Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
An implicit function learning approach for parametric modal regression
Authors: Yangchen Pan, Ehsan Imani, Amir-massoud Farahmand, Martha White
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically demonstrate on several synthetic problems that our method (i) can learn multi-valued functions and produce the conditional modes, (ii) scales well to high-dimensional inputs, and (iii) can even be more effective for certain uni-modal problems, particularly for high-frequency functions. We demonstrate that our method is competitive in a real-world modal regression problem and two regular regression datasets. |
| Researcher Affiliation | Academia | Yangchen Pan, Ehsan Imani Univ. of Alberta & Amii EMAIL; Amir-massoud Farahmand Vector Institute & Univ. of Toronto EMAIL; Martha White Univ. of Alberta EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of open-source code for its methodology. |
| Open Datasets | Yes | We construct a modal regression dataset from the Medical Cost Personal Dataset (Lantz, 2013) using the following steps. |
| Dataset Splits | No | The paper states "All of our results are averaged over 5 runs and for each run, the data is randomly split into training and testing sets." but does not explicitly mention or quantify a validation set split. |
| Hardware Specification | No | The paper does not provide specific details about the hardware specifications (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions "Tensor Flow" as a software reference but does not specify any software dependencies with version numbers that are directly used for its experiments. |
| Experiment Setup | Yes | For both our algorithm (Implicit) and MDN, we use a 16 16 tanh units NN and train by stochastic gradient descent with mini-batch size 128. We optimize both algorithms by sweeping the learning rate from {0.01, 0.001, 0.0001}. |