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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.

168,742 papers · 148 categories

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4998147196 · May 202619922001200920172026
48 results for support identification

Paper develops a novel approach to identify clusters of features in multivariate extremes.

problem Understanding the complex structure of multivariate extremes in various fields.
method Optimization-based approach to assess the dependence structure of extremes.
result Estimating clusters of features that best capture the support of extremes.

Rapid identification of object from radar cross section (RCS) signals is important for many space and military applications. This identification is a problem in pattern recognition which either neural networks or support vector machines should prove to be high-speed. Bayesian networks would also provide value but requi…

2010-05-28abs ↗pdf ↗

Study supports recovery of PDEs from noisy data using a specific regularization method.

problem Support recovery of PDEs from a single noisy trajectory.
method Applying ℓ1-regularized Pseudo-Least Squares model to a given data set.
result Support of ℓ1-c coefficients asymptotically converges to the true signed-support of the PDE.

DeepSupp detects financial support levels using attention mechanisms.

problem Traditional SR identification methods fail to adapt to modern markets.
method Multi-head attention mechanisms, dynamic correlation matrices, DBSCAN clustering.
result DeepSupp outperforms six baseline methods across six financial metrics.

ADSGD method speeds up model identification in sparse optimization.

problem Implicit model identification in sparse optimization problems.
method Accelerated Doubly Stochastic Gradient Method (ADSGD) for faster explicit model identification.
result ADSGD achieves faster explicit model identification and improved algorithm efficiency.

Paper tackles best mixed arm identification with cost constraints in bandit models.

problem Finding the best mixed arm with cost constraints in a stochastic bandit model.
method Proposes SFSR algorithm combining successive reject and score-function-based rejection criteria.
result Upper and lower bounds on mis-identification probability show exponential decay with budget.

Relational Structural Causal Models enable causal reasoning about unseen object combinations.

problem Developing a model that can reason about causal and combinatorial aspects of unseen object combinations.
method Relational Structural Causal Models extend structural causal models to include relational variables and define identification criteria.
result Proposed relational neural causal models outperform non-relational baselines on simulated traffic scenes.

The paper explores how to extrapolate from limited data points using causal mechanisms.

problem Handling distribution shifts with limited target samples.
method Formulates the extrapolation problem with a latent-variable model embodying the minimal change principle in causal mechanisms, and identifies conditions for identification.
result Theoretical understanding and practical methods for extrapolation without requiring an on-support target distribution.

Paper reduces sample complexity for bilinear systems identification to nearly constant.

problem Identifying discrete-time bilinear systems under bounded disturbances.
method Uses trajectory-dependent regressors and polynomial mean-square state growth analysis.
result Proves sample complexity of O~(1/ε)\widetilde{\mathcal O}(1/ε) for estimation error εε.

Deep learning identifies smartphone users from motion sensor data.

problem Smartphone user identification using motion sensor signals.
method Transformed motion signals into images, trained CNN for classification, used SVM for few-shot identification.
result CNN achieved 89.75% multi-class user classification and 96.72% few-shot user identification accuracy.

A new method enhances signal recovery with FDR control.

problem Challenging signal recovery in compressive sensing.
method Knockoff-guided compressive sensing framework with FDR control.
result Guaranteed FDR control leads to more accurate signal reconstruction.

Deep CNNs struggle with rare taxa; one-class classifiers help identify them.

problem Efficiently identifying rare benthic macroinvertebrates in insect monitoring.
method Combining deep CNNs with one-class classifiers to identify rare taxa.
result The proposed approach can improve rare species identification, supporting automation.

Interventional data helps identify latent factors without distributional assumptions.

problem Identifying latent factors from interventional data without distributional assumptions.
method Leveraging geometric signatures of latent factors' support from interventional data.
result Latent causal factors can be identified up to permutation and scaling given data from perfect do-interventions.

Paper tackles dynamic graph topology identification in time-varying graphs.

problem Dynamic graph topology identification in time-varying graphs.
method Proposes an online algorithm for time-varying optimization, with intrinsic temporal regularization.
result Demonstrates performance on Gaussian graphical model problem.

Objective: Patient notes in electronic health records (EHRs) may contain critical information for medical investigations. However, the vast majority of medical investigators can only access de-identified notes, in order to protect the confidentiality of patients. In the United States, the Health Insurance Portability a…

2016-06-10abs ↗pdf ↗

New fairness notion helps identify fair auditors for evaluating decision-support systems.

problem Identifying fair auditors to evaluate decision-support systems for bias.
method Introducing a non-comparative fairness notion based on desired system properties.
result The proposed fairness notion provides guarantees in terms of comparative fairness.

Modern data analysis depends increasingly on estimating models via flexible high-dimensional or nonparametric machine learning methods, where the identification of structural parameters is often challenging and untestable. In linear settings, this identification hinges on the completeness condition, which requires the …

2017-09-11abs ↗pdf ↗

End-to-end algorithm for controlling bilinear systems with probabilistic noise.

problem Controlling bilinear systems with noisy data.
method Proposes an end-to-end algorithm using statistical learning theory and robust controller design.
result Derived finite sample identification error bounds and structurally suitable for control.

