Gradient estimation is often necessary for fitting generative models wit...
This paper proposes probabilistic conformal prediction (PCP), a predicti...
This paper presents a new optimization approach to causal estimation. Gi...
In high-dimensional statistics, variable selection is an optimization pr...
In high-dimensional statistics, variable selection is an optimization pr...
Semantic hashing has become a crucial component of fast similarity searc...
Reinforcement learning (RL) in discrete action space is ubiquitous in
re...
The ability to learn new concepts with small amounts of data is a critic...
Mean-field variational inference (MFVI) has been widely applied in large...
To combine explicit and implicit generative models, we introduce
semi-im...
To address the challenge of backpropagating the gradient through categor...
Due to the high variance of policy gradients, on-policy optimization
alg...
To backpropagate the gradients through discrete stochastic layers, we en...
Semi-implicit variational inference (SIVI) is introduced to expand the
c...