A L EARNING T HEORETIC P ERSPECTIVE ON L OCAL E XPLAINABILITY
A BSTRACT
In this paper, we explore connections between interpretable machine learning and learning theory through the lens of local approximation explanations.
First, we tackle the traditional problem of performance generalization and bound the testtime predictive accuracy of a model using a notion of how locally explainable it is.
Second, we explore the novel problem of explanation generalization which is an important concern for a growing class of ﬁnite sample-based local approximation explanations.
Finally, we validate our theoretical results empirically and show that they reﬂect what can be seen in practice.
I NTRODUCTION
There has been a growing interest in interpretable machine learning, which seeks to help people better understand their models.
While interpretable machine learning encompasses a wide range of problems, it is a fairly uncontroversial hypothesis that there exists a trade-off between a model’s complexity and general notions of interpretability .
This hypothesis suggests a seemingly natural connection to the ﬁeld of learning theory, which has thoroughly explored relationships between a function class’s complexity and generalization .
However, formal connections between interpretability and learning theory remain relatively unstudied.
Though there are several notions of conveying interpretability, one common and ﬂexible approach is to use local approximations.
Formally, local approximation explanations (which we will refer to as “local explanations”) provide insight into a model’s behavior as follows: for any black-box model f ∈ F and input x , the explanation system produces a simple function, g x ( x (cid:48) )
∈ G local , which approximates f in a chosen neighborhood, x (cid:48) ∼ N x .
Crucially, the freedom to specify both G local and N x grants local explanations great versatility.
In this paper, we provide two connections between learning theory and how well f can be approximated locally (i.e. the ﬁdelity of local explanations).
Our ﬁrst result studies the standard problem of performance generalization by relating test-time predictive accuracy to a notion of local explainability.
As it turns out, our focus on local explanations leads us to unique tools and insights from a learning theory point of view.
Our second result identiﬁes and addresses an unstudied – yet important – question regarding explanation generalization .
This question pertains to a growing class of explanation systems, such as MAPLE (Plumb et al., 2018) and
RL-LIM (Yoon et al., 2019), which we call ﬁnite sample-based local explanations 1 .
These methods learn their local approximations using a common ﬁnite sample drawn from data distribution D (in contrast to canonical local approximation methods such as LIME (Ribeiro et al., 2016)) and, as a result, run the risk of overﬁtting to this ﬁnite sample.
In light of this, we answer the following question: for these explanation-learning systems, how well do the quality of local explanations generalize to data not seen during training?
We address these questions with two bounds, which we outline now.
Regarding performance generalization, we derive our ﬁrst main result, Theorem 1, which bounds the expected test mean squared error (MSE) of any f in terms of its MSE over the m samples in the training set, S = { ( x i , y i ) } mi =1 :
E ( x,y )
[Equation]
D [
[Equation]
f ( x ) − y ) 2 ]
[Equation]
[Table]
[Equation]
[Table]
[Equation]
[Table]
[Equation]
[Table]
[Equation]
[Table]
Regarding explanation generalization for ﬁnite sample-based explanation-learning systems, we apply a similar proof technique to obtain Theorem 2, which bounds the quality of the system’s explanations on unseen data in terms of their quality on the data on which the system was trained:
[Equation]
[Table]
[Equation]
[Table]
[Equation]
[Table]
[Equation]
[Table]
[Equation]
[Table]
[Equation]
[Table]
[Equation]
[Table]
Before summarizing our contributions we discuss the key terms and their relationships.
• Interpretability terms: The terms involving MNF correspond to Mirrored Neighborhood Fidelity, a metric we use to measure local explanation quality.
As we discuss in Section 3, this is a reasonable modiﬁcation of the commonly used Neighborhood Fidelity ( NF ) metric (Ribeiro et al., 2016; Plumb et al., 2018).
Intuitively, we generally expect MNF to be larger when the neighborhood sizes are larger since each g x (cid:48) is required to extrapolate farther.
• Complexity term: This term measures the complexity of the local explanation system g in terms of (a) the complexity of the local explanation class G local and (b) ρ S , a quantity that we deﬁne and refer to as the neighborhood disjointedness factor .
