99 citations · 600 across the 38 of their papers we have counts for
40 papers
Powell-Style Model-Based Derivative-Free Optimization with Complexity Guarantees
Abraar Chaudhry, Katya Scheinberg, Scholar Sun
We propose variants of model-based trust region derivative-free algorithms that are closest to methods initially proposed and implemented by Powell. These methods rely on low degre…
Stochastic ISTA/FISTA Adaptive Step Search Algorithms for Convex Composite Optimization
Lam M. Nguyen, Katya Scheinberg, Trang H. Tran
We develop and analyze stochastic variants of ISTA and a full backtracking FISTA algorithms [Beck and Teboulle, 2009, Scheinberg et al., 2014] for composite optimization without th…
First- and Second-Order Stochastic Adaptive Regularization with Cubics: High Probability Iteration and Sample Complexity
Katya Scheinberg, Miaolan Xie
We present high-probability (and expectation) complexity bounds for two versions of stochastic adaptive regularization methods with cubics (SARC), also known as regularized Newton…
Sample Complexity Analysis for Adaptive Optimization Algorithms with Stochastic Oracles
Billy Jin, Katya Scheinberg, Miaolan Xie
Several classical adaptive optimization algorithms, such as line search and trust region methods, have been recently extended to stochastic settings where function values, gradient…
Finding Optimal Policy for Queueing Models: New Parameterization
Trang H. Tran, Lam M. Nguyen, Katya Scheinberg
Queueing systems appear in many important real-life applications including communication networks, transportation and manufacturing systems. Reinforcement learning (RL) framework i…
First- and Second-Order High Probability Complexity Bounds for Trust-Region Methods with Noisy Oracles
Liyuan Cao, Albert S. Berahas, Katya Scheinberg
In this paper, we present convergence guarantees for a modified trust-region method designed for minimizing objective functions whose value and gradient and Hessian estimates are c…