Zelda E Mariet — accepted AI-conference papers

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 +5

NeurIPS 2023poster

Antibodies have become an important class of therapeutic agents to treat human diseases. To accelerate therapeutic antibody discovery, computational methods, especially machine learning, have attracted considerable interest for predicting specific interactions between antibody candidates and target…

Cited by 6 Source ↗PDF ↗code ↗Scholar ↗

2019

DppNet: Approximating Determinantal Point Processes with Deep Networks

Zelda E. Mariet, Yaniv Ovadia, Jasper Snoek

NeurIPS 2019poster

Determinantal point processes (DPPs) provide an elegant and versatile way to sample sets of items that balance the point-wise quality with the set-wise diversity of selected items. For this reason, they have gained prominence in many machine learning applications that rely on subset selection. Howev…

Cited by 13 Source ↗PDF ↗Scholar ↗

2018

Exponentiated Strongly Rayleigh Distributions

Zelda E. Mariet, Suvrit Sra, Stefanie Jegelka

NeurIPS 2018poster

Strongly Rayleigh (SR) measures are discrete probability distributions over the subsets of a ground set. They enjoy strong negative dependence properties, as a result of which they assign higher probability to subsets of diverse elements. We introduce in this paper Exponentiated Strongly Rayleigh (E…

Cited by 17 Source ↗PDF ↗Scholar ↗

2018

Maximizing Induced Cardinality Under a Determinantal Point Process

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

NeurIPS 2018poster

Determinantal point processes (DPPs) are well-suited to recommender systems where the goal is to generate collections of diverse, high-quality items. In the existing literature this is usually formulated as finding the mode of the DPP (the so-called MAP set). However, the MAP objective inherently as…

Cited by 14 Source ↗PDF ↗Scholar ↗

2017

Elementary Symmetric Polynomials for Optimal Experimental Design

Zelda E. Mariet, Suvrit Sra

NeurIPS 2017poster

We revisit the classical problem of optimal experimental design (OED) under a new mathematical model grounded in a geometric motivation. Specifically, we introduce models based on elementary symmetric polynomials; these polynomials capture "partial volumes" and offer a graded interpolation between t…

Cited by 23 Source ↗PDF ↗Scholar ↗

2016

Kronecker Determinantal Point Processes

Zelda E. Mariet, Suvrit Sra

NeurIPS 2016poster

Determinantal Point Processes (DPPs) are probabilistic models over all subsets a ground set of N items. They have recently gained prominence in several applications that rely on diverse subsets. However, their applicability to large problems is still limited due to O(N^3) complexity of core tasks su…

Cited by 35 Source ↗PDF ↗Scholar ↗