Study compares different integrals for optimal portfolio optimization with insider information.
problem Optimizing portfolios in a financial market with insider information.
method Anticipating stochastic calculus and various integrals (Russo-Vallois forward, Ayed-Kuo, Hitsuda-Skorokhod).
result The Hitsuda-Skorokhod and Ayed-Kuo integrals do not provide a financially meaningful investment strategy.
This work integrates differentiation and integration in Physics-Informed Neural Networks.
problem Solving integro-differential equations and computing integral transforms.
method Augmenting Physics-Informed Neural Networks with automatic integration.
result Solving complex integral transforms and integro-differential equations.
The ability to integrate information in the brain is considered to be an essential property for cognition and consciousness. Integrated Information Theory (IIT) hypothesizes that the amount of integrated information (Φ) in the brain is related to the level of consciousness. IIT proposes that to quantify information i…
SAIN integrates user-item feedback with content attributes for better recommendation.
problem Cold start problems in recommendation models due to sparse user-item interactions.
method SAIN uses a self-attention mechanism to capture feature interactions and an information integration layer to combine feedback and content information.
result SAIN outperforms state-of-the-art models by 2.13% on public datasets.
A new model DKMPP integrates covariates and uses an integration-free method for spatio-temporal point processes.
problem Training intractable deep spatio-temporal point processes with multimodal covariates.
method DKMPP uses a deep kernel to model complex relationships and an integration-free score matching method.
result DKMPP and score-based estimators outperform baseline models in spatio-temporal point processes.
New method uses iterated integrals to bridge geometric and homotopy information.
problem Lack of effective methods to connect geometric and homotopy information.
method Introducing Chen's iterated integrals on loop spaces.
result Upper bounds for Gromov's distortion and non-existence of small-volume cycles.
Paper designs Poisson integrators using machine learning.
problem Designing integrators that preserve Poisson geometry.
method Reformulated as an optimization problem in Hamilton-Jacobi PDE, solved using machine learning.
result Machine learning approximates solutions to Hamilton-Jacobi PDE.
Despite its great success, machine learning can have its limits when dealing with insufficient training data. A potential solution is the additional integration of prior knowledge into the training process which leads to the notion of informed machine learning. In this paper, we present a structured overview of various…
Integrates side information for robust portfolio optimization.
problem Portfolio optimization under uncertainty and side information.
method Distributionally robust optimization with optimal transport ambiguity set.
result The problem can be reformulated as a finite-dimensional optimization problem.
Proposes a method to integrate prior knowledge into trajectory prediction models.
problem Improving accuracy and robustness in trajectory prediction models.
method Continual learning approach that allows integration of arbitrary prior knowledge and probabilistic predictions.
result Outperforms non-informed and informed learning methods, using half as many observation examples.
Survey of integrating physics knowledge into machine learning models.
problem Mitigating data shortage and ensuring physical plausibility.
method Combining physics knowledge with machine learning models.
result Summarizes recent works in physics-informed machine learning.
Dropout is used to avoid overfitting by randomly dropping units from the neural networks during training. Inspired by dropout, this paper presents GI-Dropout, a novel dropout method integrating with global information to improve neural networks for text classification. Unlike the traditional dropout method in which the…
Paper introduces a novel error measure for neural networks integrating statistical and information theory.
problem No single error measure is universally best for neural network training.
method Developed a novel error measure EExpAbs and integrated it into the Levenberg-Marquardt algorithm. result Self-adaptive, dynamic learning algorithm improves both model accuracy and training process.
Recent advances in the field of network embedding have shown the low-dimensional network representation is playing a critical role in network analysis. However, most of the existing principles of network embedding do not incorporate auxiliary information such as content and labels of nodes flexibly. In this paper, we t…
The paper integrates AI and expert knowledge to optimize radiotherapy decisions.
problem Optimizing radiation dose planning considering patient-specific information.
method Integrating Gaussian process models with deep neural networks to quantify uncertainty.
result Improves AI model performance and guides clinical decision making.
The paper proposes a method to integrate prior information into penalized regression.
problem Improving predictive performance in high-dimensional tasks with prior information.
method Integrating multiple sources of prior information into penalized regression.
result The method improves predictive performance, as shown by simulations and applications.
Background: Predictive, stable and interpretable gene signatures are generally seen as an important step towards a better personalized medicine. During the last decade various methods have been proposed for that purpose. However, one important obstacle for making gene signatures a standard tool in clinics is the typica…
The paper studies a gradient system on a beta statistical manifold, proving integrability and deriving explicit expressions.
problem Investigating the geometry and integrability of a gradient system on a bivariate beta statistical manifold.
method Proving the system is Hamiltonian and admitting a Lax pair representation, deriving explicit expressions using Stirling's approximation, and identifying the Hamiltonian function.
result The gradient flow is linearizable in dual affine coordinates, and the system is completely integrable.
