Original: Accelerated Linearized Laplace Approximation for Bayesian Deep Learning
Revised: Accelerated Linearized Laplace Approximation for Bayesian Deep Learning

Original: Abstract
Revised: Abstract

Original: Laplace approximation (LA) and its linearized variant (LLA) enable effortlessadaptation of pretrained deep neural networks to Bayesian neural networks.
Revised: Laplace approximation (LA) and its linearized variant (LLA) enable effortlessadaptation of pretrained deep neural networks to Bayesian neural networks.

Original: Thegeneralized Gauss-Newton (GGN) approximation is typically introduced to improve their tractability.
Revised: Thegeneralized Gauss-Newton (GGN) approximation is typically introduced to improve their tractability.

Original: However, LA and LLA are still confronted with non-trivialinefficiency issues and should rely on Kronecker-factored, diagonal, or even lastlayer approximate GGN matrices in practical use.
Revised: However, LA and LLA are still confronted with non-trivialinefficiency issues and should rely on Kronecker-factored, diagonal, or even lastlayer approximate GGN matrices in practical use.

Original: These approximations are likelyto harm the fidelity of learning outcomes.
Revised: These approximations are likelyto harm the fidelity of learning outcomes.

Original: To tackle this issue, inspired by theconnections between LLA and neural target kernels (NTKs), we develop a Nyströmapproximation to NTKs to accelerate LLA.
Revised: To tackle this issue, inspired by theconnections between LLA and neural target kernels (NTKs), we develop a Nyströmapproximation to NTKs to accelerate LLA.

Original: Our method benefits from the capabilityof popular deep learning libraries for forward mode automatic differentiation, andenjoys reassuring theoretical guarantees.
Revised: Our method benefits from the capabilityof popular deep learning libraries for forward mode automatic differentiation, andenjoys reassuring theoretical guarantees.

Original: Extensive studies reflect the merits ofthe proposed method in aspects of both scalability and performance.
Revised: Extensive studies reflect the merits ofthe proposed method in aspects of both scalability and performance.

Original: Our methodcan even scale up to architectures like vision transformers.
Revised: Our methodcan even scale up to architectures like vision transformers.

Original: We also offer valuableablation studies to diagnose our method.
Revised: We also offer valuableablation studies to diagnose our method.

Original:  Introduction
Revised:  Introduction

Original: Deep neural networks (DNNs) excel at modeling deterministic relationships and have become de factosolutions for diverse pattern recognition problems [18, 54].
Revised: Deep neural networks (DNNs) excel at modeling deterministic relationships and have become de factosolutions for diverse pattern recognition problems [18, 54].

Original: However, DNNs fall short in reasoningabout model uncertainty [1] and suffer from poor calibration [17].
Revised: However, DNNs fall short in reasoningabout model uncertainty [1] and suffer from poor calibration [17].

Original: These issues are intolerable inrisk-sensitive scenarios like self-driving [27], healthcare [34], finance [25] etc.
Revised: These issues are intolerable inrisk-sensitive scenarios like self-driving [27], healthcare [34], finance [25] etc.

Original: Bayesian Neural Networks (BNNs) have emerged as effective prescriptions to these pathologies [37,21, 44, 16].
Revised: Bayesian Neural Networks (BNNs) have emerged as effective prescriptions to these pathologies [37,21, 44, 16].

Original: They usually proceed by estimating the posteriors over high-dimensional NN parameters.
Revised: They usually proceed by estimating the posteriors over high-dimensional NN parameters.

Original: Due to some intractable integrals, diverse approximate inference methods have been applied tolearning BNNs, spanning variational inference (VI) [1, 36], Markov chain Monte Carlo (MCMC)
Revised: Due to some intractable integrals, diverse approximate inference methods have been applied tolearning BNNs, spanning variational inference (VI) [1, 36], Markov chain Monte Carlo (MCMC)

Original: [55,4], Laplace approximation (LA) [38, 50, 30] etc.
Revised: [55,4], Laplace approximation (LA) [38, 50, 30] etc.

Original: LA has recently gained unprecedented attention because its post-hoc nature nicely suits with thepretraining-finetuning fashion in deep learning (DL).
Revised: LA has recently gained unprecedented attention because its post-hoc nature nicely suits with thepretraining-finetuning fashion in deep learning (DL).

Original: LA approximates the posterior with a Gaussianaround its maximum, whose mean and covariance are the maximum a posteriori (MAP) and theinversion of the Hessian respectively.
Revised: LA approximates the posterior with a Gaussianaround its maximum, whose mean and covariance are the maximum a posteriori (MAP) and theinversion of the Hessian respectively.

Original: It is a common practice to approximate the Hessian with thegeneralized Gauss-Newton (GGN) matrix [40] to make the whole workflow more tractable.
Revised: It is a common practice to approximate the Hessian with thegeneralized Gauss-Newton (GGN) matrix [40] to make the whole workflow more tractable.

Original: Linearized LA (LLA) [14, 22] applies LA to the first-order approximation of the NN of concern.
Revised: Linearized LA (LLA) [14, 22] applies LA to the first-order approximation of the NN of concern.

Original: LLAshares with LA the same predictive mean yet possesses a more benign covariance, which mitigatesthe underfitting issue of LA [33].
Revised: LLAshares with LA the same predictive mean yet possesses a more benign covariance, which mitigatesthe underfitting issue of LA [33].

Original: Immer et al.
Revised: Immer et al.

Original: [22] argue that LLA is more sensible than LA in thepresence of GGN approximation; LLA can perform on par with or better than popular alternatives onvarious uncertainty quantification (UQ) tasks [14, 6, 7].
Revised: [22] argue that LLA is more sensible than LA in thepresence of GGN approximation; LLA can perform on par with or better than popular alternatives onvarious uncertainty quantification (UQ) tasks [14, 6, 7].

Original: The Laplace library [6] further substantially advances LLA’s applicability, making it a simple and competing baseline for Bayesian DL.
Revised: The Laplace library [6] further substantially advances LLA’s applicability, making it a simple and competing baseline for Bayesian DL.

Original: [Figure]
Revised: [Figure]

Original: The practical adoption of LA and LLA actually entails further approximations on top of GGN.
Revised: The practical adoption of LA and LLA actually entails further approximations on top of GGN.

Original: E.g.,when using Laplace to process a pretrained ResNet [18], practitioners are recommended to resort toa Kronecker-factored (KFAC) [40] or diagonal approximation of the full GGN matrix for tractability.
Revised: E.g.,when using Laplace to process a pretrained ResNet [18], practitioners are recommended to resort toa Kronecker-factored (KFAC) [40] or diagonal approximation of the full GGN matrix for tractability.

Original: An orthogonal tactic is to apply LA/LLA to only NNs’ last layer [30].
Revised: An orthogonal tactic is to apply LA/LLA to only NNs’ last layer [30].

Original: Yet, the approximation errorsin these cases can hardly be identified, significantly undermining the fidelity of the learning outcomes.
Revised: Yet, the approximation errorsin these cases can hardly be identified, significantly undermining the fidelity of the learning outcomes.

Original: This paper aims at scaling LLA up in a more assurable way.
Revised: This paper aims at scaling LLA up in a more assurable way.

Original: We first revisit the inherent connectionsbetween Neural Tangent Kernels (NTKs) [24] and LLA [28, 22], and find that, if we can approximatethe NTKs with the inner product of some low-dimensional vector representations of the data, LLAcan be considerably accelerated.
Revised: We first revisit the inherent connectionsbetween Neural Tangent Kernels (NTKs) [24] and LLA [28, 22], and find that, if we can approximatethe NTKs with the inner product of some low-dimensional vector representations of the data, LLAcan be considerably accelerated.

Original: Given this finding, we propose to adapt the Nyström methodto approximate the NTKs of multi-output NNs, and advocate leveraging forward mode automaticdifferentiation ( fwAD ) to efficiently compute the involved Jacobian-vector products (JVPs).
Revised: Given this finding, we propose to adapt the Nyström methodto approximate the NTKs of multi-output NNs, and advocate leveraging forward mode automaticdifferentiation ( fwAD ) to efficiently compute the involved Jacobian-vector products (JVPs).

Original: Theresultant accElerated LLA (ELLA) preserves the principal structures of vanilla LLA yet withoutexplicitly computing/storing the costly GGN/Jacobian matrices for the training data.
Revised: Theresultant accElerated LLA (ELLA) preserves the principal structures of vanilla LLA yet withoutexplicitly computing/storing the costly GGN/Jacobian matrices for the training data.

Original: What’s more,we theoretically analyze the approximation error between the predictive of ELLA and that of vanilla
Revised: What’s more,we theoretically analyze the approximation error between the predictive of ELLA and that of vanilla

Original: LLA, and find that it deceases rapidly as the Nyström approximation becomes accurate.
Revised: LLA, and find that it deceases rapidly as the Nyström approximation becomes accurate.

Original: We perform extensive studies to show that ELLA can be a low-cost and effective baseline for Bayesian
Revised: We perform extensive studies to show that ELLA can be a low-cost and effective baseline for Bayesian

Original: DL.
Revised: DL.

Original: We first describe how to specify the hyperparameters of ELLA, and use an illustrative regressiontask to demonstrate the effectiveness of ELLA (see Figure 1).
Revised: We first describe how to specify the hyperparameters of ELLA, and use an illustrative regressiontask to demonstrate the effectiveness of ELLA (see Figure 1).

Original: We then experiment on standardimage classification benchmarks to exhibit the superiority of ELLA over competing baselines inaspects of both performance and scalability.
Revised: We then experiment on standardimage classification benchmarks to exhibit the superiority of ELLA over competing baselines inaspects of both performance and scalability.

Original: We further show that ELLA can even scale up to modernarchitectures like vision transformers (ViTs) [11].
Revised: We further show that ELLA can even scale up to modernarchitectures like vision transformers (ViTs) [11].

Original:  Background
Revised:  Background

Original: Consider a learning problem on D =
Revised: Consider a learning problem on D =

Original: ( X , Y )
Revised: ( X , Y )

Original: = { ( x i , y i ) }
Revised: = { ( x i , y i ) }

Original: Ni =1 , where x i ∈ X
Revised: Ni =1 , where x i ∈ X

Original: and y i ∈ R C
Revised: and y i ∈ R C

Original: (e.g.,regression) or { 0 , 1 } C (e.g., classification) refer to observations and targets respectively.
Revised: (e.g.,regression) or { 0 , 1 } C (e.g., classification) refer to observations and targets respectively.

