29 citations · 29 across the 1 of their papers we have counts for
5 papers
Learning Correlated Latent Representations with Adaptive Priors
Da Tang, Dawen Liang, Nicholas Ruozzi +1
Variational Auto-Encoders (VAEs) have been widely applied for learning compact, low-dimensional latent representations of high-dimensional data. When the correlation structure amon…
Correlated Variational Auto-Encoders
Da Tang, Dawen Liang, Tony Jebara +1
Variational Auto-Encoders (VAEs) are capable of learning latent representations for high dimensional data. However, due to the i.i.d. assumption, VAEs only optimize the singleton v…
The Deconfounded Recommender: A Causal Inference Approach to Recommendation
Yixin Wang, Dawen Liang, Laurent Charlin +1
The goal of recommendation is to show users items that they will like. Though usually framed as a prediction, the spirit of recommendation is to answer an interventional question--…
Variational Autoencoders for Collaborative Filtering
Dawen Liang, Rahul G. Krishnan, Matthew D. Hoffman +1
We extend variational autoencoders (VAEs) to collaborative filtering for implicit feedback. This non-linear probabilistic model enables us to go beyond the limited modeling capacit…
On the challenges of learning with inference networks on sparse, high-dimensional data
Rahul G. Krishnan, Dawen Liang, Matthew Hoffman
We study parameter estimation in Nonlinear Factor Analysis (NFA) where the generative model is parameterized by a deep neural network. Recent work has focused on learning such mode…