no code implementations • 2 Nov 2022 • Xuejun Zhao, Ruihao Zhu, William B. Haskell
The goal for the supplier is to develop data-driven pricing policies with sublinear regret bounds under a wide range of possible retailer inventory policies for a fixed time horizon.
no code implementations • 31 Aug 2020 • Jian Wu, William B. Haskell, Wenjie Huang, Huifu Xu
Preference robust choice models concern decision-making problems where the decision maker's (DM) utility/risk preferences are ambiguous and the evaluation is based on the worst-case utility function/risk measure from a set of plausible utility functions/risk measures.
no code implementations • 25 Mar 2020 • Abhishek Gupta, William B. Haskell
We show that if the distribution of the iterates in the Markov chain satisfy a contraction property with respect to the Wasserstein divergence, then the Markov chain admits an invariant distribution.
no code implementations • 22 Jun 2019 • Xun Zhang, William B. Haskell, Zhisheng Ye
This framework includes a number of existing deterministic and variance-reduced algorithms for function minimization as special cases, and it is also applicable to more general problems such as saddle-point problems and variational inequalities.
no code implementations • 17 May 2018 • William B. Haskell, Wenjie Huang, Huifu Xu
Decision maker's preferences are often captured by some choice functions which are used to rank prospects.
no code implementations • 11 May 2018 • Wenjie Huang, William B. Haskell
The inner loop computes the risk by solving a stochastic saddle-point problem.
no code implementations • 19 May 2017 • Renbo Zhao, William B. Haskell, Jiashi Feng
We propose a unified framework to speed up the existing stochastic matrix factorization (SMF) algorithms via variance reduction.
no code implementations • 1 Apr 2017 • Renbo Zhao, William B. Haskell, Vincent Y. F. Tan
We revisit the stochastic limited-memory BFGS (L-BFGS) algorithm.