Original: The advancein machine learning suggests using a NN g θ ( · ) :
Revised: The advancein machine learning suggests using a NN g θ ( · ) :

Original: X → R C with parameters θ ∈ R P for data fitting.
Revised: X → R C with parameters θ ∈ R P for data fitting.

Original: Despite well-performing, regularly trained NNs only capture the most likely interpretation for thedata, thus miss the ability to reason about uncertainty and are prone to overfitting and overconfidence.
Revised: Despite well-performing, regularly trained NNs only capture the most likely interpretation for thedata, thus miss the ability to reason about uncertainty and are prone to overfitting and overconfidence.

Original: BNNs [37, 21, 44] characterize model uncertainty by probabilistic principle and can holistically represent all likely interpretations.
Revised: BNNs [37, 21, 44] characterize model uncertainty by probabilistic principle and can holistically represent all likely interpretations.

Original: Typically, BNNs impose a prior p ( θ ) on NN parameters and chase the
Revised: Typically, BNNs impose a prior p ( θ ) on NN parameters and chase the

Original: Bayesian posterior p ( θ |D ) = p ( D| θ ) p ( θ ) /p
Revised: Bayesian posterior p ( θ |D ) = p ( D| θ ) p ( θ ) /p

Original: ( D ) where p ( D| θ ) =
Revised: ( D ) where p ( D| θ ) =

Original: (cid:81)
Revised: (cid:81)

Original: i p ( y i | x i , θ ) =
Revised: i p ( y i | x i , θ ) =

Original: (cid:81)
Revised: (cid:81)

Original: i p ( y i | g θ ( x i )) .
Revised: i p ( y i | g θ ( x i )) .

Original: Analytical estimation is usually intractable due to NNs’ high nonlinearity.
Revised: Analytical estimation is usually intractable due to NNs’ high nonlinearity.

Original: Thereby, BNN methodsusually find a surrogate of the true posterior q ( θ )
Revised: Thereby, BNN methodsusually find a surrogate of the true posterior q ( θ )

Original: ≈ p ( θ |D ) via approximate inference methods likevariational inference (VI) [1, 20, 36, 62, 29], Laplace approximation (LA) [38, 50], Markov chain
Revised: ≈ p ( θ |D ) via approximate inference methods likevariational inference (VI) [1, 20, 36, 62, 29], Laplace approximation (LA) [38, 50], Markov chain

Original: Monte Carlo (MCMC) [55, 4, of the negative log posterior w.r.t.
Revised: Monte Carlo (MCMC) [55, 4, of the negative log posterior w.r.t.

Original: the parameters, i.e., Σ − 1 = −∇ 2 θθ (log p ( D| θ )
Revised: the parameters, i.e., Σ − 1 = −∇ 2 θθ (log p ( D| θ )

Original: + log p ( θ ))
Revised: + log p ( θ ))

Original: | θ =ˆ θ .
Revised: | θ =ˆ θ .

Original: Without loss of generality, we base the following discussion on the isotropic Gaussian priorp ( θ ) =
Revised: Without loss of generality, we base the following discussion on the isotropic Gaussian priorp ( θ ) =

Original: N ( θ ; 0 , σ 20 I P ) 1 , so −∇ 2 θθ log p ( θ ) | θ =
Revised: N ( θ ; 0 , σ 20 I P ) 1 , so −∇ 2 θθ log p ( θ ) | θ =

Original: ˆ θ amounts to I P /σ 20 .
Revised: ˆ θ amounts to I P /σ 20 .

Original: Due to the intractability of the Hessian for NNs with massive parameters, it is a common practice touse the symmetric positive and semi-definite (SPSD) GGN matrix as a workaround, i.e.,
Revised: Due to the intractability of the Hessian for NNs with massive parameters, it is a common practice touse the symmetric positive and semi-definite (SPSD) GGN matrix as a workaround, i.e.,

Original: [Equation]
Revised: [Equation]

Original: 1 =
Revised: 1 =

Original: [Equation]
Revised: [Equation]

Original: where J ˆ θ ( x ) ≜ ∇ θ g θ ( x ) | θ =ˆ θ and Λ( x , y ) ≜ −∇ 2 gg log p ( y | g ) | g = g ˆ θ ( x ) .
Revised: where J ˆ θ ( x ) ≜ ∇ θ g θ ( x ) | θ =ˆ θ and Λ( x , y ) ≜ −∇ 2 gg log p ( y | g ) | g = g ˆ θ ( x ) .

Original: When concatenating { J ˆ θ ( x i ) ∈ R C × P } Ni =1 as a big matrix J ˆ θ , X ∈ R NC × P and organizing{ Λ( x i , y i ) ∈ R C × C } Ni =1 as a block-diagonal matrix Λ X , Y ∈ R NC × NC , we have:
Revised: When concatenating { J ˆ θ ( x i ) ∈ R C × P } Ni =1 as a big matrix J ˆ θ , X ∈ R NC × P and organizing{ Λ( x i , y i ) ∈ R C × C } Ni =1 as a block-diagonal matrix Λ X , Y ∈ R NC × NC , we have:

Original: [Equation]
Revised: [Equation]

Original: Yet, LA suffers from underfitting [33].
Revised: Yet, LA suffers from underfitting [33].

Original: This is probably because GGN approximation implicitly turnsthe original model
Revised: This is probably because GGN approximation implicitly turnsthe original model

Original: g θ ( x ) into a linear one g lin θ ( x ) =
Revised: g θ ( x ) into a linear one g lin θ ( x ) =

Original: g ˆ θ ( x )+ J ˆ θ ( x )( θ − ˆ θ ) but g θ ( x ) is still leveragedto make prediction.
Revised: g ˆ θ ( x )+ J ˆ θ ( x )( θ − ˆ θ ) but g θ ( x ) is still leveragedto make prediction.

Original: I.e., there exists a shift between posterior inference and prediction [22].
Revised: I.e., there exists a shift between posterior inference and prediction [22].

Original: Tomitigate this issue, a proposal is to predict with g lin θ ( x ) , giving rise to linearized LA (LLA) [14, 28, 22].
Revised: Tomitigate this issue, a proposal is to predict with g lin θ ( x ) , giving rise to linearized LA (LLA) [14, 28, 22].

Original: LA and LLA nicely fit the pretraining-finetuning fashion in DL – they can be post-hoc applied topretrained models where only the GGN matrices require to be estimated and further inverted.
Revised: LA and LLA nicely fit the pretraining-finetuning fashion in DL – they can be post-hoc applied topretrained models where only the GGN matrices require to be estimated and further inverted.

Original: By LAand LLA, practitioners can adapt off-the-shelf high performing DNNs to BNNs easily.
Revised: By LAand LLA, practitioners can adapt off-the-shelf high performing DNNs to BNNs easily.

Original: LLA has revealed strong results on diverse UQ problems [14, 6, 7].
Revised: LLA has revealed strong results on diverse UQ problems [14, 6, 7].

Original: The Laplace library [6] furtheradvances LLA’s applicability, and evidences LLA is competitive to popular alternatives [63, 32, 39].
Revised: The Laplace library [6] furtheradvances LLA’s applicability, and evidences LLA is competitive to popular alternatives [63, 32, 39].

Original: Scalability issue The GGN matrix of size P × P is still unamenable in modern DL scenarios, so furtherapproximations sparsifying it are always introduced.
Revised: Scalability issue The GGN matrix of size P × P is still unamenable in modern DL scenarios, so furtherapproximations sparsifying it are always introduced.

Original: The diagonal and KFAC [40] approximations arecommonly adopted ones [50, 62], where only a diagonal or block-diagonal structure of the original
Revised: The diagonal and KFAC [40] approximations arecommonly adopted ones [50, 62], where only a diagonal or block-diagonal structure of the original

Original: GNN matrix is preserved.
Revised: GNN matrix is preserved.

Original: An orthogonal tactic is to concern only a subspace of the high-dimensionalparameter space (e.g., the parameter space of the last layer [30]) to reduce the scale of the GGNmatrix.
Revised: An orthogonal tactic is to concern only a subspace of the high-dimensionalparameter space (e.g., the parameter space of the last layer [30]) to reduce the scale of the GGNmatrix.

Original: However, these strategies sacrifice the fidelity of the learning outcomes as the approximationerrors in these cases can hardly be theoretically measured.
Revised: However, these strategies sacrifice the fidelity of the learning outcomes as the approximationerrors in these cases can hardly be theoretically measured.

Original: To this end, we develop accElerated
Revised: To this end, we develop accElerated

Original: Linearized Laplace Approximation (ELLA) to push the limit of LLA in a more assurable way.
Revised: Linearized Laplace Approximation (ELLA) to push the limit of LLA in a more assurable way.

Original:  Methodology
Revised:  Methodology

Original: In this section, we first revisit the relation of LLA to Gaussian processes (GPs) and Neural Tangent
Revised: In this section, we first revisit the relation of LLA to Gaussian processes (GPs) and Neural Tangent

Original: Kernels (NTKs) [24].
Revised: Kernels (NTKs) [24].

Original: After that, we reveal how to accelerate LLA by kernel approximation.
Revised: After that, we reveal how to accelerate LLA by kernel approximation.

Original: Basedon these findings, we develop an efficient implementation of ELLA using the Nyström method [58].
Revised: Basedon these findings, we develop an efficient implementation of ELLA using the Nyström method [58].

Original: 3.1 The Gaussian Process View of LLA
Revised: 3.1 The Gaussian Process View of LLA

Original: Integrating q ( θ ) =
Revised: Integrating q ( θ ) =

Original: N ( θ ; ˆ θ , Σ ) with the linear model g lin θ ( x ) actually gives rise to a function-spaceapproximate posterior [14, 28, 22] in the form of q ( f )
Revised: N ( θ ; ˆ θ , Σ ) with the linear model g lin θ ( x ) actually gives rise to a function-spaceapproximate posterior [14, 28, 22] in the form of q ( f )

Original: =
Revised: =

Original: GP ( f | g ˆ θ ( x ) , κ LLA ( x , x ′ )) withκ LLA ( x
Revised: GP ( f | g ˆ θ ( x ) , κ LLA ( x , x ′ )) withκ LLA ( x

Original: [Equation]
Revised: [Equation]

Original: By Woodbury matrix identity [60], we have:
Revised: By Woodbury matrix identity [60], we have:

Original: [Equation]
Revised: [Equation]

Original: It follows that
Revised: It follows that

Original: [Equation]
Revised: [Equation]

Original: where κ NTK ( x , x ′ ) ≜
Revised: where κ NTK ( x , x ′ ) ≜

Original: J ˆ θ ( x ) J ˆ θ ( x ′ )
Revised: J ˆ θ ( x ) J ˆ θ ( x ′ )

Original: ⊤ denotes the neural tangent kernel (NTK)
Revised: ⊤ denotes the neural tangent kernel (NTK)

Original: [24] corresponding tog ˆ θ ( x ) .
Revised: [24] corresponding tog ˆ θ ( x ) .

