Thesis

• Allocative Poisson Factorization For Computational Social Science. Aaron Schein. PhD thesis. University of Massachusetts Amherst, 2019.
  [📄 PDF]
  
  

Probabilistic tensor decomposition

• The ALL0CORE Tensor Decomposition for Sparse Count Data. John Hood and Aaron Schein. International Conference on Artificial Intelligence and Statistics (AISTATS), 2024.
  [📄 PDF | 💻 Code]
  
  

• Bayesian Poisson Tucker Decomposition for Learning the Structure of International Relations. Aaron Schein, Mingyuan Zhou and Hanna Wallach. International Conference on Machine Learning (ICML), 2016.
  [📄 PDF | 💻 Code | 📜 Slides | 🖼️ Poster]
  
  

• Bayesian Poisson Tensor Factorization for Inferring Multilateral Relations from Sparse Dyadic Event Counts. Aaron Schein, John Paisley, David M. Blei and Hanna Wallach. Conference of Knowledge Discovery and Data Mining (KDD), 2015.
  [📄 PDF | 💻 Code | ▶️ Talk at KDD | 📜 Slides]
  
  

Probabilistic state-space models

• The Ordered Matrix Dirichlet for State-Space Models. Niklas Stoehr, Benjamin J. Radford, Ryan Cotterell and Aaron Schein. International Conference on Artificial Intelligence and Statistics (AISTATS), 2023.
  [📄 PDF | 💻 Code]
  
  

• Poisson-Randomized Gamma Dynamical Systems. Aaron Schein, Scott W. Linderman, Mingyuan Zhou, David M. Blei and Hanna Wallach. Neural Information Processing Systems (NeurIPS), 2019.
  [📄 PDF | 💻 Code]
  
  

• Poisson--Gamma Dynamical Systems. Aaron Schein, Mingyuan Zhou and Hanna Wallach. Neural Information Processing Systems (NeurIPS), 2016.
  [📄 PDF | 💻 Code | ▶️ NeurIPS oral] | 📜 Slides]
  
  

Randomized field experiments and voter turnout

• Do Billboard Advertisements Increase Voter Turnout? A Large-Scale Field Experiment? Donald P. Green, Lionel Ong, Kylan Rutherford and Aaron Schein. Quarterly Journal of Political Science (QJPS), 2024.
  [📄 PDF]
  
  

• Assessing the Effects of Friend-to-Friend Texting on Turnout in the 2020 US Presidential Election. Aaron Schein, David M. Blei and Donald P. Green. Conference on Digital Experimentation (CODE), 2021.
  [📄 PDF | ▶️ Talk at Open States Summit 2023]
  
  

• Assessing the Effects of Friend-to-Friend Texting on Turnout in the 2018 US Midterm Elections. Aaron Schein, Keyon Vafa, Dhanya Sridhar, Victor Veitch, Jeffrey Quinn, James Moffet, David M. Blei and Donald P. Green. The Web Conference (WWW), 2021.
  [📄 PDF | 💻 Replication material | ▶️ WWW talk | ▶️ IC2S2 talk]
  
  

Other political science applications

• Addressing Discretization-Induced Bias in Demographic Prediction. Evan Dong, Aaron Schein, Yixin Wang and Nikhil Garg. Conference on Fairness, Accountability, and Transparency (FAccT), 2024.
  [📄 arXiv]
  
  

• Estimating conflict losses and reporting biases. Benjamin J. Radford, Yaoyao Dai, Niklas Stoehr, Aaron Schein, Mya Fernandez and Hanif Sajid. Brief report at Proceedings of the National Academy of Sciences (PNAS), 2023.
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• An Ordinal Latent Variable Model of Conflict Intensity. Niklas Stoehr, Lucas Torroba Hennigen, Josef Valvoda, Robert West, Ryan Cotterell and Aaron Schein. Long paper at the Association for Computational Linguistics (ACL), 2023.
  [📄 PDF | 💻 Code]
  
  

Large language models (LLMs)

• Activation Scaling for Steering and Interpreting Language Models. Niklas Stoehr, Kevin Du, Vésteinn Snæbjarnarson, Robert West, Ryan Cotterell and Aaron Schein. Findings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2024.
  [📄 arXiv | 💻 Code]
  
  

• Context versus Prior Knowledge in Language Models. Kevin Du, Vésteinn Snæbjarnarson, Niklas Stoehr, Jennifer C. White, Aaron Schein and Ryan Cotterell. Long paper at the Association for Computational Linguistics (ACL), 2024.
  [📄 arXiv | 💻 Code]
  
  

• Measurement in the Age of LLMs: An Application to Ideological Scaling. Sean O'Hagan and Aaron Schein. Preprint, 2024.
  [📄 arXiv]
  
  

Cancer (epi)genomics

• Doubly Non-Central Beta Matrix Factorization for DNA Methylation Data. Aaron Schein, Anjali Nagulpally, Hanna Wallach and Patrick Flaherty. Conference on Uncertainty in Artificial Intelligence (UAI), 2021.
  [📄 PDF | 💻 Code]
  
  

• A Bayesian Nonparametric Model for Inferring Subclonal Populations from Structured DNA Sequencing Data. Shai He, Aaron Schein, Vishal Sarsani and Patrick Flaherty. Annals of Applied Statistics (AoAS), 2021.
  [📄 PDF]
  
  

Differential privacy

• Locally Private Bayesian Inference for Count Models. Aaron Schein, Zhiwei Steven Wu, Alexandra Schofield, Mingyuan Zhou and Hanna Wallach. International Conference on Machine Learning (ICML), 2019.
  [📄 PDF | 💻 Code | ▶️ ICML oral]]
  
  

• A Variational Inference Approach for Locally Private Inference of Poisson Factorization Models. Alexandra Schofield, Aaron Schein, Zhiwei Steven Wu and Hanna Wallach. NeurIPS Workshop on Privacy Preserving Machine Learning (PPML), 2018.
  [📄 PDF]