One acceleration measurement equals ~10^5 phase-space measurements for local dark matter density estimation, with acceleration outperforming Jeans modeling in both equilibrium and perturbed Milky Way simulations.
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27 Pith papers cite this work, alongside 6,526 external citations. Polarity classification is still indexing.
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representative citing papers
A Gaussian process surrogate gate inserted between generative crystal models and property oracles matches or exceeds ungated fine-tuning while using roughly one-fifth the oracle calls for heat capacity and bulk modulus.
In conjugate BLR, MFVI overestimates expected predictive variance on in-distribution points relative to the exact posterior, with overestimation aligned to training data directions.
Derives explicit bounds decomposing KL(GP || LNP) into three costs with decay rates O(e^{-c d^{2/d_x}}) for squared-exponential kernels and O(d^{-2ν/d_x}) for Matérn kernels, plus recommendations to predict variance from locations alone and use second-order pooling.
Kernels from pretrained MLIP latent spaces outperform standard acquisition methods in active learning for reactive chemistry, reducing required labels by 38% for energy error and 28% for force error.
STOMP extends direct preference optimization to the multi-objective setting via smooth Tchebysheff scalarization and standardization of observed rewards, achieving highest hypervolume in eight of nine protein engineering evaluations.
KDE-AIS trains a Gaussian process and kernel density surrogate from shared evaluations to build an adaptive importance sampling proposal that converges to the zero-variance optimum for efficient failure probability estimation.
Cosmological MBHBs coalesce in ~1 Gyr with high eccentricities; scaling relations from 30 Griffin re-simulations link dynamical-friction, hardening and total times to galaxy and orbital properties.
Force-aware Neural Tangent Kernels combined with chunked acquisition provide scalable and distribution-robust active learning for MLIPs, outperforming baselines on OC20 and remaining competitive on other benchmarks.
EDRBO uses ensemble surrogates and Wasserstein ambiguity sets to robustify BO acquisition functions against context distribution mismatch, with sublinear regret O(γ_T √T) and SOTA empirical results on continuous contexts.
Safety certification of dynamical systems is reformulated as direct classification via kernel embeddings on trajectories, bypassing recursive DP to avoid error compounding and support non-Markovian dynamics.
BACO replaces direct black-box calls in collaborative optimization with Gaussian process surrogates at both subsystem and system levels, achieving lower objectives and near-zero constraint violations on MDO benchmarks and a CRM wing problem within limited evaluations.
GSC-QEMit adaptively mitigates quantum errors using hierarchical context clustering, Gaussian-process forecasting, and contextual bandits, delivering 9% higher average logical fidelity than unmitigated runs in Qiskit Aer simulations.
mLaSDI uses multi-stage residual decoder training with periodic activations to recover high-frequency details in latent space dynamics identification, yielding lower reconstruction and prediction errors than standard LaSDI for PDEs.
Bayesian PSR with Gaussian processes and GradCoRe accelerates VQE SGD by reusing observations and minimizing per-step costs while reducing to standard PSR in special cases.
A semi-parametric framework decouples discrepancy functions from physics-based components via orthogonal Gaussian process regression for interpretable nonlinear system identification from incomplete physics.
Bayesian nonparametric infinite mixture of multi-output GPs with wavelet Besov priors and intrinsic coregionalization detects anomalies in multivariate functional data via slice sampling in a semi-supervised setting.
Composite Gaussian process models with an analytical profit function and a steady-state energy-balance residual are embedded in Bayesian optimization to improve economic performance and constraint satisfaction on a simulated multi-product chemical reactor.
By detecting trajectory disturbances, attributing them to visual causes with a VLM, and fitting a few-shot spatial disturbance model, robots build personalized danger libraries that improve later navigation.
REX-SUB combines a randomized exchange algorithm with Vecchia approximation to choose subsamples that minimize mean squared prediction error and interval scores in large-scale spatial GPs.
FEDONet augments DeepONet with Fourier-embedded trunk networks using random Fourier features, yielding lower L2 reconstruction errors than standard DeepONet on Burgers', 2D Poisson, Eikonal, Allen-Cahn, and Kuramoto-Sivashinsky equations across dataset sizes and noise levels.
