| Clarifying the Hubble constant tension with a Bayesian hierarchical model of the local distance ladder SM Feeney, DJ Mortlock, N Dalmasso Monthly Notices of the Royal Astronomical Society 476 (3), 3861-3882, 2018 | 187 | 2018 |
| Conditional density estimation tools in python and R with applications to photometric redshifts and likelihood-free cosmological inference N Dalmasso, T Pospisil, AB Lee, R Izbicki, PE Freeman, AI Malz Astronomy and Computing 30, 100362, 2020 | 66 | 2020 |
| Diagnostics for Conditional Density Models and Bayesian Inference Algorithms D Zhao, N Dalmasso, R Izbicki, AB Lee Proceedings of the 37th Conference on Uncertainty in Artificial Intelligence …, 2021 | 36 | 2021 |
| Likelihood-free frequentist inference: Bridging classical statistics and machine learning for reliable simulator-based inference N Dalmasso, L Masserano, D Zhao, R Izbicki, AB Lee Electronic Journal of Statistics 18 (2), 5045-5090, 2024 | 32* | 2024 |
| Confidence Sets and Hypothesis Testing in a Likelihood-Free Inference Setting N Dalmasso, R Izbicki, AB Lee International Conference of Machine Learning (ICML) 2020, 2020 | 30 | 2020 |
| Synthetic data applications in finance VK Potluru, D Borrajo, A Coletta, N Dalmasso, Y El-Laham, E Fons, ... arXiv preprint arXiv:2401.00081, 2023 | 24 | 2023 |
| Fair When Trained, Unfair When Deployed: Observable Fairness Measures are Unstable in Performative Prediction Settings A Mishler, N Dalmasso NeurIPS 2021 Workshop on Algorithmic Fairness through the Lens of Causality …, 2022 | 19* | 2022 |
| Validation of Approximate Likelihood and Emulator Models for Computationally Intensive Simulations N Dalmasso, AB Lee, R Izbicki, T Pospisil, I Kim, CA Lin Proceedings of the Twenty Third International Conference on Artificial …, 2020 | 13 | 2020 |
| Explicit Group Sparse Projection with Applications to Deep Learning and NMF R Ohib, N Gillis, N Dalmasso, S Shah, VK Potluru, S Plis Transactions on Machine Learning Research, 2019 | 11* | 2019 |
| Robust learning rate selection for stochastic optimization via splitting diagnostic M Sordello, N Dalmasso, H He, W Su Transactions on Machine Learning Research, 2024 | 10* | 2024 |
| Architectural Distant Reading: Using Machine Learning to Identify Typological Traits across Multiple Buildings C Ferrando, N Dalmasso, J Mai, DC Llach Proceedings of the 18th International Conference on CAAD Futures, Daejeon …, 2019 | 9 | 2019 |
| Efficient event series data modeling via first-order constrained optimization N Dalmasso, R Zhao, M Ghassemi, V Potluru, T Balch, M Veloso Proceedings of the Fourth ACM International Conference on AI in Finance, 463-471, 2023 | 7* | 2023 |
| PayVAE: A Generative Model for Financial Transactions N Dalmasso, RE Tillman, P Reddy, M Veloso AAAI-21 Workshop on Knowledge Discovery from Unstructured Data in Financial …, 2021 | 7 | 2021 |
| Deep Gaussian Mixture Ensembles Y El-Laham, N Dalmasso, E Fons, S Vyetrenko Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial …, 2023 | 6 | 2023 |
| Structural Forecasting for Short-term Tropical Cyclone Intensity Guidance T McNeely, P Khokhlov, N Dalmasso, KM Wood, AB Lee Weather and Forecasting Journal, 2023 | 6 | 2023 |
| Online Learning for Mixture of Multivariate Hawkes Processes M Ghassemi, N Dalmasso, S Lamba, VK Potluru, S Shah, T Balch, ... ICAIF '22: 3rd ACM International Conference on AI in Finance, 506–513, 2022 | 4 | 2022 |
| Structural Forecasting for Tropical Cyclone Intensity Prediction: Providing Insight with Deep Learning T McNeely, N Dalmasso, KM Wood, AB Lee NeurIPS 2020 Workshop on Tackling Climate Change with Machine Learning, 2020 | 3 | 2020 |
| Fair Wasserstein Coresets Z Xiong, N Dalmasso, S Sharma, F Lecue, D Magazzeni, VK Potluru, ... Advances in Neural Information Processing Systems 38, 2024 | 2 | 2024 |
| FairWASP: Fast and optimal fair wasserstein pre-processing Z Xiong, N Dalmasso, A Mishler, VK Potluru, T Balch, M Veloso Proceedings of the AAAI Conference on Artificial Intelligence 38 (14), 16120 …, 2024 | 2 | 2024 |
| Feature Engineering for Entity Resolution with Arabic Names: Improving Estimates of Observed Casualties in the Syrian Civil War N Dalmasso, R Mejia, J Rodu, M Price, J Murray Neurips 2020 Artificial Intelligence for Humanitarian Assistance and …, 2019 | 2 | 2019 |
Ann B. LeeDepartment of Statistics & Data Science and the Machine Learning Department, Carnegie MellonE-mail confirmado em cmu.edu