Original: Note that κ NTK ( x , x ′ ) is a matrix-valued kernel, with values in the space of C × C matrices.
Revised: Note that κ NTK ( x , x ′ ) is a matrix-valued kernel, with values in the space of C × C matrices.

Original: The main challenge then turns into the computation and inversion of the gram matrix κ NTK ( X , X ) ofsize NC × NC .
Revised: The main challenge then turns into the computation and inversion of the gram matrix κ NTK ( X , X ) ofsize NC × NC .

Original: When either N or C is large, the estimation of κ LLA still suffers from inefficiencyissue.
Revised: When either N or C is large, the estimation of κ LLA still suffers from inefficiencyissue.

Original: To address this, existing work [22] assumes independence among the C output dimensionsto cast q ( f ) into C independent GPs following [51], and randomly subsample M ≪ N data pointsto form a cheap substitute for the original gram matrix.
Revised: To address this, existing work [22] assumes independence among the C output dimensionsto cast q ( f ) into C independent GPs following [51], and randomly subsample M ≪ N data pointsto form a cheap substitute for the original gram matrix.

Original: Despite effective in some cases, theseapproximations are heuristic, lacking a clear theoretical foundation.
Revised: Despite effective in some cases, theseapproximations are heuristic, lacking a clear theoretical foundation.

Original: 3.2 Scale Up LLA by Kernel Approximation
Revised: 3.2 Scale Up LLA by Kernel Approximation

Original: We show that if we can approximate κ NTK ( x , x ′ ) with the inner product of some explicit C × Kdimensional representations of the data, i.e., κ NTK ( x , x ′ )
Revised: We show that if we can approximate κ NTK ( x , x ′ ) with the inner product of some explicit C × Kdimensional representations of the data, i.e., κ NTK ( x , x ′ )

Original: ≈ φ ( x ) φ ( x ′ )
Revised: ≈ φ ( x ) φ ( x ′ )

Original: ⊤ with φ : X → R C × K , thescalability of the whole workflow can be unleashed.
Revised: ⊤ with φ : X → R C × K , thescalability of the whole workflow can be unleashed.

Original: Concretely, letting φ X ∈ R NC × K be the concatenation of { φ ( x i ) ∈ R C × K } Ni =1 , we have
Revised: Concretely, letting φ X ∈ R NC × K be the concatenation of { φ ( x i ) ∈ R C × K } Ni =1 , we have

Original: [Equation]
Revised: [Equation]

Original: The matrix G ∈ R K × K possesses a similar formula to the Σ in the original κ LLA in Equation (3),yet much smaller.
Revised: The matrix G ∈ R K × K possesses a similar formula to the Σ in the original κ LLA in Equation (3),yet much smaller.

Original: Once having φ , it is only required to perform one forward pass of g and φ for eachtraining data to estimate G .
Revised: Once having φ , it is only required to perform one forward pass of g and φ for eachtraining data to estimate G .

Original: When K is reasonably small, e.g., < 100 , it is cheap to invert G .
Revised: When K is reasonably small, e.g., < 100 , it is cheap to invert G .

Original: 3.3 Approximate NTKs via the Nyström Method
Revised: 3.3 Approximate NTKs via the Nyström Method

Original: Typical kernel approximation means include Nyström method [45, 58] and random features-basedmethods [48, 49, 61, 43, 15, 9].
Revised: Typical kernel approximation means include Nyström method [45, 58] and random features-basedmethods [48, 49, 61, 43, 15, 9].

Original: Given that the latter routinely rely on a relatively large number ofrandom features to gain a faithful approximation (which means a large K ), we suggest leveraging the
Revised: Given that the latter routinely rely on a relatively large number ofrandom features to gain a faithful approximation (which means a large K ), we suggest leveraging the

Original: Nyström method, which captures only several principal components of the kernel, to build φ .
Revised: Nyström method, which captures only several principal components of the kernel, to build φ .

Original: To comfortably apply the Nyström method, we first rewrite κ NTK ( x , x ′ ) as a scalar-valued kernelκ
Revised: To comfortably apply the Nyström method, we first rewrite κ NTK ( x , x ′ ) as a scalar-valued kernelκ

Original: [Equation]
Revised: [Equation]

Original: x , i
Revised: x , i

Original: [Equation]
Revised: [Equation]

Original: ( x ′ , i ′ )
Revised: ( x ′ , i ′ )

Original: ⊤ where J ˆ θ ( x ,i ) ≜ ∇ θ [ g θ ( x )]
Revised: ⊤ where J ˆ θ ( x ,i ) ≜ ∇ θ [ g θ ( x )]

Original: ( i )
Revised: ( i )

Original: | θ
Revised: | θ

Original: =ˆ θ : X × [ C ] → R 1 × P 2computes the gradient of i -th output w.r.t.
Revised: =ˆ θ : X × [ C ] → R 1 × P 2computes the gradient of i -th output w.r.t.

Original: parameters.
Revised: parameters.

Original: By Mercer’s theorem [42],
Revised: By Mercer’s theorem [42],

Original: [Equation]
Revised: [Equation]

Original: where ψ k ∈ L 2 ( X × [ C ] , q ) 3 refer to the eigenfunctions of the NTK w.r.t.
Revised: where ψ k ∈ L 2 ( X × [ C ] , q ) 3 refer to the eigenfunctions of the NTK w.r.t.

Original: some probability measureq with µ k ≥ 0 as the associated eigenvalues.
Revised: some probability measureq with µ k ≥ 0 as the associated eigenvalues.

Original: Based on this, the Nyström method discovers the top- Keigenvalues as well as the corresponding eigenfunctions for kernel approximation.
Revised: Based on this, the Nyström method discovers the top- Keigenvalues as well as the corresponding eigenfunctions for kernel approximation.

Original: By definition, the eigenfunctions can represent the spectral information of the kernel:
Revised: By definition, the eigenfunctions can represent the spectral information of the kernel:

Original: [Equation]
Revised: [Equation]

Original: while being orthonormal under q :
Revised: while being orthonormal under q :

Original: [Equation]
Revised: [Equation]

Original: In our case, q ( x , i ) can be factorized as the product of the data distribution and a uniform distributionover { 1 , ..., C } , which can be trivially sampled from.
Revised: In our case, q ( x , i ) can be factorized as the product of the data distribution and a uniform distributionover { 1 , ..., C } , which can be trivially sampled from.

Original: The Nyström method draws M ( M ≥ K ) i.i.d.samples ˜
Revised: The Nyström method draws M ( M ≥ K ) i.i.d.samples ˜

Original: X
Revised: X

Original: = { ( x 1 , i 1 ) , ..., ( x M , i M ) } from q to approximate the integration in Equation (8):
Revised: = { ( x 1 , i 1 ) , ..., ( x M , i M ) } from q to approximate the integration in Equation (8):

Original: [Equation]
Revised: [Equation]

Original: Applying this equation to these samples gives rise to:
Revised: Applying this equation to these samples gives rise to:

Original: [Equation]
Revised: [Equation]

Original: then we arrive at
Revised: then we arrive at

Original: [Equation]
Revised: [Equation]

Original: where K =
Revised: where K =

Original: J ˆ θ , ˜ X J ⊤ ˆ θ , ˜ X , with J ˆ θ , ˜ X ∈ R M × P as the concatenation of { J ˆ θ ( x m , i m ) ∈ R 1 × P } Mm =1 ,and ψ k = [ ψ k ( x 1 , i 1 ) , ..., ψ k ( x M , i M )] ⊤ .
Revised: J ˆ θ , ˜ X J ⊤ ˆ θ , ˜ X , with J ˆ θ , ˜ X ∈ R M × P as the concatenation of { J ˆ θ ( x m , i m ) ∈ R 1 × P } Mm =1 ,and ψ k = [ ψ k ( x 1 , i 1 ) , ..., ψ k ( x M , i M )] ⊤ .

Original: This implies a scaled eigendecomposition problem of K .
Revised: This implies a scaled eigendecomposition problem of K .

Original: We compute the top- K eigenvalues λ 1 , ..., λ K of matrix K and record the corresponding orthonormaleigenvectors u 1 , ..., u K .
Revised: We compute the top- K eigenvalues λ 1 , ..., λ K of matrix K and record the corresponding orthonormaleigenvectors u 1 , ..., u K .

Original: Given the constraint in Equation (9), it is easy to see that:
Revised: Given the constraint in Equation (9), it is easy to see that:

Original: [Equation]
Revised: [Equation]

Original: Combining Equation (13) and (10) yields the Nyström approximation of the top- K eigenfunctions:
Revised: Combining Equation (13) and (10) yields the Nyström approximation of the top- K eigenfunctions:

Original: [Equation]
Revised: [Equation]

Original: [Equation]
Revised: [Equation]

Original: Then, the mapping φ which satisfied κ NTK ( x , x ′ )
Revised: Then, the mapping φ which satisfied κ NTK ( x , x ′ )

Original: ≈ φ ( x ) φ ( x ′ )
Revised: ≈ φ ( x ) φ ( x ′ )

Original: ⊤ can be realized as:
Revised: ⊤ can be realized as:

Original: [Equation]
Revised: [Equation]

Original: Connection to sparse approximations of GPs A recent work [57] has shown that sparse variational
Revised: Connection to sparse approximations of GPs A recent work [57] has shown that sparse variational

Original: Gaussian processes (SVGP) [53, 3] is algebraically equivalent to the Nyström approximation becausethe Evidence Lower BOund
Revised: Gaussian processes (SVGP) [53, 3] is algebraically equivalent to the Nyström approximation becausethe Evidence Lower BOund

Original: (ELBO) of the former encompasses the learning objective of the latter.
Revised: (ELBO) of the former encompasses the learning objective of the latter.

