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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.

168,695 papers · 148 categories

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17 results for “noise-adaptive”

Variational Flow Maps: Make Some Noise for One-Step Conditional Generationcs.CV

VFMs use noise adapters to conditionally generate images in one step.

problem Conditional image generation with iterative models is slow and requires explicit sampling paths.

method Developed a variational flow map framework that learns noise distributions for conditional sampling.

result VFMs achieve well-calibrated conditional samples in a single forward pass.

conditional generation variational flow maps image processing inverse problems noise adaptation

2026-03-07abs ↗pdf ↗

Noise-Adaptive Conformal Classification with Marginal Coveragestat.ME

This work introduces a noise-adaptive conformal inference method for better prediction sets in noisy data.

problem Real-world complications like random label noise limit the effectiveness of conformal inference.

method An adaptive conformal inference method capable of handling deviations from exchangeability.

result Informative prediction sets with tight marginal coverage guarantees in noisy data.

conformal inference noise-adaptive prediction sets random label noise tight marginal coverage

2025-01-29abs ↗pdf ↗

(Accelerated) Noise-adaptive Stochastic Heavy-Ball Momentummath.OC

Improved SHB method for faster convergence on strongly-convex quadratics.

problem Understanding and improving the theoretical and practical advantages of SHB.

method Noise-adaptive multi-stage algorithm for SHB with accelerated convergence.

result SHB can achieve accelerated convergence with larger mini-batch sizes.

Stochastic Heavy Ball Noise-adaptive Accelerated Convergence Strongly-Convex Quadratics Mini-batch Size

2024-01-12abs ↗pdf ↗

Concomitant DAG Learning: On the Roles of Noise Adaptivity, Sparsity, and Non-negativitystat.ML

New methods learn DAGs from noisy data, adapting to noise levels.

problem Inferring causal relationships from observational data with noise and confounding.

method Reformulate DAG learning as a continuous optimization problem over adjacency matrices, jointly inferring structure and noise levels.

result Improved robustness to heteroscedasticity and distribution shifts.

DAG learning signal processing optimization causal inference noise adaptation

2026-05-22abs ↗pdf ↗

Kernel Stein Generative Modelingstat.ML

Kernel SVGD improves high-dimensional inference with noise adaptation.

problem Challenges in high-dimensional inference with SVGD.

method Noise Conditional Kernel SVGD (NCK-SVGD) with entropic regularization.

result NCK-SVGD produces samples comparable to GANs and SGLD on computer vision benchmarks.

Kernel SVGD High-dimensional inference Noise adaptation Score estimation Stochastic Gradient Langevin Dynamics

2020-07-06abs ↗pdf ↗

Existence of Strong Randomized Equilibria in Mean-Field Games of Optimal Stopping with Common Noisemath.OC

Existence of strong randomized equilibria in mean-field games with common noise.

problem Existence of strong solutions in mean-field games of optimal stopping.

method Connection with Bank-El Karoui's representation problem and continuity assumptions.

result Existence of strong randomized mean-field equilibrium under certain conditions.

mean-field games optimal stopping strong solutions common noise adapted stopping time

2025-07-25abs ↗pdf ↗

Extra-Newton: A First Approach to Noise-Adaptive Accelerated Second-Order Methodsmath.OC

A new method for faster optimization of noisy functions.

problem Optimizing noisy functions efficiently.

method A universal and adaptive second-order method for convex functions.

result Achieves

O(σ/ \sqrt{T})

convergence for stochastic oracles and

O( 1 / T^3)

for deterministic oracles.

optimization second-order methods adaptive step-size noisy functions universal algorithm

2022-11-03abs ↗pdf ↗

Modified SPLICE and its Extension to Non-Stereo Data for Noise Robust Speech Recognitioncs.LG

In this paper, a modification to the training process of the popular SPLICE algorithm has been proposed for noise robust speech recognition. The modification is based on feature correlations, and enables this stereo-based algorithm to improve the performance in all noise conditions, especially in unseen cases. Further,…

2013-07-15abs ↗pdf ↗

Envy-Free Allocation of Indivisible Goods via Noisy Queriescs.GT

Fairly allocate items with noisy queries, reducing envy.

problem Fairly allocate indivisible goods with unknown valuations.

