
Fair Generative Modeling via Weak Supervision
Realworld datasets are often biased with respect to key demographic fac...
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Permutation Invariant Graph Generation via ScoreBased Generative Modeling
Learning generative models for graphstructured data is challenging beca...
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Learning Controllable Fair Representations
Learning data representations that are transferable and fair with respec...
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Uncertainty Autoencoders: Learning Compressed Representations via Variational Information Maximization
The goal of statistical compressive sensing is to efficiently acquire an...
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Stochastic Optimization of Sorting Networks via Continuous Relaxations
Sorting input objects is an important step in many machine learning pipe...
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Bias Correction of Learned Generative Models using LikelihoodFree Importance Weighting
A learned generative model often produces biased statistics relative to ...
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AlignFlow: Cycle Consistent Learning from Multiple Domains via Normalizing Flows
Given unpaired data from multiple domains, a key challenge is to efficie...
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Modeling Sparse Deviations for Compressed Sensing using Generative Models
In compressed sensing, a small number of linear measurements can be used...
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FlowGAN: Bridging implicit and prescribed learning in generative models
Evaluating the performance of generative models for unsupervised learnin...
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Boosted Generative Models
We propose a new approach for using unsupervised boosting to create an e...
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Contextual Symmetries in Probabilistic Graphical Models
An important approach for efficient inference in probabilistic graphical...
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node2vec: Scalable Feature Learning for Networks
Prediction tasks over nodes and edges in networks require careful effort...
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Graphite: Iterative Generative Modeling of Graphs
Graphs are a fundamental abstraction for modeling relational data. Howev...
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Best arm identification in multiarmed bandits with delayed feedback
We propose a generalization of the best arm identification problem in st...
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Variational Rejection Sampling
Learning latent variable models with stochastic variational inference is...
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Learning Policy Representations in Multiagent Systems
Modeling agent behavior is central to understanding the emergence of com...
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Streamlining Variational Inference for Constraint Satisfaction Problems
Several algorithms for solving constraint satisfaction problems are base...
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Aditya Grover
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