Publications

Autoencoding Variational Inference for Topic Models. Akash Srivastava and Charles Sutton. In International Conference on Learning Representations (ICLR). 2017.

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VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning. Akash Srivastava, Lazar Valkov, Chris Russell, Michael Gutmann and Charles Sutton. In Advances in Neural Information Processing Systems (NIPS). 2017.

[ .pdf | bib | abstract | code and data ]

Clustering with a Reject Option: Interactive Clustering as Bayesian Prior Elicitation. Akash Srivastava, James Zou, Ryan P. Adams and Charles Sutton. In Workshop on Human Interpretability in Machine Learning (co-located with ICML) and Interactive Data Exploration and Analytics Workshop,KDD (Oral). 2016.

[ .pdf | bib | abstract ]


Preprints

Variational Inference In Pachinko Allocation Machines. Akash Srivastava and Charles Sutton.

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Vadam: Fast and Scalable Variational Inference by Perturbing Adam. Mohammad Emtiyaz Khan, Zuozhu Liu, Voot Tangkaratt, Didrik Nielsen, Yarin Gal, Akash Srivastava.

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Synthesis of Differentiable Functional Programs for Lifelong Learning. Lazar Valkov, Dipak Chaudhari, Akash Srivastava, Swarat Chaudhuri and Charles Sutton.

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Thesis

Burst Detection Modulated Document Clustering: A Partially Feature-Pivoted Approach To First Story Detection. Akash Srivastava. MSc Thesis.

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