Title: Non-Asymptotic Uncertainty Quantification in High-Dimensional Learning
Venue: NeurIPS 2024
Accepted: yes
Keywords: high-dimensional regression

uncertainty quantification

model-based deep learning

debiased estimator

inverse problems
Abstract: Uncertainty quantification (UQ) is a crucial but challenging task in many high-dimensional learning problems to increase the confidence of a given predictor. We develop a new data-driven approach for UQ in regression that applies both to classical optimization approaches such as the LASSO as well as to neural networks. One of the most notable UQ techniques is the debiased LASSO, which modifies the LASSO to allow for the construction of asymptotic confidence intervals by decomposing the estimation error into a Gaussian and an asymptotically vanishing bias component. However, in real-world problems with finite-dimensional data, the bias term is often too significant to disregard, resulting in overly narrow confidence intervals. Our work rigorously addresses this issue and derives a data-driven adjustment that corrects the confidence intervals for a large class of predictors by estimating the means and variances of the bias terms from training data, exploiting high-dimensional concentration phenomena. This gives rise to non-asymptotic confidence intervals, which can help avoid overestimating certainty in critical applications such as MRI diagnosis. Importantly, our analysis extends beyond sparse regression to data-driven predictors like neural networks, enhancing the reliability of model-based deep learning. Our findings bridge the gap between established theory and the practical applicability of such methods.

Non-Asymptotic Uncertainty Quantification in High-Dimensional Learning

Abstract

Uncertainty quantification (UQ) is a crucial but challenging task in many highdimensional learning problems to increase the confidence of a given predictor. We develop a new data-driven approach for UQ in regression that applies both to classical optimization approaches such as the LASSO as well as to neural networks. One of the most notable UQ techniques is the debiased LASSO, which modifies the LASSO to allow for the construction of asymptotic confidence intervals by decomposing the estimation error into a Gaussian and an asymptotically vanishing bias component. However, in real-world problems with finite-dimensional data, the bias term is often too significant to disregard, resulting in overly narrow confidence intervals. Our work rigorously addresses this issue and derives a data-driven adjustment that corrects the confidence intervals for a large class of predictors by estimating the means and variances of the bias terms from training data, exploiting high-dimensional concentration phenomena. This gives rise to non-asymptotic confidence intervals, which can help avoid overestimating certainty in critical applications such as MRI diagnosis. Importantly, our analysis extends beyond sparse regression to data-driven predictors like neural networks, enhancing the reliability of model-based deep learning. Our findings bridge the gap between established theory and the practical applicability of such methods.

1 Introduction

The past few years have witnessed remarkable advances in high-dimensional statistical models, inverse problems, and learning methods for solving them. In particular, we have seen a surge of new methodologies and algorithms that have revolutionized our ability to extract insights from complex, high-dimensional data [1-3]. Also, the theoretical underpinnings of the techniques in these fields have achieved tremendous success. However, the development of rigorous methods for quantifying the uncertainty associated with their estimates and underlying parameters, such as constructing confidence intervals for a given solution, has lagged behind, with much of the underlying theory remaining elusive.

In high-dimensional statistics, for example, even for classical regularized estimators such as the LASSO [4-6], it was shown that a closed-form characterization of the probability distribution of the estimator in simple terms is not possible, e.g., [7, Theorem 5.1]. This, in turn, implies that it is very challenging to establish rigorous confidence intervals that would quantify the uncertainty of

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such estimated parameters. To overcome this, a series of papers [8-10] proposed and analyzed the debiased LASSO , also known as the desparsified LASSO, a procedure to fix the bias introduced by the ℓ 1 penalty in the LASSO; see [9, Corollary 11] and [11] for a discussion on the bias induced by the ℓ 1 regularizer. The debiased estimator derived in the aforementioned works has established a principled framework for obtaining sharp confidence intervals for the LASSO, initiating a statistical inference approach with UQ guarantees for high-dimensional regression problems where the number of predictors significantly exceeds the number of observations. Recently, this estimator was also extended in several directions beyond ℓ 1 -minimization which include, for example, deep unrolled algorithms [12, 13] and it has been applied to many fields like magnetic resonance imaging both with classical high-dimensional regression techniques as well as recent learning ones [12, 14]; see the paragraph related works in Section 2 below.

The idea of the debiased LASSO is that its estimation error, i.e., the difference between the debiased estimator and the ground truth, can be decomposed into a Gaussian and a remainder/bias component. It has been shown in certain cases that the ℓ ∞ norm of the remainder component vanishes with high probability, assuming an asymptotic setting, i.e., when the dimensions of the underlying model grow within a specific rate, see [10, 15] for details. In this case, the estimator is proven to be approximately Gaussian from which the confidence intervals are derived. However, in practice, one needs to be in a very high-dimensional regime with enough data for these assumptions to kick in. In many applications with a finite set of observations, the remainder term does not vanish ; it can rather be substantially large, and the confidence intervals constructed solely based on the Gaussian component fail to account for the entire estimation error . Consequently, the derived confidence intervals are narrower, resulting in an overestimation of certainty. This issue is particularly problematic in applications where it is

crucial to estimate the magnitude of a vector coefficient with a high degree of confidence, such as in medical imaging applications.

Moreover, according to the standard theory of debiased estimators, the estimation of how small the remainer term is depends on how well one can quantify the ℓ 2 and ℓ 1 bounds for the corresponding biased estimator , e.g., the LASSO [10, 15]. Although sharp oracle inequalities exist for such classical regression estimators, cf. related works , the same cannot be said about when unrolled algorithms are employed. For the latter, generalization bounds are usually not sharp or do not exist.

In this paper, we tackle the challenge of constructing valid confidence intervals around debiased estimators for the parameters in high-dimensional learning. The key difficulty lies in accounting for the remainder term in the estimation error decomposition - see Equation 3 - which hinders the development of finite-sample confidence intervals. We propose a novel non-asymptotic theory that explicitly characterizes the remainder term , enabling us to construct reliable confidence intervals in the finite-sample regime. Furthermore, we extend our framework to quantify uncertainty for the output of model-based neural networks, which, in turn, are used to solve inverse problems. This paves the way for a rigorous theory of data-driven UQ for modern deep learning techniques. We state an informal version of our main result, discussed in detail in Section 3.