method Use Gaussian noisy queries to find an envy-free allocation.

result The optimal number of queries scales as $ \frac{m^{2.5}}{Δ^2} $ for large negative-envy.

fair allocation indivisible goods noisy queries envy-freeness Gaussian noise

2026-02-06abs ↗pdf ↗

Noise-adaptive Margin-based Active Learning and Lower Bounds under Tsybakov Noise Conditionstat.ML

We present a simple noise-robust margin-based active learning algorithm to find homogeneous (passing the origin) linear separators and analyze its error convergence when labels are corrupted by noise. We show that when the imposed noise satisfies the Tsybakov low noise condition (Mammen, Tsybakov, and others 1999; Tsyb…

2014-06-20abs ↗pdf ↗

Noise Robust Online Inference for Linear Dynamic Systemsstat.CO

We revisit the Bayesian online inference problems for the linear dynamic systems (LDS) under non- Gaussian environment. The noises can naturally be non-Gaussian (skewed and/or heavy tailed) or to accommodate spurious observations, noises can be modeled as heavy tailed. However, at the cost of such noise robustness, the…

2015-04-22abs ↗pdf ↗

SMSSVD - SubMatrix Selection Singular Value Decompositionstat.AP

High throughput biomedical measurements normally capture multiple overlaid biologically relevant signals and often also signals representing different types of technical artefacts like e.g. batch effects. Signal identification and decomposition are accordingly main objectives in statistical biomedical modeling and data…

2017-10-23abs ↗pdf ↗

Towards Noise-adaptive, Problem-adaptive (Accelerated) Stochastic Gradient Descentmath.OC

Adapts SGD to noise and problem specifics for faster convergence.

problem Minimizing smooth, strongly-convex functions with varying noise and problem constants.

method Adaptive SGD with exponentially decreasing step-sizes, Nesterov acceleration, and stochastic line-search.

result Achieves near-optimal convergence rates without knowing noise or problem specifics.

Stochastic Gradient Descent Adaptivity Noise Problem-specific Convergence Rate

2021-10-21abs ↗pdf ↗

The Conjugate Domain Dichotomy: Exact Risk of M-Estimators under Infinite-Variance Noise in High Dimensionsmath.ST

This paper analyzes M-estimators under infinite-variance noise in high dimensions.

problem High-dimensional M-estimation with infinite-variance noise.

method Study of the Fenchel conjugate domain and its impact on risk.

result Exact risk of M-estimators under infinite-variance noise is derived.

M-estimation infinite variance high dimensions Fenchel conjugate regularization

2026-03-30abs ↗pdf ↗

On the Interaction of Batch Noise, Adaptivity, and Compression, under

(L_0,L_1)

-Smoothness: An SDE Approachcs.LG

Unified framework for distributed compressed SGD under $(L_0, L_1)$ -smoothness.

problem Understanding the joint effect of batch noise, adaptivity, and compression in distributed stochastic optimization.

method Developed a unified theoretical framework using SDEs that incorporate curvature-dependent terms.

result Normalizing updates in DCSGD stabilizes convergence, with normalization degree determined by noise structure and landscape regularity.

distributed optimization stochastic gradient descent compression SDE $(L_0

2025-05-30abs ↗pdf ↗

Noise-Adaptive Confidence Sets for Linear Bandits and Application to Bayesian Optimizationstat.ML

Novel confidence sets improve linear bandit performance by adapting to unknown noise levels.

problem Adapting to unknown noise levels in sequential decision-making.

method Proposed semi-adaptive and variance-adaptive confidence sets.

result Improved regret bounds and better performance in Bayesian optimization tasks.

linear bandits confidence sets regret minimization adaptive algorithms Bayesian optimization

2024-02-12abs ↗pdf ↗

Adaptive Strategies in Non-convex Optimizationcs.LG

Adaptive algorithms improve performance in non-convex optimization across various scenarios.

problem Improper handling of noise scales, gradient magnitudes, and smoothness in non-convex optimization.

method Design and analysis of noise-adaptive, scale-free, and generalized algorithms.

result Adaptive algorithms achieve optimal rates and performance in diverse optimization settings.

Adaptive Algorithms Non-convex Optimization Gradient Noise Deep Learning BatchNorm

2023-06-17abs ↗pdf ↗