64 citations · 285 across the 26 of their papers we have counts for
5 papers · 1 filter
Item Recommendation with Variational Autoencoders and Heterogenous Priors
Giannis Karamanolakis, Kevin Raji Cherian, Ananth Ravi Narayan +3
In recent years, Variational Autoencoders (VAEs) have been shown to be highly effective in both standard collaborative filtering applications and extensions such as incorporation o…
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…
Initialization and Coordinate Optimization for Multi-way Matching
Da Tang, Tony Jebara
We consider the problem of consistently matching multiple sets of elements to each other, which is a common task in fields such as computer vision. To solve the underlying NP-hard…
On Learning from Label Proportions
Felix X. Yu, Krzysztof Choromanski, Sanjiv Kumar +2
Learning from Label Proportions (LLP) is a learning setting, where the training data is provided in groups, or "bags", and only the proportion of each class in each bag is known. T…
Semistochastic Quadratic Bound Methods
Aleksandr Y. Aravkin, Anna Choromanska, Tony Jebara +1
Partition functions arise in a variety of settings, including conditional random fields, logistic regression, and latent gaussian models. In this paper, we consider semistochastic…