Theorem 1 (Informal Version) . Let x (1) , . . . , x ( l ) ∈ C N be i.i.d. data. Let b ( i ) = Ax ( i ) + ε ( i ) be a high-dimensional regression model with noise ε ( i ) ∼ CN (0 , σ 2 I N × N ) . With the data, derive, for a significance level α , a confidence radius r j ( α ) for a new sample's component x ( l +1) j . Let (ˆ x u ) ( l +1) j be the debiased estimator based on a (learned) high-dimensional regression estimator ˆ x ( i ) j . Then, it holds that

Theorem 1 has far-reaching implications that transcend the classical regularized high-dimensional regression setting. For example, it enables the establishment of rigorous confidence intervals for learning algorithms such as unrolled networks [16]. To our knowledge, obtaining rigorous UQ results for neural networks without relying on non-scalable Monte Carlo methods remains a challenging problem [17]. To address this and quantify uncertainty, our approach combines model-based prior knowledge with data-driven statistical techniques. The model-based component harnesses the Gaussian distribution of the noise to quantify the uncertainty arising from the noisy data itself. We note that the Gaussian assumption for the noise is not a limitation, and extensions to non-Gaussian distributions are also possible via, e.g., a Central Limit Theorem-type argument, as clarified by [10]. We make a Gaussian noise assumption here for the sake of clarity. Complementing this, the data-driven component is imperative for quantifying the uncertainty inherent in the estimator's performance. Moreover, our approach does not require any assumptions regarding the convergence or quality properties of the estimator . This flexibility enables the debiased method to apply to a wide range of estimators.

Contributions. The key contributions in this work are threefold 1

We solve the problem illustrated in Fig. 1 by developing a non-asymptotic theory for constructing confidence intervals around the debiased LASSO estimator . Unlike existing approaches that rely on asymptotic arguments and ignore the remainder term, our finite-sample analysis explicitly accounts for the remainder , clarifying an important theoretical gap and providing rigorous guarantees without appealing to asymptotic regimes.

We establish a general framework that extends the debiasing techniques to model-based deep learning approaches for high-dimensional regression . Our results enable the principled measurement of uncertainty for estimators learned by neural networks, a capability crucial for reliable decision-making in safety-critical applications. We test our approach with state-of-the-art unrolled networks such as the It-Net [18].

For real-world medical imaging tasks, we demonstrate that the remainder term in the debiased LASSO estimation error can be accurately modeled as a Gaussian distribution. Leveraging this finding, we derive Gaussian adjusted CIs that provide sharper uncertainty estimates than previous methods , enhancing the practical utility of debiased estimators in high-stakes medical domains.

2 Background and Problem Formulation

In numerous real-world applications, we encounter high-dimensional regression problems where the number of features far exceeds the number of observations. This scenario, known as high-dimensional regression, arises when we aim to estimate N features, described by x 0 ∈ C N from only a few m target measurements b ∈ C m , where m ≪ N . Mathematically, this can be expressed as a linear model b = Ax 0 + ε , where A ∈ C m × N is the measurement matrix and ε ∼ CN (0 , σ 2 I N × N ) is additive Gaussian noise with variance σ 2 . In the presence of sparsity, where the feature vector x 0 has only s non-zero entries ( s ≪ N ), a popular approach is to solve the LASSO, which gives an estimator ˆ x obtained by solving the following ℓ 1 -regularized optimization problem:

However, the LASSO estimator is known to exhibit a systematic bias, and its distribution is intractable, posing challenges for uncertainty quantification [7]. To address this limitation, debiasing techniques have been developed in recent years [8-10]. The debiased LASSO estimator, ˆ x u , is defined as:

where M is a correction matrix that could be chosen such that max i,j ∈{ 1 ,...,N } | ( M ˆ Σ -I N × N ) ij | is small. Here, ˆ Σ = A ∗ A m . We refer to [15] for a more detailed description of how to choose M . Remarkably, the estimation error

can be decomposed into a Gaussian component W ∼ CN (0 , σ 2 m ˆ Σ) and a remainder term R that vanishes asymptotically with high probability [15, Theorem 3.8], assuming a Gaussian measurement matrix A . Such a result was extended to matrices associated to a bounded orthonormal system like a subsampled Fourier matrix, allowing for extending the debiased LASSO to MRI [19]. The decomposition (3) and the asymptotic behavior of R enable the construction of asymptotically valid CIs for the debiased LASSO estimate, providing principled UQ for high-dimensional sparse regression problems.

However, in real-world applications involving finite data regimes, the remainder term can be significant, rendering the asymptotic confidence intervals imprecise or even misleading, as illustrated in Fig. 1. This issue is particularly pronounced in high-stakes domains like medical imaging, where reliable UQ is crucial for accurate diagnosis and treatment planning. Second, the debiasing techniques have thus far been restricted to estimators whose error is well quantifiable, leaving the challenge of how they would behave for deep learning architectures open . In such cases, the behavior of the remainder term is largely unknown, precluding the direct application of existing debiasing methods and hindering the deployment of these methods in risk-sensitive applications.

A prominent example for solving the LASSO problem in (1) with an unrolled algorithm is the ISTA [20, 21]:

Here, µ > 0 is a step-size parameter, and S λ ( x ) is the soft-thresholding operator. The work [22] interpreted each ISTA iteration as a layer of a recurrent neural network (RNN). The Learned ISTA (LISTA) approach learns the parameters W k 1 , W k 2 , λ k instead of using the fixed ISTA updates:

In this formulation, LISTA unrolls K iterations into K layers, with learnable parameters ( W k , λ k ) per layer. The parameters are learned by minimizing the reconstruction error min λ,W 1 l ∑ l i =1 ∥ x k i ( λ, W, b ( i ) , x ( i ) ) -x ( i ) ∥ 2 2 on training data ( x ( i ) , b ( i ) ) . Unrolled neural networks like LISTA have shown promise as model-based deep learning solutions for inverse problems, leveraging domain knowledge for improved performance. Such iterative end-to-end network schemes provide state-of-the-art reconstructions for inverse problems [18]. Recently, the work [12] proposes a

framework based on the debiasing step to derive confidence intervals specifically for the unrolled LISTA estimator. However, similar to the previously mentioned debiased LASSO literature, it only handles the asymptotic setting . One of the main goals of this paper is to overcome such limitation of the current theory.

Related Works.

High-dimensional regression. High-dimensional regression and sparse recovery is now a wellestablished theory, see [1, 3, 23] and references therein. In this context, several extensions of the LASSO have been proposed such as the elastic net [24], the group LASSO [25], the LASSO with a nuclear norm penalization [26], the Sorted L-One Penalized Estimation (SLOPE) [27] which adapts the ℓ 1 -norm to control the false discovery rate. In addition to convex penalty functions, concave penalties have been explored to address some limitations of the LASSO, e.g., the Smoothly Clipped Absolute Deviation (SCAD) penalty [28] and the Minimax Concave Penalty (MCP) [29]. Non-convex variants of the LASSO for ℓ p -norm ( p < 1 ) minimization were also studied [30, 31] as well as noiseblind variants such as the square-root LASSO [32, 33]. Scalable and fast algorithms for solving the LASSO and its variants include semi-smooth Newton methods [34] and IRLS [35].

