9 citations · 26 across the 13 of their papers we have counts for
14 papers
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…
Teaching Probabilistic Machine Learning in the Liberal Arts: Empowering Socially and Mathematically Informed AI Discourse
Yaniv Yacoby
We present a new undergraduate ML course at our institution, a small liberal arts college serving students minoritized in STEM, designed to empower students to critically connect t…
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…
Transparent Trade-offs between Properties of Explanations
Hiwot Belay Tadesse, Alihan Hüyük, Yaniv Yacoby +2
When explaining black-box machine learning models, it's often important for explanations to have certain desirable properties. Most existing methods `encourage' desirable propertie…
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…
Empowering First-Year Computer Science Ph.D. Students to Create a Culture that Values Community and Mental Health
Yaniv Yacoby, John Girash, David C. Parkes
Doctoral programs often have high rates of depression, anxiety, isolation, and imposter phenomenon. Consequently, graduating students may feel inadequately prepared for research-fo…
"If it didn't happen, why would I change my decision?": How Judges Respond to Counterfactual Explanations for the Public Safety Assessment
Yaniv Yacoby, Ben Green, Christopher L. Griffin +1
Many researchers and policymakers have expressed excitement about algorithmic explanations enabling more fair and responsible decision-making. However, recent experimental studies…
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…
Characterizing and Avoiding Problematic Global Optima of Variational Autoencoders
Yaniv Yacoby, Weiwei Pan, Finale Doshi-Velez
Variational Auto-encoders (VAEs) are deep generative latent variable models consisting of two components: a generative model that captures a data distribution p(x) by transforming…
Mitigating the Effects of Non-Identifiability on Inference for Bayesian Neural Networks with Latent Variables
Yaniv Yacoby, Weiwei Pan, Finale Doshi-Velez
Bayesian Neural Networks with Latent Variables (BNN+LVs) capture predictive uncertainty by explicitly modeling model uncertainty (via priors on network weights) and environmental s…