Amortized Active Learning and Inference Engine (ALINE)

This repository contains the official implementation of the NeurIPS 2025 paper: "ALINE: Joint Amortization for Bayesian Inference and Active Data Acquisition".

Installation

1. Clone the repository:

   git clone https://github.com/huangdaolang/ALINE.git
   cd ALINE

2. Create a conda environment and install dependencies:

   conda create -n aline python=3.12
   conda activate aline
   pip install -r requirements.txt

Directory

• config: hydra configuration
• distributions: customised distribution classes
• loss: loss functions
• model: ALINE architecture
• tasks: simulating task data
• utils: helper functions
• notebooks: Jupyter notebooks for evaluation and analysis

Train

Active Learning (Gaussian Process)

1D

python train_aline.py task=al_mix max_epoch=200000 burning_epoch=20000 task.dim_x=1 task.n_target_theta=2 task.n_query_init=200 task.lengthscale_lower=0.1 task.lengthscale_upper=2.0 task.design_scale=5 gamma=1 file_name='aline_al_1d.pth' checkpoint_name='ckpt_al_1d.tar' min_T=30 T=30

2D

python train_aline.py task=al_mix max_epoch=200000 burning_epoch=20000 task.dim_x=2 task.n_target_theta=3 task.n_query_init=200 task.lengthscale_lower=0.1 task.lengthscale_upper=2.0 task.design_scale=5 gamma=1 file_name='aline_al_2d.pth' checkpoint_name='ckpt_al_2d.tar' min_T=50 T=50

Location Finding

python train_aline.py task=location_finding task.theta_dist=uniform task.n_target_theta=2 task.K=1 lr=1e-3 T=30 task.n_query_init=200 max_epoch=100000 burning_epoch=20000 eval=bed eval.batch_size=1000 eval.L_final=1000000 eval.batch_size_final=200 eval.n_query_final=2000 eval.T_final=35

CES

python train_aline.py task=ces lr=1e-3 T=10 task.n_query_init=200 max_epoch=200000 burning_epoch=20000 eval=bed eval.batch_size=500 eval.L_final=10000000 eval.batch_size_final=20 eval.n_query_final=2000 eval.T_final=15

Psychometric model

python train_aline.py task=psychometric task.mask_type=["predefined"] min_T=30 T=30 gamma=0.99 max_epoch=100000 burning_epoch=10000 checkpoint_name='ckpt_psychometric.tar' file_name='aline_psychometric.pth'

Citation

If you find this work useful in your research, please consider citing our paper:

@inproceedings{huang2025aline,
  title={ALINE: Joint Amortization for Bayesian Inference and Active Data Acquisition},
  author={Huang, Daolang and Wen, Xinyi and Bharti, Ayush and Kaski, Samuel and Acerbi, Luigi},
  booktitle={Thirty-ninth Conference on Neural Information Processing Systems},
  year={2025}
}

License

This project is licensed under the MIT License.