7 citations · 16 across the 14 of their papers we have counts for
14 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…
Spline Quantile Regression
Ta-Hsin Li, Nimrod Megiddo
Quantile regression is a powerful tool capable of offering a richer view of the data as compared to least-squares regression. Quantile regression is typically performed individuall…
On the Complexity of Combinatorial Optimization on Fixed Structures
Nimrod Megiddo
Combinatorial optimization can be described as the problem of finding a feasible subset that maximizes a objective function. The paper discusses combinatorial optimization problems…
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…
On "Indifference" and Backward Induction in Games with Perfect Information
Nimrod Megiddo
Indifference of a player with respect to two distinct outcomes of a game cannot be handled by small perturbations, because the actual choice may have significant impact on other pl…
On the Use of Generative Models in Observational Causal Analysis
Nimrod Megiddo
The use of a hypothetical generative model was been suggested for causal analysis of observational data. The very assumption of a particular model is a commitment to a certain set…
Remarks on Utility in Repeated Bets
Nimrod Megiddo
The use of von Neumann -- Morgenstern utility is examined in the context of multiple choices between lotteries. Different conclusions are reached if the choices are simultaneous or…
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…
AI Descartes: Combining Data and Theory for Derivable Scientific Discovery
Cristina Cornelio, Sanjeeb Dash, Vernon Austel +6
Scientists have long aimed to discover meaningful formulae which accurately describe experimental data. A common approach is to manually create mathematical models of natural pheno…
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…
Recommending Targeted Strangers from Whom to Solicit Information on Social Media
Jalal Mahmud, Michelle X. Zhou, Nimrod Megiddo +2
We present an intelligent, crowd-powered information collection system that automatically identifies and asks target-ed strangers on Twitter for desired information (e.g., cur-rent…
Optimizing The Selection of Strangers To Answer Questions in Social Media
Jalal Mahmud, Michelle Zhou, Nimrod Megiddo +2
Millions of people express themselves on public social media, such as Twitter. Through their posts, these people may reveal themselves as potentially valuable sources of informatio…