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.
Paper optimizes AI for fighter pilots using Bayesian optimization.
problem Optimizing AI behavior for aerial dog fighting with uncertain objectives.
method Bayesian optimization with Gaussian Process surrogate and Hybrid Repeat/Multi-point Sampling.
result Improved model of objective function for better prediction of performance.
AI learns to optimize dog-fighting performance using Bayesian optimization.
problem Optimizing AI decision-making in dynamic, volatile combat environments.
method Developed Gaussian process Bayesian optimization (GPBO) techniques with RS and HRMS to improve surrogate model accuracy.
result HRMS improves surrogate model accuracy, allowing AI to more accurately predict and optimize performance.
Bayesian optimization for multi-objective and multi-point search.
problem Efficiently optimize unknown multi-objective functions with high evaluation costs.
method Proposes a Bayesian optimization algorithm that considers both multi-objective and multi-point search problems, using an acquisition function and an efficient gradient estimation method.
result Shows comparable or superior performance to heuristic methods via numerical experiments.
Optimizes neural networks training with multi-point optimization.
problem Training efficiency and loss landscape diversity of neural networks.
method Multi-point optimization technique for simultaneous training of multiple models.
result Loss landscapes of neural networks are diverse and intricate, and batch normalization makes them smoother.
Deep neural networks' loss surfaces contain every low-dimensional pattern.
problem Finding arbitrary low-dimensional patterns in neural network loss surfaces.
method Empirical and theoretical analysis of loss landscapes of deep neural networks.
result Deep universal approximators exhibit a property where arbitrary smooth patterns exist in their loss surfaces.
New sampling method makes fictitious play consistent in repeated games.
problem Fictitious play fails to be Hannan consistent in repeated games.
method Introduced sampled fictitious play with Bernoulli sampling, proving it is Hannan consistent.
result Sampled fictitious play is Hannan consistent using anti-concentration results.
TDA improves accuracy of machine learning models for repeated measurements.
problem Limited accuracy of machine learning models for repeated measurements.
method Samples from data space, builds network graph based on data topology.
result TDA classifier achieves high accuracy (up to 96.8%) in repeated measurement datasets.
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.
A new Latent Diffusion Model generates realistic reservoir facies.
problem Creating accurate reservoir facies from limited measurements.
method Proposes a Latent Diffusion Model for conditional facies generation.
result Significantly outperforms GAN-based alternatives in fidelity and realism.
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.
Bayesian HMF integrates multiple datasets for in/out-of-matrix prediction.
problem Data integration across different entity types and sparsity levels.
method Bayesian hybrid matrix factorisation model combining multiple methods.
result Consistently better in-matrix and out-of-matrix predictions compared to state-of-the-art methods.
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.
A hybrid method combines data assimilation and machine learning to predict chaotic dynamics from sparse noisy data.
problem Predicting chaotic dynamics from sparse and noisy observations.
method Iterative application of ensemble Kalman filter for data assimilation and neural network for model emulation.
result The hybrid method successfully predicts chaotic dynamics up to two Lyapunov times, retrieves positive Lyapunov exponents, and more energetic frequencies.
This research extends the Pareto/NBD model using neural networks for better out-of-sample predictions.
problem The limitations of the Pareto/NBD model in predicting out-of-sample data.
method A neural network-based extension of the Pareto/NBD model.
result The proposed method shows extraordinary predictability on repeat purchases at individual and aggregate levels.
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.
Efficient boosting method for regression with limited feedback.
problem Online boosting for regression tasks with noisy multi-point bandit feedback.
method Efficient regret minimization method with online boosting algorithm and projection-free online convex optimization.
result Improved state-of-the-art guarantees in efficiency.
The paper introduces a frequency-domain estimator for low-order systems from noisy data.
problem Estimating frequency responses of low-order systems from noisy measurements.
method Uses a quadratic data-fitting term regularized by the nuclear norm of a Loewner matrix, subject to a convex stability constraint.
result Proves a finite-sample error bound and extends it to all frequencies through rational interpolation.
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.
We present a braid-theoretic approach to combinatorially computing knot Floer homology. To a knot or link K, which is braided about the standard disk open book decomposition for (S^3,ξ_std), we associate a corresponding multi-pointed nice Heegaard diagram. We then describe an explicit algorithm for computing the associ…
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.
CRAFT improves on existing methods for sampling complex distributions.
problem Sampling from complex probability distributions.
method Combines SMC with variational inference using normalizing flows.
result Improves on Annealed Flow Transport Monte Carlo and MCMC-based Stochastic Normalizing Flows.
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.
BILBO optimizes bilevel problems without repeated lower-level optimizations.
problem Challenges in bilevel optimization, especially in noisy, constrained, and derivative-free settings.
method BILevel Bayesian Optimization (BILBO) that optimizes both levels simultaneously, using confidence-bounds and function query selection.
result Theoretical and empirical evidence of BILBO's effectiveness on various problems.
Unlike other industries in which intellectual property is patentable, the financial industry relies on trade secrecy to protect its business processes and methods, which can obscure critical financial risk exposures from regulators and the public. We develop methods for sharing and aggregating such risk exposures that …
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.
Develops a fast, accurate method for comparing networks.
problem Comparing networks with repeated observations and varying sizes/sparsity.
method A novel two-sample hypothesis testing method with theoretical guarantees.
result Outperforms existing tools in speed and accuracy, power-optimal.
Study optimizes product assortment for retailers with repeated exposures and patience costs.
problem Optimizing product assortment for online retailers with repeated exposures and varying consumer patience.
method Developed a cascade multinomial logit model to capture repeated exposures and patience costs.
result Proposed an approximation solution to the assortment optimization problem.
New method handles correlated and repeated measurements using smoothed multivariate square-root Lasso.
problem Handling correlated and repeated measurements with complex noise structure.
method Proposes a concomitant estimator that uses non-averaged measurements and leverages smoothing theory for optimization.
result Demonstrates practical benefits on various datasets (toy, simulated, real neuroimaging).
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.
New dynamics for SGD in small learning rate regime.
problem Improving stochastic gradient descent in small learning rate regime.
method Introducing stochastic modified flows and distribution dependent stochastic modified flows.
result Captures fluctuating dynamics of SGD in small learning rate - infinite width scaling regime.
Methods for prediction and tolerance intervals in non-normal models.
problem Constructing prediction and tolerance intervals for non-normal data.
method Two approaches: pivotal quantity approximation and confidence interval for mean.
result Intuitive, simple, efficient methods with proper operating characteristics.
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.
Estimates support in distributions with sampling artifacts and errors.
problem Support estimation in the presence of sampling artifacts and errors.
method Regularized weighted Chebyshev approximations with Touchard polynomials, discretized semi-infinte programming.
result Significant improvements over noiseless support estimation methods.
Ribbon: Scalable Approximation and Robust Uncertainty Quantification
problem Reliably quantifying predictive uncertainty for complex models
method Ribbon, a scalable approximation to Dirichlet-reweighted bootstrap uncertainty
result Asymptotically equivalent to a flat-prior Laplace approximation under correct likelihood specification, recovers robust sandwich covariance under misspecification
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.