Fast and Flexible Monotonic Functions with Ensembles of Lattices
Authors: Mahdi Milani Fard, Kevin Canini, Andrew Cotter, Jan Pfeifer, Maya Gupta
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate that compared to random forests, these ensembles produce similar or better accuracy, while providing guaranteed monotonicity consistent with prior knowledge, smaller model size and faster evaluation. and "7 Experiments We demonstrate the proposals on four datasets." |
| Researcher Affiliation | Industry | K. Canini, A. Cotter, M. R. Gupta, M. Milani Fard, J. Pfeifer Google Inc. 1600 Amphitheatre Parkway, Mountain View, CA 94043 {canini,acotter,mayagupta,janpf,mmilanifard}@google.com |
| Pseudocode | No | The paper describes steps for training the lattices in a numbered list format but does not present them as a formal pseudocode block or algorithm. |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-sourcing of the code for its described methodology. |
| Open Datasets | Yes | Dataset 1 is the ADULT dataset from the UCI Machine Learning Repository [19] |
| Dataset Splits | Yes | We split 800k labelled samples based on time, using the 500k oldest samples for a training set, the next 100k samples for a validation set, and the most recent 200k samples for a testing set (so the three datasets are not IID). |
| Hardware Specification | No | The paper discusses evaluation speed and memory usage, but does not provide specific hardware details (e.g., CPU/GPU models, memory size, or cloud instance types) used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'Light Touch [20]' and 'a C++ package implementation for RF' but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | The best RF on the validation set used 350 trees with a leaf size of 1 and the best Crystals model used 350 lattices with 6 features per lattice. All models were trained using logistic loss, mini-batch size of 100, and 200 loops. For each model, we chose the optimization algorithms step sizes by finding the power of 2 that maximized accuracy on the validation set. |