As we discuss in Section 4, ρ S is a value in [1 , √ m ] (where m = | S | ) that is proportional to the level of disjointedness of the neighborhoods for points in the sample S .
Intuitively, we expect ρ S to be larger when the neighborhoods sizes are smaller since smaller neighborhoods will overlap less.
Notably both our bounds capture the following key trade-off: as neighborhood widths increase,
MNF increases but ρ S decreases.
As such, our bounds are non-trivial only if the neighborhoods N x can be chosen such that MNF remains small but ρ S grows slower than ˜ O ( √ m ) (since ˆ R S ( G local ) typically decays as ˜ O
(1 / √ m ) ).
We summarize our main contributions as follows:
(1) We make a novel connection between performance generalization and local explainability, arriving at Theorem 1.
Given the relationship between MNF and ρ S , this bound roughly captures that an easier-to-interpret f enjoys better generalization guarantees, a potentially valuable result when reasoning about F is difﬁcult (e.g. for neural networks).
Further, our proof technique may be of independent theoretical interest as it provides a new way to bound the Rademacher complexity of a particular class of randomized functions (see Section 4).
(2) We motivate and explore an important generalization question about expected explanation quality.
Speciﬁcally, we arrive at Theorem 2, a bound for test MNF in terms of train MNF .
This bound suggests that practitioners can better guarantee good local explanation quality (measured by MNF ) using methods which encourage the neighborhood widths to be wider (see Section 5).
(3) We verify empirically on UCI Regression datasets that our results non-trivially reﬂect the two types of generalization in practice.
First, we demonstrate that ρ S can indeed exhibit slower than ˜ O ( √ m ) growth without signiﬁcantly increasing the MNF terms.
Also, for Theorem 2, we show that the generalization gap indeed improves with larger neighborhoods (see Section 6).
(4) Primarily to aid in our theoretical results, we propose MNF as a novel yet reasonable measure of local explainability.
Additionally, we argue that this metric presents a promising avenue for future study, as it may naturally complement NF and offer a unique advantage when evaluating local explanations on “realistic” on-distribution data (see Section 3).
R ELATED W ORK
Interpretability meets learning theory.
Semenova et al.
(2020) study the performance generalization of models learned from complex classes when they can be globally well-approximated by simpler (e.g. interpretable) classes.
In such cases, their theory argues that if the complex class has many models that perform about as optimally on training data, generalization from the complex class can be more closely bounded using the simpler class’s complexity.
In our corresponding results, we similarly aim to avoid involving the larger class’s complexity.
However, we directly study generalization via a model’s local explainability, rather than instantiate ”complex” and “simple” classes for global approximations.
The two are fundamentally different technical problems; standard learning theory results cannot be directly applied as they are done for single-function global approximations.
Statistical localized regression.
(Fan, 1993; Fan & Gijbels, 1996) are canonical results which bound the squared error of a nonparametric function deﬁned using locally ﬁt models.
These local models are both simple (e.g. linear) and similarly trained by weighting real examples with a kernel (i.e. an implied neighborhood).
However, each local model is only used to make a prediction at its source point and the theory requires shrinking the kernel width towards 0 as the sample size grows.
We instead ﬁt local models as explanations for a trained model (i.e. which is considered the “true regression function”) and more importantly, care about the ﬁdelity of each local model over whole (non-zero) neighborhoods.
Unlike localized regression, this allows us to use uniform convergence to bound test error with empirical and generalization terms.
While the previous results do not have empirical terms, the learning rates are exponential in the number of samples.
Learning Theory.
Another line of related work also studies how to explain generalization of overparameterized classes.
As standard uniform convergence on these classes often leads to vacuous bounds, a general approach that has followed from (Nagarajan & Kolter, 2019; Zhang et al., 2017;
Neyshabur et al., 2014) has been to study implications of different biases placed on learned models.
We study what would happen if an overparameterized model had an unexplored type of bias, one that is inspired by local explainability.
Additionally, our work’s technical approach parallels another line of existing results which likewise try to apply uniform convergence on separate surrogate classes.
