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..
Model-Free IRL Using Maximum Likelihood Estimation
Authors: Vinamra Jain, Prashant Doshi, Bikramjit Banerjee3951-3958
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | With four real-world population data sets in Japan and China, we demonstrate that the proposed method can estimate the transition population more accurately than existing methods. |
| Researcher Affiliation | Industry | Tomoharu Iwata, Hitoshi Shimizu NTT Communication Science Laboratories Kyoto, Japan |
| Pseudocode | Yes | Algorithm 1 shows the estimation procedure of the proposed model. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the methodology is open-source or publicly available. |
| Open Datasets | Yes | We evaluated the proposed method using four real-world population data sets: Tokyo, Osaka, Nagoya, and Beijing (Iwata et al. 2017). The Tokyo, Osaka, and Nagoya data... We used the following sources: SNS-based People Flow Data, Nightley, Inc., Shibasaki & Sekimoto Laboratory, the University of Tokyo, Micro Geo Data Forum, People Flow project, and Center for Spatial Information Science at the University of Tokyo, http: //nightley.jp/archives/1954. The Beijing data...were generated from TDrive trajectory data (Yuan et al. 2010; 2011). |
| Dataset Splits | No | The paper describes the datasets used and mentions 'prediction error of the area population at the next time step' for hyperparameter tuning, but it does not specify explicit train/validation/test dataset splits (e.g., percentages or counts). |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'ADAM (Kingma and Ba 2014)' for optimization, but it does not provide specific version numbers for any software dependencies or libraries (e.g., Python, TensorFlow, PyTorch). |
| Experiment Setup | Yes | With the proposed method, we used ten hidden units and maximized the objective function by ADAM (Kingma and Ba 2014). We use the following input vector: utℓℓ = [ τ(t), xℓ, xℓ xℓ]. We transform the input vector into a transition probability by the following three-layered, feed-forward neural network: htℓℓ = tanh(W1utℓℓ + b1), θtℓℓ = softmax(w2htℓℓ + b2). |