On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,694 papers · 148 categories

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15 results for “SVGP”

Connections and Equivalences between the Nyström Method and Sparse Variational Gaussian Processesstat.ML

Clarifies connections between Nyström and SVGP methods for scalable GPs.

problem Lack of understanding between GP and kernel methods communities.

method Investigates Nyström and SVGP methods for scalable Gaussian processes.

result Establishes connections and equivalences between Nyström and SVGP methods.

Nyström method SVGP Gaussian processes kernel methods scalability

2021-06-02abs ↗pdf ↗

Dual Parameterization of Sparse Variational Gaussian Processescs.LG

Improves SVGP methods for faster and more accurate Gaussian process inference.

problem Efficient non-conjugate Gaussian process inference.

method Dual parameterization of SVGP methods using site parameters.

result Faster and more accurate inference with tighter evidence lower bound.

Gaussian process inference variational methods natural gradient site parameters

2021-11-05abs ↗pdf ↗

Self-Attention through Kernel-Eigen Pair Sparse Variational Gaussian Processescs.LG

A new method for self-attention models that improves uncertainty estimation.

problem Overconfident predictions and lack of calibrated uncertainty in Transformers.

method Kernel-Eigen Pair Sparse Variational Gaussian Processes (KEP-SVGP) with Kernel SVD (KSVD) to handle asymmetry of attention kernels.

result Reduction in time complexity and improved performance on various benchmarks.

self-attention transformers uncertainty estimation kernel-svd variational inference

2024-02-02abs ↗pdf ↗

Uncertainty Quantification for Scientific Machine Learning using Sparse Variational Gaussian Process Kolmogorov-Arnold Networks (SVGP KAN)cs.LG

SVGP KAN integrates uncertainty quantification into Kolmogorov-Arnold networks.

problem Uncertainty quantification in scientific machine learning models.

method Sparse variational Gaussian process inference with Kolmogorov-Arnold topology.

result Demonstrated ability to distinguish aleatoric and epistemic uncertainty in various scientific applications.

uncertainty quantification scientific machine learning Kolmogorov-Arnold networks sparse variational Gaussian process interpretability

2025-12-04abs ↗pdf ↗

Conditioning Sparse Variational Gaussian Processes for Online Decision-makingcs.LG

A scalable online method for Gaussian processes that improves decision-making in various applications.

problem Scalability issues with Gaussian processes for online decision-making.

method Online variational conditioning (OVC) for SVGPs.

result OVC enables efficient online learning and decision-making with SVGPs.

Gaussian processes scalability online learning variational conditioning decision-making

2021-10-28abs ↗pdf ↗

Approximation-Aware Bayesian Optimizationcs.LG

Optimizes data acquisition in high-dimensional Bayesian optimization.

problem Suboptimal data acquisition in high-dimensional Bayesian optimization tasks.

method Utility-calibrated variational inference to align approximations with BO goals.

result Optimal data acquisition decisions under a limited computational budget.

Bayesian optimization approximation-aware data acquisition utility-calibrated variational inference

2024-06-06abs ↗pdf ↗

A Partitioned Sparse Variational Gaussian Process for Fast, Distributed Spatial Modelingcs.LG

A new method for faster spatial modeling on exascale computers.

problem Scalable, memory-efficient machine learning for spatially distributed data.

method Partitioned Sparse Variational Gaussian Process (PSVGP) with decentralized communication.

result Improved spatial predictions and better model fit with minimal overhead.

Exascale computing Spatial modeling Machine learning Gaussian process Partitioned SVGP

2025-07-22abs ↗pdf ↗

Scalable and Interpretable Scientific Discovery via Sparse Variational Gaussian Process Kolmogorov-Arnold Networks (SVGP KAN)cs.LG

SVGP KAN integrates sparse variational GP with KANs for scalable probabilistic inference.

problem Lack of probabilistic outputs in standard KANs and cubic scaling of Gaussian Process methods.

method Sparse Variational GP-KAN combines KAN topology with sparse variational inference and permutation-based importance analysis.

result Enables probabilistic KANs to handle larger datasets with linear computational complexity.