This includes PAC-Bayesian bounds, a large family of techniques that come from looking at a stochastic version of a model in parameter space (McAllester, 1998; 2003; Langford & Caruana, 2002; Langford & Shawe-Taylor, 2003).
In a different vein, some results in deep learning look at compressed, sparsiﬁed, or explicitly regularized surrogates of neural networks (Arora et al., 2018;
Dziugaite & Roy, 2017).
In our case, the surrogate class is a collection of local explanations.
M IRRORED N EIGHBORHOOD F IDELITY
In order to connect local explanations to generalization, recall that we study a measure of local interpretability which we call “mirrored neighborhood ﬁdelity” ( MNF ).
As we explain below, this quantity comes from a slight modiﬁcation to an existing metric for local approximation explanations, namely, that of neighborhood ﬁdelity ( NF ).
To deﬁne these quantities, we use the following notation.
Let X be an input space and D be a distribution over X × Y where Y ⊆ R .
Then, let F be a class of functions f : X → Y .
For our theoretical results, we speciﬁcally assume that Y is bounded as Y =
[ − B, B ] for some B >(though this does not matter for the remainder of this section).
In order to provide local explanations, we need to ﬁx a nearby region around each x ∈ X .
To this end, for any x , let N x correspond to some distribution denoting a local neighborhood at x (e.g. typically chosen to be a distribution centered at x ).
For any distribution N , we use p N ( x ) to denote its density at x .
Finally, let G be a class of local explainers g :
X × X
→ Y such that for each x ∈ X , the local explanation g ( x, · ) :
X → Y belongs to a class of (simple) functions (e.g. linear), G local .
For convenience, we denote g ( x, · ) as g x ( · ) and use it to locally approximate f in the neighborhood deﬁned by N x .
The accuracy of the local explanation system g is usually quantiﬁed by a term called “neighbhorhood ﬁdelity” which is deﬁned as follows (Ribeiro et al., 2016; 2018; Plumb et al., 2018; 2020):
[Equation]
To verbally interpet this, let us call x as the “source” point which gives rise to a local explanation
g x ( · ) and x (cid:48) the “target” points that we try to ﬁt using g .
To compute NF ( f, g ) , we need to do the following: for each source point x , we ﬁrst compute the average error in the ﬁt of g x ( · ) over draws of nearby target points x (cid:48) ∼ N x ; then, to get a more overall measure of g ’s quality, we globally average this error across draws of the source point x ∼ D .
Now, to deﬁne MNF , we take the same expression as for NF but swap x and x (cid:48) within the innermost expectation (without modifying how each variable is distributed).
In other words, we now sample a target point x from D and sample source points x (cid:48) from a distribution over points near a given x .
Since this local distribution is over source points rather than target points, just for the sake of distinguishing, we’ll refer to this as a mirrored neighborhood distribution and denote it as N mir x .
We deﬁne MNF more formally below, following which we explain how to better understand it:
Deﬁnition 3.1.
(Mirrored Neighborhood Fidelity)
We deﬁne MNF : F × G → R as
[Equation]
and with an abuse of notation, we let MNF ( f, g, x
[Equation]
:=
E x
[Equation]
Understanding MNF .
It is helpful to parse the expression for MNF in two different ways.
First, we can think of it as measuring the error in approximating every target point x ∈ X through a randomized locally-approximating function g x (cid:48) ( · ) , where x (cid:48) is randomly drawn from the neighborhood
N mir x .
A second way to parse this is in a manner similar to how we parsed NF .
To do this, ﬁrst we note that the expectations in MNF can be swapped around and rewritten as:
[Equation]
where D † and N † x (cid:48) are suitably deﬁned distributions (derived in Appendix A) that can be thought of as modiﬁed counterparts of D and N mir x (cid:48) respectively.
With this rewritten expression, one can read
MNF like NF : for each source point (now x (cid:48) ), we compute the average error in the ﬁt of the corresponding local function ( g x (cid:48) ( · ) ) over target points ( x ) drawn from the source’s local neighborhood ( N † x (cid:48) ); this error is then globally averaged over different draws of the source point ( x (cid:48) ∼ D † ).
Why MNF ?
