7 citations · 16 across the 14 of their papers we have counts for
5 papers · 1 filter
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 Efficient Interior-Point Method for Online Convex Optimization
Elad Hazan, Nimrod Megiddo
A new algorithm for regret minimization in online convex optimization is described. The regret of the algorithm after time periods is - which is the minimu…
Bayesian Experimental Design for Symbolic Discovery
Kenneth L. Clarkson, Cristina Cornelio, Sanjeeb Dash +3
This study concerns the formulation and application of Bayesian optimal experimental design to symbolic discovery, which is the inference from observational data of predictive mode…
Symbolic Regression using Mixed-Integer Nonlinear Optimization
Vernon Austel, Cristina Cornelio, Sanjeeb Dash +4
The Symbolic Regression (SR) problem, where the goal is to find a regression function that does not have a pre-specified form but is any function that can be composed of a list of…
Strategic Classification
Moritz Hardt, Nimrod Megiddo, Christos Papadimitriou +1
Machine learning relies on the assumption that unseen test instances of a classification problem follow the same distribution as observed training data. However, this principle can…