90 citations · 332 across the 63 of their papers we have counts for
4 papers · 2 filters
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…
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…