Αρχειοθήκη ιστολογίου

Αναζήτηση αυτού του ιστολογίου

Δευτέρα 14 Σεπτεμβρίου 2020

 


It's About Quality, Not Quantity: Qualitative FDG PET/CT Criteria for Therapy Response Assessment in Clinical Practice.
Related Articles It's About Quality, Not Quantity: Qualitative FDG PET/CT Criteria for Therapy Response Assessment in Clinical Practice. AJR Am J Roentgenol. 2020 08;215(2):313-324 Authors: Banks KP, Peacock JG, Gusman M, Clemenshaw MN Abstract OBJECTIVE. FDG PET/CT has emerged as an effective tool for the timely accurate assessment of how tumors respond to therapy. To standardize interpretation and reporting, numerous response criteria...
Head and Neck
Sat Sep 12, 2020 06:58
Utilization of contrast-enhanced ultrasound in the evaluation of craniofacial osseous lesions: A case report.
Related Articles Utilization of contrast-enhanced ultrasound in the evaluation of craniofacial osseous lesions: A case report. Clin Imaging. 2020 Mar;60(1):5-9 Authors: Sng WJ, Kapur J, Sundar G, Lian WQD, Tan AP Abstract A 14-year-old boy undergoing brain MRI had an incidental avidly enhancing lobulated lesion in the left superolateral orbital rim with associated cortical erosion. Apart from Contrast-enhanced Magnetic Resonance Imaging (MRI),...
Head and Neck
Sat Sep 12, 2020 06:58
The impact of purifying and background selection on the inference of population history: problems and prospects [NEW RESULTS]
Current procedures for inferring population history are generally performed under the assumption of complete neutrality - that is, by neglecting both direct selection and the effects of selection on linked sites. We here examine how the presence of direct purifying and background selection may bias demographic inference by evaluating two commonly-used methods (MSMC and fastsimcoal2), specifically studying how the underlying shape of the distribution of fitness effects (DFE) and the fraction of directly...
bioRxiv Subject Collection: Evolutionary Biology
Fri Sep 11, 2020 03:00
Gender effects in the association between airway microbiome and asthma
Gender differences are found in asthma susceptibility and severity. Accumulating evidence has linked airway microbiome dysbiosis with asthma and the airway microbial communities are found to be different by gender. However, whether gender modifies the link between airway microbiome and asthma has not been investigated.
Annals of Allergy, Asthma & Immunology
Fri Sep 11, 2020 03:00
Combined Use of Detergents and Ultrasonication for Generation of an Acellular Pig Larynx
Tissue Engineering Part A, Ahead of Print.
Tissue Engineering
Thu Sep 10, 2020 10:00
The Effects of Topographic Micropatterning on Endothelial Colony-Forming Cells
Tissue Engineering Part A, Ahead of Print.
Tissue Engineering
Thu Sep 10, 2020 10:00
Impact of Release Kinetics on Efficacy of Locally Delivered Parathyroid Hormone for Bone Regeneration Applications
Tissue Engineering Part A, Ahead of Print.
Tissue Engineering
Thu Sep 10, 2020 10:00
Acinetobacter: An Enemy after Head and Neck Cancer Operations with Microvascular Free Flap Reconstruction?
Surgical Infections, Ahead of Print.
Surgical Infections
Fri Sep 11, 2020 10:00
Future Meetings
Thyroid, Volume 30, Issue 9, Page 1393-1394, September 2020.
liebertpub1
Tue Sep 08, 2020 10:00
MiR-4461 Inhibits Tumorigenesis of Renal Cell Carcinoma by Targeting PPP1R3C
Cancer Biotherapy and Radiopharmaceuticals, Ahead of Print.
Cancer Biotherapy & Radiopharmaceuticals - Table of Contents
Fri Sep 11, 2020 10:00
Use and Effectiveness of Sperm Cryopreservation for Adolescents and Young Adults: A 37-Year Bicentric Experience
Journal of Adolescent and Young Adult Oncology, Ahead of Print.
Journal of Adolescent and Young Adult Oncology
Fri Sep 11, 2020 10:00
Novel Genomic Differences in Cell-Free Circulating DNA Profiles of Young- Versus Older-Onset Colorectal Cancer
Journal of Adolescent and Young Adult Oncology, Ahead of Print.
Journal of Adolescent and Young Adult Oncology
Fri Sep 11, 2020 10:00
Users with spinal cord injury experience of robotic Locomotor exoskeletons: a qualitative study of the benefits, limitations, and recommendations
Persons with spinal cord injury (SCI) may experience both psychological and physiological benefits from robotic locomotor exoskeleton use, and knowledgeable users may have valuable perspectives to inform futur...
Journal of NeuroEngineering and Rehabilitation - Latest Articles
Fri Sep 11, 2020 03:00
Is evolution in response to extreme events good for population persistence? [NEW RESULTS]
Climate change is predicted to increase the severity of environmental perturbations, including storms and droughts, which act as strong selective agents. These extreme events are often of finite duration (pulse disturbances). Hence, while evolution during an extreme event may be adaptive, the resulting phenotypic changes may become maladaptive when the event ends. Using individual-based models and analytic approximations that fuse quantitative genetics and demography, we explore how heritability...
bioRxiv Subject Collection: Evolutionary Biology
Fri Sep 11, 2020 03:00
Connecting Spectral Clustering to Maximum Margins and Level Sets
We study the connections between spectral clustering and the problems of maximum margin clustering, and estimation of the components of level sets of a density function. Specifically, we obtain bounds on the eigenvectors of graph Laplacian matrices in terms of the between cluster separation, and within cluster connectivity. These bounds ensure that the spectral clustering solution converges to the maximum margin clustering solution as the scaling parameter is reduced towards zero. The sensitivity...
JMLR
Wed Jan 01, 2020 02:00
Empirical Priors for Prediction in Sparse High-dimensional Linear Regression
In this paper we adopt the familiar sparse, high-dimensional linear regression model and focus on the important but often overlooked task of prediction. In particular, we consider a new empirical Bayes framework that incorporates data in the prior in two ways: one is to center the prior for the non-zero regression coefficients and the other is to provide some additional regularization. We show that, in certain settings, the asymptotic concentration of the proposed empirical Bayes posterior predictive...
