New regularization scheme for FMs improves feature interaction selection.
problem Feature selection in FMs leads to loss of feature interactions.
method Proposes a new regularization scheme for FMs with upper bound of ℓ1 regularizer. result Improves feature interaction selection without restricting sparsity patterns.
AdaEnsemble learns adaptive feature interactions for CTR prediction.
problem Learning feature interactions for CTR prediction in recommender systems and Ads ranking.
method AdaEnsemble is a Sparsely-Gated Mixture-of-Experts (SparseMoE) architecture that dynamically selects feature interaction depth.
result AdaEnsemble achieves better prediction accuracy and inference efficiency compared to state-of-the-art models.
Methodology for learning sparse models using all multiplicative interactions efficiently.
problem Learning high-order feature interactions with fine control.
method Fine Control Kernel framework, combining Fenchel Duality and Apriori algorithm.
result Efficiently solves large sparse learning problems with sparse feature screening rules.
SIAN bridges simple models to neural networks by identifying necessary feature combinations.
problem The gap between simple models and powerful neural networks in performance.
method Feature interaction detection and sparse selection algorithm.
result Competitive performance across multiple tabular datasets with optimal tradeoff.
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…
SDAMI enhances interpretable high-dimensional regression with sparse deep learning and footprint principle.
problem Personalized models for small samples and high-dimensional features with interpretability.
method Sparse Deep Additive Model with Interactions (SDAMI) combining sparsity-driven feature selection and deep subnetworks.
result SDAMI successfully identifies pure interactions with near-zero false positive rates.
SeqFM models dynamic and sequential features for better predictive analytics.
problem Inadequate handling of sequential dependencies in existing FM-based models.
method Introduces SeqFM, a novel model that incorporates multi-view self-attention to model static, dynamic, and their interactions.
result SeqFM outperforms existing models in ranking, classification, and regression tasks on six large-scale datasets.
New method identifies important features and interactions in RF models.
problem Limited theoretical understanding of local feature and interaction importance in RF models.
method Combines global and local analysis to identify frequent feature co-occurrences.
result Proves consistent recovery of true local signal features and interactions.
Randomized feature models learn interaction kernels from agent paths.
problem Learning interaction kernels from noisy agent paths.
method Randomized feature algorithm and sparse regression.
result Pruned features reduce overfitting and lower simulation cost.
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.
problem Improving GNNs' ability to model feature interactions effectively.
method Investigates feature graphs and their importance in GNNs, using experiments and theoretical support.
result Edges between interacting features are crucial for GNNs, while non-interaction edges can degrade performance.
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.
problem Heavy computational burden in considering all interactions for categorical features.
method Randomly generates chains of intersections, estimates and selects frequent patterns.
result Selected patterns are the most frequent in the data set.
CAN approximates explicit feature interactions for CTR prediction.
problem Learning explicit feature interactions from sparse features.
method Co-Action Network approximates explicit pairwise feature interactions without introducing too many additional parameters.
result CAN outperforms state-of-the-art CTR models and the cartesian product method.
This work uses decision trees to encode relevant features and their interactions into neural networks, improving model performance.
problem Overfitting in neural networks with many irrelevant variables.
method Defines a mapping to encode decision tree extracted relationships into a neural network.
result The approach outperforms fully connected neural networks and tree-based methods.
AEN-SAEs address feature starvation in sparse autoencoders by stabilizing the geometric alignment of sparse coding.
problem Feature starvation in sparse autoencoders, leading to unstable and misaligned representations.
method Adaptive Elastic Net SAEs (AEN-SAEs) combine ℓ2 and ℓ1 terms to stabilize the sparse coding map and control feature interactions. result AEN-SAEs mitigate feature starvation without heuristic resampling, maintaining competitive reconstruction abilities.
Model financial default cascades on sparse graphs via hitting times.
problem Capturing systemic risk in large, sparsely-connected financial networks.
method Dynamic particle systems with hitting times and convergence theory.
result Characterization of default time distribution in tree-like networks.
Regularization improves stability and consistency of sparse autoencoders.
problem Varying features across random seeds and training choices in SAEs.
method Added L1 or L2 penalties on encoder and decoder weights.
result L2 regularization increases cross-seed feature consistency.
RGAM builds more accurate models by preferring linear features over non-linear ones.
problem Building accurate models when linearity assumption is poor.
method Multi-stage algorithm guided by the principle of preferring linear features.
result RGAM can fit sparse generalized additive models at scale for various data types.