Original: That’s to say, ELLA implicitly deploys a sparse approximation to the original GP predictive of LLA.
Revised: That’s to say, ELLA implicitly deploys a sparse approximation to the original GP predictive of LLA.

Original: 3.4 Implementation
Revised: 3.4 Implementation

Original: As shown, the estimation of φ on a data point x degenerates as K JVPs, which can be accomplishedby invoking fwAD for K times:
Revised: As shown, the estimation of φ on a data point x degenerates as K JVPs, which can be accomplishedby invoking fwAD for K times:

Original: [Equation]
Revised: [Equation]

Original: where the model output g ˆ θ ( x ) and the JVP J ˆ θ ( x ) v k are simultaneously computed in one singleforward pass .
Revised: where the model output g ˆ θ ( x ) and the JVP J ˆ θ ( x ) v k are simultaneously computed in one singleforward pass .

Original: Prevalent DL libraries like PyTorch [47] and Jax [2] have already been armed with thecapability for fwAD .
Revised: Prevalent DL libraries like PyTorch [47] and Jax [2] have already been armed with thecapability for fwAD .

Original: Algorithm 2 details the procedure of building φ in a PyTorch-like style.
Revised: Algorithm 2 details the procedure of building φ in a PyTorch-like style.

Original: With that, we can trivially compute the posterior q ( f ) according to Equation (6), as detailed in
Revised: With that, we can trivially compute the posterior q ( f ) according to Equation (6), as detailed in

Original: Algorithm 1.
Revised: Algorithm 1.

Original: The procedures for estimating G and the posterior can both be batched for acceleration.
Revised: The procedures for estimating G and the posterior can both be batched for acceleration.

Original: Computation overhead The estimation of φ involves M forward and backward passes of g ˆ θ and theeigendecomposition of a matrix of size M × M .
Revised: Computation overhead The estimation of φ involves M forward and backward passes of g ˆ θ and theeigendecomposition of a matrix of size M × M .

Original: After that, we only need to make { v k } Kk =1 persistent,which amounts to storing K more NN copies .
Revised: After that, we only need to make { v k } Kk =1 persistent,which amounts to storing K more NN copies .

Original: Estimating G − 1 requires scanning the training setonce and inverting a matrix of K × K , which is cheap.
Revised: Estimating G − 1 requires scanning the training setonce and inverting a matrix of K × K , which is cheap.

Original: The evaluation of q ( f ) embodies that ofφ , i.e., performing K forward passes under the scope of fwAD .
Revised: The evaluation of q ( f ) embodies that ofφ , i.e., performing K forward passes under the scope of fwAD .

Original: This is similar to other BNNs thatperform S forward passes with different MC parameter samples to estimate the posterior predictive.
Revised: This is similar to other BNNs thatperform S forward passes with different MC parameter samples to estimate the posterior predictive.

Original:  Theoretical Analysis
Revised:  Theoretical Analysis

Original: In this section, we theoretically analyze the approximation error of κ ELLA ( x , x ′ ) to κ LLA ( x , x ′ ) .
Revised: In this section, we theoretically analyze the approximation error of κ ELLA ( x , x ′ ) to κ LLA ( x , x ′ ) .

Original: To reduce unnecessary complexity, this section assumes using M = K i.i.d.
Revised: To reduce unnecessary complexity, this section assumes using M = K i.i.d.

Original: MC samples when performing the Nyström method.
Revised: MC samples when performing the Nyström method.

Original: Then, κ ELLA can be reformulated as (details deferred to Appendix A.3)
Revised: Then, κ ELLA can be reformulated as (details deferred to Appendix A.3)

Original: # g ˆ θ : NN pre-trained by MAP; ( X , Y ) :
Revised: # g ˆ θ : NN pre-trained by MAP; ( X , Y ) :

Original: # training set; C : number of classes # M , K , σ 20 : hyper-parameters def estimate_ G ( φ , X , Y , K , σ 20 ): G = zeros( K , K ) for ( x , y ) in ( X , Y ) : g x , φ x = φ ( x ) Λ x ,
Revised: # training set; C : number of classes # M , K , σ 20 : hyper-parameters def estimate_ G ( φ , X , Y , K , σ 20 ): G = zeros( K , K ) for ( x , y ) in ( X , Y ) : g x , φ x = φ ( x ) Λ x ,

Original: y = hessian(nll
Revised: y = hessian(nll

Original: ( g x , y ), g x )
Revised: ( g x , y ), g x )

Original: G += φ ⊤ x Λ x , y φ x return G + eye( K ) /σdef
Revised: G += φ ⊤ x Λ x , y φ x return G + eye( K ) /σdef

Original: _q_f( φ , G − 1 , x ) g x , φ x = φ ( x ) κ x , x = φ x G − 1 φ ⊤ x return N ( g x , κ x , x ) φ = build_ φ ( g ˆ θ , X , C , M , K )
Revised: _q_f( φ , G − 1 , x ) g x , φ x = φ ( x ) κ x , x = φ x G − 1 φ ⊤ x return N ( g x , κ x , x ) φ = build_ φ ( g ˆ θ , X , C , M , K )

Original: G − 1 = inv(estimate_ G ( φ , X , Y , K , σ 20 ))
Revised: G − 1 = inv(estimate_ G ( φ , X , Y , K , σ 20 ))

Original: q_f
Revised: q_f

Original: = partial(_q_f, φ , G − 1 ) def build_ φ ( g ˆ θ , X , C , M , K ): def _ φ ( g ˆ θ , C , { v k } Kk =1 , x ): φ x = zeros( C , K ) for k in range( K ): with fwAD .enable(): g x , jvp = g
Revised: = partial(_q_f, φ , G − 1 ) def build_ φ ( g ˆ θ , X , C , M , K ): def _ φ ( g ˆ θ , C , { v k } Kk =1 , x ): φ x = zeros( C , K ) for k in range( K ): with fwAD .enable(): g x , jvp = g

Original: (ˆ θ , v k ) ( x ) φ x [: , k ]
Revised: (ˆ θ , v k ) ( x ) φ x [: , k ]

Original: = jvp return g x , φ x
Revised: = jvp return g x , φ x

Original: J ˆ θ , ˜ X
Revised: J ˆ θ , ˜ X

Original: = zeros( M ,dim( ˆ θ ))
Revised: = zeros( M ,dim( ˆ θ ))

Original: for m in range( M ): x m =
Revised: for m in range( M ): x m =

Original: uniform_sample( X ) i m =
Revised: uniform_sample( X ) i m =

Original: uniform_sample( [ C ] )
Revised: uniform_sample( [ C ] )

Original: J ˆ θ , ˜ X [ m ] =
Revised: J ˆ θ , ˜ X [ m ] =

Original: grad( g ˆ θ ( x m )
Revised: grad( g ˆ θ ( x m )

Original: [ i m ] , ˆ θ ) { λ k , u k } Kk =1 = eig
Revised: [ i m ] , ˆ θ ) { λ k , u k } Kk =1 = eig

Original: ( J ˆ θ , ˜ X J ⊤ ˆ θ , ˜ X ,top = K ) for k in range( K ): v
Revised: ( J ˆ θ , ˜ X J ⊤ ˆ θ , ˜ X ,top = K ) for k in range( K ): v

Original: [Equation]
Revised: [Equation]

Original: =
Revised: =

Original: [Equation] [Equation]
Revised: [Equation] [Equation]

Original: u
Revised: u

Original: [Equation]
Revised: [Equation]

Original: / √ λ k return partial(_ φ , g ˆ θ , C , { v k } Kk =1 ) κ ELLA (
Revised: / √ λ k return partial(_ φ , g ˆ θ , C , { v k } Kk =1 ) κ ELLA (

Original: [Equation]
Revised: [Equation]

Original: [Equation] [Equation]
Revised: [Equation] [Equation]

Original: =
Revised: =

Original: [Equation]
Revised: [Equation]

Original: θ , ˜ X (cid:104)
Revised: θ , ˜ X (cid:104)

Original: [Equation]
Revised: [Equation]

Original: , ˜ X
Revised: , ˜ X

Original: [Equation]
Revised: [Equation]

Original: ⊤ ˆ θ , X Λ X , Y
Revised: ⊤ ˆ θ , X Λ X , Y

Original: [Equation]
Revised: [Equation]

Original: ˆ θ , X
Revised: ˆ θ , X

Original: [Equation]
Revised: [Equation]

Original: ˆ θ , ˜ X
Revised: ˆ θ , ˜ X

Original: + J ˆ θ , ˜
Revised: + J ˆ θ , ˜

Original: X J ⊤ ˆ θ , ˜ X /σ 20 (cid:105) − 1 J ˆ θ , ˜ X (cid:124)
Revised: X J ⊤ ˆ θ , ˜ X /σ 20 (cid:105) − 1 J ˆ θ , ˜ X (cid:124)

Original: (cid:123)(cid:122)
Revised: (cid:123)(cid:122)

Original: (cid:125) Σ ′
Revised: (cid:125) Σ ′

Original: J ˆ θ ( x ) ⊤ .
Revised: J ˆ θ ( x ) ⊤ .

Original: Thereby, the gap between κ ELLA ( x , x ′ ) and κ LLA ( x , x ′ ) can be upper bounded by E = ∥
Revised: Thereby, the gap between κ ELLA ( x , x ′ ) and κ LLA ( x , x ′ ) can be upper bounded by E = ∥

Original: Σ ′ − Σ ∥ ,where ∥ · ∥ represents the matrix 2-norm (i.e., the spectral norm).
Revised: Σ ′ − Σ ∥ ,where ∥ · ∥ represents the matrix 2-norm (i.e., the spectral norm).

Original: In typical cases, P ≫ M , thuswith high probability K =
Revised: In typical cases, P ≫ M , thuswith high probability K =

Original: J ˆ θ , ˜ X
Revised: J ˆ θ , ˜ X

Original: J ⊤ ˆ θ , ˜ X ≻ 0 .
Revised: J ⊤ ˆ θ , ˜ X ≻ 0 .

Original: We present an upper bound of E as follows.
Revised: We present an upper bound of E as follows.

Original: Theorem 1 (Proof in Appendix A.4) .
Revised: Theorem 1 (Proof in Appendix A.4) .