JMLR
Wed Jan 01, 2020 02:00
Sparse and low-rank multivariate Hawkes processes
We consider the problem of unveiling the implicit network structure of node interactions (such as user interactions in a social network), based only on high-frequency timestamps. Our inference is based on the minimization of the least-squares loss associated with a multivariate Hawkes model, penalized by $\ell_1$ and trace norm of the interaction tensor. We provide a first theoretical analysis for this problem, that includes sparsity and low-rank inducing penalizations. This result involves a new...
JMLR
Wed Jan 01, 2020 02:00
A Data Efficient and Feasible Level Set Method for Stochastic Convex Optimization with Expectation Constraints
Stochastic convex optimization problems with expectation constraints (SOECs) are encountered in statistics and machine learning, business, and engineering. The SOEC objective and constraints contain expectations defined with respect to complex distributions or large data sets, leading to high computational complexity when solved by the algorithms that use exact functions and their gradients. Recent stochastic first order methods exhibit low computational complexity when handling SOECs but guarantee...
JMLR
Wed Jan 01, 2020 02:00
WONDER: Weighted One-shot Distributed Ridge Regression in High Dimensions
In many areas, practitioners need to analyze large data sets that challenge conventional single-machine computing. To scale up data analysis, distributed and parallel computing approaches are increasingly needed. Here we study a fundamental and highly important problem in this area: How to do ridge regression in a distributed computing environment? Ridge regression is an extremely popular method for supervised learning, and has several optimality properties, thus it is important to study. We study...
JMLR
Wed Jan 01, 2020 02:00
Nesterov's Acceleration for Approximate Newton
Optimization plays a key role in machine learning. Recently, stochastic second-order methods have attracted considerable attention because of their low computational cost in each iteration. However, these methods might suffer from poor performance when the Hessian is hard to be approximate well in a computation-efficient way. To overcome this dilemma, we resort to Nesterov's acceleration to improve the convergence performance of these second-order methods and propose accelerated approximate Newton....
JMLR
Wed Jan 01, 2020 02:00
Graph-Dependent Implicit Regularisation for Distributed Stochastic Subgradient Descent
We propose graph-dependent implicit regularisation strategies for synchronised distributed stochastic subgradient descent (Distributed SGD) for convex problems in multi-agent learning. Under the standard assumptions of convexity, Lipschitz continuity, and smoothness, we establish statistical learning rates that retain, up to logarithmic terms, single-machine serial statistical guarantees through implicit regularisation (step size tuning and early stopping) with appropriate dependence on the graph...
JMLR
Wed Jan 01, 2020 02:00
Importance Sampling Techniques for Policy Optimization
How can we effectively exploit the collected samples when solving a continuous control task with Reinforcement Learning? Recent results have empirically demonstrated that multiple policy optimization steps can be performed with the same batch by using off-distribution techniques based on importance sampling. However, when dealing with off-distribution optimization, it is essential to take into account the uncertainty introduced by the importance sampling process. In this paper, we propose and analyze...
JMLR
Wed Jan 01, 2020 02:00
A Statistical Learning Approach to Modal Regression
This paper studies the nonparametric modal regression problem systematically from a statistical learning viewpoint. Originally motivated by pursuing a theoretical understanding of the maximum correntropy criterion based regression (MCCR), our study reveals that MCCR with a tending-to-zero scale parameter is essentially modal regression. We show that the nonparametric modal regression problem can be approached via the classical empirical risk minimization. Some efforts are then made to develop a...
JMLR
Wed Jan 01, 2020 02:00
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts all text-based language problems into a text-to-text format. Our systematic...
JMLR
Wed Jan 01, 2020 02:00
Robust Asynchronous Stochastic Gradient-Push: Asymptotically Optimal and Network-Independent Performance for Strongly Convex Functions
We consider the standard model of distributed optimization of a sum of functions $F(\mathbf z) = \sum_{i=1}^n f_i(\mathbf z)$, where node $i$ in a network holds the function $f_i(\mathbf z)$. We allow for a harsh network model characterized by asynchronous updates, message delays, unpredictable message losses, and directed communication among nodes. In this setting, we analyze a modification of the Gradient-Push method for distributed optimization, assuming that (i) node $i$ is capable of generating...
JMLR
Wed Jan 01, 2020 02:00
Chaining Meets Chain Rule: Multilevel Entropic Regularization and Training of Neural Networks
We derive generalization and excess risk bounds for neural networks using a family of complexity measures based on a multilevel relative entropy. The bounds are obtained by introducing the notion of generated hierarchical coverings of neural networks and by using the technique of chaining mutual information introduced by Asadi et al. '18. The resulting bounds are algorithm-dependent and multiscale: they exploit the multilevel structure of neural networks. This, in turn, leads to an empirical risk...
JMLR
Wed Jan 01, 2020 02:00
Branch and Bound for Piecewise Linear Neural Network Verification
The success of Deep Learning and its potential use in many safety-critical applicationshas motivated research on formal verification of Neural Network (NN) models. In thiscontext, verification involves proving or disproving that an NN model satisfies certaininput-output properties. Despite the reputation of learned NN models as black boxes,and the theoretical hardness of proving useful properties about them, researchers havebeen successful in verifying some classes of models by exploiting their piecewise...
JMLR
Wed Jan 01, 2020 02:00
metric-learn: Metric Learning Algorithms in Python
metric-learn is an open source Python package implementing supervised and weakly-supervised distance metric learning algorithms. As part of scikit-learn-contrib, it provides a unified interface compatible with scikit-learn which allows to easily perform cross-validation, model selection, and pipelining with other machine learning estimators. metric-learn is thoroughly tested and available on PyPi under the MIT license.
