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.

169,341 papers · 148 categories

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9 results for “de-randomization”

De-randomizing MCMC dynamics with the diffusion Stein operatorstat.ML

A new method de-randomizes MCMC dynamics using the Stein operator.

problem Estimating complex target distributions in Bayesian inference.

method De-randomized kernel-based particle samplers that discretize the fiber-gradient Hamiltonian flow.

result GSVGD de-randomizes complex MCMC dynamics, maintaining high sample quality.

Bayesian inference MCMC dynamics Stein operator Particle samplers KL-divergence

2021-10-07abs ↗pdf ↗

De-randomized PAC-Bayes Margin Bounds: Applications to Non-convex and Non-smooth Predictorscs.LG

New bounds explain deterministic non-smooth deep nets without large Lipschitz constants.

problem Challenges in explaining generalization of deterministic non-smooth deep nets.

method De-randomized PAC-Bayes margin bounds for deterministic non-convex and non-smooth predictors.

result New bounds avoid large Lipschitz constants, providing generalization guarantees.

PAC-Bayes Deep nets ReLU-nets Non-smooth Non-convex

2020-02-23abs ↗pdf ↗

Robust Optimization for Non-Convex Objectivescs.LG

Develops a method to optimize worst-case objectives using Bayesian optimization.

problem Optimizing in the worst case over a class of non-convex objectives.

method Reduction from robust improper optimization to Bayesian optimization, followed by de-randomization for specific tasks.

result Shows that de-randomizing the solution is NP-hard in general but feasible for certain tasks.

robust optimization Bayesian optimization non-convex objectives NP-hard de-randomization

2017-07-04abs ↗pdf ↗

PAC-Bayesian Transportation Boundstat.ML

Develops a new PAC-Bayesian bound for evaluating randomness in predictors.

problem Lack of a PAC-Bayesian bound for deterministic predictors and continuous loss functions.

method PAC-Bayesian transportation bound unifying PAC-Bayesian and chaining methods.

result First PAC-Bayesian bound relating risks of any two predictors by their distance.

PAC-Bayesian Transportation Generalization Deterministic Randomness

2019-05-31abs ↗pdf ↗

(De)Randomized Smoothing for Certifiable Defense against Patch Attackscs.LG

New method defends against patch attacks with high-certainty guarantees.

problem Patch attacks on images, especially physical adversarial attacks.

method Randomized smoothing, exploiting patch constraints.

result Meaningfully large robustness certificates against patch attacks.

patch attacks certifiable defense randomized smoothing image security robustness certificates

2020-02-25abs ↗pdf ↗

Optimal Quantum Speedups for Repeatedly Nested Expectation Estimationquant-ph

Quantum algorithm speeds up nested expectation estimation by nearly quadratically.

problem Estimating repeatedly nested expectations with quantum computing.

method Proposes a quantum algorithm achieving nearly quadratic speedup over classical methods.

result Achieves nearly quadratic speedup for RNEs, up to logarithmic factors.

quantum computing nested expectations speedup repeated nesting Monte Carlo methods

2026-02-08abs ↗pdf ↗

Randomization in Optimal Execution Gamesq-fin.TR

Study shows randomized strategies can't be Nash equilibria in markets with transient price impact.

problem Existence of pure Nash equilibria in markets with transient price impact.

method Considered randomized strategies and showed that they cannot be Nash equilibria.

result Nash equilibria cannot contain randomized strategies.

optimal execution randomized strategies Nash equilibria transient price impact competitive markets

2025-03-11abs ↗pdf ↗

Deep neural networks with controlled variable selection for the identification of putative causal genetic variantscs.LG

New method uses DNN for genetic variant identification, controlling randomness and improving interpretability.

problem Challenges in interpreting deep neural networks for genetic variant identification.

method Interpretable neural network model with controlled variable selection using ensembling, knockoffs, and de-randomization.

result The proposed method leads to more discoveries compared to conventional methods.

deep neural networks variable selection genetic variants Alzheimer disease false discovery rate

2021-09-29abs ↗pdf ↗

A Kernel-Based Conditional Two-Sample Test Using Nearest Neighbors (with Applications to Calibration, Regression Curves, and Simulation-Based Inference)stat.ME

Kernel-based test detects differences between two conditional distributions efficiently.

problem Detecting differences between two conditional distributions efficiently.

method Kernel-based measure using nearest-neighbor graphs, consistent estimate with Gaussian limit.

result Asymptotic level control and universal consistency for detecting differences.

kernel-based test conditional distributions nearest-neighbor graphs asymptotic level universal consistency

2024-07-23abs ↗pdf ↗