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Found 8 papers, 2 papers with code
An Efficient Interior-Point Method for Online Convex Optimization
A new algorithm for regret minimization in online convex optimization is described.
On "Indifference" and Backward Induction in Games with Perfect Information
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 players, and cause them to act in a way that has significant impact of the indifferent player.
On the Use of Generative Models in Observational Causal Analysis
Estimating the joint probability distribution of can be useful for predicting values of variables in view of the observed values of others, but it is not sufficient for inferring causal relationships.
Remarks on Utility in Repeated Bets
The use of von Neumann -- Morgenstern utility is examined in the context of multiple choices between lotteries.
Bayesian Experimental Design for Symbolic Discovery
This study concerns the formulation and application of Bayesian optimal experimental design to symbolic discovery, which is the inference from observational data of predictive models taking general functional forms.
AI Descartes: Combining Data and Theory for Derivable Scientific Discovery
We develop a method to enable principled derivations of models of natural phenomena from axiomatic knowledge and experimental data by combining logical reasoning with symbolic regression.
Symbolic Regression using Mixed-Integer Nonlinear Optimization
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 operators, is a hard problem in machine learning, both theoretically and computationally.
Strategic Classification
Jury designs a classifier, and Contestant receives an input to the classifier, which he may change at some cost.
