We describe, at the microscopic level, the dynamics of N interacting components where the probability is very small when N is large that a given component interact more than once, directly or indirectly, up to time t, with any other component. Due to this fact, we can consider, at the macroscopic level, the quadratic s…
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48 results for “interactive component”
JODIE learns dynamic user-item embeddings from interactions, outperforming existing methods.
problem Modeling dynamic user-item interactions for accurate future predictions.
method JODIE uses coupled recurrent models with update, projection, and prediction components, and a novel t-Batch algorithm.
result JODIE outperforms state-of-the-art methods by up to 22.4% on future interaction and state change prediction tasks.
New copula-based models for binary outcomes capture complex interactions.
problem Complex interaction effects in binary outcome models.
method Additive models with copula-based components, no discretization required.
result Better or comparable predictive performance compared to other methods.
Quaternion self-attention reduces computational cost and improves performance.
problem Existing quaternion self-attention increases computational cost and diverges attention distributions.
method Proposes a shared-score quaternion self-attention mechanism.
result Reduces score-computation multiplications by 75% and softmax operations from four to one.
Proposes MCCF to distinguish latent purchasing motivations in user-item interactions.
problem Difficulty in capturing fine-grained user preferences due to complex latent motivations.
method Introduces MCCF with decomposer and combiner modules to identify and recombine latent components.
result Significant performance gains and necessity of considering multiple components demonstrated.
DeepLight: Deep Lightweight Feature Interactions for Accelerating CTR Predictions in Ad Servingcs.LG
DeepLight accelerates CTR predictions in ad serving by 46X.
problem Significantly increased serving delay and high memory usage for ad serving.
method Explicitly searching feature interactions, pruning layers, promoting sparsity.
result Accelerates model inference by 46X on Criteo dataset.
Bayesian method identifies dynamical models with uncertainty quantification.
problem Uncertainty in selecting governing equations for dynamical systems.
method Bayesian sparse identification with model averaging.
result Accurately recovers sparse interaction structures with uncertainty quantification.
Develops a sparse Bayesian model for high-dimensional data.
problem Sparse nonparametric regression with complex interactions.
method Sparse additive Gaussian process with soft interactions.
result Excellent performance in simulated and real data.
Robot learns to imitate human interactions through deep learning.
problem Teaching robots to coordinate actions with human partners.
method Deep learning framework for motion embedding, prediction, and trajectory generation.
result Importance of predictive and adaptive components for successful imitation.
RAMs improve GAMs' accuracy by fitting components to subregions of feature space.
problem Subpar accuracy in GAMs due to inability to capture feature interactions.
method Identify subregions of feature space where interactions are minimized, fitting one component per subregion.
result RAMs offer improved expressiveness compared to GAMs while maintaining interpretability.
Structural Landmarking and Interaction Modelling: on Resolution Dilemmas in Graph Classificationcs.LG
SLIM model tackles graph classification by resolving part-interaction dilemmas.
problem Difficulty in modeling graph parts and their interactions in graph classification.
method SLIM model, which solves resolution dilemmas and leverages explicit interactions.
result SLIM offers improved interpretability, accuracy, and new insights in graph representation learning.
Unified local and global explanations through functional decomposition of low dimensional structures.
problem Tackles the challenge of extracting meaningful local and global explanations from machine learning models.
method Proposes a new identification constraint to decompose the global representation into main and interaction components of arbitrary order.
result Unified local and global explanations by connecting partial dependence plots and interventional SHAP values.
A hybrid model combines piecewise linear and neural components for interpretable predictions.
problem Post-hoc interpretable methods lead to contradictory explanations and lower prediction accuracy.
method Hybrid model with piecewise linear and neural components.
result The model achieves good interpretability and state-of-the-art accuracy.
STRIDE improves explainable AI by efficiently decomposing feature interactions without subset enumeration.
problem Lack of expressive power and high computational cost in existing XAI frameworks.
method STRIDE uses a functional decomposition approach in RKHS, avoiding subset enumeration and focusing on orthogonal components.
result STRIDE achieves a 3.0 times speedup over TreeSHAP and a high R^2 of 0.93 for feature reconstruction.
TopoNTK kernel captures higher-order interactions in simplicial complexes.
problem Graph neural networks miss higher-order interactions in relational systems.
method Introduces TopoNTK, an infinite-width kernel for simplicial message passing.
result TopoNTK captures topology invisible to graph kernels, improving expressivity and interpretability.