A two-phase algorithm identifies the best arm in sparse linear bandits with fixed budget.

problem Best arm identification in sparse linear bandits with limited budget.
method Lasso and Optimal-Design (Lasso-OD) based linear best-arm identification.
result Lasso-OD achieves significant performance improvement for sparse and high-dimensional linear bandits.

A new pricing controller handles resource constraints to infer target prices effectively.

problem Resource constraints prevent fixed-price inference, leading to support exclusion.
method Formalizes support-exclusion failure, designs a target-aware controller, and uses a realized information clock.
result The controller can certify feasible target bands and log continuous local densities, leading to polynomial rates of inference.

New algorithms for best arm identification in bandits robust to misspecified parameters.

problem Inconsistent learning performance of traditional MAB algorithms when parameters are misspecified.
method Proposes two classes of asymptotically near-optimal algorithms for statistically robust MAB under fixed-budget pure exploration.
result Establishes fundamental performance limits and proposes algorithms that are asymptotically near-optimal.

Additive decoders tackle latent variables and image generation.

problem Latent variables identification and out-of-support image generation in representation learning.
method Additive decoders that can identify latent variables up to permutation and block-wise invertible transformations, and generate novel images by recombining observed factors.
result Additive decoders provide a new setting for nonlinear independent component analysis and can generate novel images by recombining observed factors.

Study quantile multi-armed bandits for identifying the best arm with a specified quantile level.

problem Identifying the arm with the highest quantile in multi-armed bandits with private rewards.
method Proposed a (non-private) and differentially private successive elimination algorithms for best-arm identification.
result The proposed algorithms are essentially optimal for quantile bandit problems, with finite sample complexity even for distributions with infinite support-size.

Optimal algorithms identify non-dominated arms in multi-output linear bandit models.

problem Identifying the Pareto Set in multi-output linear bandit models.
method Design-based algorithms for Pareto Set Identification (PSI) in a structured multi-output linear bandit model.
result Nearly optimal guarantees in both fixed-budget and fixed-confidence settings.

Work shows hallucination detection by LLMs is impossible without expert feedback.

problem Detecting hallucinations in LLMs is theoretically impossible without expert-labeled feedback.
method Investigated hallucination detection using a theoretical framework inspired by language identification.
result Automated hallucination detection is impossible for most language collections without expert-labeled feedback.

This chapter covers methods for identifying and inferring graph topologies.

problem Identifying and inferring graph topologies from multidimensional relational data.
method Overview of methods including correlation metrics, covariance selection, kernels, structural equations, and vector autoregressions.
result Supports both batch and online learning with convergence guarantees and leverages high-order statistical information.

A method identifies abrupt changes in functions with fixed confidence under noisy feedback.

problem Identifying abrupt changes in piecewise constant functions quickly and with certainty.
method Fixed-confidence piecewise constant bandit problem, focusing sampling efforts near change points.
result Asymptotically optimal method proven computationally efficient and effective in experiments.

Paper develops a new estimator for panel data with endogenous treatments, improving causal inference.

problem Challenges in causal inference for static panel data with endogenous treatments and confounding variables.
method Develops Double Machine Learning (DML) estimator for static panel models with endogenous treatments (panel IV DML). Introduces weak-identification diagnostics.
result Panel IV DML estimator improves estimation accuracy and delivers more reliable inference under weak identification.

Understanding how features interact with each other is of paramount importance in many scientific discoveries and contemporary applications. Yet interaction identification becomes challenging even for a moderate number of covariates. In this paper, we suggest an efficient and flexible procedure, called the interaction …

2016-05-28abs ↗pdf ↗

New algorithm for efficiently identifying the best arm in stochastic bandits.

problem Best arm identification in stochastic multi-armed bandits with fixed confidence.
method Sequential probability ratio tests for arm selection.
result Asymptotically optimal sample complexity and guaranteed δδ-PAC performance.

With the development and widespread use of wireless devices in recent years (mobile phones, Internet of Things, Wi-Fi), the electromagnetic spectrum has become extremely crowded. In order to counter security threats posed by rogue or unknown transmitters, it is important to identify RF transmitters not by the data cont…

2017-11-05abs ↗pdf ↗

The paper identifies generators of linear SDEs with noise types.

problem Identifying the generator of linear SDEs from their solution distribution.
method Deriving sufficient and necessary conditions for additive noise, and sufficient conditions for multiplicative noise.
result Generic conditions for identifying the generator of linear SDEs with both types of noise.

We evaluated the effectiveness of an automated bird sound identification system in a situation that emulates a realistic, typical application. We trained classification algorithms on a crowd-sourced collection of bird audio recording data and restricted our training methods to be completely free of manual intervention.…

2018-09-04abs ↗pdf ↗

Bayesian method estimates Kronecker graphical models from autoregressive processes.

problem Estimating Kronecker graphical models from autoregressive Gaussian processes.
method Bayesian approach to estimate Kronecker graphical models.
result Effectiveness demonstrated through numerical experiments and real-world data application.