93 citations · 116 across the 5 of their papers we have counts for
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Population-Based Black-Box Optimization for Biological Sequence Design
Christof Angermueller, David Belanger, Andreea Gane +5
The use of black-box optimization for the design of new biological sequences is an emerging research area with potentially revolutionary impact. The cost and latency of wet-lab exp…
Weighting Is Worth the Wait: Bayesian Optimization with Importance Sampling
Setareh Ariafar, Zelda Mariet, Ehsan Elhamifar +3
Many contemporary machine learning models require extensive tuning of hyperparameters to perform well. A variety of methods, such as Bayesian optimization, have been developed to a…
Foundations of Sequence-to-Sequence Modeling for Time Series
Vitaly Kuznetsov, Zelda Mariet
The availability of large amounts of time series data, paired with the performance of deep-learning algorithms on a broad class of problems, has recently led to significant interes…
Learning Determinantal Point Processes by Corrective Negative Sampling
Zelda Mariet, Mike Gartrell, Suvrit Sra
Determinantal Point Processes (DPPs) have attracted significant interest from the machine-learning community due to their ability to elegantly and tractably model the delicate bala…
Kronecker Determinantal Point Processes
Zelda Mariet, Suvrit Sra
Determinantal Point Processes (DPPs) are probabilistic models over all subsets a ground set of items. They have recently gained prominence in several applications that rely on…