LASSO theory. Given how ubiquitous and studied such an estimator is, it is difficult to do justice to all the papers that have contributed to such a theory. Several works have established oracle inequalities for the LASSO [36-40]. Another key theoretical result is the consistency of the LASSO in terms of variable selection. [41] and [42] established the consistency of the LASSO while [43] analyzed the sparsity behavior of the LASSO when the design matrices satisfy the Restricted Isometry Property.

Debiased estimators. After the first papers about the debiased LASSO [8-10], some works have focused on improving its finite-sample performance and computational efficiency [15, 44]. The size of the confidence intervals derived for the debiased LASSO has been proven to be sharp in the minimax sense [45]. Debiased estimators have been extended in several directions, e.g., [19, 44, 46, 47]. Recently, [48] established asymptotic normality results for a debiased estimator of convex regularizers beyond the ℓ 1 -norm. In the context of MR images, [49] explored a debiased estimator for inverse problems with a total variation regularizer. Debiased estimators have also been recently extended to unrolled estimators - see discussion in the next paragraph - in [12, 13].

Algorithm unrolling and model-based deep learning for inverse problems. The idea of unfolding the iterative steps of classical algorithms into a deep neural network architecture dates back to [22], which proposed the Learned ISTA (LISTA) to fast approximate the solution of sparse coding problems. Several works have extended and improved upon the original LISTA framework [50-55]. [56] proposed the Learned Primal-Dual algorithm, unrolling the primal-dual hybrid gradient method for tomographic reconstruction. [57] proposed the Deep Cascade of Convolutional Neural Networks (DC-CNN) for dynamic MRI reconstruction. [58] unfolded proximal gradient descent solvers to learn their parameters for 1D TV regularized problems. [16] introduced a general framework for algorithm unrolling. [59] developed MoDL, a model-based deep learning approach for MRI reconstruction that unrolls the ADMM algorithm. [60] proposed a proximal alternating direction network (PADNet) to unroll nonconvex optimization. See also the surveys for more information about unrolling and also the connection with physics-inspired methods [61, 62]. [18, 63] developed the It-Net, an unrolled proximal gradient descent scheme where the proximal operator is replaced by a U-Net. This scheme won the AAPM Challenge 2021 [64] whose goal was to identify the state-of-the-art in solving the CT inverse problem with data-driven techniques . A generalization of the previous paradigm is the learning to optimize framework that develops an optimization method by training, i.e., learning from its performance on sample problem [65, 66].

Uncertainty Quantification. There have been a few attempts to quantify uncertainty on a pixel level for unrolled networks used in imaging processing, e.g., [67]. However, such approaches are based on Bayesian networks and MC dropout [68], which requires significant inference time paired with a loss of reconstruction performance since the dropout for UQ is a strong regularizer in the neural network. Unlike prior work, our contribution focuses on a scalable data-driven method that is easily implementable in the data reconstruction pipeline.

Another method that became popular in the last couple of years is conformal prediction [69, 70], which addresses the problem of constructing prediction bands ˆ C m : X →{ subsets of B } for a given level α with the property that for a new i.i.d. pair ( a i , b i ) , we get P ( b m +1 ∈ ˆ C n ( a m +1 )) ≥ 1 -α , where the pairs ( a i , b i ) ∼ P, i = 1 , . . . , m are i.i.d. feature and response pairs from a distribution

P on A × B and the probability is over all of available data ( a i , b i ) , i = 1 , . . . , m + 1 . Such a method assumes that the regression coefficient x , in the model b i = ⟨ a i , x ⟩ + ϵ , is fixed. In contrast to that, the debiased LASSO produces confidence intervals for individual pixels x ( l +1) j , where j = 1 , . . . , N when a new set of data points b ( l +1) 1 , . . . , b ( l +1) m is given for a fixed set of measurement vectors a 1 , . . . , a m . Recently, a few papers developed conformal prediction-based uncertainty masks for imaging tasks [71, 72]. Unlike the former, our method provides computationally inexpensive confidence intervals for a new given test image. Unlike the latter, which constructs an interval-valued function for each pixel of a new image that holds in expectation (see Equation 2 in [72]), our method comes with pixel-wise coverage guarantees for each new test image.

3 Data-Driven Confidence Intervals

We now introduce our data-driven approach to correct the CIs. Instead of deriving asymptotic CIs from the decomposition ˆ x u -x 0 = W + R , by assuming that R asymptotically vanishes, we utilize data ( b ( i ) , x ( i ) ) l i =1 along with concentration techniques to estimate the size of the bias component R . We continue to leverage the prior knowledge of the Gaussian component W while extending the CIs' applicability to a broad class of estimators, including neural networks. Our method is summarized in Algorithm 1, where the data is used to estimate the radii of the CIs, and in Algorithm 2, which constructs the estimator around which the CIs are centered. The following main result proves the validity of our method.

Theorem 2. Let x (1) , . . . , x ( l ) ∈ C N be i.i.d. complex random variables representing ground truth data drawn from an unknown distribution Q . Suppose, that ε ( i ) ∼ CN (0 , σ 2 I m × m ) is noise in the high-dimensional models b ( i ) = Ax ( i ) + ε ( i ) , where A ∈ C m × N , and independent of the x ( i ) 's. Let ˆ X : C m → C N be a (learned) estimation function that maps the data b ( i ) to ˆ x ( i ) , which is an estimate for x ( i ) . Set | R ( i ) j | = | e T j ( M ˆ Σ -I N × N )(ˆ x ( i ) -x ( i ) ) | for fixed A and M . For j = 1 , . . . , N , we denote the true but unknown mean with µ j = E [ | R (1) j | ] and the unknown variance with ( σ 2 R ) j := E [( | R (1) j | -µ j ) 2 ] , respectively. Let ˆ S j = 1 l l ∑ i =1 | R ( i ) j | be the unbiased sample mean estimator and (ˆ σ 2 R ) j = 1 l -1 l ∑ i =1 ( | R ( i ) j | -ˆ S j ) 2 the unbiased variance estimator. Let α ∈ (0 , 1) and γ j ∈ ( 0 , 1 -1 lα ) . Furthermore, set the confidence regions for the sample x ( l +1) ∼ Q in the model b ( l +1) = Ax ( l +1) + ε ( l +1) as C j ( α ) = { z ∈ C : | (ˆ x u ) ( l +1) j -z | ≤ r j ( α ) } with radius

