Hybrid-MST improves preference aggregation from sparse data.
problem Recovering ratings from sparse and noisy pairwise data.
method Bayesian optimization and Bradley-Terry model for utility function, Gaussian-Hermite quadrature for EIG estimation, hybrid sampling strategy.
result Hybrid-MST outperforms state-of-the-art methods in preference aggregation.
Hybrid bootstrap improves model performance over dropout.
problem Improving predictive model performance through regularization.
method Resamples features from other training points instead of replacing them with zeros.
result Offers superior performance compared to dropout.
A new method for DPMM inference without truncation.
problem Inference in DPMM requires truncation, limiting model flexibility.
method Hybrid inference combining MCMC and variational methods.
result Efficient variational updates without increased model complexity.
Variational Bayesian inference and (collapsed) Gibbs sampling are the two important classes of inference algorithms for Bayesian networks. Both have their advantages and disadvantages: collapsed Gibbs sampling is unbiased but is also inefficient for large count values and requires averaging over many samples to reduce …
This paper addresses how well we can recover a data matrix when only given a few of its elements. We present a randomized algorithm that element-wise sparsifies the data, retaining only a few its elements. Our new algorithm independently samples the data using sampling probabilities that depend on both the squares ($\e…
A new hybrid algorithm reduces stochastic gradient evaluations for nonconvex optimization.
problem Solving stochastic composite nonconvex optimization problems efficiently.
method Proposes a new hybrid variance-reduced proximal gradient method with a stochastic gradient estimator.
result Achieves optimal stochastic oracle complexity bound with one less gradient evaluation.
A new hybrid VAE-GAN framework improves mode coverage and quality.
problem Mode collapse and poor sample quality in GANs and VAEs.
method Integrates a 'Best-of-Many-Samples' reconstruction cost and a stable synthetic likelihood estimate.
result Significant improvement in mode coverage and quality compared to hybrid VAE-GANs and plain GANs.
Hybrid method improves mutual information estimation from samples.
problem Estimating mutual information from joint distributions is challenging.
method Proposes a hybrid method combining discriminative and generative approaches.
result Hybrid methods yield tighter variational bounds on mutual information.
Hybrid approach improves reinforcement learning efficiency.
problem Improving sample efficiency in reinforcement learning.
method Approximate model from Model-Free RL, use Mean First Passage Times for reachability, design new algorithms.
result Hybrid approaches converge with fewer iterations and samples.
New hybrid RL algorithm outperforms model-free and model-based methods.
problem Improving reinforcement learning algorithms for MDPs.
method Combines model-free and model-based learning, with a PAC analysis.
result Outperforms both model-free and model-based methods in most cases.
Paper proposes a hybrid RL algorithm that combines offline and online data without needing reward info.
problem How to efficiently use online data to improve RL policies using only offline data.
method A three-stage hybrid RL algorithm that uses reward-agnostic exploration and model-based offline RL.
result The hybrid RL algorithm outperforms both pure offline and pure online RL in sample complexity.
HySRL improves RL sample efficiency with shifted-dynamics data.
problem Leveraging historical data with shifted dynamics to improve sample efficiency in RL.
method HySRL, a hybrid transfer RL algorithm that uses prior information on dynamics shift to achieve better sample complexity.
result HySRL achieves problem-dependent sample complexity and outperforms pure online RL.
D-Wave hybrid quantum-classical portfolio optimization shows classical decomposition is key, not quantum sampling.
problem Optimizing portfolios with constraints using hybrid quantum-classical methods.
method Operational decomposition audit of D-Wave's hybrid quantum-classical service on mean-variance-turnover instances.
result Classical decomposition and feasibility-aware reassembly are key to hybrid quantum-classical performance.
A new method uses ABC-SMC to infer hybrid models in bioprocesses with limited data.
problem Inference of hybrid models in bioprocesses with limited real data and high uncertainties.
method Approximate Bayesian Computation with Sequential Monte Carlo (ABC-SMC) and linear Gaussian dynamic Bayesian network (LG-DBN) for posterior distribution approximation.
result The method accelerates hybrid model inference and supports process monitoring and robust control.
