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Found 15 papers, 2 papers with code
Beyond Discretization: Learning the Optimal Solution Path
We propose an alternative approach that parameterizes the solution path with a set of basis functions and solves a \emph{single} stochastic optimization problem to learn the entire solution path.
Binary Classification with Instance and Label Dependent Label Noise
Our findings suggest that learning solely with noisy samples is impossible without access to clean samples or strong assumptions on the distribution of the data.
Active Learning in the Predict-then-Optimize Framework: A Margin-Based Approach
We develop the first active learning method in the predict-then-optimize framework.
Online Contextual Decision-Making with a Smart Predict-then-Optimize Method
We propose an algorithm that mixes a prediction step based on the "Smart Predict-then-Optimize (SPO)" method with a dual update step based on mirror descent.
Integrated Conditional Estimation-Optimization
In contrast to the standard approach of first estimating the distribution of uncertain parameters and then optimizing the objective based on the estimation, we propose an integrated conditional estimation-optimization (ICEO) framework that estimates the underlying conditional distribution of the random parameter while considering the structure of the optimization problem.
Risk Bounds and Calibration for a Smart Predict-then-Optimize Method
We develop risk bounds and uniform calibration results for the SPO+ loss relative to the SPO loss, which provide a quantitative way to transfer the excess surrogate risk to excess true risk.
Joint Online Learning and Decision-making via Dual Mirror Descent
We consider an online revenue maximization problem over a finite time horizon subject to lower and upper bounds on cost.
Stochastic In-Face Frank-Wolfe Methods for Non-Convex Optimization and Sparse Neural Network Training
The Frank-Wolfe method and its extensions are well-suited for delivering solutions with desirable structural properties, such as sparsity or low-rank structure.
Generalization Bounds in the Predict-then-Optimize Framework
A natural loss function in this environment is to consider the cost of the decisions induced by the predicted parameters, in contrast to the prediction error of the parameters.
Condition Number Analysis of Logistic Regression, and its Implications for Standard First-Order Solution Methods
When the training data is non-separable, we show that the degree of non-separability naturally enters the analysis and informs the properties and convergence guarantees of two standard first-order methods: steepest descent (for any given norm) and stochastic gradient descent.
Smart "Predict, then Optimize"
Our SPO+ loss function can tractably handle any polyhedral, convex, or even mixed-integer optimization problem with a linear objective.
Profit Maximization for Online Advertising Demand-Side Platforms
We develop an optimization model and corresponding algorithm for the management of a demand-side platform (DSP), whereby the DSP aims to maximize its own profit while acquiring valuable impressions for its advertiser clients.
Optimization and Control Computer Science and Game Theory
An Extended Frank-Wolfe Method with "In-Face" Directions, and its Application to Low-Rank Matrix Completion
Motivated principally by the low-rank matrix completion problem, we present an extension of the Frank-Wolfe method that is designed to induce near-optimal solutions on low-dimensional faces of the feasible region.
A New Perspective on Boosting in Linear Regression via Subgradient Optimization and Relatives
Furthermore, we show that these new algorithms for the Lasso may also be interpreted as the same master algorithm (subgradient descent), applied to a regularized version of the maximum absolute correlation loss function.
AdaBoost and Forward Stagewise Regression are First-Order Convex Optimization Methods
Boosting methods are highly popular and effective supervised learning methods which combine weak learners into a single accurate model with good statistical performance.
