This paper offers a nearly optimal algorithm for online linear optimization with delayed
bandit feedback. Online linear optimization with bandit feedback, or nonstochastic linear …

We propose novel algorithms with first-and second-order regret bounds for adversarial
linear bandits. These regret bounds imply that our algorithms perform well when there is an …

In this paper, we consider online decision problems with submodular loss functions. For
such problems, existing studies have only dealt with worst-case analysis. This study goes …

In this paper, we propose algorithms for online submodular optimization with tracking regret
bounds. Online submodular optimization is a generic framework for sequential decision …

The problem of monotone submodular maximization has been studied extensively due to its
wide range of applications. However, there are cases where one can only access the …

We study an extension of the stochastic submodular minimization problem, namely, the
stochastic $ L^\natural $-convex minimization problem. We develop the first polynomial-time …

Querying complex models for precise information (eg traffic models, database systems, large
ML models) often entails intense computations and results in long response times. Thus …

We propose new sequential simulation–optimization algorithms for general convex
optimization via simulation problems with high-dimensional discrete decision space. The …

We develop and analyze a set of new sequential simulation-optimization algorithms for large-
scale multi-dimensional discrete optimization via simulation problems with a convexity …

Many problems in the machine learning pipeline boil down to maximizing the expectation of
a function over a distribution. This is the classic problem of stochastic optimization. There are …