A Unifying Post-Processing Framework for Multi-Objective Learn-to-Defer Problems
Authors: Mohammad-Amin Charusaie, Samira Samadi
NeurIPS 2024 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Furthermore, we design a generalizable algorithm to estimate that solution and apply this algorithm to the COMPAS, Hatespeech, and ACSIncome datasets. Our algorithm shows improvements in terms of constraint violation over a set of learn-to-defer baselines and can control multiple constraint violations at once. |
| Researcher Affiliation | Academia | Mohammad-Amin Charusaie Max Planck Institute for Intelligent Systems Tuebingen, Germany EMAIL Samira Samadi Max Planck Institute for Intelligent Systems Tuebingen, Germany EMAIL |
| Pseudocode | Yes | Based on this optimal solution, we can design a plug-in method (see Algorithm 1 in Appendix F) to solve the constrained learning problem using empirical data. |
| Open Source Code | Yes | The code is available in https://github.com/Amin Chrs/Post Process/. |
| Open Datasets | Yes | Our algorithm shows improvements in terms of constraint violation over a set of learn-to-defer baselines and can control multiple constraint violations at once. The use of d-GNP is beyond learn-to-defer applications and can potentially obtain a solution to decision-making problems with a set of controlled expected performance measures. |
| Dataset Splits | Yes | n is the size of the set using which we fine-tune the algorithm, ϵ measures the accuracy of learned post-processing scores, and γ is a parameter that measures the sensitivity of the constraint to the change of the predictor. |
| 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 mentions training on a '1-layer feed-forward neural network' and using a 'pre-trained model [5]' but does not specify any software libraries or frameworks with version numbers (e.g., PyTorch, TensorFlow, Python version). |
| Experiment Setup | Yes | All scores, classifiers, and rejection functions are trained on a 1-layer feed-forward neural network. The human assessment is done in this dataset on 1000 cases by giving humans a description of the case and asking them whether the defendant would recidivate within two years of their most recent crime. |