
Active Multitask Learning with Committees
The cost of annotating training data has traditionally been a bottleneck...
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Learning Correlated Latent Representations with Adaptive Priors
Variational AutoEncoders (VAEs) have been widely applied for learning c...
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A New Distribution on the Simplex with AutoEncoding Applications
We construct a new distribution for the simplex using the Kumaraswamy di...
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Correlated Variational AutoEncoders
Variational AutoEncoders (VAEs) are capable of learning latent represen...
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Beta Survival Models
This article analyzes the problem of estimating the time until an event ...
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Thompson Sampling for Noncompliant Bandits
Thompson sampling, a Bayesian method for balancing exploration and explo...
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Item Recommendation with Variational Autoencoders and Heterogenous Priors
In recent years, Variational Autoencoders (VAEs) have been shown to be h...
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Subgoal Discovery for Hierarchical Dialogue Policy Learning
Developing conversational agents to engage in complex dialogues is chall...
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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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Variational Autoencoders for Collaborative Filtering
We extend variational autoencoders (VAEs) to collaborative filtering for...
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Initialization and Coordinate Optimization for Multiway Matching
We consider the problem of consistently matching multiple sets of elemen...
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FrankWolfe Algorithms for Saddle Point Problems
We extend the FrankWolfe (FW) optimization algorithm to solve constrain...
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Binary embeddings with structured hashed projections
We consider the hashing mechanism for constructing binary embeddings, th...
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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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On Learning from Label Proportions
Learning from Label Proportions (LLP) is a learning setting, where the t...
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Semistochastic Quadratic Bound Methods
Partition functions arise in a variety of settings, including conditiona...
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∝SVM for learning with label proportions
We study the problem of learning with label proportions in which the tra...
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Bethe Bounds and Approximating the Global Optimum
Inference in general Markov random fields (MRFs) is NPhard, though iden...
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Tony Jebara
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Associate Professor of Computer Science at Columbia University, Director at Netflix