| The impact of comprehensive case management on HIV client outcomes M Brennan-Ing, L Seidel, L Rodgers, J Ernst, D Wirth, D Tietz, A Morretti, ... PloS one 11 (2), e0148865, 2016 | 54 | 2016 |
| Data extraction and analysis system and tool A Moretti, K McKnight, JPG Brenes US Patent 10,311,741, 2019 | 49* | 2019 |
| AutoML using Metadata Language Embeddings I Drori, L Liu, Y Nian, SC Koorathota, JS Li, AK Moretti, J Freire, M Udell NuerIPS Workshop on Meta-Learning, 2019 | 29 | 2019 |
| Nonlinear evolution via spatially-dependent linear dynamics for electrophysiology and calcium data D Hernandez, AK Moretti, Z Wei, S Saxena, J Cunningham, L Paninski Neurons, Behavior, Data analysis and Theory, 2018 | 19 | 2018 |
| variational combinatorial sequential monte carlo methods for bayesian phylogenetic inference AK Moretti, L Zhang, CA Naesseth, H Venner, D Blei, I Pe’er Uncertainty in Artificial Intelligence, 971-981, 2021 | 18 | 2021 |
| Variational Combinatorial Sequential Monte Carlo Methods for Bayesian Phylogenetic Inference AK Moretti, L Zhang, CA Naesseth, H Venner, D Blei, I Pe’er Uncertainty in Artificial Intelligence, 971-981, 2021 | 18 | 2021 |
| Variational objectives for markovian dynamics with backward simulation A Khalil Moretti, Z Wang, L Wu, I Drori, I Pe’er ECAI 2020, 1371-1378, 2020 | 16 | 2020 |
| A Novel Variational Family for Hidden Nonlinear Markov Models D Hernandez, AK Moretti, Z Wei, S Saxena, J Cunningham, L Paninski arXiv preprint arXiv:1811.02459, 2018 | 16 | 2018 |
| Smoothing Nonlinear Variational Objectives with Sequential Monte Carlo A Moretti, Z Wang, L Wu, I Pe'er ICLR Workshop on Deep Generative Models, 2019 | 14 | 2019 |
| Particle smoothing variational objectives AK Moretti, Z Wang, L Wu, I Drori, I Pe'er arXiv preprint arXiv:1909.09734, 2019 | 13 | 2019 |
| Mining student ratings and course contents for computer science curriculum decisions A Moretti, J Gonzalez-Brenes, K McKnight, A Salleb-Aouissi EDM, 2014 | 13* | 2014 |
| Accurate Protein Structure Prediction by Embeddings and Deep Learning Representations I Drori, D Thaker, A Srivatsa, D Jeong, Y Wang, L Nan, F Wu, D Leggas, ... Machine Learning in Computational Biology, 2019 | 7 | 2019 |
| Autoencoding topographic factors A Moretti, A Stirn, G Marks, I Pe'er Journal of Computational Biology 26 (6), 546-560, 2019 | 5 | 2019 |
| Fast hyperboloid decision tree algorithms P Chlenski, E Turok, A Moretti, I Pe'er International Conference on Learning Representation (ICLR) 2024, 2023 | 2 | 2023 |
| Variational Combinatorial Sequential Monte Carlo for Bayesian Phylogenetic Inference A Moretti, L Zhang, I Pe'er Machine Learning in Computational Biology, 2020 | 2 | 2020 |
| Variational Bayesian Methods for Inferring Spatial Statistics and Nonlinear Dynamics AK Moretti Columbia University, 2021 | 1 | 2021 |
| Variational Pseudo Marginal Methods for Jet Reconstruction in Particle Physics H Yang, AK Moretti, S Macaluso, P Chlenski, CA Naesseth, I Pe'er arXiv preprint arXiv:2406.03242, 2024 | | 2024 |
| Particle Smoothing Variational Objectives A Khalil Moretti, Z Wang, L Wu, I Drori, I Pe'er arXiv e-prints, arXiv: 1909.09734, 2019 | | 2019 |
| A Note on Feynman-Kac and Particle Filters: Inference in Dynamical Systems A Moretti | | |
Itsik Pe'erDepartment of Computer Science, Columbia UniversityVerified email at cs.columbia.edu
