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Found 12 papers, 1 papers with code
Universal Majorization-Minimization Algorithms
Majorization-minimization (MM) is a family of optimization methods that iteratively reduce a loss by minimizing a locally-tight upper bound, called a majorizer.
Automatically Bounding the Taylor Remainder Series: Tighter Bounds and New Applications
We then recursively combine the bounds for the elementary functions using an interval arithmetic variant of Taylor-mode automatic differentiation.
Data-driven Science and Machine Learning Methods in Laser-Plasma Physics
Laser-plasma physics has developed rapidly over the past few decades as high-power lasers have become both increasingly powerful and more widely available.
Learning Effective Loss Functions Efficiently
We consider the problem of learning a loss function which, when minimized over a training dataset, yields a model that approximately minimizes a validation error metric.
Bayes Optimal Early Stopping Policies for Black-Box Optimization
We derive an optimal policy for adaptively restarting a randomized algorithm, based on observed features of the run-so-far, so as to minimize the expected time required for the algorithm to successfully terminate.
Learning Optimal Linear Regularizers
We present algorithms for efficiently learning regularizers that improve generalization.
Approximation Algorithms for Cascading Prediction Models
We present an approximation algorithm that takes a pool of pre-trained models as input and produces from it a cascaded model with similar accuracy but lower average-case cost.
Delay-Tolerant Algorithms for Asynchronous Distributed Online Learning
We analyze new online gradient descent algorithms for distributed systems with large delays between gradient computations and the corresponding updates.
Online Submodular Maximization under a Matroid Constraint with Application to Learning Assignments
How should we dynamically rank information sources to maximize the value of the ranking?
No-Regret Algorithms for Unconstrained Online Convex Optimization
We present an algorithm that, without such prior knowledge, offers near-optimal regret bounds with respect to _any_ choice of x*.
Online Learning of Assignments
Which ads should we display in sponsored search in order to maximize our revenue?
An Online Algorithm for Maximizing Submodular Functions
We present an algorithm for solving a broad class of online resource allocation problems.