Hybrid RL algorithms improve offline and online RL in linear MDPs.
problem Improving RL performance without single-policy concentrability.
method Developed computationally efficient algorithms for PAC and regret-minimizing RL in linear MDPs.
result Achieved sharper error or regret bounds for linear MDPs.
Novel hybrid method for Bayesian network structure learning reduces computational time without sacrificing accuracy.
problem Bayesian network structure learning efficiency and accuracy trade-off.
method Partitioned PC (pPC), p-value adjacency thresholding (PATH), hybrid greedy initialization (HGI). result pHGS achieves significant computational reductions compared to the PC algorithm without sacrificing structure learning accuracy.
A new hybrid federated learning algorithm for combining clinical and omics data.
problem Combining clinical and omics data in federated learning settings.
method Reformulated Kernel Regularized Least Squares algorithm for hybrid federated learning.
result Validation of two variants of the hybrid algorithm on well-established datasets.
A hybrid network improves MRI reconstruction from undersampled data.
problem Reducing MRI acquisition time while maintaining image quality.
method Hybrid architecture combining k-space and image domains using complex-valued and real-valued U-nets.
result Hybrid approach outperformed image-only deep learning methods in hard-to-reconstruct regions.
A hybrid K-NN and SVM technique improves classification accuracy.
problem Improving classification accuracy in pattern recognition.
method Discriminative nearest neighbour classification combined with SVM.
result The hybrid technique outperforms state-of-the-art methods.
New method finds basins of attraction without needing system models.
problem Determining basins of attraction (BoA) for nonlinear systems without prior knowledge.
method Hybrid Active Learning (HAL) method combining AST, AL, and DBS.
result Efficiently finds and labels boundary of BoA without model knowledge.
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 regularization avoids double descent in random feature models.
problem Avoiding the double descent phenomenon in random feature models.
method Combines early stopping and weight decay, using GCV for hyperparameter selection.
result Hybrid method successfully avoids double descent and achieves comparable generalization.
Hybrid method improves sampling from multimodal distributions.
problem Sampling from multimodal posterior distributions efficiently.
method Jump-Diffusion Langevin Dynamics hybrid with Metropolis.
result Calibrated hybrid method outperforms pure methods.
Proposes a new metric learning method for image recognition.
problem Improving image recognition performance using learned distance representations.
method Introduces a Generalized Hybrid Metric Loss (GHM-Loss) to learn hybrid proximity features combining geometric and probabilistic spaces.
result Demonstrates superior performance compared to existing methods on public datasets.
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.
WHAI combines autoencoding and MCMC for scalable topic modeling.
problem Training scalable deep topic models for big corpora.
method Develops a hybrid autoencoding inference network for deep latent Dirichlet allocation.
result WHAI achieves both scalability and speed in topic inference.
Paper improves AI adaptability in uncertain aerial dogfighting scenarios.
problem Adapting AI to new, volatile objective functions in aerial dogfighting.
method Hybrid Repeat/Multi-point Sampling combined with Bayesian optimization.
result Improved model of objective function leads to better AI adaptability.
NSC classifies hybrid system states for time-bounded reachability, achieving high accuracy with minimal false negatives.
problem Classifying states in hybrid systems for time-bounded reachability.
method Neural State Classification using Deep Neural Networks.
result Achieved 99.25% to 99.98% accuracy with false-negative rates reduced to 0.0015 to 0 after tuning.
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.
The hybrid Monte Carlo algorithm (HMCA) is applied for Bayesian parameter estimation of the realized stochastic volatility (RSV) model. Using the 2nd order minimum norm integrator (2MNI) for the molecular dynamics (MD) simulation in the HMCA, we find that the 2MNI is more efficient than the conventional leapfrog integr…
Many structured data-fitting applications require the solution of an optimization problem involving a sum over a potentially large number of measurements. Incremental gradient algorithms offer inexpensive iterations by sampling a subset of the terms in the sum. These methods can make great progress initially, but often…
This paper develops a semi-closed form formula for pricing variance swaps with stochastic volatility and interest rate correlation.
problem Pricing variance swaps with stochastic volatility and interest rate correlation under full correlation structure.
method Developed an efficient semi-closed form pricing formula for variance swaps using characteristic functions.
result The correlation between the underlying and interest rate significantly impacts the pricing of variance swaps.
