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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.

Pages

Posts

Future Blog Post

less than 1 minute read

Published:

This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.

Blog Post number 4

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 3

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 2

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 1

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

portfolio

publications

Learning near-optimal decisions: from SAA to robust optimization

Published in In Preparation, 2022

We propose a novel approach that directly recommends robust, near-optimal decisions based on data within the context of contextual stochastic optimization problems.

Recommended citation: Cristian, R., Perakis, G. (2022). Learning Robust Decisions for Contextual Stochastic Optimization Problems Directly from Data

End-to-end learning via constraint-enforcing approximators for linear programs with applications to supply chains

Published in AAAI-23 Main Track, 2022

We present a novel appoach in joint prediction and optimization by introducing a neural network architecture (ProjectNet) capable of approximately solving optimization problems.

Recommended citation: Cristian, R., Harsha, P., Perakis, G., Quanz, B. L., & Spantidakis, I. (2022). End-to-End Learning via Constraint-Enforcing Approximators for Linear Programs with Applications to Supply Chains.

talks

Online semigroup queries on arrays and paths on trees

Published:

We consider a common class of database range queries which consists of evaluating a given function on contiguous subranges of arrays. For instance, this may be the average or minimum of a range. Such queries are also common subroutines in various algorithms. A common approach to tackling the range query problem for semigroup operators is to precompute the answer for a small subset of ranges, and combine these solutions when answering any given query. For instance, the sum of the elements from index i to index j can be computed as the answers to the ranges from i to k and k+1 to j for i <= k < j. This introduces an inherent tradeoff between the precomputation complexity and the query complexity - the more precomputation, the less query time required. Moreover, we consider a data-driven case and design a method to make better precompuation decisions than traditional methods.

Learning near-optimal robust solutions in pricing and beyond

Published:

We develop a method for producing robust decisions in predict-optimize tasks. In particular, we introduce two notions of robustness: (1) decisions which minimize maximum cost with respect to noise/uncertainty in the objective, (2) producing stable decisions which do not change significantly under perturbation to the training data.

teaching