Original: Let c Λ be a finite constant associated with Λ , and E ′ the errorof Nyström approximation ∥
Revised: Let c Λ be a finite constant associated with Λ , and E ′ the errorof Nyström approximation ∥

Original: J ˆ θ , X J ⊤ ˆ θ , ˜ X
Revised: J ˆ θ , X J ⊤ ˆ θ , ˜ X

Original: ( J ˆ θ , ˜ X
Revised: ( J ˆ θ , ˜ X

Original: J ⊤ ˆ θ
Revised: J ⊤ ˆ θ

Original: [Equation]
Revised: [Equation]

Original: ˜ X ) − 1 J ˆ θ , ˜ X
Revised: ˜ X ) − 1 J ˆ θ , ˜ X

Original: J ⊤ ˆ θ , X − J ˆ θ , X J ⊤ ˆ θ , X ∥ .
Revised: J ⊤ ˆ θ , X − J ˆ θ , X J ⊤ ˆ θ , X ∥ .

Original: It holds that
Revised: It holds that

Original: [Equation]
Revised: [Equation]

Original: E ′ has been extensively analyzed by pioneering work [12, 5, 26], and we simply adapt the resultsdeveloped by Drineas and Mahoney [12] to our case.
Revised: E ′ has been extensively analyzed by pioneering work [12, 5, 26], and we simply adapt the resultsdeveloped by Drineas and Mahoney [12] to our case.

Original: We denote the maximum diagonal entry and the
Revised: We denote the maximum diagonal entry and the

Original: M + 1 -th largest eigenvalue of J ˆ θ , X J ⊤ ˆ θ , X by c κ and ˜ λ
Revised: M + 1 -th largest eigenvalue of J ˆ θ , X J ⊤ ˆ θ , X by c κ and ˜ λ

Original: M +1 respectively.
Revised: M +1 respectively.

Original: Theorem 2 (Error bound of Nyström approximation) .
Revised: Theorem 2 (Error bound of Nyström approximation) .

Original: With probability at least 1 − δ , it holds that:
Revised: With probability at least 1 − δ , it holds that:

Original: [Equation]
Revised: [Equation]

Original: Plugging this back to E , we arrive at the corollary below.
Revised: Plugging this back to E , we arrive at the corollary below.

Original: Corollary 1.
Revised: Corollary 1.

Original: With probability at least 1 − δ , the following bound exists:
Revised: With probability at least 1 − δ , the following bound exists:

Original: [Equation]
Revised: [Equation]

Original: As desired, the upper bound of E decreases along with the growing of the number of MC samples M .
Revised: As desired, the upper bound of E decreases along with the growing of the number of MC samples M .

Original:  Experiments
Revised:  Experiments

Original: We first discuss how to specify the hyperparameters of ELLA, then expose an interesting finding.
Revised: We first discuss how to specify the hyperparameters of ELLA, then expose an interesting finding.

Original: After that, we compare ELLA to competing baselines to evidence its merits in efficacy and scalability.
Revised: After that, we compare ELLA to competing baselines to evidence its merits in efficacy and scalability.

Original: 5.1 Specification of Hyperparameters
Revised: 5.1 Specification of Hyperparameters

Original: Given an NN g ˆ θ pretrained by MAP, we need to specify the prior variance σ 20 , the number of MCsamples in Nyström method M , and the number of remaining eigenpairs K before applying ELLA.
Revised: Given an NN g ˆ θ pretrained by MAP, we need to specify the prior variance σ 20 , the number of MCsamples in Nyström method M , and the number of remaining eigenpairs K before applying ELLA.

Original: We simply set σ 20 to 1 Nγ with γ as the weight decay coefficient used for pretraining according to [10].
Revised: We simply set σ 20 to 1 Nγ with γ as the weight decay coefficient used for pretraining according to [10].

Original: 4 8 16 32 64 128 256 512 1024 2000 K 

Revised: 4 8 16 32 64 128 256 512 1024 2000 K

Original: M 0.
Revised: M 0.

Original: (a) 4 8 16 32 64 128 256 512 1024 2000 K 

Revised: (a) 4 8 16 32 64 128 256 512 1024 2000 K

Original: M 0.
Revised: M 0.

Original: (b)    40000 N 0.
Revised: (b)    40000 N 0.

Original: T e s t N
Revised: T e s t N

Original: LL
Revised: LL

Original: ResNet20 ResNet
Revised: ResNet20 ResNet

Original: ResNet44 ResNet (c)
Revised: ResNet44 ResNet (c)

Original: [Table]
Revised: [Table]

Original: We perform an empirical study to inspect how M and K affect the quality of the Nyström approximation and the ELLA GP covariance.
Revised: We perform an empirical study to inspect how M and K affect the quality of the Nyström approximation and the ELLA GP covariance.

Original: We take 2000 MNIST images as training set X , and 256 othersas validation set X val .
Revised: We take 2000 MNIST images as training set X , and 256 othersas validation set X val .

Original: The architecture contains 2 convolutions and a linear head.
Revised: The architecture contains 2 convolutions and a linear head.

Original: Batch normalization [23] and ReLU activation are used.
Revised: Batch normalization [23] and ReLU activation are used.

Original: The number of parameters P is 29 , 034 .
Revised: The number of parameters P is 29 , 034 .

Original: Larger architecturesor larger X will render the exact evaluation of κ LLA unapproachable.
Revised: Larger architecturesor larger X will render the exact evaluation of κ LLA unapproachable.

Original: We quantify the approximationerror of the Nyström method by [Equation]
Revised: We quantify the approximationerror of the Nyström method by ϵ Nyström ≜ ∥

Original:  X ∥ ,and that from κ ELLA to κ LLA by ϵ ELLA ≜ 1 | X val | [Equation] x
Revised: J ˆ θ , X J ⊤ ˆ θ , ˜ X ( J ˆ θ , ˜ X J ⊤ ˆ θ , ˜ X ) − 1 J ˆ θ , ˜

Original: [Equation] X val ∥ κ ELLA ( x , x ) − κ LLA ( x , x )∥ /
Revised: X J ⊤ ˆ θ , X − J ˆ θ , X J ⊤ ˆ θ , X ∥ / ∥ J ˆ θ , X J ⊤ ˆ θ , X ∥ ,and that from κ ELLA to κ LLA by ϵ ELLA ≜ 1 | X val | (cid:80) x ∈ X val ∥ κ ELLA ( x , x ) − κ LLA ( x , x )

Original: ∥  κ LLA ( x , x ) ∥ .
Revised: ∥ / ∥ κ LLA ( x , x ) ∥ .

Original: Wevary M from 4 to 2000 and K from 4 to M , and report the approximation errors in Figure 2 (a)-(b).
Revised: Wevary M from 4 to 2000 and K from 4 to M , and report the approximation errors in Figure 2 (a)-(b).

Original: We notice that 1) the larger K the better; 2) when fixing K , ϵ Nyström and ϵ ELLA decay as M grows; 3) ϵ
Revised: We notice that 1) the larger K the better; 2) when fixing K , ϵ Nyström and ϵ ELLA decay as M grows;3)

Original:  Nyström decays more rapidly than ϵ ELLA , echoing Theorem 1.
Revised: ϵ Nyström decays more rapidly than ϵ ELLA , echoing Theorem 1.

Original: Given that ELLA needs to store Kvectors of size P , small K is desired for efficiency.
Revised: Given that ELLA needs to store Kvectors of size P , small K is desired for efficiency.

Original: K ∈ [16 , 32] seems to be a reasonable choicegiven Figure 2.
Revised: K ∈ [16 , 32] seems to be a reasonable choicegiven Figure 2.

Original: Unless otherwise stated, we set M = 2000 and K = 20 in the following experiments.
Revised: Unless otherwise stated, we set M = 2000 and K = 20 in the following experiments.

Original: ELLA (or LLA) finds a GP posterior, so predicts with a variant of the aforementioned posterior predictive:
Revised: ELLA (or LLA) finds a GP posterior, so predicts with a variant of the aforementioned posterior predictive:

Original: [Equation]
Revised: [Equation]

Original: In classification tasks, we use 512 MC samples to approximate the last expectation and it is cheap.
Revised: In classification tasks, we use 512 MC samples to approximate the last expectation and it is cheap.

Original: 5.2 The Overfitting Issue of LLA
Revised: 5.2 The Overfitting Issue of ELLA

Original: Reinspecting Equation (1) and (6), we see, with more training data involved, the covariance in
Revised: Reinspecting Equation (1) and (6), we see, with more training data involved, the covariance in

Original: LA, LLA, and ELLA shrinks and the uncertainty dissipates.
Revised: LA, LLA, and ELLA shrinks and the uncertainty dissipates.

Original: However, under ubiquitous modelmisspecification [41], should the uncertainty vanish that fast?
Revised: However, under ubiquitous modelmisspecification [41], should the uncertainty vanish that fast?

Original: We perform a study with ResNets [18]on CIFAR-10 [31] to find an answer.
Revised: We perform a study with ResNets [18]on CIFAR-10 [31] to find an answer.

Original: Concretely, we randomly subsample N data from the trainingset of CIFAR-10, and fit ELLA on them.
Revised: Concretely, we randomly subsample N data from the training setof CIFAR-10, and fit ELLA on them.

Original: We depict the test negative log-likelihood (NLL), accuracy,and expected calibration error (ECE) [17] of the ELLA in Figure 2 (c) and Appendix B.1.
Revised: We depict the test negative log-likelihood (NLL), accuracy, andexpected calibration error (ECE) [17] of the ELLA in Figure 2 (c) and Appendix B.1.

Original: We alsoprovide the corresponding results of LLA ∗ in Appendix B.1.

Original: The V-shape NLL curves across settingsreflect the overfitting issue of ELLAand LLA ∗ (or more generally, LLA).
Revised: The V-shapecurves of NLL and ECE across settings reflect an overfitting issue of ELLA (or more generally, LLA).

Original: To address this issue, we advocate performing early stop when applying ELLA/LLA to big data .
Revised: To address this issue, we advocate performing early stop when applying ELLA to big data .

Original: Specifically, when iterating over the training data to estimate G (see Equation (6)), we continuouslyrecord the NLL of the current ELLA posterior on some validation data, and stop when there is a trendof overfitting.
Revised: Specifically,when iterating over the training data to estimate G (see Equation (6)), we continuously record the NLL of the current ELLA posterior on some validation data, and stop when there is a trend ofoverfitting.

Original: If we cannot access a validation set easily, we can apply strong data augmentation tosome training data to form a substitute of the validation set.
Revised: If we cannot access a validation set easily, we can apply strong data augmentation tosome training data to form a substitute of the validation set.

