90 citations · 332 across the 63 of their papers we have counts for
5 papers · 1 filter
Theoretical limitations of Encoder-Decoder GAN architectures
Sanjeev Arora, Andrej Risteski, Yi Zhang
Encoder-decoder GANs architectures (e.g., BiGAN and ALI) seek to add an inference mechanism to the GANs setup, consisting of a small encoder deep net that maps data-points to their…
Beyond Log-concavity: Provable Guarantees for Sampling Multi-modal Distributions using Simulated Tempering Langevin Monte Carlo
Rong Ge, Holden Lee, Andrej Risteski
A key task in Bayesian statistics is sampling from distributions that are only specified up to a partition function (i.e., constant of proportionality). However, without any assump…
Provable benefits of representation learning
Sanjeev Arora, Andrej Risteski
There is general consensus that learning representations is useful for a variety of reasons, e.g. efficient use of labeled data (semi-supervised learning), transfer learning and un…
Extending and Improving Wordnet via Unsupervised Word Embeddings
Mikhail Khodak, Andrej Risteski, Christiane Fellbaum +1
This work presents an unsupervised approach for improving WordNet that builds upon recent advances in document and sense representation via distributional semantics. We apply our m…
On the ability of neural nets to express distributions
Holden Lee, Rong Ge, Tengyu Ma +2
Deep neural nets have caused a revolution in many classification tasks. A related ongoing revolution -- also theoretically not understood -- concerns their ability to serve as gene…