Fixed-point algorithms for learning determinantal point processes. - Zelda Mariet; Suvrit Sra (ICML, 2015)

Fixed-point algorithms for determinantal point processes. - Zelda Mariet; Suvrit Sra (CoRR, 2015)

Kronecker Determinantal Point Processes. - Zelda E. Mariet; Suvrit Sra (NIPS, 2016)

Diversity Networks. - Zelda Mariet; Suvrit Sra (ICLR (Poster), 2016)

Elementary Symmetric Polynomials for Optimal Experimental Design. - Zelda E. Mariet; Suvrit Sra (NIPS, 2017)

Maximizing Induced Cardinality Under a Determinantal Point Process. - Jennifer A. Gillenwater; Alex Kulesza; Sergei Vassilvitskii; Zelda E. Mariet (NeurIPS, 2018)

Exponentiated Strongly Rayleigh Distributions. - Zelda E. Mariet; Suvrit Sra; Stefanie Jegelka (NeurIPS, 2018)

Learning Determinantal Point Processes by Sampling Inferred Negatives. - Zelda Mariet; Mike Gartrell; Suvrit Sra (CoRR, 2018)

Learning Determinantal Point Processes by Corrective Negative Sampling. - Zelda Mariet; Mike Gartrell; Suvrit Sra (AISTATS, 2019)

Foundations of Sequence-to-Sequence Modeling for Time Series. - Zelda Mariet; Vitaly Kuznetsov (AISTATS, 2019)

A Tree-Based Method for Fast Repeated Sampling of Determinantal Point Processes. - Jennifer Gillenwater; Alex Kulesza; Zelda Mariet; Sergei Vassilvitskii (ICML, 2019)

DppNet: Approximating Determinantal Point Processes with Deep Networks. - Zelda E. Mariet; Yaniv Ovadia; Jasper Snoek (NeurIPS, 2019)

Learning with generalized negative dependence: probabilistic models of diversity for machine learning. - Zelda E. Mariet (2019)

Population-Based Black-Box Optimization for Biological Sequence Design. - Christof Angermüller; David Belanger 002; Andreea Gane; Zelda Mariet; David Dohan; Kevin Murphy 002; Lucy J. Colwell; D. Sculley (ICML, 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)

Faster & More Reliable Tuning of Neural Networks: Bayesian Optimization with Importance Sampling. - Setareh Ariafar; Zelda Mariet; Dana H. Brooks; Jennifer G. Dy; Jasper Snoek (AISTATS, 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; et al. (CoRR, 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)

Understanding the bias-variance tradeoff of Bregman divergences. - Ben Adlam; Neha Gupta; Zelda Mariet; Jamie Smith (CoRR, 2022)

Ensembling over Classifiers: a Bias-Variance Perspective. - Neha Gupta; Jamie Smith; Ben Adlam; Zelda Mariet (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)

Plex: Towards Reliability using Pretrained Large Model Extensions. - Dustin Tran; Jeremiah Z. Liu; Michael W. Dusenberry; Du Phan; Mark Collier; Jie Ren 006; Kehang Han; Zi Wang; Zelda Mariet; Huiyi Hu; Neil Band; Tim G. J. Rudner; et al. (CoRR, 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; et al. (Trans. Mach. Learn. Res., 2022)

Ensembles of Classifiers: a Bias-Variance Perspective. - Neha Gupta; Jamie Smith; Ben Adlam; Zelda E. Mariet (Trans. Mach. Learn. Res., 2022)

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 (NeurIPS, 2023)

Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities. - Gheorghe Comanici; Eric Bieber; Mike Schaekermann; Ice Pasupat; Noveen Sachdeva; Inderjit S. Dhillon; Marcel Blistein; Ori Ram; Dan Zhang; Evan Rosen; Luke Marris; Sam Petulla; et al. (CoRR, 2025)