Hybrid SA algorithm optimizes index tracking for large indices.
problem Optimizing index tracking for large indices with financial constraints.
method Hybrid simulated annealing algorithm.
result Algorithm finds optimal solutions for past and future returns.
A hybrid model combines machine learning with a land surface model to improve soil moisture predictions.
problem Improving soil moisture predictions in climatological situations.
method Noah land-surface model integrated with Gaussian Processes, using autoregressive model for out-of-sample results.
result 3-fold reduction in RMSE using one-year leave-one-out cross-validation.
A new algorithm improves credit scoring accuracy for imbalanced data.
problem Poor classification of minority class in credit scoring data sets.
method Weighted-Hybrid-Sampling-Boost (WHSBoost) algorithm with balanced data sampling.
result WHSBoost outperforms other methods in credit scoring accuracy.
Hybrid LLM generates synthetic data preserving causal parameters.
problem Synthetic data fails to accurately estimate causal effects.
method Combines model-based covariate synthesis with separately learned propensity and outcome models.
result Hybrid framework ensures causal structure in synthetic data.
We introduce a simulation scheme for Brownian semistationary processes, which is based on discretizing the stochastic integral representation of the process in the time domain. We assume that the kernel function of the process is regularly varying at zero. The novel feature of the scheme is to approximate the kernel fu…
Improved function approximation for noisy data.
problem Efficient estimation of conditional expectations from highly polluted data.
method Hybrid approach combining Christoffel sampling and optimal experimental design.
result Improved computational efficiency and sample complexity compared to existing methods.
GraphVite efficiently trains node embeddings for large networks.
problem Scaling node embedding algorithms to handle large networks (tens of millions of nodes).
method CPU-GPU hybrid system with parallel edge sampling and negative sampling.
result GraphVite achieves high performance (50x faster) for large networks.
Combining automata learning and model-based testing generates sufficient training data for hybrid systems.
problem Challenges in constructing models of hybrid systems that combine physical and digital behavior.
method Exploits a combination of automata learning and model-based testing to generate sufficient training data.
result Recurrent neural networks trained with this data achieved significantly better results compared to models learned from random data.
In this paper, we consider the problem of pricing discretely-sampled variance swaps based on a hybrid model of stochastic volatility and stochastic interest rate with regime-switching. Our modelling framework extends the Heston stochastic volatility model by including the CIR stochastic interest rate and model paramete…
RP1 uses active learning to improve world model in fewest samples.
problem Improving sample efficiency in MBRL for continuous control tasks.
method RP1 views MBRL as active learning, using a hybrid objective function and principled termination.
result Statistically significant gains over existing approaches on continuous control tasks.
Defines non-parabolic curves in spatial hybrid space with applications.
problem Defining and analyzing non-parabolic spatial hybrid framed curves.
method Definition and proof of existence and uniqueness theorem for non-parabolic spatial hybrid framed curves.
result Existence and uniqueness theorem for non-parabolic spatial hybrid framed curves.
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.
Hybrid QML model improves recovery rate prediction accuracy.
problem Complex nonlinear dependencies, high-dimensional feature spaces, and limited sample sizes in recovery rate forecasting.
method Hybrid Quantum Machine Learning (QML) with Amplitude Encoding, leveraging PQC and qubit data compression.
result Significantly lower RMSE (0.228) compared to classical models.
Interactive IL beats BC by state-wise annotation cost.
problem Behavior Cloning struggles with annotation cost in sequential decision making.
method Proved Stagger and Warm Stagger algorithms to outperform BC.
result Interactive and hybrid IL methods outperform BC with state-wise annotation.
A fast method estimates correlations in hybrid systems using observable market data.
problem Estimating instantaneous correlations in hybrid systems from observable data.
method Empirical correlations between observable market quantities are used to estimate state variables' correlations. Linear systems are involved, and the matrix is converted to positive semidefinite if necessary.
result The estimates are reasonably accurate, especially with more than 1,000 data points.
Framework for sensitivity analysis in biomanufacturing processes.
problem High complexity and uncertainty in biomanufacturing processes.
method Shapley value estimation for linear and nonlinear pKG models, using quasi-Monte Carlo and antithetic sampling.
result Improved efficiency and accuracy in sensitivity analysis for biomanufacturing processes.