JMLR
Wed Jan 01, 2020 02:00
Targeted Fused Ridge Estimation of Inverse Covariance Matrices from Multiple High-Dimensional Data Classes
We consider the problem of jointly estimating multiple inverse covariance matrices from high-dimensional data consisting of distinct classes. An $\ell_2$-penalized maximum likelihood approach is employed. The suggested approach is flexible and generic, incorporating several other $\ell_2$-penalized estimators as special cases. In addition, the approach allows specification of target matrices through which prior knowledge may be incorporated and which can stabilize the estimation procedure in high-dimensional...
JMLR
Wed Jan 01, 2020 02:00
Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adapting
We study contextual bandit learning with an abstract policy class and continuous action space. We obtain two qualitatively different regret bounds: one competes with a smoothed version of the policy class under no continuity assumptions, while the other requires standard Lipschitz assumptions. Both bounds exhibit data-dependent “zooming” behavior and, with no tuning, yield improved guarantees for benign problems. We also study adapting to unknown smoothness parameters, establishing a price-of-adaptivity...
JMLR
Wed Jan 01, 2020 02:00
Online Sufficient Dimension Reduction Through Sliced Inverse Regression
Sliced inverse regression is an effective paradigm that achieves the goal of dimension reduction through replacing high dimensional covariates with a small number of linear combinations. It does not impose parametric assumptions on the dependence structure. More importantly, such a reduction of dimension is sufficient in that it does not cause loss of information. In this paper, we adapt the stationary sliced inverse regression to cope with the rapidly changing environments. We propose to...
JMLR
Wed Jan 01, 2020 02:00
Convergence Rates for the Stochastic Gradient Descent Method for Non-Convex Objective Functions
We prove the convergence to minima and estimates on the rate of convergence for the stochastic gradient descent method in the case of not necessarily locally convex nor contracting objective functions. In particular, the analysis relies on a quantitative use of mini-batches to control the loss of iterates to non-attracted regions. The applicability of the results to simple objective functions arising in machine learning is shown.
JMLR
Wed Jan 01, 2020 02:00
Representation Learning for Dynamic Graphs: A Survey
Graphs arise naturally in many real-world applications including social networks, recommender systems, ontologies, biology, and computational finance. Traditionally, machine learning models for graphs have been mostly designed for static graphs. However, many applications involve evolving graphs. This introduces important challenges for learning and inference since nodes, attributes, and edges change over time. In this survey, we review the recent advances in representation learning for dynamic graphs,...
JMLR
Wed Jan 01, 2020 02:00
A Unified Framework of Online Learning Algorithms for Training Recurrent Neural Networks
We present a framework for compactly summarizing many recent results in efficient and/or biologically plausible online training of recurrent neural networks (RNN). The framework organizes algorithms according to several criteria: (a) past vs. future facing, (b) tensor structure, (c) stochastic vs. deterministic, and (d) closed form vs. numerical. These axes reveal latent conceptual connections among several recent advances in online learning. Furthermore, we provide novel mathematical intuitions...
JMLR
Wed Jan 01, 2020 02:00
Generalized Optimal Matching Methods for Causal Inference
We develop an encompassing framework for matching, covariate balancing, and doubly-robust methods for causal inference from observational data called generalized optimal matching (GOM). The framework is given by generalizing a new functional-analytical formulation of optimal matching, giving rise to the class of GOM methods, for which we provide a single unified theory to analyze tractability and consistency. Many commonly used existing methods are included in GOM and, using their GOM interpretation,...
JMLR
Wed Jan 01, 2020 02:00
Probabilistic Learning on Graphs via Contextual Architectures
We propose a novel methodology for representation learning on graph-structured data, in which a stack of Bayesian Networks learns different distributions of a vertex's neighbourhood. Through an incremental construction policy and layer-wise training, we can build deeper architectures with respect to typical graph convolutional neural networks, with benefits in terms of context spreading between vertices. First, the model learns from graphs via maximum likelihood estimation without using target labels....
JMLR
Wed Jan 01, 2020 02:00
GraKeL: A Graph Kernel Library in Python
The problem of accurately measuring the similarity between graphs is at the core of many applications in a variety of disciplines. Graph kernels have recently emerged as a promising approach to this problem. There are now many kernels, each focusing on different structural aspects of graphs. Here, we present GraKeL, a library that unifies several graph kernels into a common framework. The library is written in Python and adheres to the scikit-learn interface. It is simple to use and can be naturally...
JMLR
Wed Jan 01, 2020 02:00
Gradient Descent for Sparse Rank-One Matrix Completion for Crowd-Sourced Aggregation of Sparsely Interacting Workers
We consider worker skill estimation for the single-coin Dawid-Skene crowdsourcing model. In practice, skill-estimation is challenging because worker assignments are sparse and irregular due to the arbitrary and uncontrolled availability of workers. We formulate skill estimation as a rank-one correlation-matrix completion problem, where the observed components correspond to observed label correlation between workers. We show that the correlation matrix can be successfully recovered and skills are...
JMLR
Wed Jan 01, 2020 02:00
pyts: A Python Package for Time Series Classification
pyts is an open-source Python package for time series classification. This versatile toolbox provides implementations of many algorithms published in the literature, preprocessing functionalities, and data set loading utilities. pyts relies on the standard scientific Python packages numpy, scipy, scikit-learn, joblib, and numba, and is distributed under the BSD-3-Clause license. Documentation contains installation instructions, a detailed user guide, a full API description, and concrete self-contained...
JMLR
Wed Jan 01, 2020 02:00
Monte Carlo Gradient Estimation in Machine Learning
This paper is a broad and accessible survey of the methods we have at our disposal for Monte Carlo gradient estimation in machine learning and across the statistical sciences: the problem of computing the gradient of an expectation of a function with respect to parameters defining the distribution that is integrated; the problem of sensitivity analysis. In machine learning research, this gradient problem lies at the core of many learning problems, in supervised, unsupervised and reinforcement learning....
JMLR
Wed Jan 01, 2020 02:00
Latent Simplex Position Model: High Dimensional Multi-view Clustering with Uncertainty Quantification
High dimensional data often contain multiple facets, and several clustering patterns can co-exist under different variable subspaces, also known as the views. While multi-view clustering algorithms were proposed, the uncertainty quantification remains difficult --- a particular challenge is in the high complexity of estimating the cluster assignment probability under each view, and sharing information among views. In this article, we propose an approximate Bayes approach --- treating the similarity...
