The key assumption underlying linear Markov Decision Processes (MDPs) is...
Score matching is an alternative to maximum likelihood (ML) for estimati...
Sparse linear regression is a central problem in high-dimensional statis...
Much of reinforcement learning theory is built on top of oracles that ar...
Measuring the stability of conclusions derived from Ordinary Least Squar...
Sparse linear regression with ill-conditioned Gaussian random designs is...
Partially Observable Markov Decision Processes (POMDPs) are a natural an...
For many inference problems in statistics and econometrics, the unknown
...
Sparse linear regression is a fundamental problem in high-dimensional
st...
As in standard linear regression, in truncated linear regression, we are...
Generative neural networks have been empirically found very promising in...
In the model of online caching with machine learned advice, introduced b...
The Earth Mover Distance (EMD) between two sets of points A, B ⊆R^d with...
We extend the multi-pass streaming model to sliding window problems, and...