A stochastic probing problem consists of a set of elements whose values are independent
random variables. The algorithm knows the distributions of these variables, but not the …

We introduce and study a general scheduling problem that we term the Polytope Scheduling
problem (PSP). In this problem, jobs can have different arrival times and sizes, and the rates …

Decision trees are popular classification models, providing high accuracy and intuitive
explanations. However, as the tree size grows the model interpretability deteriorates …

In the stochastic submodular cover problem, the goal is to select a subset of stochastic items
of minimum expected cost to cover a submodular function. Solutions in this setting …

We present approximation algorithms for two problems: Stochastic Boolean Function
Evaluation (SBFE) and Stochastic Submodular Set Cover (SSSC). Our results for SBFE …

We study the generalized min sum set cover (GMSSC) problem, wherein given a collection
of hyperedges E with arbitrary covering requirements {ke∊ Z+: e∊ E}, the goal is to find an …

Stochastic optimization is a widely used approach for optimization under uncertainty, where
uncertain input parameters are modeled by random variables. Exact or approximation …

We consider a natural model of online preference aggregation, where sets of preferred items
R1, R2,..., Rt,..., along with a demand for kt items in each Rt, appear online. Without prior …

In the submodular cover problem, we are given a non-negative monotone submodular
function f over a ground set E of items, and the goal is to choose a smallest subset S⊆ E …

We present a new approximation algorithm for the stochastic submodular set cover (SSSC)
problem called adaptive dual greedy. We use this algorithm to obtain a 3-approximation …