Stein's method compares probability distributions through the study of a class of linear
operators called Stein operators. While mainly studied in probability and used to underpin …

We propose a general purpose variational inference algorithm that forms a natural
counterpart of gradient descent for optimization. Our method iteratively transports a set of …

We derive a new discrepancy statistic for measuring differences between two probability
distributions based on combining Stein's identity and the reproducing kernel Hilbert space …

Improving the generalization of deep networks is an important open challenge, particularly
in domains without plentiful data. The mixup algorithm improves generalization by linearly …

This paper demonstrates how to recover causal graphs from the score of the data
distribution in non-linear additive (Gaussian) noise models. Using score matching …

The empirical success of reinforcement learning (RL) in contact-rich manipulation leaves
much to be understood from a model-based perspective, where the key difficulties are often …

Abstract Approximate Markov chain Monte Carlo (MCMC) offers the promise of more rapid
sampling at the cost of more biased inference. Since standard MCMC diagnostics fail to …

To improve the efficiency of Monte Carlo estimation, practitioners are turning to biased
Markov chain Monte Carlo procedures that trade off asymptotic exactness for computational …

In the stochastic contextual low-rank matrix bandit problem, the expected reward of an action
is given by the inner product between the action's feature matrix and some fixed, but initially …

Achieving optimal statistical performance while ensuring the privacy of personal data is a
challenging yet crucial objective in modern data analysis. However, characterizing the …