Gaussian processes with error-in-variables generate uncertainty-aware equation of state tables for gold from DFT data across extreme densities and temperatures.
Framework extracts capacity, degradation rate, and dV/dQ features from 25 BESS modules that statistically distinguish 25 faulty cell groups from 325 non-faulty ones, while resistance does not.
An extension of PFGS adds posterior probability of constraint satisfaction and Monte Carlo robustness estimation as Pareto objectives for interactive candidate selection in Bayesian optimization, demonstrated on an 8D CHO cell culture simulator.
citing papers explorer
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An Acceleration is Worth a Hundred Thousand Phase Space Measurements
One acceleration measurement equals ~10^5 phase-space measurements for local dark matter density estimation, with acceleration outperforming Jeans modeling in both equilibrium and perturbed Milky Way simulations.
-
Surrogate-Gated Generation and Foundation-Model Embeddings for Bayesian Materials Design
A Gaussian process surrogate gate inserted between generative crystal models and property oracles matches or exceeds ungated fine-tuning while using roughly one-fifth the oracle calls for heat capacity and bulk modulus.
-
Gaussian Mean Field Variational Inference can Overestimate Predictive Variance
In conjugate BLR, MFVI overestimates expected predictive variance on in-distribution points relative to the exact posterior, with overestimation aligned to training data directions.
-
Three Costs of Amortizing Gaussian Process Inference with Neural Processes
Derives explicit bounds decomposing KL(GP || LNP) into three costs with decay rates O(e^{-c d^{2/d_x}}) for squared-exponential kernels and O(d^{-2ν/d_x}) for Matérn kernels, plus recommendations to predict variance from locations alone and use second-order pooling.
-
Pretrained Model Representations as Acquisition Signals for Active Learning of MLIPs
Kernels from pretrained MLIP latent spaces outperform standard acquisition methods in active learning for reactive chemistry, reducing required labels by 38% for energy error and 28% for force error.
-
Pareto-Optimal Offline Reinforcement Learning via Smooth Tchebysheff Scalarization
STOMP extends direct preference optimization to the multi-objective setting via smooth Tchebysheff scalarization and standardization of observed rewards, achieving highest hypervolume in eight of nine protein engineering evaluations.
-
Surrogate-Guided Adaptive Importance Sampling for Failure Probability Estimation
KDE-AIS trains a Gaussian process and kernel density surrogate from shared evaluations to build an adaptive importance sampling proposal that converges to the zero-variance optimum for efficient failure probability estimation.
-
Scaling Relations for Binary Black Hole Merger Times from Cosmological Initial Conditions
Cosmological MBHBs coalesce in ~1 Gyr with high eccentricities; scaling relations from 30 Griffin re-simulations link dynamical-friction, hardening and total times to galaxy and orbital properties.
-
Force-Aware Neural Tangent Kernels for Scalable and Robust Active Learning of MLIPs
Force-aware Neural Tangent Kernels combined with chunked acquisition provide scalable and distribution-robust active learning for MLIPs, outperforming baselines on OC20 and remaining competitive on other benchmarks.
-
Ensemble Distributionally Robust Bayesian Optimisation with Continuous Context
EDRBO uses ensemble surrogates and Wasserstein ambiguity sets to robustify BO acquisition functions against context distribution mismatch, with sublinear regret O(γ_T √T) and SOTA empirical results on continuous contexts.
-
Safety Certification is Classification
Safety certification of dynamical systems is reformulated as direct classification via kernel embeddings on trajectories, bypassing recursive DP to avoid error compounding and support non-Markovian dynamics.
-
Bayesian Algorithm for Collaborative Optimization with Application to Aircraft Design
BACO replaces direct black-box calls in collaborative optimization with Gaussian process surrogates at both subsystem and system levels, achieving lower objectives and near-zero constraint violations on MDO benchmarks and a CRM wing problem within limited evaluations.