JMLR
Wed Jan 01, 2020 02:00
Convergence of Sparse Variational Inference in Gaussian Processes Regression
Gaussian processes are distributions over functions that are versatile and mathematically convenient priors in Bayesian modelling. However, their use is often impeded for data with large numbers of observations, $N$, due to the cubic (in $N$) cost of matrix operations used in exact inference. Many solutions have been proposed that rely on $M \ll N$ inducing variables to form an approximation at a cost of $\mathcal{O}\left(NM^2\right)$. While the computational cost appears linear in $N$, the true...
JMLR
Wed Jan 01, 2020 02:00
Tensor Train Decomposition on TensorFlow (T3F)
Tensor Train decomposition is used across many branches of machine learning. We present T3F—a library for Tensor Train decomposition based on TensorFlow. T3F supports GPU execution, batch processing, automatic differentiation, and versatile functionality for the Riemannian optimization framework, which takes into account the underlying manifold structure to construct efficient optimization methods. The library makes it easier to implement machine learning papers that rely on the Tensor Train decomposition....
JMLR
Wed Jan 01, 2020 02:00
AI Explainability 360: An Extensible Toolkit for Understanding Data and Machine Learning Models
As artificial intelligence algorithms make further inroads in high-stakes societal applications, there are increasing calls from multiple stakeholders for these algorithms to explain their outputs. To make matters more challenging, different personas of consumers of explanations have different requirements for explanations. Toward addressing these needs, we introduce AI Explainability 360, an open-source Python toolkit featuring ten diverse and state-of-the-art explainability methods and two evaluation...
JMLR
Wed Jan 01, 2020 02:00
A Unified Framework for Structured Graph Learning via Spectral Constraints
Graph learning from data is a canonical problem that has received substantial attention in the literature. Learning a structured graph is essential for interpretability and identification of the relationships among data. In general, learning a graph with a specific structure is an NP-hard combinatorial problem and thus designing a general tractable algorithm is challenging. Some useful structured graphs include connected, sparse, multi-component, bipartite, and regular graphs. In this paper, we introduce...
JMLR
Wed Jan 01, 2020 02:00
A General System of Differential Equations to Model First-Order Adaptive Algorithms
First-order optimization algorithms play a major role in large scale machine learning. A new class of methods, called adaptive algorithms, was recently introduced to adjust iteratively the learning rate for each coordinate. Despite great practical success in deep learning, their behavior and performance on more general loss functions are not well understood. In this paper, we derive a non-autonomous system of differential equations, which is the continuous-time limit of adaptive optimization methods....
JMLR
Wed Jan 01, 2020 02:00
On lp-Support Vector Machines and Multidimensional Kernels
In this paper, we extend the methodology developed for Support Vector Machines (SVM) using the $\ell_2$-norm ($\ell_2$-SVM) to the more general case of $\ell_p$-norms with $p>1$ ($\ell_p$-SVM). We derive second order cone formulations for the resulting dual and primal problems. The concept of kernel function, widely applied in $\ell_2$-SVM, is extended to the more general case of $\ell_p$-norms with $p>1$ by defining a new operator called multidimensional kernel. This object gives rise...
JMLR
Wed Jan 01, 2020 02:00
A Regularization-Based Adaptive Test for High-Dimensional GLMs
In spite of its urgent importance in the era of big data, testing high-dimensional parameters in generalized linear models (GLMs) in the presence of high-dimensional nuisance parameters has been largely under-studied, especially with regard to constructing powerful tests for general (and unknown) alternatives. Most existing tests are powerful only against certain alternatives and may yield incorrect Type 1 error rates under high-dimensional nuisance parameter situations. In this paper, we propose...
JMLR
Wed Jan 01, 2020 02:00
Path-Based Spectral Clustering: Guarantees, Robustness to Outliers, and Fast Algorithms
We consider the problem of clustering with the longest-leg path distance (LLPD) metric, which is informative for elongated and irregularly shaped clusters. We prove finite-sample guarantees on the performance of clustering with respect to this metric when random samples are drawn from multiple intrinsically low-dimensional clusters in high-dimensional space, in the presence of a large number of high-dimensional outliers. By combining these results with spectral clustering with respect to LLPD, we...
JMLR
Wed Jan 01, 2020 02:00
Apache Mahout: Machine Learning on Distributed Dataflow Systems
Apache Mahout is a library for scalable machine learning (ML) on distributed dataflow systems, offering various implementations of classification, clustering, dimensionality reduction and recommendation algorithms. Mahout was a pioneer in large-scale machine learning in 2008, when it started and targeted MapReduce, which was the predominant abstraction for scalable computing in industry at that time. Mahout has been widely used by leading web companies and is part of several commercial cloud offerings....
JMLR
Wed Jan 01, 2020 02:00
(1 + epsilon)-class Classification: an Anomaly Detection Method for Highly Imbalanced or Incomplete Data Sets
Anomaly detection is not an easy problem since distribution of anomalous samples is unknown a priori. We explore a novel method that gives a trade-off possibility between one-class and two-class approaches, and leads to a better performance on anomaly detection problems with small or non-representative anomalous samples. The method is evaluated using several data sets and compared to a set of conventional one-class and two-class approaches.
JMLR
Wed Jan 01, 2020 02:00
Distributed Minimum Error Entropy Algorithms
Minimum Error Entropy (MEE) principle is an important approach in Information Theoretical Learning (ITL). It is widely applied and studied in various fields for its robustness to noise. In this paper, we study a reproducing kernel-based distributed MEE algorithm, DMEE, which is designed to work with both fully supervised data and semi-supervised data. The divide-and-conquer approach is employed, so there is no inter-node communication overhead. Similar as other distributed algorithms, DMEE significantly...