Then, it holds that

Theorem 2 presents a way to achieve conservative confidence intervals that are proven to be valid, i.e., are proven to contain the true parameter with a probability of 1 -α . Its main advantage is that there are no assumptions on the distribution Q (except that σ 2 R exists), making it widely applicable. Hence, Theorem 2 includes the worst-case distribution showing a way to quantify uncertainty even in such an ill-posed setting. Especially in medical imaging, such certainty guarantees are crucial for accurate diagnosis. The proof exploits the Gaussianity of the component W as well as an empirical version of Chebyshev's inequality, which is tight when there is no information on the underlying distribution. The detailed proof can be found in Appendix B. For a thorough discussion on Theorem 2 including practical simplifications and the dependence of γ j on the confidence interval length, we refer to Appendix A.

More certainty comes with the price of larger confidence intervals. If there is additional information on the distribution of R , like the ability to be approximated by a Gaussian distribution, then the confidence intervals become tighter. This case, which includes relevant settings such as MRI, is discussed in Section 4.

Algorithm 1 Estimation of Confidence Radius

1: Input: Estimation function ˆ X , dictionary matrix A , correction matrix M , data points ( b ( i ) , x ( i ) ) l i =1 , significance level α

2: for i = 1 , . . . , l do

3: Compute ˆ x ( i ) , R ( i ) ∈ C N via ˆ x ( i ) = ˆ X ( b ( i ) ) and R ( i ) j = e T j ( M ˆ Σ -I N × N )(ˆ x ( i ) -x ( i ) ) .

4: end for

5: for j = 1 , . . . , N do

8: end for

9: Output: Radii of confidence regions ( r j ( α )) N j =1

Algorithm 2 Construction of Confidence Regions

1: Input: Estimation function ˆ X , dictionary matrix A , correction matrix M , measurement b , radii ( r j ( α )) N j =1 (derived from Algorithm 1 or Theorem 3)

2: Compute estimator ˆ x = ˆ X ( b ) .

3: Construct debiased estimator via ˆ x u = ˆ x + 1 m MA ∗ ( b -A ˆ x )

4: for j = 1 , . . . , N do

5: Construct confidence region C j ( α ) = { z ∈ C | | ˆ x u j -z | ≤ r j ( α ) }

6: end for

7: Output: Debiased estimator x u and confidence regions ( C j ( α )) N j =1

4 Confidence Intervals for Gaussian Remainders

Valid confidence intervals, i.e., those with correct coverage probability, can be derived most straightforwardly when the distribution of the remainder term is known and easily characterized. In such cases, more informative distributional assumptions lead to potentially tighter confidence intervals compared to Theorem 2, which makes no assumptions about the remainder component. In this section, we derive non-asymptotic confidence intervals assuming the remainder term to be approximated by a Gaussian distribution.

Theorem 3. Let ˆ x u ∈ C N be a debiased estimator for x ∈ C N with a remainder term R ∼ CN (0 , Σ R /m ) . Then, C j ( α ) = { z ∈ C | | z -ˆ x u j | ≤ r j ( α ) } with radius

is valid, i.e. P ( x j ∈ C j ( α )) ≥ 1 -α .

For the proof, we refer to Appendix B. We note, however, that the theorem can be generalized beyond the Gaussian case. In particular, we present in Appendix C a proof of this theorem for heavy-tailed distributions. In Appendix E, we demonstrate empirically that the Gaussian assumption for the remainder term holds in a wide range of relevant practical settings. This validation enables the application of the proposed confidence intervals derived under this assumption. These confidence intervals strike a careful balance between non-asymptotic reliability, ensuring valid coverage even in finite-sample regimes, and tightness, providing informative and precise uncertainty estimates. By leveraging the Gaussian approximation, which becomes increasingly accurate in higher dimensions as illustrated in Figure 7, our framework offers a principled and computationally efficient approach to quantifying uncertainty in high-dimensional prediction problems. The variance of R can be estimated with the given data using, e.g., the unbiased estimator for the variance as in Theorem 2.

5 Numerical Experiments

We evaluate the performance of our non-asymptotic confidence intervals through extensive numerical experiments across two settings: (i.) the classical debiased LASSO framework to contrast our nonasymptotic confidence intervals against the asymptotic ones. (ii.) the learned framework where we employ learned estimators, specifically the U-net [73] as well as the It-Net [18], to reconstruct real-world MR images and quantify uncertainty. Our experiments demonstrate the importance of properly accounting for the remainder term in practical, non-asymptotic regimes. Each experiment follows the same structure:

Data Generation and Management: We fix the forward operator A and generate n > 2 feature vectors x ( i ) n i =1 and noise vectors ε ( i ) n i =1 with ε ( i ) ∼ CN (0 , σ 2 I m × m ) . We obtain observations b ( i ) n i =1 via b ( i ) = Ax ( i ) + ε ( i ) . We split the data ( b ( i ) , x ( i ) ) n i =1 into an estimation dataset of size l and a test dataset of size k ( l + k = n ). If we learn an estimator, we further split the data into training, estimation, and test sets.

Reconstruction: Depending on the experiment, we obtain a reconstruction function ˆ X in one of the following ways: for the classical LASSO setting, we use the LASSO; for the learned estimator experiment, we train a U-Net [73] or It-net [18] on the training data to serve as the reconstruction function ˆ X .

Estimation of Confidence Radii: We run Algorithm 1 with A, ˆ X,M (that is chosen according to [15]), the estimation data ( b ( i ) , x ( i ) ) l i =1 , and a predefined significance level α ∈ (0 , 1) to obtain radii r j ( α ) N j =1 . We optimize over γ , therefore the γ we use is the optimal one which leads to the smallest confidence intervals. To construct the final confidence intervals, the radii need to be centered according to the debiased estimator. For every new measurement b , we run Algorithm 2 to obtain tailored confidence intervals for the feature vector x corresponding to b . In addition, we compute the CI for the Gaussian adjustment based on Theorem 3 using the estimation set to quantify the variance of R with the unbiased estimator for the variance as before.

