86 citations · 433 across the 35 of their papers we have counts for
8 papers · 1 filter
BayReL: Bayesian Relational Learning for Multi-omics Data Integration
Ehsan Hajiramezanali, Arman Hasanzadeh, Nick Duffield +2
High-throughput molecular profiling technologies have produced high-dimensional multi-omics data, enabling systematic understanding of living systems at the genome scale. Studying…
Quantifying the multi-objective cost of uncertainty
Byung-Jun Yoon, Xiaoning Qian, Edward R. Dougherty
Various real-world applications involve modeling complex systems with immense uncertainty and optimizing multiple objectives based on the uncertain model. Quantifying the impact of…
Bayesian Graph Neural Networks with Adaptive Connection Sampling
Arman Hasanzadeh, Ehsan Hajiramezanali, Shahin Boluki +4
We propose a unified framework for adaptive connection sampling in graph neural networks (GNNs) that generalizes existing stochastic regularization methods for training GNNs. The p…
NADS: Neural Architecture Distribution Search for Uncertainty Awareness
Randy Ardywibowo, Shahin Boluki, Xinyu Gong +2
Machine learning (ML) systems often encounter Out-of-Distribution (OoD) errors when dealing with testing data coming from a distribution different from training data. It becomes im…
Probabilistic Best Subset Selection via Gradient-Based Optimization
Mingzhang Yin, Nhat Ho, Bowei Yan +2
In high-dimensional statistics, variable selection recovers the latent sparse patterns from all possible covariate combinations. This paper proposes a novel optimization method to…
Pairwise Supervised Hashing with Bernoulli Variational Auto-Encoder and Self-Control Gradient Estimator
Siamak Zamani Dadaneh, Shahin Boluki, Mingzhang Yin +2
Semantic hashing has become a crucial component of fast similarity search in many large-scale information retrieval systems, in particular, for text data. Variational auto-encoders…
Uncertainty Quantification for Deep Context-Aware Mobile Activity Recognition and Unknown Context Discovery
Zepeng Huo, Arash PakBin, Xiaohan Chen +6
Activity recognition in wearable computing faces two key challenges: i) activity characteristics may be context-dependent and change under different contexts or situations; ii) unk…
Learnable Bernoulli Dropout for Bayesian Deep Learning
Shahin Boluki, Randy Ardywibowo, Siamak Zamani Dadaneh +2
In this work, we propose learnable Bernoulli dropout (LBD), a new model-agnostic dropout scheme that considers the dropout rates as parameters jointly optimized with other model pa…