5 papers
Sample Efficient Reinforcement Learning with REINFORCE
Junzi Zhang, Jongho Kim, Brendan O'Donoghue +1
Policy gradient methods are among the most effective methods for large-scale reinforcement learning, and their empirical success has prompted several works that develop the foundat…
Anderson Accelerated Douglas-Rachford Splitting
Anqi Fu, Junzi Zhang, Stephen Boyd
We consider the problem of non-smooth convex optimization with linear equality constraints, where the objective function is only accessible through its proximal operator. This prob…
Robust Super-Level Set Estimation using Gaussian Processes
Andrea Zanette, Junzi Zhang, Mykel J. Kochenderfer
This paper focuses on the problem of determining as large a region as possible where a function exceeds a given threshold with high probability. We assume that we only have access…
Consistency and Computation of Regularized MLEs for Multivariate Hawkes Processes
Xin Guo, Anran Hu, Renyuan Xu +1
This paper proves the consistency property for the regularized maximum likelihood estimators (MLEs) of multivariate Hawkes processes (MHPs). It also develops an alternating minimiz…
Globally Convergent Type-I Anderson Acceleration for Non-Smooth Fixed-Point Iterations
Junzi Zhang, Brendan O'Donoghue, Stephen Boyd
We consider the application of the type-I Anderson acceleration to solving general non-smooth fixed-point problems. By interleaving with safe-guarding steps, and employing a Powell…