arXiv research

New regularization scheme for FMs improves feature interaction selection.

AdaEnsemble learns adaptive feature interactions for CTR prediction.

Methodology for learning sparse models using all multiplicative interactions efficiently.

SIAN bridges simple models to neural networks by identifying necessary feature combinations.

Inspired by the immense success of deep learning, graph neural networks (GNNs) are widely used to learn powerful node representations and have demonstrated promising performance on different graph learning tasks. However, most real-world graphs often come with high-dimensional and sparse node features, rendering the le…

In various web applications like targeted advertising and recommender systems, the available categorical features (e.g., product type) are often of great importance but sparse. As a widely adopted solution, models based on Factorization Machines (FMs) are capable of modelling high-order interactions among features for …

SDAMI enhances interpretable high-dimensional regression with sparse deep learning and footprint principle.

New method identifies important features and interactions in RF models.

Randomized feature models learn interaction kernels from agent paths.

Taking into account high-order interactions among covariates is valuable in many practical regression problems. This is, however, computationally challenging task because the number of high-order interaction features to be considered would be extremely large unless the number of covariates is sufficiently small. In thi…

Learning sparse linear models with two-way interactions is desirable in many application domains such as genomics. l1-regularised linear models are popular to estimate sparse models, yet standard implementations fail to address specifically the quadratic explosion of candidate two-way interactions in high dimensions, a…

This study explores how feature graphs enhance GNNs' performance in modeling interactions.

Generalized linear models with nonlinear feature transformations are widely used for large-scale regression and classification problems with sparse inputs. Memorization of feature interactions through a wide set of cross-product feature transformations are effective and interpretable, while generalization requires more…

This paper is concerned with the problems of interaction screening and nonlinear classification in a high-dimensional setting. We propose a two-step procedure, IIS-SQDA, where in the first step an innovated interaction screening (IIS) approach based on transforming the original $p$-dimensional feature vector is propose…

Random Intersection Chains selects important interactions from categorical features.

CAN approximates explicit feature interactions for CTR prediction.

This work uses decision trees to encode relevant features and their interactions into neural networks, improving model performance.

AEN-SAEs address feature starvation in sparse autoencoders by stabilizing the geometric alignment of sparse coding.

Model financial default cascades on sparse graphs via hitting times.

Regularization improves stability and consistency of sparse autoencoders.

RGAM builds more accurate models by preferring linear features over non-linear ones.

New algorithm learns sparse linear MDPs with polynomial interactions, improving sample complexity.

Ising models describe the joint probability distribution of a vector of binary feature variables. Typically, not all the variables interact with each other and one is interested in learning the presumably sparse network structure of the interacting variables. However, in the presence of latent variables, the convention…

Predictive modeling applications increasingly use data representing people's behavior, opinions, and interactions. Fine-grained behavior data often has different structure from traditional data, being very high-dimensional and sparse. Models built from these data are quite difficult to interpret, since they contain man…

Visual reranking is effective to improve the performance of the text-based video search. However, existing reranking algorithms can only achieve limited improvement because of the well-known semantic gap between low level visual features and high level semantic concepts. In this paper, we adopt interactive video search…

Sparser Random Feature Models via IMP (ShRIMP) efficiently learns sparse models for high-dimensional data.

User response prediction makes a crucial contribution to the rapid development of online advertising system and recommendation system. The importance of learning feature interactions has been emphasized by many works. Many deep models are proposed to automatically learn high-order feature interactions. Since most featu…

Feature selection can efficiently identify the most informative features with respect to the target feature used in training. However, state-of-the-art vector-based methods are unable to encapsulate the relationships between feature samples into the feature selection process, thus leading to significant information los…

New method initializes MLPs for tabular data with tree-based feature interactions.

DCN-V2 improves deep & cross network for web-scale learning to rank systems.

Method learns dynamics from sparse, irregular feature data.

A new method for embedding sparse high-order interactions.

BiSHop tackles tabular data challenges with sparse Hopfield layers.

BaGGLS models biological interactions using Bayesian shrinkage for interpretability.

Quadratic regression involves modeling the response as a (generalized) linear function of not only the features $x^{j_1}$ but also of quadratic terms $x^{j_1}x^{j_2}$. The inclusion of such higher-order "interaction terms" in regression often provides an easy way to increase accuracy in already-high-dimensional problem…

A new method uses CPD to efficiently model feature interactions in non-sequential data.

We develop a class of rules spanning the range between quadratic discriminant analysis and naive Bayes, through a path of sparse graphical models. A group lasso penalty is used to introduce shrinkage and encourage a similar pattern of sparsity across precision matrices. It gives sparse estimates of interactions and pro…

New models improve machine learning accuracy and transparency in finance.

A theory of feature geometry using spectral analysis of weight matrices.

With the growing importance of personalized recommendation, numerous recommendation models have been proposed recently. Among them, Matrix Factorization (MF) based models are the most widely used in the recommendation field due to their high performance. However, MF based models suffer from cold start problems where us…

SPARTAN learns sparse interaction graphs between objects in scenes.

Sparse Bayesian learning algorithm for estimating interaction kernels in Motsch-Tadmor model.

A framework detects nonlinear and interaction effects in epidemiological data with uncertainty quantification.

Neural networks are usually not the tool of choice for nonparametric high-dimensional problems where the number of input features is much larger than the number of observations. Though neural networks can approximate complex multivariate functions, they generally require a large number of training observations to obtai…

We propose a novel diverse feature selection method based on determinantal point processes (DPPs). Our model enables one to flexibly define diversity based on the covariance of features (similar to orthogonal matching pursuit) or alternatively based on side information. We introduce our approach in the context of Bayes…

In this study, we analyzed the activity of monkey V1 neurons responding to grating stimuli of different orientations using inference methods for a time-dependent Ising model. The method provides optimal estimation of time-dependent neural interactions with credible intervals according to the sequential Bayes estimation…

Factorization Machine (FM) is a widely used supervised learning approach by effectively modeling of feature interactions. Despite the successful application of FM and its many deep learning variants, treating every feature interaction fairly may degrade the performance. For example, the interactions of a useless featur…

Deep filtering improves robustness of models from noisy, sparse data.