Evaluation: We use the test dataset ( b ( i ) , x ( i ) ) n i = l +1 to evaluate our adjustments. For each b ( i ) , we run Algorithm 2 to obtain confidence intervals C ( i ) j ( α ) N j =1 for x ( i ) . We estimate P ( x ( i ) j ∈ C j ( α )) by h j ( α ) = 1 k ∑ n i = l +1 ✶ { x ( i ) j ∈ C j ( α ) } and average over all components h ( α ) = 1 N ∑ N j =1 h j ( α ) . Since performance on the support S , the non-zero pixels, is crucial, we define the hit rate on S as h ( i ) S = 1 | S | ∑ N j =1 ✶ { x ( i ) j ∈ C j ( α ) } and average h S ( α ) = 1 k ∑ n i = l +1 h ( i ) S . Note that the support may change with i . Moreover, we do the same for the CI based on the Gaussian-adjusted radii.

5.1 UQ for Sparse Model-Based Regression

We consider a setting aligned with existing debiased LASSO literature, e.g., [9] to demonstrate our approach's extension of current UQ methods. The forward operator is a complex Gaussian matrix A ∈ C m × N with dimensions N = 10000 , m = 0 . 6 N , and A ij ∼ CN (0 , 1) . We generate n = 750 s = 0 . 1 N -sparse features x ( i ) by randomly selecting m distinct indices from 1 , . . . , N and drawing magnitudes from CN (0 , 1) . With relative noise ∥ ε ( i ) ∥ ∥ Ax ( i ) ∥ ≈ 0 . 2 , we split the data ( b ( i ) , x ( i ) ) n i =1 into l = 500 estimation and k = 250 test data. For reconstruction, we solve the LASSO ˆ X ( b ) := argmin x ∈ C N 1 m ∥ Ax -b ∥ + λ ∥ x ∥ 1 with λ = 10 σ √ m (2 + √ 12 log( N )) following [19].

With significance level α = 0 . 05 , we run Algorithm 1 to obtain confidence radii, choosing M = I N × N [15] and exploiting the relaxation (9). Averaged over the l estimation data points, the ℓ 2 and ℓ ∞ norm ratios are: ∥ R ∥ 2 ∥ W ∥ 2 = 0 . 9993 and ∥ R ∥ ∞ ∥ W ∥ ∞ = 1 . 1581 . In existing literature, the ℓ ∞ norm is typically measured when the remainder term vanishes, as it is relevant for pixel-wise confidence intervals. Here, the remainder term is of comparable order as the Gaussian term and hence, too significant to neglect in confidence intervals derivation.

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Evaluating it on the remaining k = 250 data points, the data-driven and Gaussian-adjusted averaged hit rates are h (0 . 05) = 1 , h S (0 . 05) = 1 and h G (0 . 05) = 0 . 9691 , h G S (0 . 05) = 0 . 8948 , respectively. Neglecting the remainder term yields h W (0 . 05) = 0 . 8692 and h W S (0 . 05) = 0 . 6783 , which is substantially lower and violates the specified 0 . 05 significance level. Fig. 2 presents confidence intervals of each type for one data point x ( i ) . A detailed visualization of h j (0 . 05) , h ( i ) S (0 . 05) , h G j (0 . 05) , and ( h G S ) ( i ) (0 . 05) is illustrated in Fig. 4c and 4i in the appendix. Further experiments with different sparse regression settings, including subsampled Fourier matrices, are also presented in Appendix D.

5.2 UQ for MRI Reconstruction with Unrolled Neural Networks

We extend the debiasing approach to model-based deep learning for MRI reconstruction using the U-Net and It-Net on single-coil knee images from the NYU fastMRI dataset 2 [74, 75]. Here, the forward operator is the undersampled Fourier operator PF ∈ C m × N with N = 320 × 320 , m = 0 . 6 N , the Fourier matrix F and a radial mask P , see Figure 5b. The noise level σ is chosen such that the relative noise is approximately 0 . 1 . The data is split into training (33370 slices), validation (5346 slices), estimation (1372 slices), and test (100 slices) datasets.

We then train an It-Net [18] with 8 layers, a combination of MS-SSIM [76] and ℓ 1 -losses and Adam optimizer with learning rate 5 e -5 for 15 epochs to obtain our reconstruction function ˆ X .

With significance level α = 0 . 1 , we run Algorithm 1 to construct confidence radii, this time for the real part of the image instead of the magnitude, choosing M = I N × N [15] and exploiting the relaxation (9). Averaged over the l estimation data points, we have ∥ R ∥ 2 ∥ W ∥ 2 = 0 . 38 and ∥ R ∥ ∞ ∥ W ∥ ∞ = 0 . 49 , which indicates that the remainder term is significant and cannot be neglected. Evaluating the test data, the averages of the data-driven adjustment hit rates are h (0 . 1) = 0 . 9998 , h S (0 . 1) = 0 . 9995 and the averages of the Gaussian adjusted hit rates are h G (0 . 1) = 0 . 9485 , h G S (0 . 1) = 0 . 9353 , where the support refers to the largest 10% of the image pixels as the background cannot be clearly delineated numerically. Neglecting the remainder term, the hit rates of the asymptotic CIs are h W (0 . 1) = 0 . 9306 and h W S (0 . 1) = 0 . 9152 . As in the sparse regression setting, they are significantly lower. Fig. 3 presents confidence intervals based on the data-driven adjustment and the asymptotic confidence intervals for a region in one image x ( i ) . In addition, it contains a box plot showing the distribution of the hit rates based on the Gaussian adjustment and the asymptotic hit rates. More experiments for UQ for MRI reconstruction can be found in Appendix D and Tables 2 and 3.

(a) ground truth

0

10

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(b) w/ data adjustment

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(c) w/o data adjustment

hit rates all gauss

hit rates all asymp

0.850

0.875

0.900

0.925

0.950

0.975

1.000

Coverage

(d) coverage over all components

6 Final Remarks

In this work, we proposed a data-driven uncertainty quantification method that derives non-asymptotic confidence intervals based on debiased estimators. Our approach corrects asymptotic confidence intervals by incorporating an estimate of the remainder component and has solid theoretical foundations. While the correction can be based on prior knowledge, e.g., a Gaussian distribution of the remainder term, we also derive CI based on a data-driven adjustment without further information. This data-driven nature enhances its applicability to a wide range of estimators, including modelbased deep-learning techniques. We conducted experiments that confirm our theoretical findings, demonstrating that even in classical sparse regression settings, the remainder term is too significant to be neglected. Furthermore, we applied the proposed method to MRI, achieving significantly better rates on the image support.