NKI integrates obfuscated datasets using nonlinear kernels for improved data collaboration.
problem Privacy-preserving data collaboration with reduced reconstruction risk.
method Formulates linear kernel integration, kernelizes it, and introduces graph regularization and centering constraints.
result NKI improves classification accuracy over existing linear integration methods under nonlinear dimensionality reduction.
GPCCA integrates multi-modal data with missing values, improving clustering accuracy.
problem Integrating and analyzing multi-modal data with missing values and partial observations.
method Generalized Probabilistic Canonical Correlation Analysis (GPCCA) for unsupervised multi-modal data integration and dimensionality reduction.
result GPCCA outperforms existing methods in capturing essential patterns across modalities and provides robust low-dimensional embeddings.
New BdryMatérn GP model for reliable boundary integration on irregular domains.
problem Incorporating boundary information in Gaussian process models for complex phenomena.
method Proposes a novel BdryMatérn GP framework with a new covariance kernel derived via path integral and stochastic PDE.
result Sample paths from the BdryMatérn GP satisfy desired boundaries with smoothness control on derivatives.
Physics-informed kernel learning integrates physical priors into machine learning models.
problem Tackles the integration of physical laws into machine learning models for improved accuracy and efficiency.
method Uses Fourier methods to approximate the kernel and minimizes a physics-informed risk function.
result Demonstrates PIKL outperforms physics-informed neural networks and traditional PDE solvers in various scenarios.
New bounds for optimal transport using Gaussian processes and rate-distortion functions.
problem Finding bounds for entropic optimal transport with mutual information constraints.
method Lifting technique to construct a Gaussian process and applying the majorizing measure theorem.
result Maximum expected inner product is equivalent to a truncated integral involving the rate-distortion function.
The Fredholm integral equation of the first kind improves solutions for ill-posed supervised learning problems with limited data.
problem Ill-posed supervised learning problems with insufficient data.
method Using the Fredholm integral equation of the first kind (FIFK) with semi-supervised assumptions and MSDF methods.
result Improved accuracy and stability in solutions for ill-posed problems.
AIME embeds multi-omics data to adjust confounders and find related features.
problem Extracting meaningful relationships between complex omics data types while accounting for confounders.
method Autoencoder-based deep learning approach that incorporates clinical confounders.
result AIME effectively adjusts for confounders and extracts biologically relevant features.
New integration method improves BSDE-based PDE solvers.
problem Discretization bias in standard BSDE-based solvers.
method Proposed Stratonovich-based BSDE formulation with stochastic Heun integration.
result Eliminates bias issues and outperforms EM-based variants.
MaGNet integrates local and global graph information for interpretable results.
problem Lack of interpretability and inability to learn varying-order representations in GNNs.
method Model-agnostic Graph Neural Network (MaGNet) framework integrating estimation and interpretation models.
result MaGNet effectively integrates information of various orders and provides meaningful, interpretable results.
A novel method integrates feature and topology views for unsupervised graph representation learning.
problem Lack of mutual information across feature and topology views in graph representation learning.
method Proposes a multi-view representation learning module and a common representation learning module using mutual information maximization and reconstruction loss minimization.
result Demonstrates effectiveness in integrating feature and topology views, achieving comparable or better performance than supervised methods.
This paper proposes new methods for ALR that consider informativeness, representativeness, and diversity.
problem Efficiently label samples for regression models with limited labeled data.
method Integrates informativeness, representativeness, and diversity in pool-based sequential active learning.
result Demonstrates effectiveness of new ALR approaches on 12 datasets.
CP2 uses geometric information to improve conformal prediction robustness.
problem CP fails under geometric data shifts, losing coverage guarantees.
method Integrates geometric pose information into CP via canonicalization.
result Integrating geometric information with CP ensures robustness under geometric shifts.
Although information extraction and coreference resolution appear together in many applications, most current systems perform them as ndependent steps. This paper describes an approach to integrated inference for extraction and coreference based on conditionally-trained undirected graphical models. We discuss the advan…
We study an intrinsic distribution, called polar, on the space of l-dimensional integral elements of the higher order contact structure on jet spaces. The main result establishes that this exterior differential system is the prolongation of a natural system of PDEs, named pasting conditions, on sections of the bundle…
Proposes a method to integrate learner models robustly against misspecifications.
problem Misspecifications in learner models and parameter sharing patterns degrade prediction accuracy.
method Sequentially incorporates additional learners based on user-specified parameter sharing patterns.
result Data-adaptively selects the most suitable way of parameter sharing to enhance predictive performance.
