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Found 17 papers, 4 papers with code
Global Solver and Its Efficient Approximation for Variational Bayesian Low-rank Subspace Clustering
However, Bayesian learning is often obstructed by computational difficulty: the rigorous Bayesian learning is intractable in many models, and its variational Bayesian (VB) approximation is prone to suffer from local minima.
Breakdown Point of Robust Support Vector Machine
For learning parameters such as the regularization parameter in our algorithm, we derive a simple formula that guarantees the robustness of the classifier.
Trimmed Density Ratio Estimation
Density ratio estimation is a vital tool in both machine learning and statistical community.
Boosting the kernelized shapelets: Theory and algorithms for local features
We consider binary classification problems using local features of objects.
A successive difference-of-convex approximation method for a class of nonconvex nonsmooth optimization problems
We consider a class of nonconvex nonsmooth optimization problems whose objective is the sum of a smooth function and a finite number of nonnegative proper closed possibly nonsmooth functions (whose proximal mappings are easy to compute), some of which are further composed with linear maps.
Optimistic Robust Optimization With Applications To Machine Learning
In this paper, we explore an optimistic, or best-case view of uncertainty and show that it can be a fruitful approach.
Position-based Multiple-play Bandit Problem with Unknown Position Bias
Motivated by online advertising, we study a multiple-play multi-armed bandit problem with position bias that involves several slots and the latter slots yield fewer rewards.
A refined convergence analysis of pDCA$_e$ with applications to simultaneous sparse recovery and outlier detection
Moreover, for a large class of loss functions and regularizers, the KL exponent of the corresponding potential function are shown to be 1/2, which implies that the pDCA$_e$ is locally linearly convergent when applied to these problems.
Robust Bayesian Model Selection for Variable Clustering with the Gaussian Graphical Model
Even more severe, small insignificant partial correlations due to noise can dramatically change the clustering result when evaluating for example with the Bayesian Information Criteria (BIC).
Multiple-Instance Learning by Boosting Infinitely Many Shapelet-based Classifiers
Classifiers based on a single shapelet are not sufficiently strong for certain applications.
Convex Fairness Constrained Model Using Causal Effect Estimators
However, MD quantifies not only discrimination but also explanatory bias which is the difference of outcomes justified by explanatory features.
Theory and Algorithms for Shapelet-based Multiple-Instance Learning
We propose a new formulation of Multiple-Instance Learning (MIL), in which a unit of data consists of a set of instances called a bag.
BODAME: Bilevel Optimization for Defense Against Model Extraction
Model extraction attacks have become serious issues for service providers using machine learning.
A Gradient Method for Multilevel Optimization
Although application examples of multilevel optimization have already been discussed since the 1990s, the development of solution methods was almost limited to bilevel cases due to the difficulty of the problem.
Convergence Error Analysis of Reflected Gradient Langevin Dynamics for Globally Optimizing Non-Convex Constrained Problems
Gradient Langevin dynamics and a variety of its variants have attracted increasing attention owing to their convergence towards the global optimal solution, initially in the unconstrained convex framework while recently even in convex constrained non-convex problems.
A Framework for Bilevel Optimization on Riemannian Manifolds
We provide convergence and complexity analysis for the proposed hypergradient descent algorithm on manifolds.