Proposes Deep Scenes for interaction-aware scene understanding in reinforcement learning for autonomous driving.
problem Leveraging deep reinforcement learning for high-level decision making in autonomous driving requires handling variable-length sequences of different object types and interactions.
method Introduces Deep Scenes architecture, an extension of Deep Sets or Graph Convolutional Networks, to learn complex interaction-aware scene representations.
result Graph-Q and DeepScene-Q algorithms outperform state-of-the-art methods in evaluations with SUMO.
Surrogate-based analysis of interactions via local effect smooths
problem Detecting and characterizing feature interactions in machine learning models
method Surrogate-based analysis using generalized additive models
result Empirical validation of effectiveness for pairwise interactions
SurvFD and SurvSHAP-IQ provide interpretable survival models by analyzing feature interactions.
problem Non-additivity of hazard and survival functions limits standard additive explanation methods.
method SurvFD decomposes higher-order effects into time-dependent and time-independent components, extending Shapley interactions to time-indexed functions.
result SurvFD and SurvSHAP-IQ offer a new perspective on survival explanations, explicitly characterizing feature interactions.
Two-stage recommender systems show better performance when components interact rather than operate independently.
problem Two-stage recommender systems are often treated as sums of their parts, ignoring interactions between components.
method Used synthetic and real-world data to demonstrate interactions between ranker and nominators. Derived a generalization lower bound and proposed a Mixture-of-Experts approach to learn optimal item pools.
result Independent nominator training can lead to performance on par with random recommendations, highlighting the importance of interactions.
We consider a system of diffusion processes that interact through their empirical mean and have a stabilizing force acting on each of them, corresponding to a bistable potential. There are three parameters that characterize the system: the strength of the intrinsic stabilization, the strength of the external random per…
We study large deviations and rare default clustering events in a dynamic large heterogeneous portfolio of interconnected components. Defaults come as Poisson events and the default intensities of the different components in the system interact through the empirical default rate and via systematic effects that are comm…
New method recovers causal networks from short time-series data.
problem Inferring causal relationships from short time-series data in complex systems.
method Large-scale Nonlinear Granger Causality (lsNGC) approach.
result Captures meaningful interactions from limited observational data.
This paper tackles causal interactions in mixtures of DAGs using interventions.
problem Learning causal interactions among variables governed by a mixture of causal systems.
method Establishes necessary and sufficient conditions for intervention size, designs an adaptive algorithm.
result Identifies true edges in a mixture of DAGs using optimal or near-optimal interventions.
InteractE: Improving Convolution-based Knowledge Graph Embeddings by Increasing Feature Interactionscs.LG
InteractE improves link prediction in KGs by increasing feature interactions.
problem Improving link prediction in knowledge graphs by inferring missing links.
method Feature permutation, novel feature reshaping, circular convolution.
result InteractE outperforms ConvE on multiple KG datasets.
High-Dimensional Inference with the generalized Hopfield Model: Principal Component Analysis and Correctionscond-mat.stat-mech
We consider the problem of inferring the interactions between a set of N binary variables from the knowledge of their frequencies and pairwise correlations. The inference framework is based on the Hopfield model, a special case of the Ising model where the interaction matrix is defined through a set of patterns in the …
Framework for robust matrix estimation with side information.
problem High-dimensional matrix estimation with restrictive structure.
method Flexible framework decomposing matrix into four components: interaction, row, column, and residual.
result Improved imputation accuracy and treatment-effect estimation with side information.
Method tracks conversational flow in debates.
problem Understanding how ideas flow in debates.
method Tracking interactive component of debates.
result Winners use interactive component better than losers.
NRI model learns interactions from data without labels.
problem Learning interactions in systems from unlabeled data.
method Variational auto-encoder with graph neural networks.
result NRI model accurately recovers interactions and predicts dynamics.
Study reveals self-attention's role in learning and generalizing interactions.
problem Understanding self-attention's theoretical role in neural architectures.
method Interacting entities analysis, including multi-agent RL and genetic sequences.
result Self-attention efficiently represents, learns, and generalizes pairwise interactions.
Modelling sparsity, heterogeneity, reciprocity and community structure in temporal interaction datastat.ML
Novel network models capture social interaction features like reciprocity and community.
problem Modeling temporal social interaction data with reciprocity and community structure.
method Self-exciting Hawkes point processes with conditional intensity function.
result Proposed model outperforms competing approaches for link prediction.
JigSaw discovers high-order interactions from random forests.
problem Identifying complex interactions in biological data.
method Random forest structure analysis to find high-order interactions.
result JigSaw can explain outcomes in heart disease and breast cancer.
