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05/09/2022
Selectively Contextual Bandits
Contextual bandits are widely used in industrial personalization systems...
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03/24/2021
Active Multitask Learning with Committees
The cost of annotating training data has traditionally been a bottleneck...
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06/14/2019
Learning Correlated Latent Representations with Adaptive Priors
Variational Auto-Encoders (VAEs) have been widely applied for learning c...
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05/28/2019
A New Distribution on the Simplex with Auto-Encoding Applications
We construct a new distribution for the simplex using the Kumaraswamy di...
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05/14/2019
Correlated Variational Auto-Encoders
Variational Auto-Encoders (VAEs) are capable of learning latent represen...
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05/09/2019
Beta Survival Models
This article analyzes the problem of estimating the time until an event ...
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12/03/2018
Thompson Sampling for Noncompliant Bandits
Thompson sampling, a Bayesian method for balancing exploration and explo...
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07/17/2018
Item Recommendation with Variational Autoencoders and Heterogenous Priors
In recent years, Variational Autoencoders (VAEs) have been shown to be h...
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04/20/2018
Subgoal Discovery for Hierarchical Dialogue Policy Learning
Developing conversational agents to engage in complex dialogues is chall...
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04/16/2018
A refinement of Bennett's inequality with applications to portfolio optimization
A refinement of Bennett's inequality is introduced which is strictly tig...
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02/16/2018
Variational Autoencoders for Collaborative Filtering
We extend variational autoencoders (VAEs) to collaborative filtering for...
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11/02/2016
Initialization and Coordinate Optimization for Multi-way Matching
We consider the problem of consistently matching multiple sets of elemen...
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10/25/2016
Frank-Wolfe Algorithms for Saddle Point Problems
We extend the Frank-Wolfe (FW) optimization algorithm to solve constrain...
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11/16/2015
Binary embeddings with structured hashed projections
We consider the hashing mechanism for constructing binary embeddings, th...
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03/04/2015
Bethe Learning of Conditional Random Fields via MAP Decoding
Many machine learning tasks can be formulated in terms of predicting str...
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02/24/2014
On Learning from Label Proportions
Learning from Label Proportions (LLP) is a learning setting, where the t...
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09/05/2013
Semistochastic Quadratic Bound Methods
Partition functions arise in a variety of settings, including conditiona...
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06/04/2013
∝SVM for learning with label proportions
We study the problem of learning with label proportions in which the tra...
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12/31/2012
