Andrew Bennett

Machine Learning Researcher

Morgan Stanley

Biography

I am a machine learning researcher in the Machine Learning Research group at Morgan Stanley. I have diverse research interests in the area of machine learning, with particular interest in causal machine learning, data-driven decision making, reinforcement learning, econometrics, and probabilistic machine learning. Before that, I was a PhD student at Cornell University in the Computer Science department, supervised by Nathan Kallus.

Interests

Causal Machine Learning
Data-driven Decision Making
Reinforcement Learning
Econometrics
Probabilistic Machine Learning

Education

Doctor of Philosophy in Computer Science, 2023
Cornell University
Master of Science in Computer Science, 2016
The University of Melbourne
Bachelor of Science, 2013
The University of Melbourne

Publications

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Andrew Bennett, Nathan Kallus, Miruna Oprescu, Wen Sun, Kaiwen Wang (2024). Efficient and Sharp Off-Policy Evaluation in Robust Markov Decision Processes. Accepted at NeurIPS.

Kashif Rasul, Andrew Bennett, Pablo Vicente, Umang Gupta, Hena Ghonia, Anderson Schneider, Yuriy Nevmyvaka (2024). VQ-TR: Vector Quantized Attention for Time Series Forecasting. International Conference on Learning Representations (ICLR).

Miruna Oprescu, Andrew Bennett, Nathan Kallus (2024). Low-rank MDPs with Continuous Action Spaces. International Conference on Artificial Intelligence and Statistics (AISTATS).

Andrew Bennett, Nathan Kallus, Xiaojie Mao, Whitney Newey, Vasilis Syrgkanis, Masatoshi Uehara (2024). Inference on strongly identified functionals of weakly identified functions. Major revision in JRSS:B.

Masatoshi Uehara, Haruka Kiyohara, Andrew Bennett, Victor Chernozhukov, Nan Jiang, Nathan Kallus, Chengchun Shi, Wen Sun (2023). Future-Dependent Value-Based Off-Policy Evaluation in POMDPs. International Conference on Neural Information Processing Systems (NeurIPS).

Andrew Bennett, Nathan Kallus (2023). Proximal Reinforcement Learning: Efficient Off-Policy Evaluation in Partially Observed Markov Decision Processes. Operations Research (OR).

Andrew Bennett, Nathan Kallus, Xiaojie Mao, Whitney Newey, Vasilis Syrgkanis, Masatoshi Uehara (2023). Source Condition Double Robust Inference on Functionals of Inverse Problems. arXiv preprint.

Andrew Bennett, Nathan Kallus, Xiaojie Mao, Whitney Newey, Vasilis Syrgkanis, Masatoshi Uehara (2023). Minimax Instrumental Variable Regression and L2 Convergence Guarantees without Identification or Closedness. Conference on Learning Theory (COLT).

Andrew Bennett, Nathan Kallus (2023). The Variational Method of Moments. Journal of the Royal Statistical Society Series B: Statistical Methodology (JRSS:B).

Andrew Bennett, Dipendra Misra, Nathan Kallus (2023). Provable Safe Reinforcement Learning with Binary Feedback. International Conference on Artificial Intelligence and Statistics (AISTATS).

Andrew Bennett, Dipendra Misra, Nga Than (2021). Have you tried Neural Topic Models? Comparative Analysis of Neural and Non-Neural Topic Models with Application to COVID-19 Twitter Data. The 3rd Workshop on Data Science for Social Good (DSSG-21).

Andrew Bennett, Nathan Kallus, Lihong Li, Ali Mousavi (2021). Off-policy Evaluation in Infinite-Horizon Reinforcement Learning with Latent Confounders. International Conference on Artificial Intelligence and Statistics (AISTATS).

Andrew Bennett, Nathan Kallus (2020). Efficient Policy Learning from Surrogate-Loss Classification Reductions. International Conference on Machine Learning (ICML).

Andrew Bennett, Nathan Kallus, Tobias Schnabel (2019). Deep Generalized Method of Moments for Instrumental Variable Analysis. Advances in Neural Information Processing Systems (NeurIPS).

Andrew Bennett, Nathan Kallus (2019). Policy Evaluation with Latent Confounders via Optimal Balance. Advances in Neural Information Processing Systems (NeurIPS).

Dipendra Misra, Andrew Bennett, Valts Blukis, Eyvind Niklasson, Max Shatkhin, Yoav Artzi (2018). Mapping Instructions to Actions in 3D Environments with Visual Goal Prediction. Conference on Empirical Methods in Natural Language Processing (EMNLP).

Steven Xu, Andrew Bennett, Doris Hoogeveen, Jey Han Lau, Timothy Baldwin (2018). Preferred answer selection in stack overflow: Better text representations... and metadata, metadata, metadata. The 4th Workshop on Noisy User-generated Text (W-NUT), at EMNLP.

Valts Blukis, Nataly Brukhim, Andrew Bennett, Ross Knepper, Yoav Artzi (2018). Following High-level Navigation Instructions on a Simulated Quadcopter with Imitation Learning. Robotics: Science and Systems (RSS).

Doris Hoogeveen, Andrew Bennett, Yitong Li, Karin M. Verspoor, Timothy Baldwin (2018). Detecting Misflagged Duplicate Questions in Community Question-Answering Archives. The AAAI Conference on Web and Social Media (ICWSM).

Claudia Yan, Dipendra Misra, Andrew Bennnett, Aaron Walsman, Yonatan Bisk, Yoav Artzi (2018). CHALET: Cornell House Agent Learning Environment. Technical Report.

Andrew Bennett, Timothy Baldwin, Jey Han Lau, Diana McCarthy, Francis Bond (2016). LexSemTM: A Semantic Dataset Based on All-words Unsupervised Sense Distribution Learning. The Annual Meeting of the Association for Computational Linguistics (ACL).

Andrew Bennett (2016). Unsupervised All-words Sense Distribution Learning. Masters Thesis.