JMLR
Wed Jan 01, 2020 02:00
On Stationary-Point Hitting Time and Ergodicity of Stochastic Gradient Langevin Dynamics
Stochastic gradient Langevin dynamics (SGLD) is a fundamental algorithm in stochastic optimization. Recent work by Zhang et al. (2017) presents an analysis for the hitting time of SGLD for the first and second order stationary points. The proof in Zhang et al. (2017) is a two-stage procedure through bounding the Cheeger's constant, which is rather complicated and leads to loose bounds. In this paper, using intuitions from stochastic differential equations, we provide a direct analysis for the...
JMLR
Wed Jan 01, 2020 02:00
Optimal Algorithms for Continuous Non-monotone Submodular and DR-Submodular Maximization
In this paper we study the fundamental problems of maximizing a continuous non-monotone submodular function over the hypercube, both with and without coordinate-wise concavity. This family of optimization problems has several applications in machine learning, economics, and communication systems. Our main result is the first $\frac{1}{2}$-approximation algorithm for continuous submodular function maximization; this approximation factor of $\frac{1}{2}$ is the best possible for algorithms that only...
JMLR
Wed Jan 01, 2020 02:00
Community-Based Group Graphical Lasso
A new strategy for probabilistic graphical modeling is developed that draws parallels to community detection analysis. The method jointly estimates an undirected graph and homogeneous communities of nodes. The structure of the communities is taken into account when estimating the graph and at the same time, the structure of the graph is accounted for when estimating communities of nodes. The procedure uses a joint group graphical lasso approach with community detection-based grouping, such that some...
JMLR
Wed Jan 01, 2020 02:00
Fast Bayesian Inference of Sparse Networks with Automatic Sparsity Determination
Structure learning of Gaussian graphical models typically involves careful tuning of penalty parameters, which balance the tradeoff between data fidelity and graph sparsity. Unfortunately, this tuning is often a “black art” requiring expert experience or brute-force search. It is therefore tempting to develop tuning-free algorithms that can determine the sparsity of the graph adaptively from the observed data in an automatic fashion. In this paper, we propose a novel approach, named BISN (Bayesian...
JMLR
Wed Jan 01, 2020 02:00
Kymatio: Scattering Transforms in Python
The wavelet scattering transform is an invariant and stable signal representation suitable for many signal processing and machine learning applications. We present the Kymatio software package, an easy-to-use, high-performance Python implementation of the scattering transform in 1D, 2D, and 3D that is compatible with modern deep learning frameworks, including PyTorch and TensorFlow/Keras. The transforms are implemented on both CPUs and GPUs, the latter offering a significant speedup over the former....
JMLR
Wed Jan 01, 2020 02:00
Tensor Regression Networks
Convolutional neural networks typically consist of many convolutional layers followed by one or more fully connected layers. While convolutional layers map between high-order activation tensors, the fully connected layers operate on flattened activation vectors. Despite empirical success, this approach has notable drawbacks. Flattening followed by fully connected layers discards multilinear structure in the activations and requires many parameters. We address these problems by incorporating tensor...
JMLR
Wed Jan 01, 2020 02:00
Fast Rates for General Unbounded Loss Functions: From ERM to Generalized Bayes
We present new excess risk bounds for general unbounded loss functions including log loss and squared loss, where the distribution of the losses may be heavy-tailed. The bounds hold for general estimators, but they are optimized when applied to $\eta$-generalized Bayesian, MDL, and empirical risk minimization estimators. In the case of log loss, the bounds imply convergence rates for generalized Bayesian inference under misspecification in terms of a generalization of the Hellinger metric as long...
JMLR
Wed Jan 01, 2020 02:00
Kernel-estimated Nonparametric Overlap-Based Syncytial Clustering
Commonly-used clustering algorithms usually find ellipsoidal, spherical or other regular-structured clusters, but are more challenged when the underlying groups lack formal structure or definition. Syncytial clustering is the name that we introduce for methods that merge groups obtained from standard clustering algorithms in order to reveal complex group structure in the data. Here, we develop a distribution-free fully-automated syncytial clustering algorithm that can be used with $k$-means and...
JMLR
Wed Jan 01, 2020 02:00
Expected Policy Gradients for Reinforcement Learning
We propose expected policy gradients (EPG), which unify stochastic policy gradients (SPG) and deterministic policy gradients (DPG) for reinforcement learning. Inspired by expected sarsa, EPG integrates (or sums) across actions when estimating the gradient, instead of relying only on the action in the sampled trajectory. For continuous action spaces, we first derive a practical result for Gaussian policies and quadratic critics and then extend it to a universal analytical method, covering a broad...
JMLR
Wed Jan 01, 2020 02:00
Agnostic Estimation for Phase Retrieval
The goal of noisy high-dimensional phase retrieval is to estimate an $s$-sparse parameter $\boldsymbol{\beta}^*\in \mathbb{R}^d$ from $n$ realizations of the model $Y = (\mathbf{X}^T \boldsymbol{\beta}^*)^2 + \varepsilon$. Based on this model, we propose a significant semi-parametric generalization called misspecified phase retrieval (MPR), in which $Y = f(\mathbf{X}^T \boldsymbol{\beta}^*, \varepsilon)$ with unknown $f$ and $\operatorname{Cov}(Y, (\mathbf{X}^T \boldsymbol{\beta}^*)^2) > 0$....
JMLR
Wed Jan 01, 2020 02:00
Skill Rating for Multiplayer Games. Introducing Hypernode Graphs and their Spectral Theory
We consider the skill rating problem for multiplayer games, that is how to infer player skills from game outcomes in multiplayer games. We formulate the problem as a minimization problem $\arg \min_{s} s^T \Delta s$ where $\Delta$ is a positive semidefinite matrix and $s$ a real-valued function, of which some entries are the skill values to be inferred and other entries are constrained by the game outcomes. We leverage graph-based semi-supervised learning (SSL) algorithms for this problem. We apply...
JMLR
Wed Jan 01, 2020 02:00
A Class of Parallel Doubly Stochastic Algorithms for Large-Scale Learning
We consider learning problems over training sets in which both, the number of training examples and the dimension of the feature vectors, are large. To solve these problems we propose the random parallel stochastic algorithm (RAPSA). We call the algorithm random parallel because it utilizes multiple parallel processors to operate on a randomly chosen subset of blocks of the feature vector. RAPSA is doubly stochastic since each processor utilizes a random set of functions to compute the stochastic...
