Search Results for author:
Found 7 papers, 0 papers with code
Tightly Robust Optimization via Empirical Domain Reduction
Data-driven decision-making is performed by solving a parameterized optimization problem, and the optimal decision is given by an optimal solution for unknown true parameters.
Causality and Robust Optimization
A standard modification would thus utilize causal discovery algorithms for preventing cofounding bias in feature selection.
Empirical Hypothesis Space Reduction
Selecting appropriate regularization coefficients is critical to performance with respect to regularized empirical risk minimization problems.
Regret Bounds for Online Portfolio Selection with a Cardinality Constraint
Online portfolio selection is a sequential decision-making problem in which a learner repetitively selects a portfolio over a set of assets, aiming to maximize long-term return.
Unbiased Objective Estimation in Predictive Optimization
Predictive optimization, however, suffers from the problem of a calculated optimal solution’s being evaluated too optimistically, i. e., the value of the objective function is overestimated.
Causal Bandits with Propagating Inference
In this setting, the arms are identified with interventions on a given causal graph, and the effect of an intervention propagates throughout all over the causal graph.
Efficient Sublinear-Regret Algorithms for Online Sparse Linear Regression with Limited Observation
Under these assumptions, we present polynomial-time sublinear-regret algorithms for the online sparse linear regression.
