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Found 18 papers, 5 papers with code
Optimal Activation Functions for the Random Features Regression Model
The asymptotic mean squared test error and sensitivity of the Random Features Regression model (RFR) have been recently studied.
Distributed Optimization, Averaging via ADMM, and Network Topology
For simple algorithms such as gradient descent the dependency of the convergence time with the topology of this network is well-known.
A Family of Pairwise Multi-Marginal Optimal Transports that Define a Generalized Metric
Favoring its use are its metric properties.
Exact inference under the perfect phylogeny model
We use a careful combination of algorithms, software, and hardware, to develop EXACT: a tool that can explore the space of all possible phylogenetic trees, and performs exact inference under the PPM with noisy data.
Estimating Cellular Goals from High-Dimensional Biological Data
The latter approach is important because often the known/observed biochemical reactions are not enough to explain observations, and hence there is a need to extend automatically the model complexity by learning new chemical reactions.
Tractable $n$-Metrics for Multiple Graphs
In the context of comparing graphs, we are the first to show the existence of multi-distances that simultaneously incorporate the useful property of alignment consistency (Nguyen et al. 2011), and a generalized metric property.
An Explicit Convergence Rate for Nesterov's Method from SDP
The framework of Integral Quadratic Constraints (IQC) introduced by Lessard et al. (2014) reduces the computation of upper bounds on the convergence rate of several optimization algorithms to semi-definite programming (SDP).
How is Distributed ADMM Affected by Network Topology?
Here we provide a full characterization of the convergence of distributed over-relaxed ADMM for the same type of consensus problem in terms of the topology of the underlying graph.
Markov Chain Lifting and Distributed ADMM
The time to converge to the steady state of a finite Markov chain can be greatly reduced by a lifting operation, which creates a new Markov chain on an expanded state space.
Tuning Over-Relaxed ADMM
The framework of Integral Quadratic Constraints (IQC) reduces the computation of upper bounds on the convergence rate of several optimization algorithms to a semi-definite program (SDP).
Generative Adversarial Active Learning
We propose a new active learning by query synthesis approach using Generative Adversarial Networks (GAN).
A metric for sets of trajectories that is practical and mathematically consistent
Metrics on the space of sets of trajectories are important for scientists in the field of computer vision, machine learning, robotics, and general artificial intelligence.
An Explicit Rate Bound for the Over-Relaxed ADMM
In this paper we provide an exact analytical solution to this SDP and obtain a general and explicit upper bound on the convergence rate of the entire family of over-relaxed ADMM.
The Boundary Forest Algorithm for Online Supervised and Unsupervised Learning
We describe a new instance-based learning algorithm called the Boundary Forest (BF) algorithm, that can be used for supervised and unsupervised learning.
Proximal operators for multi-agent path planning
We address the problem of planning collision-free paths for multiple agents using optimization methods known as proximal algorithms.
Shape and Illumination from Shading using the Generic Viewpoint Assumption
The Generic Viewpoint Assumption (GVA) states that the position of the viewer or the light in a scene is not special.
Methods for Integrating Knowledge with the Three-Weight Optimization Algorithm for Hybrid Cognitive Processing
In this context, we focus on the Three-Weight Algorithm, which aims to solve general optimization problems.
An Improved Three-Weight Message-Passing Algorithm
We describe how the powerful "Divide and Concur" algorithm for constraint satisfaction can be derived as a special case of a message-passing version of the Alternating Direction Method of Multipliers (ADMM) algorithm for convex optimization, and introduce an improved message-passing algorithm based on ADMM/DC by introducing three distinct weights for messages, with "certain" and "no opinion" weights, as well as the standard weight used in ADMM/DC.
