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Found 8 papers, 1 papers with code
Online Structured Prediction with Fenchel--Young Losses and Improved Surrogate Regret for Online Multiclass Classification with Logistic Loss
We extend the exploit-the-surrogate-gap framework to online structured prediction with \emph{Fenchel--Young losses}, a large family of surrogate losses including the logistic loss for multiclass classification, obtaining finite surrogate regret bounds in various structured prediction problems.
Data-Driven Projection for Reducing Dimensionality of Linear Programs: Generalization Bound and Learning Methods
On the theoretical side, a natural question is: how much data is sufficient to ensure the quality of recovered solutions?
Rethinking Warm-Starts with Predictions: Learning Predictions Close to Sets of Optimal Solutions for Faster $\text{L}$-/$\text{L}^\natural$-Convex Function Minimization
The main technical difficulty lies in learning predictions that are provably close to sets of all optimal solutions, for which we present an online-gradient-descent-based method.
Improved Generalization Bound and Learning of Sparsity Patterns for Data-Driven Low-Rank Approximation
Specifically, for rank-$k$ approximation using an $m \times n$ learned sketching matrix with $s$ non-zeros in each column, they proved an $\tilde{\mathrm{O}}(nsm)$ bound on the \emph{fat shattering dimension} ($\tilde{\mathrm{O}}$ hides logarithmic factors).
Lazy and Fast Greedy MAP Inference for Determinantal Point Process
One classical and practical method is the lazy greedy algorithm, which is applicable to general submodular function maximization, while a recent fast greedy algorithm based on the Cholesky factorization is more efficient for DPP MAP inference.
Sample Complexity of Learning Heuristic Functions for Greedy-Best-First and A* Search
Motivated by this emerging approach, we study the sample complexity of learning heuristic functions for GBFS and A*.
Discrete-Convex-Analysis-Based Framework for Warm-Starting Algorithms with Predictions
Augmenting algorithms with learned predictions is a promising approach for going beyond worst-case bounds.
Multi-dimensional Graph Fourier Transform
The proposed methods are applicable to a wide variety of data that can be regarded as signals on Cartesian product graphs.
