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Found 29 papers, 7 papers with code
Molecular Graph Generation by Decomposition and Reassembling
Designing molecular structures with desired chemical properties is an essential task in drug discovery and material design.
Analyzing Tree Architectures in Ensembles via Neural Tangent Kernel
This kernel leads to the remarkable finding that only the number of leaves at each depth is relevant for the tree architecture in ensemble learning with an infinite number of trees.
Fast Rank-1 NMF for Missing Data with KL Divergence
We propose a fast non-gradient-based method of rank-1 non-negative matrix factorization (NMF) for missing data, called A1GM, that minimizes the KL divergence from an input matrix to the reconstructed rank-1 matrix.
Additive Poisson Process: Learning Intensity of Higher-Order Interaction in Poisson Processes
We present the Additive Poisson Process (APP), a novel framework that can model the higher-order interaction effects of the intensity functions in Poisson processes using projections into lower-dimensional space.
A Neural Tangent Kernel Perspective of Infinite Tree Ensembles
In practical situations, the tree ensemble is one of the most popular models along with neural networks.
Unintended Effects on Adaptive Learning Rate for Training Neural Network with Output Scale Change
A multiplicative constant scaling factor is often applied to the model output to adjust the dynamics of neural network parameters.
Fast Tucker Rank Reduction for Non-Negative Tensors Using Mean-Field Approximation
We present an efficient low-rank approximation algorithm for non-negative tensors.
The Volume of Non-Restricted Boltzmann Machines and Their Double Descent Model Complexity
The double descent risk phenomenon has received much interest in the machine learning and statistics community.
Sample Space Truncation on Boltzmann Machines
We present a lightweight variant of Boltzmann machines via sample space truncation, called a truncated Boltzmann machine (TBM), which has not been investigated before while can be naturally introduced from the log-linear model viewpoint.
A Deep Architecture for Log-Linear Models
We present a novel perspective on deep learning architectures using a partial order structure, which is naturally incorporated into the information geometric formulation of the log-linear model.
Learning Joint Intensity in a Multivariate Poisson Process on Statistical Manifolds
Learning of the model is achieved via convex optimization, thanks to the dually flat statistical manifold generated by the log-linear model.
Additive Poisson Process: Learning Intensity of Higher-Order Interaction in Stochastic Processes
We present the Additive Poisson Process (APP), a novel framework that can model the higher-order interaction effects of the intensity functions in stochastic processes using lower dimensional projections.
Fast Rank Reduction for Non-negative Matrices via Mean Field Theory
We propose an efficient matrix rank reduction method for non-negative matrices, whose time complexity is quadratic in the number of rows or columns of a matrix.
Double Descent Risk and Volume Saturation Effects: A Geometric Perspective
The appearance of the double-descent risk phenomenon has received growing interest in the machine learning and statistics community, as it challenges well-understood notions behind the U-shaped train-test curves.
Hierarchical Probabilistic Model for Blind Source Separation via Legendre Transformation
We present a novel blind source separation (BSS) method, called information geometric blind source separation (IGBSS).
Bias-Variance Trade-Off in Hierarchical Probabilistic Models Using Higher-Order Feature Interactions
However, it is well known that increasing the number of parameters also increases the complexity of the model which leads to a bias-variance trade-off.
Learning Graph Representation via Formal Concept Analysis
We present a novel method that can learn a graph representation from multivariate data.
Transductive Boltzmann Machines
We present transductive Boltzmann machines (TBMs), which firstly achieve transductive learning of the Gibbs distribution.
Legendre Decomposition for Tensors
We present a novel nonnegative tensor decomposition method, called Legendre decomposition, which factorizes an input tensor into a multiplicative combination of parameters.
Bias-Variance Decomposition for Boltzmann Machines
We achieve bias-variance decomposition for Boltzmann machines using an information geometric formulation.
Finding Statistically Significant Interactions between Continuous Features
The search for higher-order feature interactions that are statistically significantly associated with a class variable is of high relevance in fields such as Genetics or Healthcare, but the combinatorial explosion of the candidate space makes this problem extremely challenging in terms of computational efficiency and proper correction for multiple testing.
Tensor Balancing on Statistical Manifold
To theoretically prove the correctness of the algorithm, we model tensors as probability distributions in a statistical manifold and realize tensor balancing as projection onto a submanifold.
Selective Inference Approach for Statistically Sound Predictive Pattern Mining
The main obstacle of this problem is in the difficulty of taking into account the selection bias, i. e., the bias arising from the fact that patterns are selected from extremely large number of candidates in databases.
Halting in Random Walk Kernels
Random walk kernels measure graph similarity by counting matching walks in two graphs.
Fast and Memory-Efficient Significant Pattern Mining via Permutation Testing
Westfall-Young light opens the door to significant pattern mining on large datasets that previously led to prohibitive runtime or memory costs.
Identifying Higher-order Combinations of Binary Features
Finding statistically significant interactions between binary variables is computationally and statistically challenging in high-dimensional settings, due to the combinatorial explosion in the number of hypotheses.
Significant Subgraph Mining with Multiple Testing Correction
An open question, however, is whether this strategy of excluding untestable hypotheses also leads to greater statistical power in subgraph mining, in which the number of hypotheses is much larger than in itemset mining.
Rapid Distance-Based Outlier Detection via Sampling
Distance-based approaches to outlier detection are popular in data mining, as they do not require to model the underlying probability distribution, which is particularly challenging for high-dimensional data.
Efficient network-guided multi-locus association mapping with graph cuts
We present SConES, a new efficient method to discover sets of genetic loci that are maximally associated with a phenotype, while being connected in an underlying network.
