ... The models tuning-parameters are then estimated by cross-validation. |