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.

168,695 papers · 148 categories

Trend · papers per month

count % of corpus

7 results for “prior-guided”

On the Convergence of Prior-Guided Zeroth-Order Optimization Algorithmsstat.ML

This paper analyzes and guarantees convergence of prior-guided ZO algorithms.

problem Understanding convergence properties of prior-guided zeroth-order optimization algorithms.

method Analysis of convergence under a greedy descent framework with various gradient estimators, and development of ARS algorithm.

result Convergence guarantee for prior-guided random gradient-free (PRGF) algorithms and accelerated random search (ARS) algorithm.

zeroth-order optimization prior information convergence analysis gradient estimation greedy descent

2021-07-21abs ↗pdf ↗

Query-Efficient Black-box Adversarial Attacks Guided by a Transfer-based Priorcs.LG

New methods improve black-box adversarial attacks with fewer queries.

problem Efficiently craft adversarial examples without access to gradients.

method Prior-guided random gradient-free algorithms based on biased sampling and gradient averaging.

result Both methods require fewer queries and achieve higher success rates.

adversarial attacks black-box query efficiency gradient-based prior-guided

2022-03-13abs ↗pdf ↗

Model-Informed Flows for Bayesian Inferencecs.LG

New MIF architecture improves posterior approximations in Bayesian models.

problem Challenges in variational inference for complex hierarchical models.

method Combines VIP and autoregressive flow with prior information and hierarchical ordering.

result Empirically, MIF delivers tighter posterior approximations and state-of-the-art performance.

Bayesian inference Variational inference Hierarchical models Flows Prior information

2025-05-30abs ↗pdf ↗

Residual Pathway Priors for Soft Equivariance Constraintscs.LG

RPPs improve deep learning models with soft equivariance constraints.

problem Balancing expressiveness and inductive biases in deep learning.

method Introducing Residual Pathway Priors (RPPs) to convert hard constraints into soft priors.

result RPPs enable models to learn structured solutions while retaining flexibility.

Residual Pathway Priors Soft Equivariance Deep Learning Inductive Biases Equivariance

2021-12-02abs ↗pdf ↗

Improving Black-box Adversarial Attacks with a Transfer-based Priorcs.LG

We consider the black-box adversarial setting, where the adversary has to generate adversarial perturbations without access to the target models to compute gradients. Previous methods tried to approximate the gradient either by using a transfer gradient of a surrogate white-box model, or based on the query feedback. Ho…

2019-06-17abs ↗pdf ↗

Bayesian Nonlinear PDE Inference via Gaussian Process Collocation with Application to the Richards Equationstat.ME

Develops a new Gaussian process method for efficient Bayesian inference of plant root parameters in the Richards equation.

problem Estimating unknown parameters in nonlinear PDEs for agricultural studies.

method Gaussian process collocation with importance sampling and Bayesian optimization.

result Our method yields robust estimates with uncertainty quantification for plant root parameters.

Bayesian inference Gaussian process Richards equation Plant root parameters Importance sampling

2025-10-27abs ↗pdf ↗

Efficient Black-box Adversarial Attacks via Bayesian Optimization Guided by a Function Priorcs.LG

P-BO reduces black-box adversarial attacks by 10x with Bayesian optimization and function prior.

problem Efficiently generating adversarial examples against black-box models.

method Prior-guided Bayesian Optimization (P-BO) with a function prior initialized from a surrogate model.

result Significantly reduces the number of queries needed for adversarial attacks.

Bayesian Optimization Adversarial Attacks Black-box Models Function Prior Gaussian Process

2024-05-29abs ↗pdf ↗