Original: Cross validation is also required by [7],but in the sake of finding a proper Gaussian prior variance σ 20 .
Revised: Cross validation is also required by [7],but in the sake of finding a proper Gaussian prior variance σ 20 .

Original: An additional benefit of early stoppingis that it significantly reduces the time cost for handling large data (e.g., ImageNet [8]).
Revised: An additional benefit of early stoppingis that it significantly reduces ELLA’s time cost for handling large data (e.g., ImageNet [8]).

Original: [Figure] [Figure]
Revised: [Figure] [Figure]

Revised: ResNet

Original: 5.3 Illustrative Regression
Revised: 5.3 Illustrative Regression

Original: We build a regression problem with N = 16 samples from y = sin 2 x + ϵ, ϵ ∼ N (0 , 0 . 2) as shown in
Revised: We build a regression problem with N = 16 samples from y = sin 2 x + ϵ, ϵ ∼ N (0 , 0 . 2) as shown in

Original: Figure 1.
Revised: Figure 1.

Original: The model is a MLP with 3 hidden layers and tanh activations, and we pretrain it by MAPfor 1000 iterations.
Revised: The model is a MLP with 3 hidden layers and tanh activations, and we pretrain it by MAPfor 1000 iterations.

Original: For ELLA, we set M = 16 and K = 5 for efficiency.
Revised: For ELLA, we set M = 16 and K = 5 for efficiency.

Original: Unless stated otherwise, weuse the interfaces of Laplace [6] to implement LLA, LLA-KFAC, LLA-Diag, and LLA ∗ .
Revised: Unless stated otherwise, weuse the interfaces of Laplace [6] to implement LLA, LLA-KFAC, LLA-Diag, and LLA ∗ .

Original: Comparedto LLA-KFAC, LLA-Diag, and LLA ∗ , ELLA delivers a closer approximation to LLA.
Revised: Comparedto LLA-KFAC, LLA-Diag, and LLA ∗ , ELLA delivers a closer approximation to LLA.

Original: 5.4 CIFAR-10 Classification
Revised: 5.4 CIFAR-10 Classification

Original: Then, we assess ELLA on CIFAR-10 [31] with ResNet architectures [18].
Revised: Then, we assess ELLA on CIFAR-10 [31] with ResNet architectures [18].

Original: We obtain pretrainedmodels from the open source community.
Revised: We obtain pretrainedmodels from the open source community.

Original: Apart from MAP, LLA ∗ , LLA-Diag, LLA-KFAC, wefurther introduce last-layer LLA with KFAC approximation (LLA ∗ -KFAC) and mean-field VI via
Revised: Apart from MAP, LLA ∗ , LLA-Diag, LLA-KFAC, wefurther introduce last-layer LLA with KFAC approximation (LLA ∗ -KFAC) and mean-field VI via

Original: Bayesian finetuning [10] (MFVI-BF) 4 as baselines.
Revised: Bayesian finetuning [10] (MFVI-BF) 4 as baselines.

Original: LLA cannot be directly applied as the GGNmatrices are huge.
Revised: LLA cannot be directly applied as the GGNmatrices are huge.

Original: These methods all locate in the family of Gaussian approximate posteriors and areall post-hoc applied to the pretrained models to establish fair comparison.
Revised: These methods all locate in the family of Gaussian approximate posteriors and areall post-hoc applied to the pretrained models to establish fair comparison.

Original: Regarding the setups, weuse M = 2000 and K = 20 for ELLA; 5 we use 20 MC samples to estimate the posterior predictiveof MFVI-BF (as it incurs 20 NN forward passes), and use 512 ones for the other methods as stated.
Revised: Regarding the setups, weuse M = 2000 and K = 20 for ELLA; 5 we use 20 MC samples to estimate the posterior predictiveof MFVI-BF (as it incurs 20 NN forward passes), and use 512 ones for the other methods as stated.

Original: We present the comparison on test accuracy, NLL, and ECE in Table 1.
Revised: We present the comparison on test accuracy, NLL, and ECE in Table 1.

Original: As shown, ELLA exhibitssuperior NLL and ECE across settings.
Revised: As shown, ELLA exhibitssuperior NLL and ECE across settings.

Original: MFVI-BF also gives good NLL.
Revised: MFVI-BF also gives good NLL.

Original: LLA ∗ and LLA ∗ -KFACcan improve the uncertainty and calibration of MAP, yet underperforming ELLA.
Revised: LLA ∗ and LLA ∗ -KFACcan improve the uncertainty and calibration of MAP, yet underperforming ELLA.

Original: LLA-Diag and
Revised: LLA-Diag and

Original: LLA-KFAC fail for unclear reasons (also reported by [9]), so are excluded from the following studies.
Revised: LLA-KFAC fail for unclear reasons (also reported by [9]), so are excluded from the following studies.

Original: We then examine the models on the widely used out-of-distribution (OOD) generalization/robustnessbenchmark CIFAR-10 corruptions [19] and report the results in Figure 3 and Appendix B.1.
Revised: We then examine the models on the widely used out-of-distribution (OOD) generalization/robustnessbenchmark CIFAR-10 corruptions [19] and report the results in Figure 3 and Appendix B.1.

Original: It isprominent that ELLA surpasses the baselines in aspects of NLL and ECE at various levels of skew,implying its ability to make conservative predictions for OOD inputs.
Revised: It isprominent that ELLA surpasses the baselines in aspects of NLL and ECE at various levels of skew,implying its ability to make conservative predictions for OOD inputs.

Original: We further evaluate the models on the combination of the CIFAR-10 test data and the OOD SVHNtest data.
Revised: We further evaluate the models on the combination of the CIFAR-10 test data and the OOD SVHNtest data.

Original: The predictions on SVHN are all regarded as wrong due to label shift.
Revised: The predictions on SVHN are all regarded as wrong due to label shift.

Original: We plot the averageerror rate of the samples with ≤ τ ( τ ∈ [0 , 1] ) confidence in Figure 4 (a).
Revised: We plot the averageerror rate of the samples with ≤ τ ( τ ∈ [0 , 1] ) confidence in Figure 4 (a).

Original: As shown, ELLA makesless mistakes than the baselines under various confidence thresholds.
Revised: As shown, ELLA makesless mistakes than the baselines under various confidence thresholds.

Original: Figure 4 (b) displays how Kimpacts the test NLL.
Revised: Figure 4 (b) displays how Kimpacts the test NLL.

Original: We see that K ∈ [20 , 30] can already lead to good performance, reflecting theefficiency of ELLA.
Revised: We see that K ∈ [20 , 30] can already lead to good performance, reflecting theefficiency of ELLA.

Original: Figure 4 (c) shows the comparison on the time used for predicting all CIFAR-10test data.
Revised: Figure 4 (c) shows the comparison on the time used for predicting all CIFAR-10test data.

Original: ELLA is slightly slower than MFVI and substantially faster than non-last-layer LLA.
Revised: ELLA is slightly slower than MFVI and substantially faster than non-last-layer LLA.

Original: [Table]
Revised: [Table]

Original: N e g a ti v e L og - li k e li hood
Revised: N e g a ti v e L og - li k e li hood

Original: ELLAMAP
Revised: ELLAMAP

Original: [Table]
Revised: [Table]

Original: 5.5 ImageNet Classification
Revised: 5.5 ImageNet Classification

Original: We apply ELLA to ImageNet classification [8] to demonstrate its scalability.
Revised: We apply ELLA to ImageNet classification [8] to demonstrate its scalability.

Original: The experiment settingsare identical with those for CIFAR-10.
Revised: The experiment settingsare identical with those for CIFAR-10.

Original: We observe that all LLA methods implemented with Laplacewould cause out-of-memory (OOM) errors, thus take only MAP and MFVI-BF as baselines.
Revised: We observe that all LLA methods implemented with Laplacewould cause out-of-memory (OOM) errors, thus take only MAP and MFVI-BF as baselines.

Original: Table 2presents the comparison on test accuracy, NLL, and ECE with ResNet architectures.
Revised: Table 2presents the comparison on test accuracy, NLL, and ECE with ResNet architectures.

Original: We see that
Revised: We see that

Original: ELLA maintains its superiority in ECE while MFVI-BF can induce higher accuracy and lower NLL.
Revised: ELLA maintains its superiority in ECE while MFVI-BF can induce higher accuracy and lower NLL.

Original: This may be attributed to that the pretrained MAP model lies at a sharp maxima of the true posteriorso it is necessary to properly adjust the mean of the Gaussian approximate
Revised: This may be attributed to that the pretrained MAP model lies at a sharp maxima of the true posteriorso it is necessary to properly adjust the mean of the Gaussian approximate

Original: [Table] [Table]
Revised: [Table] [Table]

Original: We lastly apply ELLA to ViT-B [11].
Revised: We lastly apply ELLA to ViT-B [11].

Original: We compare ELLA to MAPin Table 3 as all other baselines incur OOM errors or crushinglylong running time.
Revised: We compare ELLA to MAPin Table 3 as all other baselines incur OOM errors or crushinglylong running time.

Original: As shown, ELLA beats MAP in multiple aspects.
Revised: As shown, ELLA beats MAP in multiple aspects.

Original: Figure 5 shows the results of ELLA and MAP on ImageNetcorruptions [19].
Revised: Figure 5 shows the results of ELLA and MAP on ImageNetcorruptions [19].

Original: They are consistent with those for CIFAR-10corruptions.
Revised: They are consistent with those for CIFAR-10corruptions.

Original: We reveal by this experiment that ELLA can be amore applicable and scalable method than most of existing BNNs.
Revised: We reveal by this experiment that ELLA can be amore applicable and scalable method than most of existing BNNs.

Original:  Related Work
Revised:  Related Work

Original: LA [38, 50, 14, 30, 22, 6, 7] locally approximates the true posterior with a Gaussian, analogous to
Revised: LA [38, 50, 14, 30, 22, 6, 7] locally approximates the true posterior with a Gaussian, analogous to

Original: VI with Gaussian variationals [16, 1, 36, 52, 46] and SWAG [39].
Revised: VI with Gaussian variationals [16, 1, 36, 52, 46] and SWAG [39].

Original: LA can be applied to pretrainedmodels effortlessly while the acquired posteriors are potentially restrictive (see Section 5.5).
Revised: LA can be applied to pretrainedmodels effortlessly while the acquired posteriors are potentially restrictive (see Section 5.5).