JMLR
Wed Jan 01, 2020 02:00
Dynamical Systems as Temporal Feature Spaces
Parametrised state space models in the form of recurrent networks are often used in machine learning to learn from data streams exhibiting temporal dependencies. To break the black box nature of such models it is important to understand the dynamical features of the input-driving time series that are formed in the state space. We propose a framework for rigorous analysis of such state representations in vanishing memory state space models such as echo state networks (ESN). In particular, we consider...
JMLR
Wed Jan 01, 2020 02:00
Bayesian Closed Surface Fitting Through Tensor Products
Closed surfaces provide a useful model for $3$-d shapes, with the data typically consisting of a cloud of points in $\mathbb{R}^3$. The existing literature on closed surface modeling focuses on frequentist point estimation methods that join surface patches along the edges, with surface patches created via Bézier surfaces or tensor products of B-splines. However, the resulting surfaces are not smooth along the edges and the geometric constraints required to join the surface patches lead to computational...
JMLR
Wed Jan 01, 2020 02:00
Optimal Bipartite Network Clustering
We study bipartite community detection in networks, or more generally the network biclustering problem. We present a fast two-stage procedure based on spectral initialization followed by the application of a pseudo-likelihood classifier twice. Under mild regularity conditions, we establish the weak consistency of the procedure (i.e., the convergence of the misclassification rate to zero) under a general bipartite stochastic block model. We show that the procedure is optimal in the sense that it...
JMLR
Wed Jan 01, 2020 02:00
Tslearn, A Machine Learning Toolkit for Time Series Data
tslearn is a general-purpose Python machine learning library for time series that offers tools for pre-processing and feature extraction as well as dedicated models for clustering, classification and regression. It follows scikit-learn's Application Programming Interface for transformers and estimators, allowing the use of standard pipelines and model selection tools on top of tslearn objects. It is distributed under the BSD-2-Clause license, and its source code is available at https://github.com/tslearn-team/tslearn.
JMLR
Wed Jan 01, 2020 02:00
Noise Accumulation in High Dimensional Classification and Total Signal Index
Great attention has been paid to Big Data in recent years. Such data hold promise for scientific discoveries but also pose challenges to analyses. One potential challenge is noise accumulation. In this paper, we explore noise accumulation in high dimensional two-group classification. First, we revisit a previous assessment of noise accumulation with principal component analyses, which yields a different threshold for discriminative ability than originally identified. Then we extend our scope to its...
JMLR
Wed Jan 01, 2020 02:00
Regularized Estimation of High-dimensional Factor-Augmented Vector Autoregressive (FAVAR) Models
A factor-augmented vector autoregressive (FAVAR) model is defined by a VAR equation that captures lead-lag correlations amongst a set of observed variables $X$ and latent factors $F$, and a calibration equation that relates another set of observed variables $Y$ with $F$ and $X$. The latter equation is used to estimate the factors that are subsequently used in estimating the parameters of the VAR system. The FAVAR model has become popular in applied economic research, since it can summarize a large...
JMLR
Wed Jan 01, 2020 02:00
Provably robust estimation of modulo 1 samples of a smooth function with applications to phase unwrapping
Consider an unknown smooth function $f: [0,1]^d \rightarrow \mathbb{R}$, and assume we are given $n$ noisy mod 1 samples of $f$, i.e., $y_i = (f(x_i) + \eta_i) \bmod 1$, for $x_i \in [0,1]^d$, where $\eta_i$ denotes the noise. Given the samples $(x_i,y_i)_{i=1}^{n}$, our goal is to recover smooth, robust estimates of the clean samples $f(x_i) \bmod 1$. We formulate a natural approach for solving this problem, which works with angular embeddings of the noisy mod 1 samples over the unit circle,...
JMLR
Wed Jan 01, 2020 02:00
GluonTS: Probabilistic and Neural Time Series Modeling in Python
We introduce the Gluon Time Series Toolkit (GluonTS), a Python library for deep learning based time series modeling for ubiquitous tasks, such as forecasting and anomaly detection. GluonTS simplifies the time series modeling pipeline by providing the necessary components and tools for quick model development, efficient experimentation and evaluation. In addition, it contains reference implementations of state-of-the-art time series models that enable simple benchmarking of new algorithms.
JMLR
Wed Jan 01, 2020 02:00
On the consistency of graph-based Bayesian semi-supervised learning and the scalability of sampling algorithms
This paper considers a Bayesian approach to graph-based semi-supervised learning. We show that if the graph parameters are suitably scaled, the graph-posteriors converge to a continuum limit as the size of the unlabeled data set grows. This consistency result has profound algorithmic implications: we prove that when consistency holds, carefully designed Markov chain Monte Carlo algorithms have a uniform spectral gap, independent of the number of unlabeled inputs. Numerical experiments illustrate...
JMLR
Wed Jan 01, 2020 02:00
Identifiability and Consistent Estimation of Nonparametric Translation Hidden Markov Models with General State Space
This paper considers hidden Markov models where the observations are given as the sum of a latent state which lies in a general state space and some independent noise with unknown distribution. It is shown that these fully nonparametric translation models are identifiable with respect to both the distribution of the latent variables and the distribution of the noise, under mostly a light tail assumption on the latent variables. Two nonparametric estimation methods are proposed and we prove that the...
JMLR
Wed Jan 01, 2020 02:00
Distributed Feature Screening via Componentwise Debiasing
Feature screening is a powerful tool in processing high-dimensional data. When the sample size N and the number of features p are both large, the implementation of classic screening methods can be numerically challenging. In this paper, we propose a distributed screening framework for big data setup. In the spirit of 'divide-and-conquer', the proposed framework expresses a correlation measure as a function of several component parameters, each of which can be distributively estimated using a natural...
