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06/06/2023
AVIDa-hIL6: A Large-Scale VHH Dataset Produced from an Immunized Alpaca for Predicting Antigen-Antibody Interactions
Antibodies have become an important class of therapeutic agents to treat...
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07/07/2022
Pre-training helps Bayesian optimization too
Bayesian optimization (BO) has become a popular strategy for global opti...
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06/21/2022
Ensembling over Classifiers: a Bias-Variance Perspective
Ensembles are a straightforward, remarkably effective method for improvi...
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02/08/2022
Understanding the bias-variance tradeoff of Bregman divergences
This paper builds upon the work of Pfau (2013), which generalized the bi...
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09/16/2021
Automatic prior selection for meta Bayesian optimization with a case study on tuning deep neural network optimizers
The performance of deep neural networks can be highly sensitive to the c...
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06/07/2021
Uncertainty Baselines: Benchmarks for Uncertainty Robustness in Deep Learning
High-quality estimates of uncertainty and robustness are crucial for num...
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06/05/2020
Population-Based Black-Box Optimization for Biological Sequence Design
The use of black-box optimization for the design of new biological seque...
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02/23/2020
Weighting Is Worth the Wait: Bayesian Optimization with Importance Sampling
Many contemporary machine learning models require extensive tuning of hy...
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01/07/2019
DPPNet: Approximating Determinantal Point Processes with Deep Networks
Determinantal Point Processes (DPPs) provide an elegant and versatile wa...
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05/09/2018
Foundations of Sequence-to-Sequence Modeling for Time Series
The availability of large amounts of time series data, paired with the p...
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02/15/2018
Learning Determinantal Point Processes by Sampling Inferred Negatives
Determinantal Point Processes (DPPs) have attracted significant interest...
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05/26/2016
Kronecker Determinantal Point Processes
Determinantal Point Processes (DPPs) are probabilistic models over all s...
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11/16/2015