| From word embeddings to document distances M Kusner, Y Sun, N Kolkin, K Weinberger International conference on machine learning, 957-966, 2015 | 1486 | 2015 |
| Counterfactual fairness MJ Kusner, JR Loftus, C Russell, R Silva arXiv preprint arXiv:1703.06856, 2017 | 560 | 2017 |
| Grammar variational autoencoder MJ Kusner, B Paige, JM Hernández-Lobato International Conference on Machine Learning, 1945-1954, 2017 | 396 | 2017 |
| Bayesian Optimization with Inequality Constraints. JR Gardner, MJ Kusner, ZE Xu, KQ Weinberger, JP Cunningham ICML 2014, 937-945, 2014 | 240 | 2014 |
| Gans for sequences of discrete elements with the gumbel-softmax distribution MJ Kusner, JM Hernández-Lobato arXiv preprint arXiv:1611.04051, 2016 | 207 | 2016 |
| Cost-sensitive tree of classifiers Z Xu, M Kusner, K Weinberger, M Chen International conference on machine learning, 133-141, 2013 | 121 | 2013 |
| Supervised Word Mover's Distance G Huang, C Guo, MJ Kusner, Y Sun, F Sha, KQ Weinberger Advances in Neural Information Processing Systems, 3270-3278, 2016 | 115 | 2016 |
| When worlds collide: integrating different counterfactual assumptions in fairness C Russell, MJ Kusner, JR Loftus, R Silva Advances in Neural Information Processing Systems 30. Pre-proceedings 30, 2017 | 85 | 2017 |
| Classifier cascades and trees for minimizing feature evaluation cost Z Xu, MJ Kusner, KQ Weinberger, M Chen, O Chapelle The Journal of Machine Learning Research 15 (1), 2113-2144, 2014 | 77 | 2014 |
| Stochastic neighbor compression M Kusner, S Tyree, K Weinberger, K Agrawal International Conference on Machine Learning, 622-630, 2014 | 70 | 2014 |
| Deep manifold traversal: Changing labels with convolutional features JR Gardner, P Upchurch, MJ Kusner, Y Li, KQ Weinberger, K Bala, ... arXiv preprint arXiv:1511.06421, 2015 | 61 | 2015 |
| Fast distributed k-center clustering with outliers on massive data G Malkomes, MJ Kusner, W Chen, KQ Weinberger, B Moseley Advances in Neural Information Processing Systems 28, 1063-1071, 2015 | 53 | 2015 |
| Causal reasoning for algorithmic fairness JR Loftus, C Russell, MJ Kusner, R Silva arXiv preprint arXiv:1805.05859, 2018 | 52 | 2018 |
| TAPAS: tricks to accelerate (encrypted) prediction as a service A Sanyal, MJ Kusner, A Gascón, V Kanade ICML, 2018 | 50 | 2018 |
| Blind justice: fairness with encrypted sensitive attributes N Kilbertus, A Gascón, MJ Kusner, M Veale, KP Gummadi, A Weller ICML, 2018 | 50 | 2018 |
| QUOTIENT: two-party secure neural network training and prediction N Agrawal, A Shahin Shamsabadi, MJ Kusner, A Gascón Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications …, 2019 | 49 | 2019 |
| Feature-cost sensitive learning with submodular trees of classifiers M Kusner, W Chen, Q Zhou, ZE Xu, K Weinberger, Y Chen Proceedings of the AAAI Conference on Artificial Intelligence 28 (1), 2014 | 49 | 2014 |
| Differentially private Bayesian optimization M Kusner, J Gardner, R Garnett, K Weinberger International conference on machine learning, 918-927, 2015 | 40 | 2015 |
| A Generative Model For Electron Paths J Bradshaw, MJ Kusner, B Paige, MHS Segler, JM Hernández-Lobato ICLR, 2019 | 33* | 2019 |
| A model to search for synthesizable molecules J Bradshaw, B Paige, MJ Kusner, MHS Segler, JM Hernández-Lobato arXiv preprint arXiv:1906.05221, 2019 | 31 | 2019 |
Kilian WeinbergerCornell UniversityEmail verificata su cornell.edu