JMLR
Wed Jan 01, 2020 02:00
NEVAE: A Deep Generative Model for Molecular Graphs
Deep generative models have been praised for their ability to learn smooth latent representations of images, text, and audio, which can then be used to generate new, plausible data. Motivated by these success stories, there has been a surge of interest in developing deep generative models for automated molecule design. However, these models face several difficulties due to the unique characteristics of molecular graphs—their underlying structure is not Euclidean or grid-like, they remain isomorphic...
JMLR
Wed Jan 01, 2020 02:00
Convergences of Regularized Algorithms and Stochastic Gradient Methods with Random Projections
We study the least-squares regression problem over a Hilbert space, covering nonparametric regression over a reproducing kernel Hilbert space as a special case. We first investigate regularized algorithms adapted to a projection operator on a closed subspace of the Hilbert space. We prove convergence results with respect to variants of norms, under a capacity assumption on the hypothesis space and a regularity condition on the target function. As a result, we obtain optimal rates for regularized...
JMLR
Wed Jan 01, 2020 02:00
Prediction regions through Inverse Regression
Predicting a new response from a covariate is a challenging task in regression, which raises new question since the era of high-dimensional data. In this paper, we are interested in the inverse regression method from a theoretical viewpoint. Theoretical results for the well-known Gaussian linear model are well-known, but the curse of dimensionality has increased the interest of practitioners and theoreticians into generalization of those results for various estimators, calibrated for the high-dimension...
JMLR
Wed Jan 01, 2020 02:00
Practical Locally Private Heavy Hitters
We present new practical local differentially private heavy hitters algorithms achieving optimal or near-optimal worst-case error and running time -- TreeHist and Bitstogram. In both algorithms, server running time is $\tilde O(n)$ and user running time is $\tilde O(1)$, hence improving on the prior state-of-the-art result of Bassily and Smith [STOC 2015] requiring $O(n^{5/2})$ server time and $O(n^{3/2})$ user time. With a typically large number of participants in local algorithms (in the millions),...
JMLR
Wed Jan 01, 2020 02:00
High-dimensional Linear Discriminant Analysis Classifier for Spiked Covariance Model
Linear discriminant analysis (LDA) is a popular classifier that is built on the assumption of common population covariance matrix across classes. The performance of LDA depends heavily on the quality of estimating the mean vectors and the population covariance matrix. This issue becomes more challenging in high-dimensional settings where the number of features is of the same order as the number of training samples. Several techniques for estimating the covariance matrix can be found in the literature....
JMLR
Wed Jan 01, 2020 02:00
Neyman-Pearson classification: parametrics and sample size requirement
The Neyman-Pearson (NP) paradigm in binary classification seeks classifiers that achieve a minimal type II error while enforcing the prioritized type I error controlled under some user-specified level $\alpha$. This paradigm serves naturally in applications such as severe disease diagnosis and spam detection, where people have clear priorities among the two error types. Recently, Tong, Feng, and Li (2018) proposed a nonparametric umbrella algorithm that adapts all scoring-type classification methods...
JMLR
Wed Jan 01, 2020 02:00
MFE: Towards reproducible meta-feature extraction
Automated recommendation of machine learning algorithms is receiving a large deal of attention, not only because they can recommend the most suitable algorithms for a new task, but also because they can support efficient hyper-parameter tuning, leading to better machine learning solutions. The automated recommendation can be implemented using meta-learning, learning from previous learning experiences, to create a meta-model able to associate a data set to the predictive performance of machine learning...
JMLR
Wed Jan 01, 2020 02:00
DESlib: A Dynamic ensemble selection library in Python
DESlib is an open-source python library providing the implementation of several dynamic selection techniques. The library is divided into three modules: (i) dcs, containing the implementation of dynamic classifier selection methods (DCS); (ii) des, containing the implementation of dynamic ensemble selection methods (DES); (iii) static, with the implementation of static ensemble techniques. The library is fully documented (documentation available online on Read the Docs), has a high test coverage...
JMLR
Wed Jan 01, 2020 02:00
ProxSARAH: An Efficient Algorithmic Framework for Stochastic Composite Nonconvex Optimization
We propose a new stochastic first-order algorithmic framework to solve stochastic composite nonconvex optimization problems that covers both finite-sum and expectation settings. Our algorithms rely on the SARAH estimator and consist of two steps: a proximal gradient and an averaging step making them different from existing nonconvex proximal-type algorithms. The algorithms only require an average smoothness assumption of the nonconvex objective term and additional bounded variance assumption if...
JMLR
Wed Jan 01, 2020 02:00
Universal Latent Space Model Fitting for Large Networks with Edge Covariates
Latent space models are effective tools for statistical modeling and visualization of network data. Due to their close connection to generalized linear models, it is also natural to incorporate covariate information in them. The current paper presents two universal fitting algorithms for networks with edge covariates: one based on nuclear norm penalization and the other based on projected gradient descent. Both algorithms are motivated by maximizing the likelihood function for an existing class of...
JMLR
Wed Jan 01, 2020 02:00
Bayesian Model Selection with Graph Structured Sparsity
We propose a general algorithmic framework for Bayesian model selection. A spike-and-slab Laplacian prior is introduced to model the underlying structural assumption. Using the notion of effective resistance, we derive an EM-type algorithm with closed-form iterations to efficiently explore possible candidates for Bayesian model selection. The deterministic nature of the proposed algorithm makes it more scalable to large-scale and high-dimensional data sets compared with existing stochastic search...
JMLR
Wed Jan 01, 2020 02:00
Orlicz Random Fourier Features
Kernel techniques are among the most widely-applied and influential tools in machine learning with applications at virtually all areas of the field. To combine this expressive power with computational efficiency numerous randomized schemes have been proposed in the literature, among which probably random Fourier features (RFF) are the simplest and most popular. While RFFs were originally designed for the approximation of kernel values, recently they have been adapted to kernel derivatives, and hence...
