Develops numerical methods for PDEs on hypergraphs and networks.
problem Solving PDEs on complex geometric structures like hypergraphs and networks.
method Hybrid finite element methods, focusing on hybrid discontinuous Galerkin methods.
result Derives numerical approximations for PDEs on hypergraphs and networks.
Hybrid framework optimizes reinsurance using generative models and reinforcement learning.
problem Traditional reinsurance optimization relies on restrictive assumptions and static designs.
method Combines VAEs for joint distribution learning and PPO for dynamic treaty parameter adaptation.
result Hybrid method produces more resilient outcomes with higher surpluses and lower tail risk.
Develops a hybrid deep learning model for stock price prediction.
problem Predicting daily stock prices in the stock market.
method Representation learning with Stock2Vec embedding and temporal convolutional layers.
result Achieves better performance on stock price prediction than benchmarks.
Most existing deep reinforcement learning (DRL) frameworks consider either discrete action space or continuous action space solely. Motivated by applications in computer games, we consider the scenario with discrete-continuous hybrid action space. To handle hybrid action space, previous works either approximate the hyb…
Hybrid framework predicts Arctic permafrost decline, risks infrastructure, and provides tools.
problem Tackles permafrost decline and infrastructure risk assessment in Arctic territories.
method Hybrid physics-machine learning framework integrating 2.9 million observations.
result Projects mean permafrost fraction decline of -20.3 pp under RCP8.5 forcing, with high-risk zones identified.
Hybrid model predicts stock prices using online forum sentiments and popularity.
problem Predicting stock prices accurately considering investor sentiment.
method XLNET for sentiment analysis, BiLSTM-highway model integration, combining post popularity.
result Hybrid model outperforms traditional methods in stock price prediction.
Paper develops a framework to identify latent dynamics from high-dimensional data.
problem Identifying latent dynamics from high-dimensional time-series data.
method Combines physics inductive bias and learn-to-identify strategy.
result Meta-HyLaD framework effectively identifies hybrid latent dynamics.
Hybrid systems are characterized by having an interaction between continuous dynamics and discrete events. The contribution of this paper is to provide hybrid systems with a novel geometric formulation so that controls can be added. Using this framework we describe some new global controllability tests for hybrid contr…
Massively parallel architectures such as the GPU are becoming increasingly important due to the recent proliferation of data. In this paper, we propose a key class of hybrid parallel graphlet algorithms that leverages multiple CPUs and GPUs simultaneously for computing k-vertex induced subgraph statistics (called graph…
Improves decision complexity in hybrid environments.
problem Complexity in hybrid decision-making problems.
method General extension of DEC framework, model aggregation approach.
result Improved regret bounds for linear Q*/V* MDPs.
Paper improves SVaR estimation for stress testing under macro scenarios using a hybrid GPR-HS framework.
problem Numerical instability in traditional SVaR estimation under extreme shocks.
method Extends GPR-HS framework to forward-looking stress scenarios with SACS for stable covariance.
result Stable SVaR ranges from -2.1020% to -2.2231%, preserving coherence property.
Unified framework for hybrid learning and optimization via active inference.
problem Sequential decisions in black-box evaluations requiring both task improvement and uncertainty reduction.
method Pragmatic Curiosity (PraC) framework that evaluates queries by balancing information gain and pragmatic value.
result Unified approach reduces decision risk and improves coverage of critical regions without task-specific rules.
Hybrid framework merges data and domain knowledge for better spatial interpolation.
problem Spatial interpolation overlooks domain knowledge and limits to spatial coordinates.
method Integrates data-driven features with rule-assisted spatial dependency function mapping.
result Superior performance in two application scenarios, capturing localized features.
Framework for robust decision making in changing environments with privacy constraints.
problem Interactive decision making in changing environments with constraints.
method Hybrid Decision Making with Structured Observations (hybrid DMSO) framework, local differentially private decision making, query-based learning, robust and smooth decision making.
result Strong connections and bounds derived for DEC, SQ dimension, local minimax complexity, learnability, and joint differential privacy.
Unified reinforcement learning methods using hybrid inference.
problem Combining model-based and model-free reinforcement learning approaches.
method Control as Hybrid Inference (CHI) framework.
result CHI algorithm balances model-based and model-free learning.
Hybrid ML ensemble predicts market risk and generates alpha.
problem Forecasting short-horizon market risk and generating alpha.
method Neural networks and tree-based voting models integrated for trading signal.
result Sharpe ratio of 2.51 and annualized CAPM alpha of +0.28.
