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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.

169,051 papers · 148 categories

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9 results for “SGP”

Online Anomaly Detection with Sparse Gaussian Processescs.LG

Paper proposes SGP-Q for efficient online anomaly detection with concept drift adaptation.

problem Efficient online anomaly detection for time-series data with concept drift.

method Sparse Gaussian processes with Q-function (SGP-Q).

result SGP-Q achieves better anomaly detection results with concept drift adaptation.

anomaly detection sparse Gaussian processes concept drift online learning time-series data

2019-05-14abs ↗pdf ↗

Stochastic Gradient Push for Distributed Deep Learningcs.LG

SGP combines PushSum with stochastic gradient updates for robust distributed deep learning.

problem Synchronization issues in distributed deep learning.

method Stochastic Gradient Push (SGP) using PushSum for approximate distributed averaging.

result SGP converges to a stationary point at the same rate as SGD and achieves consensus.

distributed deep learning SGP PushSum stochastic gradient consensus

2018-11-27abs ↗pdf ↗

Sparse Gaussian Process Variational Autoencodersstat.ML

Develops SGP-VAE for efficient sparse GP inference in multi-dimensional datasets.

problem Sparse GP approximations and missing data in multi-dimensional spatio-temporal datasets.

method Leverages partial inference networks for sparse GP approximations and amortized variational inference.

result Outperforms multi-output GPs and structured VAEs in various experiments.

Gaussian process variational autoencoder sparse approximation spatio-temporal data missing data

2020-10-20abs ↗pdf ↗

Regularized Sparse Gaussian Processesstat.ML

Regularizes sparse Gaussian processes for better model performance.

problem Efficient learning of inducing inputs in latent variable models.

method Proposes a regularization approach to balance reconstruction and approximation performance.

result Improves both inference and prediction performance in latent variable models.

Gaussian processes sparse approximation regularization latent variable models variational inference

2019-10-13abs ↗pdf ↗

Stability and Generalization of Push-Sum Based Decentralized Optimization over Directed Graphscs.LG

Unified framework for stability and generalization of Push-Sum in decentralized learning over directed graphs.

problem Understanding stability and generalization of Push-Sum in decentralized learning over directed networks.

method Developed a unified uniform-stability framework for SGP algorithm, incorporating imbalance-aware consistency bounds.

result Established finite-iteration stability and optimization guarantees for convex and non-convex objectives.

Push-Sum Decentralized Learning Directed Graphs Stability Generalization

2026-02-24abs ↗pdf ↗

Regret Bounds for Safe Gaussian Process Bandit Optimizationcs.LG

Safe Gaussian Process Bandit Optimization with sub-linear regret bounds.

problem Sequential decision-making under uncertainty and safety constraints.

method Developed SGP-UCB, a safe variant of GP-UCB with modifications to respect safety constraints.

result First sub-linear regret bounds for safe Gaussian Process Bandit Optimization.

Gaussian Processes Bandit Optimization Safety Constraints Regret Bounds

2020-05-05abs ↗pdf ↗

Local Stability and Performance of Simple Gradient Penalty mu-Wasserstein GANcs.LG

Wasserstein GAN(WGAN) is a model that minimizes the Wasserstein distance between a data distribution and sample distribution. Recent studies have proposed stabilizing the training process for the WGAN and implementing the Lipschitz constraint. In this study, we prove the local stability of optimizing the simple gradien…

2018-10-05abs ↗pdf ↗

Calibrating Transformers via Sparse Gaussian Processescs.LG

SGPA calibrates transformer uncertainty for safety-critical tasks.

problem Uncertainty estimation in transformer models for safety-critical domains.

method Bayesian inference in transformer's output space using sparse Gaussian processes.

result SGPA-based Transformers improve in-distribution calibration and out-of-distribution robustness.

Transformer Uncertainty Estimation Bayesian Inference Sparse Gaussian Processes Calibration

2023-03-04abs ↗pdf ↗

Maximum Entropy Vector Kernels for MIMO system identificationeess.SY

Recent contributions have framed linear system identification as a nonparametric regularized inverse problem. Relying on $\ell_2$ -type regularization which accounts for the stability and smoothness of the impulse response to be estimated, these approaches have been shown to be competitive w.r.t classical parametric met…

2015-08-12abs ↗pdf ↗