-
GSC-QEMit: A Telemetry-Driven Hierarchical Forecast-and-Bandit Framework for Adaptive Quantum Error Mitigation
GSC-QEMit adaptively mitigates quantum errors using hierarchical context clustering, Gaussian-process forecasting, and contextual bandits, delivering 9% higher average logical fidelity than unmitigated runs in Qiskit Aer simulations.
-
mLaSDI: Multi-stage latent space dynamics identification
mLaSDI uses multi-stage residual decoder training with periodic activations to recover high-frequency details in latent space dynamics identification, yielding lower reconstruction and prediction errors than standard LaSDI for PDEs.
-
Bayesian Parameter Shift Rule in Variational Quantum Eigensolvers
Bayesian PSR with Gaussian processes and GradCoRe accelerates VQE SGD by reusing observations and minimizing per-step costs while reducing to standard PSR in special cases.
-
Orthogonal Discrepancy Kernels for Learning with Partial Physics
A semi-parametric framework decouples discrepancy functions from physics-based components via orthogonal Gaussian process regression for interpretable nonlinear system identification from incomplete physics.
-
Bayesian Nonparametric Detection of Anomalies in Multivariate Functional Data
Bayesian nonparametric infinite mixture of multi-output GPs with wavelet Besov priors and intrinsic coregionalization detects anomalies in multivariate functional data via slice sampling in a semi-supervised setting.
-
Bayesian Optimization of a Multi-Product Chemical Reactor Using Composite Models and Partial Physics Knowledge
Composite Gaussian process models with an analytical profit function and a steady-state energy-balance residual are embedded in Bayesian optimization to improve economic performance and constraint satisfaction on a simulated multi-product chemical reactor.
-
Don't Fool Me Twice: Adapting to Adversity in the Wild with Experience-Driven Reasoning
By detecting trajectory disturbances, attributing them to visual causes with a VLM, and fitting a few-shot spatial disturbance model, robots build personalized danger libraries that improve later navigation.
-
REX-SUB: A Scalable Subsampling Strategy for Modeling Large Spatial Datasets
REX-SUB combines a randomized exchange algorithm with Vecchia approximation to choose subsamples that minimize mean squared prediction error and interval scores in large-scale spatial GPs.
-
FEDONet : Fourier-Embedded DeepONet for Spectrally Accurate Operator Learning
FEDONet augments DeepONet with Fourier-embedded trunk networks using random Fourier features, yielding lower L2 reconstruction errors than standard DeepONet on Burgers', 2D Poisson, Eikonal, Allen-Cahn, and Kuramoto-Sivashinsky equations across dataset sizes and noise levels.
-
Development of an uncertainty-aware equation of state for gold
Gaussian processes with error-in-variables generate uncertainty-aware equation of state tables for gold from DFT data across extreme densities and temperatures.
-
Health feature extraction from battery energy storage system field fault data
Framework extracts capacity, degradation rate, and dV/dQ features from 25 BESS modules that statistically distinguish 25 faulty cell groups from 325 non-faulty ones, while resistance does not.
-
A Human-in-the-Loop Bayesian Optimization Framework for Constraint-Aware Bioprocess Development
An extension of PFGS adds posterior probability of constraint satisfaction and Monte Carlo robustness estimation as Pareto objectives for interactive candidate selection in Bayesian optimization, demonstrated on an 8D CHO cell culture simulator.
-
Solution of the Newtonian plane Couette flow with dynamic wall slip using machine-learning methods
PINNs and DeepONets solve Newtonian plane Couette flow with dynamic wall slip; DeepONet achieves 0.36% mean relative error on unseen cases and 540X speedup over numerical methods.
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Gaussian Process Reconstruction of Cosmological Parameters with Gravitational Wave Sirens using Machine Learning
Gaussian Process Regression on mock GW siren catalogues reconstructs comoving distance and derivatives, showing that derivative diagnostics at specific redshifts best separate cosmological models while background data alone does not.
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Uncertainty Estimation for Deep Reconstruction in Actuatic Disaster Scenarios with Autonomous Vehicles
Evidential Deep Learning outperforms other methods in accuracy, calibration, and speed for uncertainty-aware scalar field reconstruction in aquatic environments using autonomous vehicles.