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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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10 results for “meta-priors”

Bayesian Meta-Prior Learning Using Empirical Bayescs.LG

Proposes a new method to learn meta-priors from data.

problem Improving learning systems with domain knowledge and controlling parameter learning rates.

method Hierarchical Empirical Bayes approach to decouple learning rates of features.

result Meta-prior learning improves performance and convergence time in various applications.

Bayesian learning Empirical Bayes Meta-prior Parameter learning rates Operational management

2020-02-04abs ↗pdf ↗

Recent Advances in Autoencoder-Based Representation Learningcs.LG

Review of autoencoder-based representation learning with meta-priors.

problem Learning useful representations with little supervision.

method Use of meta-priors and three mechanisms to enforce disentanglement and hierarchical organization.

result Autoencoder-based models can enforce disentanglement and hierarchical organization.

representation learning autoencoder meta-priors disentanglement hierarchical organization

2018-12-12abs ↗pdf ↗

A Novel Predictive-Coding-Inspired Variational RNN Model for Online Prediction and Recognitioncs.LG

PV-RNN model uses predictive coding to predict and recognize data sequences.

problem Addressing variational RNNs' challenges in learning meaningful latent representations and transferring future observations.

method Inspired by predictive coding, PV-RNN dynamically changes latent stochasticity and uses prediction errors for information transfer.

result PV-RNN outperforms standard variational Bayes models in predicting unseen sequences and generalizes well.

predictive coding variational RNN latent variables stochastic dynamics robot imitation

2018-11-04abs ↗pdf ↗

Weakly Augmented Variational Autoencoder in Time Series Anomaly Detectioncs.LG

Proposes a new VAE framework for anomaly detection in time series data.

problem Data scarcity leads to latent holes and discontinuous regions in latent space, causing non-robust reconstructions.

method Combines VAEs with self-supervised learning to address data scarcity and improve anomaly detection.

result Improves robustness of anomaly detection in time series data by addressing latent holes and discontinuities.

Time Series Anomaly Detection Variational Autoencoder Self-Supervised Learning Data Scarcity Latent Space

2024-01-07abs ↗pdf ↗

Bayesian meta learning for trustworthy uncertainty quantificationstat.ML

Bayesian meta learning improves uncertainty quantification in regression.

problem Trusting uncertainty quantification in Bayesian regression.

method Trust-Bayes framework for Bayesian meta learning, optimizing for trustworthy uncertainty quantification.

result Lower bounds and sample complexity for trustworthy uncertainty quantification are characterized.

Bayesian meta learning trustworthy uncertainty quantification Monte Carlo simulation Gaussian process regression

2024-07-27abs ↗pdf ↗

Toward Multimodal Model-Agnostic Meta-Learningcs.LG

Multimodal MAML adapts faster to tasks from a diverse distribution.

problem Limitations of gradient-based meta-learners in learning from multimodal task distributions.

method Augmenting MAML with a capability to identify and adapt to tasks from a multimodal distribution.

result Multimodal MAML adapts faster to tasks from a multimodal distribution.

meta-learning model-agnostic multimodal gradient-based few-shot

2018-12-18abs ↗pdf ↗

Robust Sequential Tracking via Bounded Information Geometry and Non-Parametric Field Actionsstat.ML

This paper solves the normalizability crisis in sequential inference by introducing bounded information geometry.

problem Structural failure in standard sequential inference architectures when dealing with extreme outliers.

method Non-parametric field actions and bounded information geometry to truncate infinite tails of spatial distributions.

result Empirical benchmarks across three domains show robust estimation without infinite-tailed distributional assumptions.

sequential inference bounded information geometry non-parametric field outliers LiDAR tracking

2026-03-13abs ↗pdf ↗

Modified Meta-Thompson Sampling for Linear Bandits and Its Bayes Regret Analysisstat.ML

Modified Meta-TS for linear contextual bandits reduces regret.

problem Optimizing decision-making in dynamic environments with context vectors.

method Meta-TSLB algorithm for linear contextual bandits, analyzing Bayes regret.

result Derives an

O((m+\log(m))\sqrt{n\log(n)})

bound on Bayes regret.

Meta-Thompson Sampling Linear Bandits Contextual Bandits Bayes Regret Meta-Learning

2024-09-10abs ↗pdf ↗

Clustering-based Meta Bayesian Optimization with Theoretical Guaranteecs.LG

Meta-BO method clusters and learns from prior tasks to optimize heterogeneous functions.

problem Optimizing multiple functions with historical data and scalability issues.

method Clustering-based meta-learning, surrogate prototypes, adaptive weighting policies.

result Scalable and robust meta-BO method improves convergence to global optimum.

Bayesian Optimization Meta-learning Heterogeneous Functions Scalability Clustering

2025-03-08abs ↗pdf ↗

Prior-Aligned Meta-RL: Thompson Sampling with Learned Priors and Guarantees in Finite-Horizon MDPscs.LG

New meta-reinforcement learning method improves performance in finite-horizon MDPs.

problem Improving meta-reinforcement learning in finite-horizon MDPs with shared optimal action-value functions.

method Proposes MTSRL and MTSRL+ algorithms with learned priors and covariance, coupled with prior-alignment technique for meta-regret guarantees.

result Achieves meta-regret guarantees with learned priors and covariance, outperforming prior-independent RL and bandit-only meta-baselines.

meta-reinforcement learning finite-horizon MDPs Thompson sampling prior-alignment meta-regret guarantees

2025-10-06abs ↗pdf ↗