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Found 8 papers, 0 papers with code
Learning to Price Supply Chain Contracts against a Learning Retailer
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.
Preference Robust Optimization with Quasi-Concave Choice Functions for Multi-Attribute Prospects
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.
Convergence of Recursive Stochastic Algorithms using Wasserstein Divergence
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.
A Unifying Framework for Variance Reduction Algorithms for Finding Zeroes of Monotone Operators
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.
Preference Elicitation and Robust Optimization with Multi-Attribute Quasi-Concave Choice Functions
Decision maker's preferences are often captured by some choice functions which are used to rank prospects.
Stochastic Approximation for Risk-aware Markov Decision Processes
The inner loop computes the risk by solving a stochastic saddle-point problem.
A Unified Framework for Stochastic Matrix Factorization via Variance Reduction
We propose a unified framework to speed up the existing stochastic matrix factorization (SMF) algorithms via variance reduction.
Stochastic L-BFGS: Improved Convergence Rates and Practical Acceleration Strategies
We revisit the stochastic limited-memory BFGS (L-BFGS) algorithm.