Limitations and Future Directions. While our method corrects for the remainder term, larger remainder terms necessitate greater corrections, resulting in wider confidence intervals. The goal is that those intervals should be narrow enough to be informative but wide enough to be realistic, given the sample size and variability in the data. Therefore, it is crucial to achieve a small remainder term to avoid excessively large confidence intervals. Additionally, the accuracy of our method depends on the quality of the estimates for the mean and variance of the remainder term, which improves with more available data. Additionally, the length of the intervals can be minimized over a larger parameter set, provided that more data is available. We leave as a future direction to study the sharpness of the proposed confidence intervals and radii for a given amount of data. Moreover, we would like to investigate how the length of the confidence intervals could be improved when estimating higher moments. We believe that our method is applicable to a wide variety of deep learning architectures, including vision transformers in MRI, e.g., [77]. Testing the generality of the method with state-of-the-art architectures for different problems would demonstrate its broad usefulness.

Acknowledgments

We gratefully acknowledge financial support with funds provided by the German Federal Ministry of Education and Research in the grant 'SparseMRI3D+: Compressive Sensing und Quantifizierung von Unsicherheiten für die beschleunigte multiparametrische quantitative Magnetresonanztomografie (FZK 05M20WOA)' .

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Supplementary material to the paper Non-Asymptotic Uncertainty Quantification in HighDimensional Learning .

In this supplement to the paper, we present in Section A a detailed discussion about aspects of the main result that are not mentioned in the main body of the paper. Moreover, Section B presents the proof Theorem 2 and Theorem 3. The former establishes data-driven confidence intervals, while the latter assumes the remainder component to be approximated by a Gaussian distribution. Appendix C constructs confidence intervals similar to Theorem 3 under the assumption of a heavy-tailed distribution. In Section D, we confirm our theoretical findings with several numerical experiments for classical high-dimensional regression as well as model-based neural networks. In Section E, we visualize the approximate Gaussian distribution of the remainder terms, demonstrating the applicability of Theorem 3 in relevant settings.

A Further Discussion of Main Result

In this appendix, we provide an in-depth discussion of various technical aspects and practical considerations regarding our main theoretical results, Theorem 2, including analysis of confidence interval radii, probabilistic interpretations, and implementation-oriented relaxations of the theoretical assumptions.

Length of radius: To minimize the length of the radius, γ j ∈ ( 0 , 1 -1 lα ) should be chosen as the minimizer of the problem

In order to minimize over a large set for a given significance level α , a large number of data l is needed. For fixed estimates ˆ S j and (ˆ σ 2 R ) j , more data leads to a potentially smaller confidence interval length. If we assume R j = 0 , it follows that ˆ S j = 0 and (ˆ σ 2 R ) j = 0 . Then, γ = 1 is a valid choice, for which the function σ ( M ˆ Σ M ∗ ) 1 / 2 jj √ m √ log ( 1 γ j α ) is well-defined and is minimized. In this case, the radius coincides with the asymptotic radius derived in [9, 10, 15] (except for that these works handle the real case) and the ones in [12] with M = I N × N . In this sense, the asymptotic confidence intervals can be seen as a special case of the proposed method.

The significance level α depends on γ j and l to assure c l ( · ) to be well-defined. For a large dataset

Probabilistic discussion: The probability in (5) is over the randomness of the noise as well as Q . The confidence circles C j ( α ) consist of two random variables, the debiased estimator ˆ x u j and the radius r j ( α ) . The former depends on the random noise and potentially on training data, while the latter depends on the estimators ˆ S j and ˆ σ R , which in turn depend on both the noise and the data x (1) , . . . , x ( l ) .

A crucial requirement of applying the empirical version of Chebyshev's inequality [78] is the independence and identical distribution of the variables | R (1) j | , . . . , | R ( l ) j | . Therefore, it is essential that the estimator function ˆ X is independent of the data x (1) , . . . , x ( l ) . To achieve this, we train the estimator function ˆ X using a dataset that is independent of the data x (1) , . . . , x ( l ) , used for estimating R (1) , . . . , R (1) . However, the mean and variance of | R (1) | and hence of | R ( i ) | depend on the variance of the noise ε (1) , i.e. σ 2 . Thus, different noise levels σ require a new estimation of the mean and variance of | R (1) | . Throughout this paper, we assume the noise level to be fixed and known. The latter assumption is motivated by two factors. First, the size of the confidence intervals relies on σ . Given that the primary focus of this paper is to determine the size of the confidence intervals based on the remainder term R (1) , we seek to mitigate other influencing factors such as noise level estimation. Second, in domains like medical imaging, there is substantial knowledge about the noise level. For instance, the noise level in MRI can be directly measured from the scanner [79]. If the noise level is unknown, there are methods to estimate it. In the debiased LASSO literature, the most used method

is the scaled LASSO [80]. Other methods for sparse regression, either in the LASSO context or more general for high-dimensional models, are [81-86].

Relaxation of assumptions in practice: In practice, it is often the case, that | R ( i ) 1 | , . . . , | R ( i ) N | are identical distributed resulting in µ 1 = . . . = µ N and ( σ 2 R ) 1 = . . . = ( σ 2 R ) N . Although the proof requires independence of the | R ( i ) j | , there are cases when it might suffice to relax this assumption by estimating the mean and variance pixel-wise uniformly, i.e.,

In addition to saving computational resources, accuracy improves due to the higher number of samples. Furthermore, instead of solving the optimization problem (7) for every j ∈ { 1 , . . . , N } , it might be a good idea to choose γ 1 = . . . = γ N as the minimizer of

Then, one γ can be used for computing the potentially different radii r j ( α ) .

Type of uncertainty: The decomposition of the estimation error into a Gaussian term and a remainder term allows for handling both types of uncertainty, aleatoric and epistemic, almost separately. With the Gaussian term W , we quantify the aleatoric uncertainty from the inherent measurement noise. The remainder term R handles the epistemic uncertainty, which we quantify using a purely data-driven approach since for two different backward models (e.g., two different neural networks), it can be used to compare the estimation error of both models with respect to the ground truth. In this sense, our technique is rather a more general inferential uncertainty method.

