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Found 12 papers, 0 papers with code
Safe screening rules for L0-regression
We give safe screening rules to eliminate variables from regression with L0 regularization or cardinality constraint.
Solution Path of Time-varying Markov Random Fields with Discrete Regularization
More specifically, we show that the entire solution path of the time-varying MRF for all sparsity levels can be obtained in $\mathcal{O}(pT^3)$, where $T$ is the number of time steps and $p$ is the number of unknown parameters at any given time.
Optimal Robust Classification Trees
In many high-stakes domains, the data used to drive machine learning algorithms is noisy (due to e. g., the sensitive nature of the data being collected, limited resources available to validate the data, etc).
Memory-Aware Partitioning of Machine Learning Applications for Optimal Energy Use in Batteryless Systems
By leveraging interkernel data dependencies, these energy-bounded execution cycles minimize the number of system activations and nonvolatile data transfers, and thus the total energy overhead.
Scalable Inference of Sparsely-changing Gaussian Markov Random Fields
Most of the existing methods for the inference of time-varying Markov random fields (MRFs) rely on the \textit{regularized maximum likelihood estimation} (MLE), that typically suffer from weak statistical guarantees and high computational time.
Scalable Inference of Sparsely-changing Markov Random Fields with Strong Statistical Guarantees
In this paper, we study the problem of inferring time-varying Markov random fields (MRF), where the underlying graphical model is both sparse and changes sparsely over time.
Supermodularity and valid inequalities for quadratic optimization with indicators
We show that the convex hull of the epigraph of the quadratic can be obtaining from inequalities for the underlying supermodular set function by lifting them into nonlinear inequalities in the original space of variables.
Ideal formulations for constrained convex optimization problems with indicator variables
Motivated by modern regression applications, in this paper, we study the convexification of a class of convex optimization problems with indicator variables and combinatorial constraints on the indicators.
Learning Optimal Classification Trees: Strong Max-Flow Formulations
To fill this gap in the literature, we propose a flow-based MIP formulation for optimal binary classification trees that has a stronger linear programming relaxation.
Rank-one Convexification for Sparse Regression
Sparse regression models are increasingly prevalent due to their ease of interpretability and superior out-of-sample performance.
Sparse and Smooth Signal Estimation: Convexification of L0 Formulations
Signal estimation problems with smoothness and sparsity priors can be naturally modeled as quadratic optimization with $\ell_0$-"norm" constraints.
Intrusion Prevention and Detection in Grid Computing - The ALICE Case
A remarkable example of a Grid in High Energy Physics (HEP) research is used in the ALICE experiment at European Organization for Nuclear Research CERN.
