On Human-Aligned Risk Minimization
Authors: Liu Leqi, Adarsh Prasad, Pradeep K. Ravikumar
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically study these risk measures, and demonstrate their improved performance on desiderata such as fairness, in contrast to the traditional workhorse of expected loss minimization. |
| Researcher Affiliation | Academia | Liu Leqi Carnegie Mellon University Pittsburgh, PA 15213 leqil@cs.cmu.edu Adarsh Prasad Carnegie Mellon University Pittsburgh, PA 15213 adarshp@cs.cmu.edu Pradeep Ravikumar Carnegie Mellon Universit Pittsburgh, PA 15213 pradeepr@cs.cmu.edu |
| Pseudocode | No | The paper describes an 'iterative update rule' for optimization using mathematical equations but does not present it as formally labeled pseudocode or an algorithm block. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository. |
| Open Datasets | Yes | Using the fair ML toolkit version of the COMPAS recidivism dataset [1]... we use the UTKFace dataset [31] to train a neural network |
| Dataset Splits | No | The paper mentions '2000 training and 20000 testing data points' and 'With a 90% and 10% train-test split', but does not explicitly specify a separate validation split or its proportions. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., GPU models, CPU types, or memory specifications). |
| Software Dependencies | No | The paper mentions software like 'logistic regression model', 'neural network', 'mini-batch stochastic gradient descent', and 'AI Fairness 360 toolkit [2]', but does not provide specific version numbers for any of these or other software dependencies. |
| Experiment Setup | Yes | We fix the squared error (θ; (x, y)) = 1/2(y - x^Tθ)^2 as our loss function. ... logistic regression model with L2 regularization. ... For EHRM, we have chosen b to be .3... To minimize empirical human risk, we have used a variant of mini-batch stochastic gradient descent. At each step t, θt+1 = θt - ηt/B Σi=1 to B wt i θ (θ; Zi), where wt i = w POLY(Fn( (θt; Zi))), Fn( ) is the empirical CDF of the mini-batch losses, B is the mini-batch size and ηt is the learning rate. |