Hybrid LSTM-PPO optimizes dynamic portfolios with better performance.
problem Dynamic portfolio optimization under non-stationary market conditions.
method Combines LSTM for forecasting and PPO for adaptive portfolio adjustments.
result Hybrid framework outperforms single-model and equal-weight approaches in various metrics.
The paper introduces FMCI and hybrid decoding for hidden Markov models.
problem Computing distributions and decoding hidden state sequences in HMMs.
method Finite Markov chain imbedding (FMCI) and hybrid decoding.
result Hybrid decoding improves performance over traditional methods.
Interpretable machine learning has become a strong competitor for traditional black-box models. However, the possible loss of the predictive performance for gaining interpretability is often inevitable, putting practitioners in a dilemma of choosing between high accuracy (black-box models) and interpretability (interpr…
Hybrid QNN-LSTM predicts financial stock market trends using quantum computing.
problem Complex temporal dependencies and market fluctuations in financial time-series forecasting.
method Custom QNN regressor with hybrid optimization strategies.
result Hybrid models integrate quantum computing into financial forecasting workflows.
AI model predicts stock prices using social media data and hybrid neural networks.
problem Predicting stock price movements during the COVID-19 pandemic.
method Integrates social media trends and historical stock data using a hybrid CNN-BLSTM framework.
result The proposed framework outperforms traditional models in predicting stock price movements.
Hybrid model integrates GATv2 and geostatistics for better spatial prediction and uncertainty.
problem Accurate spatial prediction and uncertainty quantification in epidemiology and risk analysis.
method Integrates Graph Attention Network (GATv2) with model-based geostatistics (MBG) to capture relational and spatial dependencies.
result Hybrid model improves predictive accuracy and uncertainty quantification compared to standalone models.
This work proposes robust reinforcement learning methods using both offline and online data.
problem Designing robust policies against parameter uncertainties in high-dimensional systems.
method Proposes RPQ for model-free learning with historical data and HyTQ for hybrid learning with both historical and online data.
result Unified analysis and theoretical guarantees for robust optimal policies in high-dimensional systems.
In this paper we present a hybrid active sampling strategy for pairwise preference aggregation, which aims at recovering the underlying rating of the test candidates from sparse and noisy pairwise labelling. Our method employs Bayesian optimization framework and Bradley-Terry model to construct the utility function, th…
Many applications in different domains produce large amount of time series data. Making accurate forecasting is critical for many decision makers. Various time series forecasting methods exist which use linear and nonlinear models separately or combination of both. Studies show that combining of linear and nonlinear mo…
A framework for computing holonomy groups of hybrid systems to achieve forward motion.
problem Achieving forward motion from periodic leg motion.
method Developing a framework for computing holonomy groups of hybrid systems.
result Computing holonomy groups of hybrid systems to achieve non-zero net motion.
A new hybrid framework reduces quantum runtime and noise effects.
problem Challenges in deploying deep QFMs on real quantum hardware.
method Iterative Quantum Feature Maps (IQFMs) combining shallow QFMs and classical augmentation weights.
result Numerical experiments show IQFMs outperforming quantum convolutional neural networks.
Study combines quantum and classical deep learning for better credit risk assessment.
problem Enhancing accuracy and efficiency in credit risk evaluation.
method Hybrid Quantum-Classical Deep Neural Network for Row-Type Dependent Predictive Analysis.
result Proposed framework enhances predictive models for different loan categories.
A new hybrid GNN framework tackles oversmoothing in graph data.
problem Oversmoothing in graph convolutional networks limits their expressive power and generalization.
method Combines traditional GCN filters with band-pass filters defined via geometric scattering and introduces an attention framework.
result Improves expressive power and generalization of graph convolutional networks.
Study develops hybrid model to mitigate stablecoin liquidity risk.
problem Increasing integration of stablecoins introduces liquidity risk during market stress.
method Hybrid monetary architecture with 100% reserve backing and liquidity facilities.
result Demonstrates significant reduction in peg deviations and stress persistence.
Generative synthetic data can preserve predictive accuracy but distort causal inference.
problem Distortion of average treatment effect estimates in synthetic data.
method Hybrid synthetic-data framework that generates covariates while modeling treatment and outcome mechanisms separately.
result Hybrid synthesis improves causal fidelity compared to fully generative baselines.
A hybrid framework for American option pricing under time-varying rough volatility.
problem Pricing American options under time-varying rough volatility.
method Signature method combined with gradient-boosted ensemble for Hurst parameter estimation, regime switch, and Random Fourier Features for acceleration.
result The proposed hybrid framework improves performance over fixed-roughness baselines and reduces duality gaps in some regimes.