JMLR
Wed Jan 01, 2020 02:00
ThunderGBM: Fast GBDTs and Random Forests on GPUs
Gradient Boosting Decision Trees (GBDTs) and Random Forests (RFs) have been used in many real-world applications. They are often a standard recipe for building state-of-the-art solutions to machine learning and data mining problems. However, training and prediction are very expensive computationally for large and high dimensional problems. This article presents an efficient and open source software toolkit called ThunderGBM which exploits the high-performance Graphics Processing Units (GPUs) for...
JMLR
Wed Jan 01, 2020 02:00
Estimation of a Low-rank Topic-Based Model for Information Cascades
We consider the problem of estimating the latent structure of a social network based on the observed information diffusion events, or cascades, where the observations for a given cascade consist of only the timestamps of infection for infected nodes but not the source of the infection. Most of the existing work on this problem has focused on estimating a diffusion matrix without any structural assumptions on it. In this paper, we propose a novel model based on the intuition that an information is...
JMLR
Wed Jan 01, 2020 02:00
Change Point Estimation in a Dynamic Stochastic Block Model
We consider the problem of estimating the location of a single change point in a network generated by a dynamic stochastic block model mechanism. This model produces community structure in the network that exhibits change at a single time epoch. We propose two methods of estimating the change point, together with the model parameters, before and after its occurrence. The first employs a least-squares criterion function and takes into consideration the full structure of the stochastic block model...
JMLR
Wed Jan 01, 2020 02:00
Union of Low-Rank Tensor Spaces: Clustering and Completion
We consider the problem of clustering and completing a set of tensors with missing data that are drawn from a union of low-rank tensor spaces. In the clustering problem, given a partially sampled tensor data that is composed of a number of subtensors, each chosen from one of a certain number of unknown tensor spaces, we need to group the subtensors that belong to the same tensor space. We provide a geometrical analysis on the sampling pattern and subsequently derive the sampling rate that guarantees...
JMLR
Wed Jan 01, 2020 02:00
Quadratic Decomposable Submodular Function Minimization: Theory and Practice
We introduce a new convex optimization problem, termed quadratic decomposable submodular function minimization (QDSFM), which allows to model a number of learning tasks on graphs and hypergraphs. The problem exhibits close ties to decomposable submodular function minimization (DSFM) yet is much more challenging to solve. We approach the problem via a new dual strategy and formulate an objective that can be optimized through a number of double-loop algorithms. The outer-loop uses either random coordinate...
JMLR
Wed Jan 01, 2020 02:00
The weight function in the subtree kernel is decisive
Tree data are ubiquitous because they model a large variety of situations, e.g., the architecture of plants, the secondary structure of RNA, or the hierarchy of XML files. Nevertheless, the analysis of these non-Euclidean data is difficult per se. In this paper, we focus on the subtree kernel that is a convolution kernel for tree data introduced by Vishwanathan and Smola in the early 2000's. More precisely, we investigate the influence of the weight function from a theoretical perspective and in...
JMLR
Wed Jan 01, 2020 02:00
Stochastic Conditional Gradient Methods: From Convex Minimization to Submodular Maximization
This paper considers stochastic optimization problems for a large class of objective functions, including convex and continuous submodular. Stochastic proximal gradient methods have been widely used to solve such problems; however, their applicability remains limited when the problem dimension is large and the projection onto a convex set is computationally costly. Instead, stochastic conditional gradient algorithms are proposed as an alternative solution which rely on (i) Approximating gradients...
JMLR
Wed Jan 01, 2020 02:00
Smoothed Nonparametric Derivative Estimation using Weighted Difference Quotients
Derivatives play an important role in bandwidth selection methods (e.g., plug-ins), data analysis and bias-corrected confidence intervals. Therefore, obtaining accurate derivative information is crucial. Although many derivative estimation methods exist, the majority require a fixed design assumption. In this paper, we propose an effective and fully data-driven framework to estimate the first and second order derivative in random design. We establish the asymptotic properties of the proposed derivative...
JMLR
Wed Jan 01, 2020 02:00
Sparse Projection Oblique Randomer Forests
Decision forests, including Random Forests and Gradient Boosting Trees, have recently demonstrated state-of-the-art performance in a variety of machine learning settings. Decision forests are typically ensembles of axis-aligned decision trees; that is, trees that split only along feature dimensions. In contrast, many recent extensions to decision forests are based on axis-oblique splits. Unfortunately, these extensions forfeit one or more of the favorable properties of decision forests based on...
JMLR
Wed Jan 01, 2020 02:00
Unique Sharp Local Minimum in L1-minimization Complete Dictionary Learning
We study the problem of globally recovering a dictionary from a set of signals via $\ell_1$-minimization. We assume that the signals are generated as i.i.d. random linear combinations of the $K$ atoms from a complete reference dictionary $D^*\in \mathbb R^{K\times K}$, where the linear combination coefficients are from either a Bernoulli type model or exact sparse model. First, we obtain a necessary and sufficient norm condition for the reference dictionary $D^*$ to be a sharp local minimum of the...
JMLR
Wed Jan 01, 2020 02:00
Stochastic Nested Variance Reduction for Nonconvex Optimization
We study nonconvex optimization problems, where the objective function is either an average of $n$ nonconvex functions or the expectation of some stochastic function. We propose a new stochastic gradient descent algorithm based on nested variance reduction, namely, Stochastic Nested Variance-Reduced Gradient descent ($\text{SNVRG}$). Compared with conventional stochastic variance reduced gradient ($\text{SVRG}$) algorithm that uses two reference points to construct a semi-stochastic gradient with...
JMLR
Wed Jan 01, 2020 02:00
Multiparameter Persistence Landscapes
An important problem in the field of Topological Data Analysis is defining topological summaries which can be combined with traditional data analytic tools. In recent work Bubenik introduced the persistence landscape, a stable representation of persistence diagrams amenable to statistical analysis and machine learning tools. In this paper we generalise the persistence landscape to multiparameter persistence modules providing a stable representation of the rank invariant. We show that multiparameter...
JMLR
Wed Jan 01, 2020 02:00

Δεν υπάρχουν σχόλια:

Δημοσίευση σχολίου

Σημείωση: Μόνο ένα μέλος αυτού του ιστολογίου μπορεί να αναρτήσει σχόλιο.