Papers

• Neural Distributed Source Coding     [code]
  Jay Whang, Anish Acharya, Hyeji Kim, Alexandros G. Dimakis.
  Preprint.

• Composing Normalizing Flows for Inverse Problems
  Jay Whang, Erik M. Lindgren, Alexandros G. Dimakis.
  The 38th International Conference on Machine Learning (ICML), 2021.
  Best Paper Award at UAI 2021 Workshop on Tractable Probabilistic Modeling
  (Short version accepted to NeurIPS 2020 Workshop on Deep Learning and Inverse Problems)

• Model-Based Deep Learning
  Nir Shlezinger, Jay Whang, Yonina Eldar, Alex Dimakis.
  Preprint.

• Solving Inverse Problems with a Flow-based Noise Model
  Jay Whang, Qi Lei, Alexandros G. Dimakis.
  The 38th International Conference on Machine Learning (ICML), 2021.
  (Short version accepted to NeurIPS 2020 Workshop on Deep Learning and Inverse Problems)

• Model-based Deep Learning: Key Approaches And Design Guidelines
  Nir Shlezinger, Jay Whang, Yonina Eldar, Alex Dimakis.
  Audience Choice Award at IEEE Data Science & Learning Workshop (DSLW 2021).

• Training Variational Autoencoders with Buffered Stochastic Variational Inference
  Rui Shu, Hung Bui, Jay Whang, Stefano Ermon.
  The 22nd International Conference on Artificial Intelligence and Statistics (AISTATS), 2019.

• Fast Exploration with Simplified Models and Approximately Optimistic Planning in Model Based Reinforcement Learning
  Ramtin Keramati*, Jay Whang*, Patrick Cho* and Emma Brunskill.
  Preprint.

• Strategic Exploration in Object-Oriented Reinforcement Learning
  Ramtin Keramati*, Jay Whang*, Patrick Cho* and Emma Brunskill.
  The 35th International Conference on Machine Learning (ICML) Workshop on Exploration in RL, 2018.

• Using Generative Models for Semi-Supervised Learning
  Daniel De Freitas Adiwardana*, Akihiro Matsukawa*, Jay Whang*.
  Technical Report.

• Exploring Batch Normalization in Recurrent Neural Networks
  Jay Whang*, Akihiro Matsukawa*.
  Technical Report.

(* equal contribution)