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Found 16 papers, 5 papers with code
Bayesian Transfer Learning
Transfer learning is a burgeoning concept in statistical machine learning that seeks to improve inference and/or predictive accuracy on a domain of interest by leveraging data from related domains.
The Stochastic Proximal Distance Algorithm
Stochastic versions of proximal methods have gained much attention in statistics and machine learning.
Bregman Power k-Means for Clustering Exponential Family Data
Recent progress in center-based clustering algorithms combats poor local minima by implicit annealing, using a family of generalized means.
Wi-Fi and Bluetooth Contact Tracing Without User Intervention
Previous contact tracing systems required the users to perform many manual actions, such as installing smartphone applications, joining wireless networks, or carrying custom user devices.
Computational tools for assessing gene therapy under branching process models of mutation
In our model, a single-hit mutation carries a slight proliferative advantage over a wild-type stem cells.
Uniform Concentration Bounds toward a Unified Framework for Robust Clustering
Recent advances in center-based clustering continue to improve upon the drawbacks of Lloyd's celebrated $k$-means algorithm over $60$ years after its introduction.
Community Detection in Weighted Multilayer Networks with Ambient Noise
We call this model for multilayer weighted networks the Stochastic Block (with) Ambient Noise Model (SBANM) and develop an associated community detection algorithm.
Kernel k-Means, By All Means: Algorithms and Strong Consistency
We show the method implicitly performs annealing in kernel feature space while retaining efficient, closed-form updates, and we rigorously characterize its convergence properties both from computational and statistical points of view.
Simple and Scalable Sparse k-means Clustering via Feature Ranking
In this paper, we propose a novel framework for sparse k-means clustering that is intuitive, simple to implement, and competitive with state-of-the-art algorithms.
Entropy Regularized Power k-Means Clustering
Despite its well-known shortcomings, $k$-means remains one of the most widely used approaches to data clustering.
Structural Risk Minimization for $C^{1,1}(\mathbb{R}^d)$ Regression
Recently, the following smooth function approximation problem was proposed: given a finite set $E \subset \mathbb{R}^d$ and a function $f: E \rightarrow \mathbb{R}$, interpolate the given information with a function $\widehat{f} \in \dot{C}^{1, 1}(\mathbb{R}^d)$ (the class of first-order differentiable functions with Lipschitz gradients) such that $\widehat{f}(a) = f(a)$ for all $a \in E$, and the value of $\mathrm{Lip}(\nabla \widehat{f})$ is minimal.
Combination of Hyperband and Bayesian Optimization for Hyperparameter Optimization in Deep Learning
Deep learning has achieved impressive results on many problems.
Automatic Conflict Detection in Police Body-Worn Audio
Automatic conflict detection has grown in relevance with the advent of body-worn technology, but existing metrics such as turn-taking and overlap are poor indicators of conflict in police-public interactions.
Generalized Linear Model Regression under Distance-to-set Penalties
Estimation in generalized linear models (GLM) is complicated by the presence of constraints.
An MM Algorithm for Split Feasibility Problems
Furthermore, we show that the Euclidean norm appearing in the proximity function of the non-linear split feasibility problem can be replaced by arbitrary Bregman divergences.
Stochastic Variational Inference for Hidden Markov Models
Variational inference algorithms have proven successful for Bayesian analysis in large data settings, with recent advances using stochastic variational inference (SVI).
