Publications

Why Exposure Bias Matters: An Imitation Learning Perspective of Error Accumulation in Language Generation

K. Arora, L. El Asri, H. Bahuleyan, and J. Chi Kit Cheung. Association for Computational Linguistics (ACL), 2022

Generative Models; Imitation Learning

Variational Selective Autoencoder: Learning from Partially-Observed Heterogeneous Data

Y. Gong, H. Hajimirsadeghi, J. He, T. Durand, and G. Mori. International Conference on Artificial Intelligence and Statistics (AISTATS), 2021

Generative Models

Polarized-VAE: Proximity Based Disentangled Representation Learning for Text Generation

V. Balasubramanian, I. Kobyzev, H. Bahuleyan, and O. Vechtomova. Conference of the European Chapter of the Association for Computational Linguistics (EACL), 2021

Generative Models

Wavelet Flow: Fast Training of High Resolution Normalizing Flows

J. Yu, K. Derpanis, and M. Brubaker. Conference on Neural Information Processing Systems (NeurIPS), 2020

Computer Vision; Generative Models

Evaluating Lossy Compression Rates of Deep Generative Models

*S. Huang, *A. Makhzani, Y. Cao, and R. Grosse. International Conference on Machine Learning (ICML), 2020

Generative Models; Learning And Generalization

Normalizing Flows: An Introduction and Review of Current Methods

I. Kobyzev, S. Prince, and M. Brubaker. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2020

Generative Models; Unsupervised Learning

On Minimax Optimality of GANs for Robust Mean Estimation

G. W. Ding, R. Huang, Y. Yu, and K. Wu. International Conference on Artificial Intelligence and Statistics (AISTATS), 2020

Generative Models; Unsupervised Learning

Variational Selective Autoencoder

*Y. Gong, *H. Hajimirsadeghi, *J. He, *M. Nawhal, T. Durand, and *G. Mori. Advances in Approximate Bayesian Inference (AABI), 2019

Generative Models; Unsupervised Learning

Arbitrarily-conditioned Data Imputation

M. Carvalho, T. Durand, J. He, N. Mehrasa, and G. Mori. Advances in Approximate Bayesian Inference (AABI), 2019

Generative Models; Unsupervised Learning

Noise Flow: Noise Modeling with Conditional Normalizing Flows

A. Abdelhamed, M. Brubaker, and M. S. Brown. International Conference on Computer Vision (ICCV), 2019

Computer Vision; Generative Models

Lifelong GAN: Continual Learning for Conditional Image Generation

*M. Zhai, *L. Chen, F. Tung, J. He, M. Nawhal, and G. Mori. International Conference on Computer Vision (ICCV), 2019

Generative Models; Learning And Generalization

LayoutVAE: Stochastic Scene Layout Generation from a Label Set

A. Jyothi, T. Durand, J. He, L. Sigal, and G. Mori. International Conference on Computer Vision (ICCV), 2019

Computer Vision; Generative Models