DeepFM combines deep learning and factorization machines for CTR prediction.
problem Maximizing CTR in recommender systems through sophisticated feature interactions.
method End-to-end learning framework combining DNN and PNN with shared feature input.
result DeepFM-D outperforms existing models, achieving over 10% improvement in CTR.
On Agents and Equilibriaq-fin.GN
This essay discusses the advantages of a probabilistic agent-based approach to questions in theoretical economics, from the nature of economic agents, to the nature of the equilibria supported by their interactions. One idea we propose is that "agents" are meta-individual, hierarchically structured objects, that includ…
Proposes FEFM and DeepFEFM for CTR prediction, outperforming state-of-the-art models.
problem Click-through rate prediction in online applications.
method Field-Embedded Factorization Machine (FEFM) and its deep counterpart DeepFEFM, combining feature embeddings and deep neural networks.
result FEFM and DeepFEFM outperform existing models in CTR prediction tasks.
Attributing forecast gaps to component models in complex model suites
problem Attributing forecast gaps between model-suite forecasts and realized outcomes
method Formalizing walk analysis and adapting order-independent attribution frameworks
result Deriving efficient formulas for elementwise and vectorized gap attribution
GENESIS generates and samples 3D scenes by capturing object interactions.
problem Lack of models that explicitly capture object interactions in scene generation.
method Object-centric latent variables, spatial GMM, amortized inference, autoregressive prior.
result First object-centric generative model of 3D visual scenes.
Vector Space of Cyclesstat.ML
Develop a variational framework for statistical inference on cyclic interactions.
problem Estimating and comparing large-scale recurrent organization in directed interactions.
method Represent directed interactions as edge flows on a simplicial complex and evolve under an energy-minimizing dynamical system.
result Separate transient interaction components from persistent harmonic flows, yielding a low-dimensional cycle space.
Paper addresses theoretical risks in neural MCCFR, proposing Robust Deep MCCFR for improved performance.
problem Theoretical risks in neural MCCFR, especially in large games.
method Adaptive framework with selective component deployment, including target networks, exploration, and variance-aware training.
result Robust Deep MCCFR achieves significant exploitability improvements in both Kuhn and Leduc Poker.
The extended Wild sums considered in this article generalize the classi- cal Wild sums of statistical physics. We first show how to obtain explicit solutions for the evolution equation of a large system where the interactions are given by a single, but general, interacting kernel which involves m components, for a fixe…
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.
Proposes a credit scoring system for Aave accounts.
problem Scoring the creditworthiness of DeFi accounts.
method Tree-based binary classifier for predicting delinquency.
result Validated the credit scoring system for Aave accounts.
The paper tackles attributing forecast gaps in complex model suites.
problem Attributing forecast gaps to individual component models in complex model suites.
method Formalized walk analysis, adapted LMDI and Shapley value approaches.
result Developed efficient formulas for gap attribution in practical portfolio-scale examples.
iRF detects stable high-order interactions in genomics data.
problem Understanding high-order interactions in genomics data.
method Iterative Random Forest algorithm (iRF) for stable high-order interaction detection.
result iRF identifies stable high-order interactions with computational cost similar to Random Forest.
A new method for sparse regression using principal components.
problem Wide data with many features and few observations.
method Combines lasso () sparsity with quadratic penalty towards principal components.
result Powerful feature selection and group-wise shrinkage.
RKT model improves knowledge tracing by considering exercise relations and student forget behavior.
problem Traditional KT models fail to consider both exercise relations and student forget behavior.
method RKT model uses relation-aware self-attention to incorporate exercise relations and student forget behavior.
result RKT model outperforms state-of-the-art KT methods on real-world datasets.
MGpi model predicts social actions in group interactions.
problem Social interaction among multiple agents and groups.
method Deep neural network with Kinesic-Proxemic-Message gate for social signal gating.
result Achieves state-of-the-art performance in group identification.
Study identifies stable configurations of intertwined threads with repulsive interactions.
problem Stable configurations of entangled systems with repulsive interactions.
method Analysis of steepest descent flow of an energy functional.
result Existence and uniqueness of stable configuration of two layers drifting apart at rate.
Study on existence of ground states on curved spaces with conditions on potential growth.
problem Existence of ground states for aggregation-diffusion models on Cartan-Hadamard manifolds.
method Investigation of a free energy functional on Cartan-Hadamard manifolds, considering entropy and interaction energies.
result Necessary and sufficient conditions for existence of ground states are found, depending on the growth of the attractive potential.