Original: VIenjoys higher flexibility yet relies on costly training; BayesAdapter [10] seems to be a remedy to theissue but its accessibility is still lower than LA.
Revised: VIenjoys higher flexibility yet relies on costly training; BayesAdapter [10] seems to be a remedy to theissue but its accessibility is still lower than LA.

Original: SWAG stores a series of SGD iterates to heuristicallyconstruct an approximate posterior and is empirically weaker than LA/LLA [6].
Revised: SWAG stores a series of SGD iterates to heuristicallyconstruct an approximate posterior and is empirically weaker than LA/LLA [6].

Original: Though more expressive approaches like deep ensembles [32] and MCMC [55, 4, 63] can explicitlyexplore diverse posterior modes, they face limitations in efficiency and scalability.
Revised: Though more expressive approaches like deep ensembles [32] and MCMC [55, 4, 63] can explicitlyexplore diverse posterior modes, they face limitations in efficiency and scalability.

Original: What’s more, it hasbeen shown that LA/LLA can perform on par with or better than deep ensembles and cyclical MCMCon multiple benchmarks [6].
Revised: What’s more, it hasbeen shown that LA/LLA can perform on par with or better than deep ensembles and cyclical MCMCon multiple benchmarks [6].

Original: This may be attributed to the un-identified, complicated relationshipsbetween the parameters space and the function space of DNNs [59].
Revised: This may be attributed to the un-identified, complicated relationshipsbetween the parameters space and the function space of DNNs [59].

Original: Also of note that LA can embracedeep ensembles to capture multiple posterior modes [13].
Revised: Also of note that LA can embracedeep ensembles to capture multiple posterior modes [13].

Original:  Conclustion
Revised:  Conclustion

Original: This paper proposes ELLA, a simple, effective, and reliable approach for Bayesian deep learning.
Revised: This paper proposes ELLA, a simple, effective, and reliable approach for Bayesian deep learning.

Original: ELLA addresses the unreliability issues of existing approximations to LLA and is implemented basedon Nyström approximation.
Revised: ELLA addresses the unreliability issues of existing approximations to LLA and is implemented basedon Nyström approximation.

Original: We offer theoretical guarantees for ELLA and perform extensive studiesto testify its efficacy and scalability.
Revised: We offer theoretical guarantees for ELLA and perform extensive studiesto testify its efficacy and scalability.

Original: ELLA currently accounts for only the predictive, and extendingit to estimate model evidence for model selection [38] deserves future investigation.
Revised: ELLA currently accounts for only the predictive, and extendingit to estimate model evidence for model selection [38] deserves future investigation.

Original: Checklist
Revised: Checklist

Original: For all authors...
Revised: For all authors...

Original: (a) Do the main claims made in the abstract and introduction accurately reflect the paper’s
Revised: (a) Do the main claims made in the abstract and introduction accurately reflect the paper’s

Original: contributions and scope?
Revised: contributions and scope?

Original: [Yes]
Revised: [Yes]

Original: (b) Did you describe the limitations of your work?
Revised: (b) Did you describe the limitations of your work?

Original: [Yes] See Section Related Work.
Revised: [Yes] See Section Related Work.

Original: (c) Did you discuss any potential negative societal impacts of your work?
Revised: (c) Did you discuss any potential negative societal impacts of your work?

Original: [N/A](d)
Revised: [N/A]

Original:  Have you read the ethics review guidelines and ensured that your paper conforms tothem?
Revised: (d) Have you read the ethics review guidelines and ensured that your paper conforms tothem?

Original: [Yes]
Revised: [Yes]

Original: If you are including theoretical results...
Revised: If you are including theoretical results...

Original: (a) Did you state the full set of assumptions of all theoretical results?
Revised: (a) Did you state the full set of assumptions of all theoretical results?

Original: [N/A]
Revised: [N/A]

Original: (b) Did you include complete proofs of all theoretical results?
Revised: (b) Did you include complete proofs of all theoretical results?

Original: [Yes] See Appendix.3.
Revised: [Yes] See Appendix.3.

Original: If you ran experiments...
Revised: If you ran experiments...

Original: (a) Did you include the code, data, and instructions needed to reproduce the main experi-
Revised: (a) Did you include the code, data, and instructions needed to reproduce the main experi-

Original: mental results (either in the supplemental material or as a URL)?
Revised: mental results (either in the supplemental material or as a URL)?

Original: [Yes] See supplemen-
Revised: [Yes] See supplemen-

Original: tal material.
Revised: tal material.

Original: (b) Did you specify all the training details (e
Revised: (b) Did you specify all the training details (e

Original: g., data splits, hyperparameters,
Revised: g., data splits, hyperparameters,

Original: how they
Revised: how they

Original: were chosen)?
Revised: were chosen)?

Original: [Yes] See Section Experiments.
Revised: [Yes] See Section Experiments.

Original: (c) Did you report error bars (e
Revised: (c) Did you report error bars (e

Original: g., with respect to the random seed after running experiments multiple times)?
Revised: g., with respect to the random seed after running experiments multiple times)?

Original: [No]
Revised: [No]

Original: (d) Did you include the total amount of compute and the type of resources used (e g., typeof GPUs, internal cluster, or cloud provider)?
Revised: (d) Did you include the total amount of compute and the type of resources used (e.g., typeof GPUs, internal cluster, or cloud provider)?

Original: [No]
Revised: [No]

Original: If you are using existing assets (e
Revised: If you are using existing assets (e

Original: g., code, data, models) or curating/releasing new assets...
Revised: g., code, data, models) or curating/releasing new assets...

Original: (a) If your work uses existing assets, did you cite the creators?
Revised: (a) If your work uses existing assets, did you cite the creators?

Original: [Yes](b) Did you mention the license of the assets?
Revised: [Yes](b) Did you mention the license of the assets?

Original: [N/A]
Revised: [N/A](c)

Original: (c) Did you include any new assets either in the supplemental material or as a URL?
Revised: Did you include any new assets either in the supplemental material or as a URL?

Original: [No]
Revised: [No]

Original: (d) Did you discuss whether and how consent was obtained from people whose data you’re
Revised: (d) Did you discuss whether and how consent was obtained from people whose data you’re

Original: using/curating?
Revised: using/curating?

Original: [N/A
Revised: [N/A]

Original: (e) Did you discuss whether the data you are using/curating contains personally identifiable
Revised: (e) Did you discuss whether the data you are using/curating contains personally identifiable

Original: information or offensive content?
Revised: information or offensive content?

Original: [N/A]
Revised: [N/A]

Original: If you used crowdsourcing or conducted research with human subjects...
Revised: If you used crowdsourcing or conducted research with human subjects...

Original: (a) Did you include the full text of instructions given to participants and screenshots, if
Revised: (a) Did you include the full text of instructions given to participants and screenshots, if

Original: applicable?
Revised: applicable?

Original: [N/A]
Revised: [N/A]

Original: (b) Did you describe any potential participant risks, with links to Institutional Review
Revised: (b) Did you describe any potential participant risks, with links to Institutional Review

Original: Board (IRB) approvals, if applicable?
Revised: Board (IRB) approvals, if applicable?

Original: [N/A]
Revised: [N/A]

Original: (c) Did you include the estimated hourly wage paid to participants and the total amount
Revised: (c) Did you include the estimated hourly wage paid to participants and the total amount

Original: spent on participant compensation?
Revised: spent on participant compensation?

Original: [N/A]
Revised: [N/A]

Original: A Proof
Revised: A Proof

Original: A.1 Derivation of Equation (5)
Revised: A.1 Derivation of Equation (5)

Original: Given
Revised: Given

Original: [Equation]
Revised: [Equation]

Original: we have
Revised: we have

Original: [Equation]
Revised: [Equation]

Original: A.2 Derivation of Equation (6)
Revised: A.2 Derivation of Equation (6)

Original: [Equation]
Revised: [Equation]

Original: (Woodbury matrix
Revised: (Woodbury matrix

Original: [Equation]
Revised: [Equation]

Original: A.3 Derivation of Equation (18)
Revised: A.3 Derivation of Equation (18)

Original: When M = K , the eigenvalues and eigenvectors of K =
Revised: When M = K , the eigenvalues and eigenvectors of K =

Original: J ˆ θ , ˜ X
Revised: J ˆ θ , ˜ X

Original: J ⊤ ˆ θ , ˜ X can be organized assquare matrices diag ( λ ) =
Revised: J ⊤ ˆ θ , ˜ X can be organized assquare matrices diag ( λ ) =

Original: diag ( λ 1 , ..., λ K ) and U =
Revised: diag ( λ 1 , ..., λ K ) and U =

Original: [ u 1 , ..., u K ] respectively.
Revised: [ u 1 , ..., u K ] respectively.

Original: And φ ( x ) =[
Revised: And φ ( x ) =[

Original: J ˆ θ ( x ) v 1 , ..., J ˆ θ ( x ) v K ]
Revised: J ˆ θ ( x ) v 1 , ..., J ˆ θ ( x ) v K ]

Original: =
Revised: =

Original: J ˆ θ ( x ) J ⊤ ˆ θ , ˜ X
Revised: J ˆ θ ( x ) J ⊤ ˆ θ , ˜ X

Original: U diag ( λ ) − 12 .
Revised: U diag ( λ ) − 12 .

Original: Of note that U is a orthogonal matrix where
Revised: Of note that U is a orthogonal matrix where

Original: UU ⊤ =
Revised: UU ⊤ =

Original: U ⊤ U
Revised: U ⊤ U

Original: = I K ⇒
Revised: = I K ⇒

Original: U − 1 = U ⊤ , and by definition, J ˆ θ , ˜ X J ⊤ ˆ θ , ˜ X
Revised: U − 1 = U ⊤ , and by definition, J ˆ θ , ˜ X J ⊤ ˆ θ , ˜ X

Original: = U diag ( λ ) U ⊤ .
Revised: = U diag ( λ ) U ⊤ .

Original: Thenκ ELLA ( x , x ′ ) =
Revised: Thenκ ELLA ( x , x ′ ) =

Original: φ ( x ) (cid:104) (cid:88)
Revised: φ ( x ) (cid:104) (cid:88)

Original: [Equation]
Revised: [Equation]

Original: A.4 Proof of Theorem
Revised: A.4 Proof of Theorem

Original: Theorem 1.
Revised: Theorem 1.