Hybrid Bayesian-conformal framework improves uncertainty quantification in healthcare predictions.
problem Jointly satisfying distribution-free coverage guarantees and risk-adaptive precision in clinical decision-making.
method Integrates Bayesian hierarchical random forests with group-aware conformal calibration, using posterior uncertainties to weight conformity scores.
result Achieves target coverage (94.3% vs 95% target) with adaptive precision, 21% narrower intervals for low-uncertainty cases.
The performance of a modulation classifier is highly sensitive to channel signal-to-noise ratio (SNR). In this paper, we focus on amplitude-phase modulations and propose a modulation classification framework based on centralized data fusion using multiple radios and the hybrid maximum likelihood (ML) approach. In order…
Hybrid model combines risk measures for better portfolio allocation.
problem Optimizing portfolios with various risk measures.
method Mean-variance hybrid model combining spectral risk measure and quantile optimization.
result Hybrid model outperforms classical mean-variance model in risk allocation.
Hybrid framework prevents forgetting in continual learning.
problem Avoiding forgetting in learning new tasks without forgetting old ones.
method Hybrid continual learning framework combining architecture growth and experience replay.
result Hybrid approach effectively avoids forgetting across multiple tasks.
BITS for GAPS uses Bayesian methods to improve surrogate model accuracy in complex systems.
problem Improving surrogate model accuracy in complex physical systems with uncertainty.
method Bayesian Information-Theoretic Sampling for hierarchical Gaussian Process Surrogates.
result Increased expected information gain and predictive accuracy by targeting high-uncertainty regions.
Hybrid model speeds up galaxy simulations by incorporating baryonic properties.
problem Inaccurate baryonic properties in dark matter-only simulations.
method Combining analytic models and machine learning for faster, more accurate simulations.
result Hybrid model outperforms machine learning alone for some baryonic properties.
Hybrid controller combines model-based and policy-based reinforcement learning.
problem Combining model-based and policy-based reinforcement learning for stability and robustness.
method Designs a hybrid controller that interpolates a model-based linear controller and a differentiable policy.
result Proven to maintain stability and universal approximation properties.
Hybrid framework combines PGMs and TNs for complex probabilistic modeling.
problem Combining quantum-like correlations into PGM models.
method Introducing decoherence to convert probabilistic TN models into PGMs.
result Hybrid models can represent and combine strengths of both PGMs and TNs.
Paper proposes a hybrid MTL framework for improved stock market prediction accuracy.
problem Inaccurate stock market predictions due to financial data's complexities.
method Multi-layer hybrid MTL structure with Transformer, BiGRU, and KAN.
result Achieved low MAE (1.078), MAPE (0.012), and high R^2 (0.98) compared to other models.
A novel double-space tensor-product RKHS framework for hybrid uncertainty sensitivity analysis.
problem Quantifying the influence of hybrid aleatory and epistemic uncertainties on high-dimensional system responses.
method A novel double-space tensor-product RKHS framework for sensitivity analysis under hybrid uncertainty.
result Concurrent double Möbius inversion orthogonally decomposes global dependence measure into pure aleatory effects, pure epistemic effects, and their interaction contributions.
Hybrid model outperforms benchmarks in financial forecasting.
problem Robust asset price forecasting in finance.
method Combining LSTM with Neural Levy Processes using Grey Wolf Optimizer and ANN calibration.
result Hybrid model outperforms base LSTM and other models.
Hybrid machine learning improves gallstone risk prediction.
problem Complex gallstone disease risk factors and interactions.
method Adaptive LASSO for variable selection, BART for interactions, differential equations for interpretation.
result Enhanced prediction accuracy and actionable insights.
Generative Adversarial Networks (GANs) can achieve state-of-the-art sample quality in generative modelling tasks but suffer from the mode collapse problem. Variational Autoencoders (VAE) on the other hand explicitly maximize a reconstruction-based data log-likelihood forcing it to cover all modes, but suffer from poore…
This thesis evaluates text-based vs audio-based classification of mental health interviews.
problem Classifying psychiatric illness using text-based methods.
method Design and evaluate a text classification network on mental health interviews, using belabBERT.
result Text-based classification is a strong alternative to audio-based methods.
In this paper, we propose hybrid building/floor classification and floor-level two-dimensional location coordinates regression using a single-input and multi-output (SIMO) deep neural network (DNN) for large-scale indoor localization based on Wi-Fi fingerprinting. The proposed scheme exploits the different nature of th…
Method improves simulation accuracy by mitigating distribution shift in hybrid systems.
problem Mitigating distribution shift in machine-learning augmented hybrid simulation.
method Tangent-space regularized estimator to control distribution shift.
result Marked improvements in simulation accuracy, especially for systems with high distribution shift.