17 citations · 35 across the 7 of their papers we have counts for
7 papers
Stochastic Approximation with Biased MCMC for Expectation Maximization
Samuel Gruffaz, Kyurae Kim, Alain Oliviero Durmus +1
The expectation maximization (EM) algorithm is a widespread method for empirical Bayesian inference, but its expectation step (E-step) is often intractable. Employing a stochastic…
Inverse Protein Folding Using Deep Bayesian Optimization
Natalie Maus, Yimeng Zeng, Daniel Allen Anderson +5
Inverse protein folding -- the task of predicting a protein sequence from its backbone atom coordinates -- has surfaced as an important problem in the "top down", de novo design of…
Practical and Matching Gradient Variance Bounds for Black-Box Variational Bayesian Inference
Kyurae Kim, Kaiwen Wu, Jisu Oh +1
Understanding the gradient variance of black-box variational inference (BBVI) is a crucial step for establishing its convergence and developing algorithmic improvements. However, e…
Learning to Select Pivotal Samples for Meta Re-weighting
Yinjun Wu, Adam Stein, Jacob Gardner +1
Sample re-weighting strategies provide a promising mechanism to deal with imperfect training data in machine learning, such as noisily labeled or class-imbalanced data. One such st…
Black Box Adversarial Prompting for Foundation Models
Natalie Maus, Patrick Chao, Eric Wong +1
Prompting interfaces allow users to quickly adjust the output of generative models in both vision and language. However, small changes and design choices in the prompt can lead to…
A Reduction of the Elastic Net to Support Vector Machines with an Application to GPU Computing
Quan Zhou, Wenlin Chen, Shiji Song +3
The past years have witnessed many dedicated open-source projects that built and maintain implementations of Support Vector Machines (SVM), parallelized for GPU, multi-core CPUs an…