Zelda Mariet

According to our database, Zelda Mariet authored at least 25 papers between 2015 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2023

AVIDa-hIL6: A Large-Scale VHH Dataset Produced from an Immunized Alpaca for Predicting Antigen-Antibody Interactions.

Hirofumi Tsuruta, Hiroyuki Yamazaki, Ryota Maeda, Ryotaro Tamura, Jennifer N. Wei, Zelda E. Mariet, Poomarin Phloyphisut, Hidetoshi Shimokawa, Joseph R. Ledsam, Lucy J. Colwell, Akihiro Imura

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

Ensembles of Classifiers: a Bias-Variance Perspective.

Neha Gupta, Jamie Smith, Ben Adlam, Zelda E. Mariet

Trans. Mach. Learn. Res., 2022

Sparse MoEs meet Efficient Ensembles.

James Urquhart Allingham, Florian Wenzel, Zelda E. Mariet, Basil Mustafa, Joan Puigcerver, Neil Houlsby, Ghassen Jerfel, Vincent Fortuin, Balaji Lakshminarayanan, Jasper Snoek, Dustin Tran, Carlos Riquelme Ruiz, Rodolphe Jenatton

Trans. Mach. Learn. Res., 2022

Plex: Towards Reliability using Pretrained Large Model Extensions.

Dustin Tran, Jeremiah Z. Liu, Michael W. Dusenberry, Du Phan, Mark Collier, Jie Ren, Kehang Han, Zi Wang, Zelda Mariet, Huiyi Hu, Neil Band, Tim G. J. Rudner, Karan Singhal, Zachary Nado, Joost van Amersfoort, Andreas Kirsch, Rodolphe Jenatton, Nithum Thain, Honglin Yuan, Kelly Buchanan, Kevin Murphy, D. Sculley, Yarin Gal, Zoubin Ghahramani, Jasper Snoek, Balaji Lakshminarayanan

CoRR, 2022

Pre-training helps Bayesian optimization too.

Zi Wang, George E. Dahl, Kevin Swersky, Chansoo Lee, Zelda Mariet, Zachary Nado, Justin Gilmer, Jasper Snoek, Zoubin Ghahramani

CoRR, 2022

Ensembling over Classifiers: a Bias-Variance Perspective.

Neha Gupta, Jamie Smith, Ben Adlam, Zelda Mariet

CoRR, 2022

Understanding the bias-variance tradeoff of Bregman divergences.

Ben Adlam, Neha Gupta, Zelda Mariet, Jamie Smith

CoRR, 2022

2021

Automatic prior selection for meta Bayesian optimization with a case study on tuning deep neural network optimizers.

Zi Wang, George E. Dahl, Kevin Swersky, Chansoo Lee, Zelda Mariet, Zachary Nado, Justin Gilmer, Jasper Snoek, Zoubin Ghahramani

CoRR, 2021

Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning.

Zachary Nado, Neil Band, Mark Collier, Josip Djolonga, Michael W. Dusenberry, Sebastian Farquhar, Angelos Filos, Marton Havasi, Rodolphe Jenatton, Ghassen Jerfel, Jeremiah Z. Liu, Zelda Mariet, Jeremy Nixon, Shreyas Padhy, Jie Ren, Tim G. J. Rudner, Yeming Wen, Florian Wenzel, Kevin Murphy, D. Sculley, Balaji Lakshminarayanan, Jasper Snoek, Yarin Gal, Dustin Tran

CoRR, 2021

Faster & More Reliable Tuning of Neural Networks: Bayesian Optimization with Importance Sampling.

Setareh Ariafar, Zelda Mariet, Dana H. Brooks, Jennifer G. Dy, Jasper Snoek

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Weighting Is Worth the Wait: Bayesian Optimization with Importance Sampling.

Setareh Ariafar, Zelda Mariet, Ehsan Elhamifar, Dana H. Brooks, Jennifer G. Dy, Jasper Snoek

CoRR, 2020

Population-Based Black-Box Optimization for Biological Sequence Design.

Christof Angermüller, David Belanger, Andreea Gane, Zelda Mariet, David Dohan, Kevin Murphy, Lucy J. Colwell, D. Sculley

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Learning with generalized negative dependence: probabilistic models of diversity for machine learning.

Zelda E. Mariet

PhD thesis, 2019

DppNet: Approximating Determinantal Point Processes with Deep Networks.

Zelda E. Mariet, Yaniv Ovadia, Jasper Snoek

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

A Tree-Based Method for Fast Repeated Sampling of Determinantal Point Processes.

Jennifer Gillenwater, Alex Kulesza, Zelda Mariet, Sergei Vassilvitskii

Proceedings of the 36th International Conference on Machine Learning, 2019

Foundations of Sequence-to-Sequence Modeling for Time Series.

Zelda Mariet, Vitaly Kuznetsov

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

Learning Determinantal Point Processes by Corrective Negative Sampling.

Zelda Mariet, Mike Gartrell, Suvrit Sra

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

Learning Determinantal Point Processes by Sampling Inferred Negatives.

Zelda Mariet, Mike Gartrell, Suvrit Sra

CoRR, 2018

Exponentiated Strongly Rayleigh Distributions.

Zelda E. Mariet, Suvrit Sra, Stefanie Jegelka

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Maximizing Induced Cardinality Under a Determinantal Point Process.

Jennifer A. Gillenwater, Alex Kulesza, Sergei Vassilvitskii, Zelda E. Mariet

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2017

Elementary Symmetric Polynomials for Optimal Experimental Design.

Zelda E. Mariet, Suvrit Sra

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

2016

Diversity Networks.

Zelda Mariet, Suvrit Sra

Proceedings of the 4th International Conference on Learning Representations, 2016

Kronecker Determinantal Point Processes.

Zelda E. Mariet, Suvrit Sra

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

2015

Fixed-point algorithms for determinantal point processes.

Zelda Mariet, Suvrit Sra

CoRR, 2015

Fixed-point algorithms for learning determinantal point processes.

Zelda Mariet, Suvrit Sra

Proceedings of the 32nd International Conference on Machine Learning, 2015