Original: Let c Λ be a finite constant associated with Λ , and E ′ the error of Nyström approximation∥
Revised: Let c Λ be a finite constant associated with Λ , and E ′ the error of Nyström approximation∥

Original: J ˆ θ , X J ⊤ ˆ θ , ˜ X
Revised: J ˆ θ , X J ⊤ ˆ θ , ˜ X

Original: ( J ˆ θ , ˜ X J ⊤ ˆ θ , ˜ X ) − 1 J ˆ θ , ˜
Revised: ( J ˆ θ , ˜ X J ⊤ ˆ θ , ˜ X ) − 1 J ˆ θ , ˜

Original: X J ⊤ ˆ θ , X − J ˆ θ , X J ⊤ ˆ θ , X ∥ .
Revised: X J ⊤ ˆ θ , X − J ˆ θ , X J ⊤ ˆ θ , X ∥ .

Original: It holds that
Revised: It holds that

Original: E ≤ σ 40 c Λ E ′ + σ 20 .
Revised: E ≤ σ 40 c Λ E ′ + σ 20 .

Original: Proof.
Revised: Proof.

Original: With K
Revised: With K

Original: =
Revised: =

Original: J ˆ θ , ˜ X
Revised: J ˆ θ , ˜ X

Original: J ⊤ ˆ
Revised: J ⊤ ˆ

Original: [Equation] [Equation]
Revised: [Equation] [Equation]

Original: X ≻ 0 , let P =
Revised: X ≻ 0 , let P =

Original: J ⊤ ˆ θ , ˜ X K
Revised: J ⊤ ˆ θ , ˜ X K

Original: [Equation]
Revised: [Equation]

Original: 1 J ˆ θ , ˜ X .
Revised: 1 J ˆ θ , ˜ X .

Original: By Woodbury matrix identity,
Revised: By Woodbury matrix identity,

Original: [Equation]
Revised: [Equation]

Original: It is interesting to see that P is a projector: P 2 = P and PJ
Revised: It is interesting to see that P is a projector: P 2 = P and PJ

Original: ⊤ ˆ θ , ˜ X
Revised: ⊤ ˆ θ , ˜ X

Original: = J ⊤ ˆ θ , ˜ X .
Revised: = J ⊤ ˆ θ , ˜ X .

Original: Then
Revised: Then

Original: [Equation]
Revised: [Equation]

Original: By Woodbury matrix identity again,
Revised: By Woodbury matrix identity again,

Original: [Equation]
Revised: [Equation]

Original: Let T =
Revised: Let T =

Original: ( PJ ⊤ ˆ θ , X Λ X , Y J ˆ θ , X P + I P /σ 20 ) − 1 ,
Revised: ( PJ ⊤ ˆ θ , X Λ X , Y J ˆ θ , X P + I P /σ 20 ) − 1 ,

Original: so PJ ⊤ ˆ θ , X Λ X , Y J ˆ θ , X P = T − 1 − I P /σ 20 .
Revised: so PJ ⊤ ˆ θ , X Λ X , Y J ˆ θ , X P = T − 1 − I P /σ 20 .

Original: It follows that
Revised: It follows thatΣ

Original: [Equation]
Revised: [Equation]

Original: As a result
Revised: As a result

Original: [Equation] [Equation]
Revised: [Equation] [Equation]

Original: where we leverage the fact that the eigenvalue of the projector P is either 1 or 0 .
Revised: where we leverage the fact that the eigenvalue of the projector P is either 1 or 0 .

Original: And
Revised: And

Original: [Equation]
Revised: [Equation]

Original: where λ min ( · ) denotes the smallest eigenvalue of a matrix.
Revised: where λ min ( · ) denotes the smallest eigenvalue of a matrix.

Original: The last inequality holds due to that theeigenvalues of PJ ⊤ ˆ θ , X Λ X , Y J ˆ θ , X P + I P /σ 20 and J ⊤ ˆ θ , X Λ X , Y J ˆ θ , X
Revised: The last inequality holds due to that theeigenvalues of PJ ⊤ ˆ θ , X Λ X , Y J ˆ θ , X P + I P /σ 20 and J ⊤ ˆ θ , X Λ X , Y J ˆ θ , X

Original: + I P /σ 20 are larger than or equalto 1 /σ 20 as PJ ⊤ ˆ θ , X Λ X , Y J
Revised: + I P /σ 20 are larger than or equalto 1 /σ 20 as PJ ⊤ ˆ θ , X Λ X , Y J

Original: ˆ θ , X P and J ⊤ ˆ θ , X Λ X , Y J ˆ θ , X are SPSD.
Revised: ˆ θ , X P and J ⊤ ˆ θ , X Λ X , Y J ˆ θ , X are SPSD.

Original: To estimate the upper bound of ∥ T − Σ ∥ , we lay out the following Lemma.
Revised: To estimate the upper bound of ∥ T − Σ ∥ , we lay out the following Lemma.

Original: Lemma 1 (Proposition of Weinstein–Aronszajn identity) .
Revised: Lemma 1 (Proposition of Weinstein–Aronszajn identity) .

Original: If A and B are matrices of size m × n andn × m respectively, the non-zero eigenvalues of AB and BA are the same.
Revised: If A and B are matrices of size m × n andn × m respectively, the non-zero eigenvalues of AB and BA are the same.

Original: It follows that
Revised: It follows that

Original: [Equation] [Equation]
Revised: [Equation] [Equation]

Original: ∥ Λ X , Y ( J ˆ θ , X J ⊤ ˆ θ , ˜ X
Revised: Λ X , Y ( J ˆ θ , X J ⊤ ˆ θ , ˜ X

Original: ( J ˆ θ , ˜ X J ⊤ ˆ θ , ˜ X ) − 1 J ˆ θ , ˜
Revised: ( J ˆ θ , ˜ X J ⊤ ˆ θ , ˜ X ) − 1 J ˆ θ , ˜

Original: X J ⊤ ˆ θ , X − J ˆ θ , X J ⊤ ˆ θ , X ) ∥ ≤ σ 40 ∥Λ X , Y ∥∥ J ˆ θ , X J ⊤ ˆ θ , ˜ X ( J ˆ θ , ˜ X J ⊤ ˆ θ , ˜ X
Revised: X J ⊤ ˆ θ , X − J ˆ θ , X J ⊤ ˆ θ , X ) ∥ ≤ σ 40 ∥

Original: [Equation] − [Equation]
Revised: Λ X , Y ∥∥

Original:  ˆ θ [Equation] ˜ X[Equation]
Revised: J ˆ θ , X J ⊤ ˆ θ , ˜ X

Original: ⊤ ˆ θ

Original: [Equation] X [Equation] ˆ θ 
Revised: ( J ˆ θ , ˜ X J ⊤ ˆ θ , ˜ X ) − 1 J ˆ θ , ˜

Original: [Equation] X [Equation] ⊤ ˆ θ 
Revised: X J ⊤ ˆ θ , X − J ˆ θ , X J ⊤ ˆ θ , X ∥ .

Original: [Equation] X ∥ . It is easy to show that ∥ Λ X , Y ∥ ≤ c Λ with c Λ as a finite constant.
Revised: It is easy to show that ∥ Λ X , Y ∥ ≤ c Λ with c Λ as a finite constant.

Original: For example, when handlingregression tasks, − log p ( y | g ) typically boils down to 1 2 σ 2 noise ( g − y )
Revised: For example, when handlingregression tasks, − log p ( y | g ) typically boils down to 1 2 σ 2 noise ( g − y )

Original: ⊤ ( g − y ) where σ 2 noise refers tothe variance of data noise, so −∇ 2 gg log p ( y | g ) =
Revised: ⊤ ( g − y ) where σ 2 noise refers tothe variance of data noise, so −∇ 2 gg log p ( y | g ) =

Original: 1 σ 2 noise I C and c Λ = 1 σ 2 noise .
Revised: 1 σ 2 noise I C and c Λ = 1 σ 2 noise .

Original: When facing classification cases, − log p ( y | g ) becomes the softmax cross-entropy loss.
Revised: When facing classification cases, − log p ( y | g ) becomes the softmax cross-entropy loss.

Original: It is guaranteed that c Λ ≤ 2 because
Revised: It is guaranteed that c Λ ≤ 2 because

Original: [Equation]
Revised: [Equation]

Original: To summarize, we have the following bound for the approximation error E :
Revised: To summarize, we have the following bound for the approximation error E :

Original: [Equation]
Revised: [Equation]

Original: [Figure]

Original: B More experiments
Revised: B More experiments

Original: [Figure]

Original: B.1 More Results on CIFAR- 
Revised: B.1 More Results on CIFAR-

Original: [Figure]
Revised: [Figure]

Original: Figure 6 presents how the test accuracy and ECE of ELLA on CIFAR-10 vary w.r.t.
Revised: Figure 6 present how the test accuracy and ECE of ELLA on CIFAR-10 vary w.r.t.

Original: the number oftraining data N .
Revised: the number of training data N .

Original: Figure 7, Figure 8, and Figure 9 show the results of the considered methods on CIFAR-10 corruptionsusing ResNet-20, ResNet-32, and ResNet-44 architectures respectively.
Revised: Figure 7, Figure 8, and Figure 9 show the results of the considered methods on CIFAR-10 corruptionsusing ResNet-20, ResNet-32, and ResNet-44 architectures respectively.

Original: We can see that ELLAsurpasses the baselines in aspects of NLL and ECE at various levels of skew.
Revised: We can see that ELLAsurpasses the baselines in aspects of NLL and ECE at various levels of skew.

Original: These results signify
Revised: These results signify

Original: ELLA’s ability to make conservative predictions for OOD inputs.
Revised: ELLA’s ability to make conservative predictions for OOD inputs.

Original: Figure 10 shows how the test NLL of LLA ∗ on CIFAR-10 varies w.r.t. the number of training data N . These results, as well as those in Section 5.2, reflect that the overfitting issue of LLA generally exists. We can also see that properly tuning the prior precision can alleviate the overfitting of LLA ∗ to someextent but cannot fully resolve it.
Revised: [Figure]

Original: [Figure]
Revised: [Figure]

Original: [Figure]

Original: [Figure]
Revised: [Figure]

Original: N e g a ti v e L og - li k e li
Revised: N e g a ti v e L og - li k e li

Revised: [Figure]

Revised: [Figure]

Revised: g

Original: [Figure]
Revised: [Figure]

Revised: ti

Original: [Figure]
Revised: [Figure]

Original: L
Revised: L

Original: [Figure]
Revised: [Figure]

Original: [Figure]