| Diversity networks: neural network compression using determinantal point processes Z Mariet, S Sra International Conference on Learning Representations, 2015 | 164 | 2015 |
| Uncertainty Baselines: Benchmarks for uncertainty & robustness in deep learning Z Nado, N Band, M Collier, J Djolonga, MW Dusenberry, S Farquhar, ... arXiv preprint arXiv:2106.04015, 2021 | 97 | 2021 |
| Plex: Towards Reliability using Pretrained Large Model Extensions D Tran, J Liu, MW Dusenberry, D Phan, M Collier, J Ren, K Han, Z Wang, ... arXiv preprint arXiv:2207.07411, 2022 | 88 | 2022 |
| Foundations of Sequence-to-Sequence Modeling for Time Series Z Mariet, V Kuznetsov The 22nd International Conference on Artificial Intelligence and Statistics …, 2019 | 76* | 2019 |
| Fixed-point algorithms for learning determinantal point processes Z Mariet, S Sra Proceedings of the 32nd International Conference on Machine Learning (ICML …, 2015 | 63 | 2015 |
| Outlier detection in heterogeneous datasets using automatic tuple expansion C Pit-Claudel, Z Mariet, R Harding, S Madden Technical Report MIT-CSAIL-TR-2016-002, CSAIL, MIT, 32 Vassar Street …, 2016 | 51* | 2016 |
| Population-based black-box optimization for biological sequence design C Angermueller, D Belanger, A Gane, Z Mariet, D Dohan, K Murphy, ... International Conference on Machine Learning, 324-334, 2020 | 46 | 2020 |
| Kronecker Determinantal Point Processes Z Mariet, S Sra Neural Information Processing Systems, 2016 | 30 | 2016 |
| Pre-trained Gaussian processes for Bayesian optimization Z Wang, GE Dahl, K Swersky, C Lee, Z Mariet, Z Nado, J Gilmer, J Snoek, ... arXiv preprint arXiv:2109.08215, 2021 | 27* | 2021 |
| A Tree-Based Method for Fast Repeated Sampling of Determinantal Point Processes J Gillenwater, A Kulesza, Z Mariet, S Vassilvtiskii International Conference on Machine Learning, 2260-2268, 2019 | 27 | 2019 |
| Elementary symmetric polynomials for optimal experimental design ZE Mariet, S Sra Advances in Neural Information Processing Systems, 2139-2148, 2017 | 21 | 2017 |
| Exponentiated strongly Rayleigh distributions Z Mariet, S Sra, S Jegelka Advances in neural information processing systems, 2018 | 17 | 2018 |
| Sparse MoEs meet Efficient Ensembles JU Allingham, F Wenzel, ZE Mariet, B Mustafa, J Puigcerver, N Houlsby, ... arXiv preprint arXiv:2110.03360, 2021 | 16 | 2021 |
| Maximizing induced cardinality under a determinantal point process JA Gillenwater, A Kulesza, S Vassilvitskii, ZE Mariet Advances in Neural Information Processing Systems 31, 2018 | 13 | 2018 |
| Faster & More Reliable Tuning of Neural Networks: Bayesian Optimization with Importance Sampling S Ariafar, Z Mariet, D Brooks, J Dy, J Snoek International Conference on Artificial Intelligence and Statistics, 3961-3969, 2021 | 11* | 2021 |
| Learning determinantal point processes by corrective negative sampling Z Mariet, M Gartrell, S Sra The 22nd International Conference on Artificial Intelligence and Statistics …, 2019 | 10* | 2019 |
| DPPNet: Approximating Determinantal Point Processes with Deep Networks ZE Mariet, Y Ovadia, J Snoek Advances in Neural Information Processing Systems, 3223-3234, 2019 | 10 | 2019 |
| Ensembles of Classifiers: a Bias-Variance Perspective N Gupta, J Smith, B Adlam, ZE Mariet Transactions on Machine Learning Research, 2022 | 8* | 2022 |
| Biological Sequence Design using Batched Bayesian Optimization D Belanger, S Vora, Z Mariet, R Deshpande, D Dohan, C Angermueller, ... | 7* | |
| Distilling ensembles improves uncertainty estimates ZE Mariet, R Jenatton, F Wenzel, D Tran Third symposium on advances in approximate bayesian inference, 2020 | 5 | 2020 |
Suvrit SraTU Munich, MITVerified email at mit.edu