B Proofs

Proof of Theorem 2. The statement x ( l +1) j ∈ C j ( α ) is equivalent to | (ˆ x u ) ( l +1) j -x ( l +1) j | ≤ r j ( α ) . To prove (5), we show that

In the next step, we write the radius r ( α ) as the sum r ( α ) = r W ( α ) + r R ( α ) . According to the decomposition (ˆ x u ) ( l +1) j -x ( l +1) j = W j + R j we obtain for fixed j ∈ { 1 , . . . , N }

where the last step follows from the pigeonhole principle. To estimate the first summand, we set

For estimating the term P ( | R j | ≥ r R j ( α ) ) , we set r R j ( α ) = c l ( α ) · (ˆ σ R ) j + ˆ S j . This choice leads to

Now, we apply an empirical version of Chebyshev's inequality [78, 87]. This leads to

where we used in the last step, that (1 -γ j ) α < α < 1 . To summarize,

Proof of Theorem 3. Since W ∼ CN (0 , σ 2 m M ˆ Σ M ∗ ) and R ∼ CN (0 , 1 m Σ R ) the estimation error ˆ x u -x 0 = W + R follows again a multivariate normal distribution with zero mean and covariance matrix 1 m ( σ 2 M ˆ Σ M ∗ +Σ R ) . By exploiting the Gaussian distribution, we obtain

Thus, we have

which needs to be equal to α > 0 . Therefore,

C Heavy-Tailed Version of Theorem 3

The following version of Theorem 3 gives an example of how to derive the radii of the confidence intervals in the case of a known, heavy-tailed distribution of the remainder term.

Theorem 4. Let ˆ x u ∈ C N be a debiased estimator for x ∈ C N with a remainder term following a complex t-distribution with a degree of freedom ν > 2 , i.e. R ∼ C t ν (0 , Σ R ) . Then, C j ( α ) = { z ∈ C | | z -ˆ x u j | ≤ r j ( α ) } with radius

is valid, i.e. P ( x j ∈ C j ( α )) ≥ 1 -α .

Proof. We can bound the estimation error | ˆ x u j -x j | analogously to the proof of Theorem 2 by

The distribution of P ( | W j | > r j W ( α )) is determined by the Gaussian noise and can be computed as

similarly to Theorem 2. If R ∼ C t ν (0 , Σ R ) , then the marginal distribution R j is also complex t-distributed, i.e. R j ∼ (0 , (Σ R ) jj ) (see [88]). Moreover, the probability density function of | R j | is

Hence,

Setting this probability equal to (1 -γ j ) α requires

Since we assumed ν > 2 the inequality (1 -γ j ) -2 /ν α -2 ν > 1 holds. Combining r j ( α ) = r j W ( α ) + r R j ( α ) concludes the proof.

D Further Numerical Evaluation

To confirm our theoretical findings claiming that the incorporation of the bias component renders the confidence intervals more robust, we present additional numerical experiments here.

UQfor Classical Model-Based Regression For the experiments described here, we use Tfocs [89]. Analogous to the experiment described in Section 5.1, we run further experiments in the classical sparse regression setting when the measurement matrix is a Gaussian and subsampled Fourier matrix. The different settings including the results can be found in Table 1. The results show that the Gaussian adjustment of our proposed method significantly increases the hit rates, especially on the support, while moderately increasing the confidence interval length. Our data-driven adjustment achieves even better hit rates, but the confidence intervals are larger. Although in well-posed settings, like the second column of Table 1, the hit rates h W (0 . 05) based on asymptotic confidence intervals lead overall to almost 95% , however on the support , which are the crucial features, the asymptotic hit rates fail. In particular, our corrections are essential in ill-posed regression problems as the third Gaussian column. The hit rates for the asymptotic CIs and the corrected ones with Gaussian adjustment are visualized in more detail in Figure 4.

UQfor MRI Reconstruction with Neural Networks In this section, we present more experiments for UQ for MRI reconstruction with neural networks. Our experimental settings, as well as our code for this experiment, are based on the paper and code 3 by [18]. The dataset used for conducting the experiments is the fastMRI single-coil knee dataset. For documentation, see [74, 75].

Table 2 represents the results obtained by learning the reconstruction function ˆ X using the It-net with 8 layers, with 60% , 40% and 30% radial undersampling and for noise levels obtained by adding complex gaussian noise with standard deviation σ = 60 and σ = 84 , respectively. Similarly, Table 3 shows the results obtained by the U-Net. In Figure 6, the asymptotic hit rates and the Gaussian adjusted ones for the 95% confidence level are compared in a box plot for each experiment. Note that for these experiments, we construct only the confidence intervals for the real part of the image, as the

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fastMRI dataset provides only real-valued data. Due to this artifact, the investigation of the imaginary part's distribution is not possible, and the Gaussian-adjusted confidence intervals are not applicable to the magnitude. For this reason, and to ensure a valid comparison of the three confidence interval types, we decided to investigate the confidence intervals for the real part, even in the asymptotic and data-driven setting.

All It-Net and U-Nets are trained with a combination of the MS-SSIM-loss [76], the ℓ 1 -loss and the Adam optimizer with a learning rate of 5 e -5 , epsilon of 1 e -4 and weight decay parameter 1 e -5 .

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The It-Nets were trained for 15 epochs, and the U-Nets were trained for 20 epochs, both with batch size 40 . Every U-Net has 2 input and output channels, 24 base channels, and encodes the image to a size of 20 × 20 and at most 384 channels. The It-Net employs the U-Net in each layer as a residual network and has a data consistency part around each U-Net in every layer.

Comparing the tables, the It-Net has, in general, better hit rates as well as a better R/W ratio than the U-Net due to its more accurate reconstruction. Further, the hit rates for all the pixels are higher than those obtained only for the support. For achieving reliable results for safety-critical applications, obtaining hit rates higher than the confidence level is crucial, especially on the support. Otherwise, one might achieve a certain confidence level overall but cannot trust the pixels of interest.

The experiments were conducted using Pytorch 1 . 9 on a desktop with AMD EPYC 7F52 16-Core CPUs and NVIDIA A100 PCIe030 030 40GB GPUs. The code for the experiments can be found in the supplementary material. The execution time of the code is around 5 hours for each It-Net, 2 hours for each U-Net, and around 30 minutes for the rest of each experiment. So, in total, this gives us a time of 48 hours for the MRI reconstruction experiments. The execution time for the classical model-based regression experiments takes 5 to 30 minutes each; therefore, in total it is less than 3 hours.

E Distribution Visualization of Remainder Term

In Figure 7, we present a series of histograms illustrating the empirical distribution of the remainder term's real part across all experimental settings in sparse regression, conducted in this

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paper. These histograms provide evidence that the remainder term can be approximated by a Gaussian distribution, with the approximation becoming increasingly precise as the dimensionality increases. Across low-dimensional scenarios, the empirical distributions exhibit some deviations from the Gaussian form, but these discrepancies diminish as the dimensionality grows larger. In high-dimensional regimes, the empirical distributions demonstrate an exceptional degree of convergence to the Gaussian approximation. This close alignment lends strong support to the validity of the key assumption of Theorem 3, allowing a Gaussian adjustment to the confidence intervals.

