306 citations · 2.2k across the 100 of their papers we have counts for
9 papers · 1 filter
Breaking the Curse of Horizon: Infinite-Horizon Off-Policy Estimation
Qiang Liu, Lihong Li, Ziyang Tang +1
We consider the off-policy estimation problem of estimating the expected reward of a target policy using samples collected by a different behavior policy. Importance sampling (IS)…
Stein Variational Gradient Descent as Moment Matching
Qiang Liu, Dilin Wang
Stein variational gradient descent (SVGD) is a non-parametric inference algorithm that evolves a set of particles to fit a given distribution of interest. We analyze the non-asympt…
Variational Inference with Tail-adaptive f-Divergence
Dilin Wang, Hao Liu, Qiang Liu
Variational inference with α-divergences has been widely used in modern probabilistic machine learning. Compared to Kullback-Leibler (KL) divergence, a major advantage of using α-d…
Regularization Matters: Generalization and Optimization of Neural Nets v.s. their Induced Kernel
Colin Wei, Jason D. Lee, Qiang Liu +1
Recent works have shown that on sufficiently over-parametrized neural nets, gradient descent with relatively large initialization optimizes a prediction function in the RKHS of the…
Off-Policy Evaluation and Learning from Logged Bandit Feedback: Error Reduction via Surrogate Policy
Yuan Xie, Boyi Liu, Qiang Liu +3
When learning from a batch of logged bandit feedback, the discrepancy between the policy to be learned and the off-policy training data imposes statistical and computational challe…
Stein Variational Gradient Descent Without Gradient
Jun Han, Qiang Liu
Stein variational gradient decent (SVGD) has been shown to be a powerful approximate inference algorithm for complex distributions. However, the standard SVGD requires calculating…