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06/13/2023
Vector-Quantized Graph Auto-Encoder
In this work, we addresses the problem of modeling distributions of grap...
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12/01/2022
GrannGAN: Graph annotation generative adversarial networks
We consider the problem of modelling high-dimensional distributions and ...
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12/07/2021
Permutation Equivariant Generative Adversarial Networks for Graphs
One of the most discussed issues in graph generative modeling is the ord...
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04/07/2021
Learned transform compression with optimized entropy encoding
We consider the problem of learned transform compression where we learn ...
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11/25/2019
Improving VAE generations of multimodal data through data-dependent conditional priors
One of the major shortcomings of variational autoencoders is the inabili...
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03/26/2019
Sparse Learning for Variable Selection with Structures and Nonlinearities
In this thesis we discuss machine learning methods performing automated ...
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10/24/2018
Continual Classification Learning Using Generative Models
Continual learning is the ability to sequentially learn over time by acc...
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05/16/2018
Structured nonlinear variable selection
We investigate structured sparsity methods for variable selection in reg...
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04/19/2018
Large-scale Nonlinear Variable Selection via Kernel Random Features
We propose a new method for input variable selection in nonlinear regres...
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10/02/2017
Learning Predictive Leading Indicators for Forecasting Time Series Systems with Unknown Clusters of Forecast Tasks
We present a new method for forecasting systems of multiple interrelated...
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06/27/2017
Forecasting and Granger Modelling with Non-linear Dynamical Dependencies
Traditional linear methods for forecasting multivariate time series are ...
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05/27/2017
Lifelong Generative Modeling
Lifelong learning is the problem of learning multiple consecutive tasks ...
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07/07/2015
