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Zelda Mariet
Zelda Mariet
Principal Research Scientist & Co-founder @ Bioptimus
Dirección de correo verificada de bioptimus.com - Página principal
Título
Citado por
Citado por
Año
Diversity networks: neural network compression using determinantal point processes
Z Mariet, S Sra
International Conference on Learning Representations, 2015
1702015
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
1022022
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
1002021
Foundations of Sequence-to-Sequence Modeling for Time Series
Z Mariet, V Kuznetsov
The 22nd International Conference on Artificial Intelligence and Statistics …, 2019
77*2019
Fixed-point algorithms for learning determinantal point processes
Z Mariet, S Sra
Proceedings of the 32nd International Conference on Machine Learning (ICML …, 2015
662015
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
532020
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
53*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
34*2021
Kronecker Determinantal Point Processes
Z Mariet, S Sra
Neural Information Processing Systems, 2016
332016
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
282019
Elementary symmetric polynomials for optimal experimental design
ZE Mariet, S Sra
Advances in Neural Information Processing Systems, 2139-2148, 2017
212017
Sparse MoEs meet Efficient Ensembles
JU Allingham, F Wenzel, ZE Mariet, B Mustafa, J Puigcerver, N Houlsby, ...
arXiv preprint arXiv:2110.03360, 2021
182021
Exponentiated strongly Rayleigh distributions
Z Mariet, S Sra, S Jegelka
Advances in neural information processing systems, 2018
172018
Maximizing induced cardinality under a determinantal point process
JA Gillenwater, A Kulesza, S Vassilvitskii, ZE Mariet
Advances in Neural Information Processing Systems 31, 2018
132018
DPPNet: Approximating Determinantal Point Processes with Deep Networks
ZE Mariet, Y Ovadia, J Snoek
Advances in Neural Information Processing Systems, 3223-3234, 2019
122019
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
11*2019
Biological Sequence Design using Batched Bayesian Optimization
D Belanger, S Vora, Z Mariet, R Deshpande, D Dohan, C Angermueller, ...
10*
Ensembles of Classifiers: a Bias-Variance Perspective
N Gupta, J Smith, B Adlam, ZE Mariet
Transactions on Machine Learning Research, 2022
9*2022
Distilling ensembles improves uncertainty estimates
ZE Mariet, R Jenatton, F Wenzel, D Tran
Third symposium on advances in approximate bayesian inference, 2020
62020
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Artículos 1–20