9 citations · 26 across the 13 of their papers we have counts for
8 papers · 1 filter
Latent Similarity Gaussian Processes: A Theory-Grounded Approach to Personalized Suicide-Risk Forecasting for Clinical Decision-Support
Yaniv Yacoby, Weiwei Pan, Hope Neveux +4
Forecasting suicide risk is difficult due to the high heterogeneity of patients and the low base rate of suicide-related events (SREs). We present Latent Similarity Gaussian Proces…
Improving Forecasts of Suicide Attempts for Patients with Little Data
Genesis Hang, Annie Chen, Hope Neveux +2
Ecological Momentary Assessment (EMA) studies provide real-time data on suicidal thoughts and behaviors, but forecasting suicide attempts remains challenging: attempts are rare, an…
Neural Stochastic Differential Equations on Compact State Spaces: Theory, Methods, and Application to Suicide Risk Modeling
Malinda Lu, Yue-Jane Liu, Matthew K. Nock +1
Ecological Momentary Assessment (EMA) studies enable the collection of high-frequency self-reports of suicidal thoughts and behaviors (STBs) via smartphones. Latent stochastic diff…
Towards Model-Agnostic Posterior Approximation for Fast and Accurate Variational Autoencoders
Yaniv Yacoby, Weiwei Pan, Finale Doshi-Velez
Inference for Variational Autoencoders (VAEs) consists of learning two models: (1) a generative model, which transforms a simple distribution over a latent space into the distribut…
An Empirical Analysis of the Advantages of Finite- v.s. Infinite-Width Bayesian Neural Networks
Jiayu Yao, Yaniv Yacoby, Beau Coker +2
Comparing Bayesian neural networks (BNNs) with different widths is challenging because, as the width increases, multiple model properties change simultaneously, and, inference in t…
BaCOUn: Bayesian Classifers with Out-of-Distribution Uncertainty
Théo Guénais, Dimitris Vamvourellis, Yaniv Yacoby +2
Traditional training of deep classifiers yields overconfident models that are not reliable under dataset shift. We propose a Bayesian framework to obtain reliable uncertainty estim…
Failure Modes of Variational Autoencoders and Their Effects on Downstream Tasks
Yaniv Yacoby, Weiwei Pan, Finale Doshi-Velez
Variational Auto-encoders (VAEs) are deep generative latent variable models that are widely used for a number of downstream tasks. While it has been demonstrated that VAE training…
Uncertainty-Aware (UNA) Bases for Deep Bayesian Regression Using Multi-Headed Auxiliary Networks
Sujay Thakur, Cooper Lorsung, Yaniv Yacoby +2
Neural Linear Models (NLM) are deep Bayesian models that produce predictive uncertainties by learning features from the data and then performing Bayesian linear regression over the…