New algorithm learns sparse linear MDPs with polynomial interactions, improving sample complexity.
problem Learning optimal policies in sparse linear MDPs with limited interactions and unknown features.
method Developed a polynomial-time algorithm using feature selection and emulator for sparse linear MDPs.
result First polynomial-time algorithm for learning near-optimal policies in k-sparse linear MDPs.
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…
IntHT solves sparse quadratic regression in sub-quadratic time and space.
problem Sparse quadratic regression in high-dimensional problems.
method Interaction Hard Thresholding (IntHT) is a variant of Iterative Hard Thresholding tailored for quadratic structures.
result IntHT provably converges to a consistent estimate under high-dimensional sparse recovery assumptions.
Sparser Random Feature Models via IMP (ShRIMP) efficiently learns sparse models for high-dimensional data.
problem Learning sparse models for high-dimensional data with sparse variable dependencies.
method Iterative Magnitude Pruning applied to Random Feature Models.
result ShRIMP achieves better or competitive test accuracy compared to state-of-the-art methods.
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.
problem No dedicated neural network architecture for tabular data.
method Sparse tree-based initialization for MLPs.
result New method outperforms default MLP initialization and gradient boosting.
DCN-V2 improves deep & cross network for web-scale learning to rank systems.
problem Efficiently learning feature interactions in large-scale recommender systems.
method Proposes DCN-V2, an improved framework for deep & cross network learning.
result DCN-V2 outperforms state-of-the-art algorithms on benchmark datasets.
Method learns dynamics from sparse, irregular feature data.
problem Learn system dynamics from sparse, irregularly sampled feature time series.
method Formulates as high-dimensional linear regression using signatures.
result Oracle bound on prediction error with explicit sampling dependencies.
A new method for embedding sparse high-order interactions.
problem Learning embeddings from sparse high-order interaction events.
method Hybridizing sparse hypergraph and matrix Gaussian processes.
result Strong asymptotic bounds on sparsity ratio.
BiSHop tackles tabular data challenges with sparse Hopfield layers.
problem Non-rotationally invariant data structure and feature sparsity in tabular data.
method Sequential column-wise and row-wise processing through interconnected directional learning modules with generalized sparse modern Hopfield layers.
result BiSHop surpasses current SOTA methods with significantly less hyperparameter tuning.
BaGGLS models biological interactions using Bayesian shrinkage for interpretability.
problem Interpreting complex interactions in high-dimensional biological data.
method Bayesian group global-local shrinkage prior with variational approximation.
result BaGGLS outperforms other methods in interaction detection and scalability.
A new method uses CPD to efficiently model feature interactions in non-sequential data.
problem Efficiently modeling feature interactions in non-sequential data with high computational and memory costs.
method Implicitly represent model parameters as a tensor, factorize into a compact Tensor Train (TT) format, and use Canonical Polyadic (CP) Decomposition for invariance to feature ordering.
result The proposed CP-based predictor outperforms other TN-based predictors on sparse data and matches neural network performance on dense non-sequential tasks.
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.
problem Black-box machine learning models lack interpretability in regulated industries.
method Introducing generalized groves of neural additive models with clear feature categories and interactions.
result Generalized groves of neural additive models achieve high accuracy with predominantly linear and sparse nonlinear components.
A theory of feature geometry using spectral analysis of weight matrices.
problem Current methods decompose neural network activations into sparse linear features, losing geometric structure.
method Develops a theory by analyzing the spectra of weight-derived matrices, introducing the frame operator.
result Features collapse onto single eigenspaces, organizing into tight frames, and admit discrete classification.
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.
problem Capturing structured interactions between entities in complex settings remains challenging.
method SPARTAN is a Transformer-based world model that learns sparse, context-dependent interaction graphs through sparsity regularization on attention patterns.
result SPARTAN achieves significantly improved few-shot adaptation to dynamics changes and robustness against distractors.
Sparse Bayesian learning algorithm for estimating interaction kernels in Motsch-Tadmor model.
problem Data-driven identification of asymmetric interaction kernels in the Motsch-Tadmor model.
method Variational framework reformulating kernel identification as a subspace identification problem; sparse Bayesian learning algorithm with informative priors.
result Accurate, robust, and interpretable estimation of interaction kernels across various noise levels and data regimes.
A framework detects nonlinear and interaction effects in epidemiological data with uncertainty quantification.
problem Lack of reliable inference for ML-discovered nonlinearities and interactions in epidemiological data.
method Combines Bayesian sparse regression, tree ensembles, and Shapley values.
result Valid uncertainty quantification for feature effects at the individual level.
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.
problem Challenges in understanding microscopic interactions from noisy, sparse, and biased data.
method Statistical approach based on deep filtering of nonlinear feature networks.
result Physicochemical models are more robust, transparent, and generalize better.