Non-Convex Robust Hypothesis Testing using Sinkhorn Uncertainty Sets

Kernekoncepter

Introducing a novel framework for robust hypothesis testing using Sinkhorn uncertainty sets to optimize detectors efficiently.

Resumé

The article introduces a new framework for non-convex robust hypothesis testing, focusing on optimizing detectors to minimize type-I and type-II errors. It discusses the challenges in existing methods and proposes an exact mixed-integer exponential conic reformulation for global optimization. The article also explores the connection between robust hypothesis testing and regularized risk functions, highlighting the computational efficiency and statistical performance of the proposed framework through a numerical study.

1. Introduction

   • Hypothesis testing is fundamental in statistics.
   • Distributionally robust hypothesis testing addresses challenges with unknown true distributions.

2. Robust Detector Construction

   • Uncertainty sets are crucial for computational tractability and performance.
   • Existing approaches construct uncertainty sets using various statistical divergences.

3. Sinkhorn Discrepancy

   • Utilizing Sinkhorn discrepancy-based uncertainty sets for robust hypothesis testing.
   • Proposed framework balances computational efficiency with statistical performance.

4. Optimization Methodology

   • Exact mixed-integer conic formulation introduced for solving non-convex problems.
   • Convex approximation method discussed as an alternative approach.

5. Regularization Effects

   • Regularized formulations of non-robust risk functions explored under different scaling regimes.

6. Numerical Experiments

   • Performance evaluation on synthetic and real datasets showcasing superiority over baseline methods.

Statistik

"The maximum of type-I/type-II error is 0.165, with computational time 264.1s."
"Training Size: 50, Testing Size: 2115, Data Dimension: 784 (MNIST)"
"Training Size: 50, Testing Size: 2000, Data Dimension: 1024 (CIFAR-10)"
"Training Size: 12, Testing Size: 10, Data Dimension: 56 (Lung Cancer)"
"Training Size: 20000, Testing Size: 3662, Data Dimension: 39 (Sepsis)"

Citater

"Our proposed framework balances the trade-off between computational efficiency and statistical testing performance."
"Sinkhorn discrepancy-based uncertainty set has received great attention due to its data-driven nature and flexibility."
"The contributions include obtaining finite-dimensional optimization reformulation under the random feature model."

Vigtigste indsigter udtrukket fra

Non-Convex Robust Hypothesis Testing using Sinkhorn Uncertainty Sets

by Jie Wang,Rui... kl. arxiv.org 03-25-2024

https://arxiv.org/pdf/2403.14822.pdf

Dybere Forespørgsler

How can probabilistic constraints be utilized more effectively in feasibility checks

Probabilistic constraints can be effectively utilized in feasibility checks by incorporating them into the optimization process as a way to ensure that the solution satisfies certain probabilistic properties. One approach is to use probabilistic constraints to define uncertainty sets, which contain candidate distributions for each hypothesis. By formulating these constraints within an optimization framework, one can ensure that the solution obtained meets the desired probabilistic criteria.
In feasibility checks, probabilistic constraints can help in assessing the robustness and reliability of solutions by considering various scenarios or uncertainties. These constraints provide a way to account for ambiguity or variability in data, leading to more realistic and reliable decision-making processes.
By leveraging probabilistic constraints effectively in feasibility checks, one can enhance the robustness and accuracy of models while also providing insights into potential risks and uncertainties associated with different decisions or solutions.

What are the implications of regularization parameters on hyper-parameter tuning in robust testing models

The regularization parameters play a crucial role in hyper-parameter tuning for robust testing models. These parameters control the trade-off between model complexity and generalization performance. In robust testing models, such as those utilizing Sinkhorn uncertainty sets, regularization parameters like ε (regularization parameter) and ρ (radius) impact how much emphasis is placed on minimizing worst-case errors versus regularizing against overfitting.
When tuning hyper-parameters in robust testing models, it is essential to consider how changes in regularization parameters affect model performance. For example:

Increasing ε may lead to smoother approximations but could result in underfitting.
Adjusting ρ affects the size of ambiguity sets around empirical distributions; larger values increase conservatism but might sacrifice sensitivity.
Balancing these parameters optimally requires understanding their individual impacts on model behavior.
Hyper-parameter tuning involves finding a balance between these factors through cross-validation or grid search techniques while considering trade-offs between computational efficiency and statistical performance.

How can the proposed framework be extended to address more complex statistical problems beyond hypothesis testing

The proposed framework for non-convex robust hypothesis testing using Sinkhorn uncertainty sets has significant potential for extension beyond hypothesis testing into more complex statistical problems. Some ways this framework could be extended include:

Change-point Detection: Adapting the methodology to detect changes not only based on hypotheses but also dynamically evolving data patterns.

Anomaly Detection: Utilizing uncertain set constructions from empirical distributions derived from samples for anomaly detection tasks where identifying rare events is critical.

Optimization Problems: Applying similar concepts of distributional uncertainty sets within optimization problems involving risk management or portfolio selection under uncertain conditions.

Machine Learning Models: Integrating this framework into machine learning algorithms like neural networks for improved generalization capabilities under distributional shifts.

By extending this framework beyond traditional hypothesis testing scenarios, researchers can explore new avenues where handling distributional uncertainties plays a crucial role in decision-making processes across diverse domains such as finance, healthcare, cybersecurity, etc., offering enhanced predictive capabilities and resilience against unforeseen variations or adversarial perturbations.