Paper proposes a network framework for prosumers to manage peak loads in Iran.
problem Balancing renewable prosumers' self-sufficiency with grid integration under uncertainty.
method Distributed contextual stochastic optimization (DCSO) framework with consensus-based sharing.
result Integration of prediction and optimization reduces peak loads and costs.
We consider a problem of data integration. Consider determining which genes affect a disease. The genes, which we call predictor objects, can be measured in different experiments on the same individual. We address the question of finding which genes are predictors of disease by any of the experiments. Our formulation i…
The paper predicts diseases using both clinical and genomics data.
problem Clinical predictions using genomics data are not common.
method Integrated clinical and genomics datasets, machine learning, Principal Component Analysis for feature selection.
result 73% accuracy in predicting 75 disease classes.
This paper evaluates heterogeneous information fusion using multi-task Gaussian processes in the context of geological resource modeling. Specifically, it empirically demonstrates that information integration across heterogeneous information sources leads to superior estimates of all the quantities being modeled, compa…
Statsformer validates and adapts LLM-derived semantic priors for improved supervised learning.
problem Unreliable semantic priors from LLMs can degrade supervised learning performance.
method Adapts LLM-derived feature scores into a family of learner-specific prior-injection mechanisms, calibrating their influence using out-of-fold validation.
result Improves prediction performance by adaptively downweighting unreliable LLM priors, ensuring a guardrailed statistical learning system.
Unified physics-informed learning method improves generalization performance.
problem Lack of theoretical analysis for hybrid settings with incomplete physical constraints.
method Unified residual form unifying collocation and variational methods, establishing generalization performance governed by affine variety dimension.
result Generalization performance is determined by affine variety dimension, not just the number of parameters.
A model integrates CNN and LSTM with LLM for better stock forecasting.
problem Complex stock market prediction with volatile patterns and multifaceted data.
method Two-Level Conv-LSTM Neural Network with LLM for holistic stock advising.
result Improved prediction accuracy and contextual insights.
We undertake a study of markets from the perspective of a financial agent with limited access to information. The set of wealth processes available to the agent is structured with reasonable economic properties, instead of the usual practice of taking it to consist of stochastic integrals against a semimartingale integ…
scICML integrates multi-omics data from single cells using co-clustering.
problem High noise and sparsity in multi-omics data from single cells.
method Information-theoretic co-clustering-based multi-view learning.
result Improves clustering performance and provides biological insights.
Proposes a new model for more accurate demand forecasting considering dynamic contextual information.
problem Traditional methods fail to capture spatio-temporal and dynamic contextual dependencies in demand forecasting.
method Integrates temporal, relational, spatial, and dynamic contextual dependencies using a Context Integrated Graph Neural Network (CIGNN).
result CIGNN outperforms state-of-the-art baselines in multi-step ahead demand forecasting.
A new deep learning framework captures multi-scale spatio-temporal dependencies.
problem Designing and analyzing deep learning models for complex spatio-temporal analytics.
method Developed an I2DRNN model with three modules for integrating and learning multi-scale spatio-temporal data. result The I2DRNN model outperforms classical and state-of-the-art models in capturing meaningful multi-scale spatio-temporal dependencies. Physics-informed machine learning models improve biomolecular system simulations.
problem Modeling unresolved interactions beyond classical force fields.
method Physics-informed neural networks and operator learning.
result Accurate, mechanistic, generalizable models for long-timescale kinetics.
CopulaGNN integrates graph representational and correlational roles for better node-level predictions.
problem Graphs encode diverse roles in node-level prediction tasks, but GNNs struggle with correlational information.
method Copula theory to describe multivariate dependence, integrating representational and correlational graph information.
result CopulaGNN improves GNN performance on regression tasks by leveraging both types of graph information.
GEAR uses graphs to integrate and reason over multiple evidence for fact verification.
problem Fact verification requires integrating and reasoning over multiple pieces of evidence.
method GEAR employs a graph-based framework to transfer information among evidence and uses BERT for improved performance.
result GEAR achieves a promising test FEVER score of 67.10% on a large-scale benchmark dataset.
Bayesian DOE accelerates experimental design with improved efficiency.
problem Enhancing experimental design efficiency and reliability.
method Bayesian framework, conditional density estimation, informative data selection.
result Significantly improved computational efficiency of experimental design.