20 citations · 23 across the 11 of their papers we have counts for
11 papers
Heuristics for Combinatorial Optimization via Value-based Reinforcement Learning: A Unified Framework and Analysis
Orit Davidovich, Shimrit Shtern, Segev Wasserkrug +1
Since the 1990s, considerable empirical work has been carried out to train statistical models, such as neural networks (NNs), as learned heuristics for combinatorial optimization (…
Finding Probably Approximate Optimal Solutions by Training to Estimate the Optimal Values of Subproblems
Nimrod Megiddo, Segev Wasserkrug, Orit Davidovich +1
The paper is about developing a solver for maximizing a real-valued function of binary variables. The solver relies on an algorithm that estimates the optimal objective-function va…
An Agent-Based Framework for the Automatic Validation of Mathematical Optimization Models
Alexander Zadorojniy, Segev Wasserkrug, Eitan Farchi
Recently, using Large Language Models (LLMs) to generate optimization models from natural language descriptions has became increasingly popular. However, a major open question is h…
Making a Case for Research Collaboration Between Artificial Intelligence and Operations Research Experts
Radhika Kulkarni, Gianluca Brero, Yu Ding +7
In 2021, INFORMS, ACM SIGAI, and the Computing Community Consortium (CCC) hosted three workshops to explore synergies between Artificial Intelligence (AI) and Operations Research (…
Achieving PAC Guarantees in Mechanism Design through Multi-Armed Bandits
Takayuki Osogami, Hirota Kinoshita, Segev Wasserkrug
We analytically derive a class of optimal solutions to a linear program (LP) for automated mechanism design that satisfies efficiency, incentive compatibility, strong budget balanc…
From Large Language Models and Optimization to Decision Optimization CoPilot: A Research Manifesto
Segev Wasserkrug, Leonard Boussioux, Dick den Hertog +4
Significantly simplifying the creation of optimization models for real-world business problems has long been a major goal in applying mathematical optimization more widely to impor…
Who Benefits from a Multi-Cloud Market? A Trading Networks Based Analysis
Segev Wasserkrug, Takayuki Osogami
In enterprise cloud computing, there is a big and increasing investment to move to multi-cloud computing, which allows enterprises to seamlessly utilize IT resources from multiple…
Mechanism Learning for Trading Networks
Takayuki Osogami, Segev Wasserkrug, Elisheva S. Shamash
We study the problem of designing mechanisms for trading networks that satisfy four desired properties: dominant-strategy incentive compatibility, efficiency, weak budget balance (…
Online Learning in Supply-Chain Games
Nicolò Cesa-Bianchi, Tommaso Cesari, Takayuki Osogami +2
We study a repeated game between a supplier and a retailer who want to maximize their respective profits without full knowledge of the problem parameters. After characterizing the…
A Game Theoretic Model for Strategic Coopetition in Business Networks
Segev Wasserkrug, Eitan Farchi
Private blockchain is driving the creation of business networks, resulting in the creation of new value or new business models to the enterprises participating in the network. Such…
A Model for Reasoning with Uncertain Rules in Event Composition Systems
Segev Wasserkrug, Avigdor Gal, Opher Etzion
In recent years, there has been an increased need for the use of active systems - systems required to act automatically based on events, or changes in the environment. Such systems…