On Making Stochastic Classifiers Deterministic
Authors: Andrew Cotter, Maya Gupta, Harikrishna Narasimhan
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conclude, in Section 5, with experiments on six datasets comparing these strategies on different problems where stochastic classifiers arise. 5 Experiments We experimentally evaluate the different strategies described above for approximating a stochastic classifier with a deterministic classifier. |
| Researcher Affiliation | Industry | Google Research 1600 Amphitheatre Pkwy, Mountain View, CA 94043 {acotter,hnarasimhan,mayagupta}@google.com |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code made available at: https://github.com/google-research/google-research/tree/master/stochastic_to_deterministic |
| Open Datasets | Yes | We use use a variety of fairness datasets with binary protected attributes: (1) COMPAS [24], where the goal is the predict recidivism with gender as the protected attribute; (2) Communities & Crime [25]... (3) Law School [27]... (4) UCI Adult [25]... (5) Wiki Toxicity [28]... |
| Dataset Splits | Yes | Table 1: Comparison of de-randomization approaches on ROC matching tasks...Train Test...Train Test... and Figure 1: Test set ROC curves for the Black group and overall population in the Law School dataset. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependency details, such as library or solver names with version numbers. |
| Experiment Setup | Yes | For hashing, we first map the input features to 2128 clusters (using a 128-bit cryptographic hash function), and apply a pairwise independent hash function to map it to 232 buckets... For Var Bin, we choose a direction β uniformly at random from the unit 2 sphere, project instances onto this direction, and have the cluster mapping divide the projected values into k = 25 contiguous bins... Additionally, we find that adding the random numbers r1, . . . , r|C| was unnecessary and take rc = 0 for all c... |