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Omnichannel retail has emerged as the new standard in today's commerce landscape, with retailers integrating their physical and online channels to enhance the customer shopping experience. However, such integration presents significant challenges for retai ...

Within the context of contemporary machine learning problems, efficiency of optimization process depends on the properties of the model and the nature of the data available, which poses a significant problem as the complexity of either increases ad infinit ...

We show that the satisfiability problem for the quantifier-free theory of product structures with the equicardinality relation is inNP. As an application, we extend the combinatory array logic fragmentto handle cardinality constraints. The resulting fragme ...

We consider fundamental algorithmic number theoretic problems and their relation to a class of block structured Integer Linear Programs (ILPs) called 2-stage stochastic. A 2-stage stochastic ILP is an integer program of the form min{c(T)x vertical bar Ax = ...

Maximal subgraph mining is increasingly important in various domains, including bioinformatics, genomics, and chemistry, as it helps identify common characteristics among a set of graphs and enables their classification into different categories. Existing ...

Pearl's do calculus is a complete axiomatic approach to learn the identifiable causal effects from observational data. When such an effect is not identifiable, it is necessary to perform a collection of often costly interventions in the system to learn the ...

Dataflow programming is a methodology that enables the development of high-level, parametric programs that are independent of the underlying platform. This approach is particularly useful for heterogeneous platforms, as it eliminates the need to rewrite ap ...

We study the computational complexity of the optimal transport problem that evaluates the Wasser- stein distance between the distributions of two K-dimensional discrete random vectors. The best known algorithms for this problem run in polynomial time in th ...

In the current era of big data, aggregation queries on high-dimensional datasets are frequently utilized to uncover hidden patterns, trends, and correlations critical for effective business decision-making. Data cubes facilitate such queries by employing p ...

Future low-carbon societies will need to store vast amounts of electricity to stabilize electricity grids and to power electric vehicles. Vehicle-to-grid allows vehicle owners and grid operators to share the costs of electricity storage by making the batte ...

Linear Arithmetic with Stars

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Quantitative Methods for Omnichannel Decision-Making

Universal and adaptive methods for robust stochastic optimization

NP Satisfiability for Arrays as Powers

The double exponential runtime is tight for 2-stage stochastic ILPs

Scalable maximal subgraph mining with backbone-preserving graph convolutions

Minimum Cost Intervention Design for Causal Effect Identification

Design Space Exploration for Partitioning Dataflow Program on CPU-GPU Heterogeneous System

Discrete Optimal Transport with Independent Marginals is #P-Hard

Interactive-time Exploration, Querying, and Analysis of Large High-dimensional Datasets

Vehicle-to-Grid for Reliable Frequency Regulation