Kolmogorov-Arnold Networks Gaussian Processes Sparse Variational Inference Interpretable Scientific Discovery Permutation-Based Importance Analysis

2025-11-29abs ↗pdf ↗

Near-Optimal Approximations for Bayesian Inference in Function Spacestat.ML

A scalable algorithm approximates Bayesian posteriors in RKHS with improved efficiency.

problem Scalable inference for Bayes posteriors in infinite-dimensional spaces.

method Approximate Langevin diffusion projection onto first M components, using law of total probability and sufficiency assumption.

result The method recovers SVGP as a special case and is provably close to optimal for convex and Lipschitz continuous likelihoods.

Bayesian inference RKHS Langevin diffusion approximation scalability

2025-02-25abs ↗pdf ↗

A piece-wise constant approximation for non-conjugate Gaussian Process modelscs.LG

This paper proposes a method to approximate non-Gaussian likelihoods in Gaussian Processes.

problem Approximating non-Gaussian likelihoods in Gaussian Processes.

method Proposes a piece-wise constant approximation for the inverse-link function.

result Yields a closed form solution for the SVGP lower bound.

Gaussian Processes Non-conjugate likelihoods Piece-wise constant approximation Sparse Variational Gaussian Processes Inverse-link function

2022-04-22abs ↗pdf ↗

Inverse-Free Sparse Variational Gaussian Processesstat.ML

New method trains sparse Gaussian processes without matrix inversion.

problem Costly training of Gaussian processes at scale.

method Inverse-free approach using matmul-only natural-gradient updates.

result Significantly improved stability and convergence in training.

Gaussian processes sparse approximations natural gradients matrix multiplications inverse-free

2026-04-01abs ↗pdf ↗

Sparse Gaussian Process Hyperparameters: Optimize or Integrate?stat.ML

Sparse Gaussian process hyperparameters optimized using MCMC.

problem Hyperparameter uncertainty leads to biased estimates and underestimation of predictive uncertainty.

method Proposes an MCMC algorithm to sample from the hyperparameter posterior in sparse Gaussian process regression.

result Significantly improves sampling efficiency in the Gaussian likelihood case.

Gaussian process hyperparameter optimization sparse GP MCMC variational inference

2022-11-04abs ↗pdf ↗

Recurrent Memory for Online Interdomain Gaussian Processescs.LG

A novel online GP model captures long-term memory in sequential data.

problem Capturing long-term memory in sequential data online.

method Integrates HiPPO framework into interdomain GP, leveraging time-varying orthogonal projections as inducing variables.

result OHSVGP outperforms existing online GP methods in predictive performance, long-term memory preservation, and computational efficiency.

Gaussian Processes Online Learning Long-Term Memory Sequential Data Orthogonal Projections

2025-02-12abs ↗pdf ↗

Computation-Aware Gaussian Processes: Model Selection And Linear-Time Inferencecs.LG

This work improves Gaussian process model selection for large datasets.

problem Prohibitively high computational cost in Gaussian process model selection.

method Linear-time scaling and computational uncertainty tradeoff.

result Computation-aware Gaussian processes can be trained on large datasets efficiently.

Gaussian processes model selection large-scale datasets computational efficiency hyperparameter optimization

2024-11-01abs ↗pdf ↗

Streaming PAC-Bayes Gaussian process regression with a performance guarantee for online decision makingcs.LG

New online GP algorithm offers performance guarantees for streaming data.

problem Training and inference of GPs require all historic data, limiting online decision-making.

method Developed a new theoretical framework based on PAC-Bayes theory, optimizing empirical risk and parameter divergence.

result Offers both a guarantee of generalized performance and good accuracy.

Gaussian process online decision-making streaming data PAC-Bayes theory generalization guarantee

2022-10-16abs ↗pdf ↗