In Figure 8 we present a series of histograms representing the empirical distribution of the remainder term's real part for the six different experimental settings, for the U-Net, conducted in this paper.

Figure 9 represents the histograms for the It-Net experiments. In most scenarios, the real part of the remainder term is Gaussian distributed with mean 0 . The only exceptions are Figures 8e and 8f, which correspond to the U-Net experiments with 30% undersampling.

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Justification: The limitations of this paper are discussed in Section 6. In particular, we discussed that larger remainder terms necessitate greater corrections, resulting in wider confidence intervals. Also, the accuracy of our method depends on the quality of the estimates for the mean and variance of the remainder term, which improves with more available data. Lastly, we left the sharpness of the provided results as a future work.

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Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper focuses on Uncertainty Quantification (UQ) in high-dimensional regression. The authors develop a new data-driven approach that applies both to classical optimization methods, such as the LASSO (which imposes an l1l1​ penalty on the weights), and to neural networks. They address the limitations of traditional UQ techniques like the debiased LASSO, which often produce overly narrow confidence intervals due to significant bias in finite-dimensional data. The authors derive non-asymptotic confidence intervals by estimating the means and variances of bias terms from training data, thus enhancing the reliability of confidence intervals for a large class of predictors.\n\nSTRENGTHS:\n1. The paper seems to improve existing methods, though this is hard to tell (see weaknesses).\n\nWEAKNESSES:\n1. The paper uses non-standard notation, making it difficult to read. In Theorem 1, $b$ seems to represent the target, which is typically denoted as $y$. Additionally, the relationship between the matrix A and the vectors $x$ is unclear. The l-1​ norm in equation (1) is applied to $x$, but $x$ is also referred to as IID data in Theorem 1. Generally, the l-1​ norm is used to penalize weights, commonly denoted by $w$, $\\theta$, or $\\beta$, rather than the input data for LASSO regression.\n2. The paper does not clearly state the type of uncertainty being quantified, which could be clarified by addressing the first issue.\n3. Some acronyms are not defined (e.g., MR, ITSA, LASSO).\n4. Figure 1 is poorly presented. The images are very small with excessive white space in between, forcing readers to zoom in significantly. As a result, the caption becomes difficult to read.\n\nQUESTIONS:\nSee weaknesses.\n\nLIMITATIONS:\nThe authors do discuss the limitations of their method, but due to the lack of clarity in the text, it is difficult to assess these limitations effectively.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 1, "SOUNDNESS_CORRECTNESS": 2, "DATE": "2024-06-25"}

comments: SUMMARY:
This work develops an uncertainty quantification technique based on the debiased LASSO. The error is decomposed into noise and bias terms, which allows non-asymptotic confidence intervals to be derived. An empirical version of Chebyshev's inequality allows for their construction when the bias term is only assumed to have finite second moment, while sharper estimates are obtained in the setting where it is Gaussian. Numerical examples are given.

STRENGTHS:
This is a good paper and in my opinion should probably be accepted.

WEAKNESSES:
The main weakness is that the proposed method is a competitor to conformal prediction, however there is no comparison of these methods or even mention of this. Some discussion on conformal prediction, and the relative merits of the new technique, are probably required for publication.

The figures are too small, making them hard to interpret. This is compounded by the size of the text in the images. Their presentation should be rethought and fixed.

QUESTIONS:
Can you please address the above issues?

LIMITATIONS:
Limitations are discussed adequately. As mentioned, there is no mention of conformal prediction.
TITLE: Official Review
SOUNDNESS_CORRECTNESS: 4
DATE: 2024-07-12

comments: SUMMARY:
Improve debiasing technique for better estimation/inference for high-dim models.

STRENGTHS:
Non-asymptotic result which helps in better numerical performance compared to asymptotic CIs.

General idea which can be extended to other statistical models.

WEAKNESSES:
NA

QUESTIONS:
Can similar to Theorem 3 results be provided for other well-known distributions? Maybe some heavy-tail dist?

LIMITATIONS:
NA
TITLE: Official Review
SOUNDNESS_CORRECTNESS: 3
DATE: 2024-07-13

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper presents a framework for constructing non-asymptotic confidence intervals around the debiased LASSO estimator. It derives a data-driven adjustment whereby the means and variances of the bias term of the debiased LASSO are estimated from the data and used to correct the confidence intervals. The framework is applied to the learned estimator from unrolled neural networks for real-world image reconstruction tasks, where the two moments are shown to be sufficient for modeling the bias term.\n\nSTRENGTHS:\n- The non-asymptotic treatment is a promising and worthwhile extension to the debiased LASSO that's likely to benefit a variety of high-dimensional regression applications. \n- It's a convenient plug-in method around existing estimators of the debiased LASSO.\n- The experiments include representative settings where the remainder term is significant, and the relative norm $||R||/||W||$ is quantified for each experiment.\n- The coverage levels in the experiments are convincing overall, aside from a few remaining questions (see \"Questions\")\n\nWEAKNESSES:\nSee \"Questions\" for questions regarding the proofs and interpretation of experimental results.\n\nThe text and figure formatting could be improved for clarity:\n- Please make the figures larger. The figures are missing axis labels and/or legends. Also, the tick and axis labels are too small.\n- Please label individual subfigures in addition to describing them in the figure caption (e.g., \"(a) w/o data adjustment\" for Figure 1).\n- For subfigures 1(d) and 1(e), and similar figures throughout the text, it would be helpful to overlay the confidence level $1-\\alpha$ in a horizontal lines.\n- For Figure 3, please display (b) and (c) on the same y-axis scale.\n- L49: confusing phrasing, \"when the dimensions of the problem grow\" to describe the asymptotic setting\n\nQUESTIONS:\n- For confidence intervals with significance level $\\alpha$, the method often seems to have coverage beyond $1-\\alpha$. Is the method prone to inefficiency or overcoverage of the CIs? It would be great to see some discussions in the experiments section as to where we could gain precision, for instance from the optimization of $\\gamma$, and also refer to Section A in the main text.\n- What is meant by the \"image support?\" Could the authors please elaborate in general on what $S$ means and illustrate $S$ in the case of the MRI images for some selected $i$?\n- Why is $|W_j| \\sim {\\rm Rice}$ in L595 and not half-normal?\n\nLIMITATIONS:\nThe authors acknowledge that the accuracy of the method depends on the quality of the moment estimates and the ability to minimize the length over a larger parameter set, both of which depend on the data size. They also discuss opportunities to explore higher moments and other neural net architectures.", "TITLE": "Official Review", "RECOMMENDATION": 7, "REVIEWER_CONFIDENCE": 3, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-07-23"}