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Found 9 papers, 5 papers with code
Detection of Unobserved Common Causes based on NML Code in Discrete, Mixed, and Continuous Variables
Causal discovery in the presence of unobserved common causes from observational data only is a crucial but challenging problem.
mdx: A Cloud Platform for Supporting Data Science and Cross-Disciplinary Research Collaborations
The growing amount of data and advances in data science have created a need for a new kind of cloud platform that provides users with flexibility, strong security, and the ability to couple with supercomputers and edge devices through high-performance networks.
Selective Sampling-based Scalable Sparse Subspace Clustering
Sparse subspace clustering (SSC) represents each data point as a sparse linear combination of other data points in the dataset.
Statistical Learnability of Generalized Additive Models based on Total Variation Regularization
A generalized additive model (GAM, Hastie and Tibshirani (1987)) is a nonparametric model by the sum of univariate functions with respect to each explanatory variable, i. e., $f({\mathbf x}) = \sum f_j(x_j)$, where $x_j\in\mathbb{R}$ is $j$-th component of a sample ${\mathbf x}\in \mathbb{R}^p$.
Grafting for Combinatorial Boolean Model using Frequent Itemset Mining
To overcome this computational difficulty, we propose an algorithm GRAB (GRAfting for Boolean datasets), which efficiently learns CBM within the $L_1$-regularized loss minimization framework.
DS-MLR: Exploiting Double Separability for Scaling up Distributed Multinomial Logistic Regression
Scaling multinomial logistic regression to datasets with very large number of data points and classes is challenging.
WordRank: Learning Word Embeddings via Robust Ranking
Then, based on this insight, we propose a novel framework WordRank that efficiently estimates word representations via robust ranking, in which the attention mechanism and robustness to noise are readily achieved via the DCG-like ranking losses.
Totally Corrective Boosting with Cardinality Penalization
If actual quantum optimization were to be used with this algorithm in the future, we would expect equivalent or superior results at much smaller time and energy costs during training.
Distributed Stochastic Optimization of the Regularized Risk
Many machine learning algorithms minimize a regularized risk, and stochastic optimization is widely used for this task.