While both NF and MNF are closely related measures of local explanation quality on f , studying MNF allows us to make connections between local explainability and different notions of generalization (Sections 4 and 5).
At a high-level, our results don’t apply to NF because the overall distribution of the points that are “ﬁt” to calculate NF (i.e., the target points) is not the same as the test data distribution D , instead being D perturbed by N x .
Rather, we prefer this target distribution to be D , which is the case for MNF , for us to be able to neatly bound test-time quantities via a local explainability term.
Otherwise, we would have to end up introducing many cumbersome terms.
Furthermore, MNF may also be of practical interest to the interpretability community, as it potentially offers a unique advantage over NF when the intended usage of local explanations is centered around understanding how f works on the speciﬁc learning task it was trained on.
Speciﬁcally, we present as an exploratory argument that selecting the target point distribution to be D rather than D perturbed by N x (as for NF ) may better emphasize the ability of g to approximate f at realistic input points.
This is relevant for ML (and deep learning especially) because (a) high-dimensional datasets often exhibit signiﬁcant feature dependencies and adherence to lower dimensional manifolds; (b) f can often be highly unpredictable and unstable when extrapolating beyond training data.
Thus, when one measures NF with standard neighborhood choices that ignore feature dependencies (e.g. most commonly N x = N ( x, σI ) ), the resulting target distribution may concentrate signiﬁcantly on regions that are non-relevant to the original learning task at hand.
As we illustrate in a toy example, this can lead to overemphasis on ﬁtting noisy off-manifold behavior, deteriorating the ﬁt of explanations relative to task-relevant input regions (we defer a more detailed discussion of this point and of other trade-offs between NF and MNF to Appendix A).
G ENERALIZATION OF M ODEL P ERFORMANCE VIA MNF
The generalization error of the function f is typically bounded by some notion of the representational complexity of F .
While standard results bound complexity in terms of parameter count, there is theoretical value in deriving bounds involving other novel terms.
By doing so, one might understand how regularizing for those terms can affect the representation capacity, and in turn, the generalization error of f .
Especially when f ’s complexity may be intractable to bound on its own, introducing these terms provides a potentially useful new way to understand f ’s generalization.
Here speciﬁcally, we are interested in establishing a general connection between the representation complexity and the local explainability of any f .
This naturally requires coming up with a notion that appropriately quantiﬁes the complexity of G , which we discuss in the ﬁrst part of this section.
As we shall see, G ’s complexity can be expressed in terms of G local , which is generally less complex and more amenable to standard analysis than F in practical settings where interpretability is desired.
In the second part, we then relate this quantity to the generalization of f to derive our ﬁrst main result.
Key technical challenge: bounding the complexity of G .
The overall idea behind how one could tie the notions of generalization and local explainability is fairly intuitive.
For example, consider a simpliﬁed setting where we approximate f by dividing X into K disjoint pieces, i.e. neighborhoods, and then approximating each neighborhood via a simple (say, linear) model.
Then, one could bound the generalization error of f as the sum of two quantities: ﬁrst, the error in approximating f via the piecewise linear model, and second, a term involving the complexity of said piecewise model.
It is straightforward to show that this complexity term grows polynomially with the piece-count,
K , and also the complexity of the simple local approximator (see Appendix C.0.1).
Similarly, one could hope to bound the generalization error of f in terms of MNF ( f, g ) and the complexity of G .
However, the key challenge here is that the class G is a much more complex class than the above class of piecewise linear models.
For example, a straightforward piece-count-based complexity bound would be inﬁnitely large since there are effectively inﬁnitely many unique pieces in g .
Our core technical contribution here is to bound the (Rademacher) complexity of G in this more ﬂexible local explanation setting.
At a high level, the resulting bound (to be stated shortly) grows inversely with “the level of overlap” between the neighborhoods { N mir x | x ∈ X} , quantiﬁed as:
Deﬁnition 4.1.
(Neighborhood disjointedness factor)
Given a dataset S ∈ ( X × Y ) m , we deﬁne the neighborhood disjointedness factor ρ S as
[Equation]
Understanding the disjointedness factor.
ρ S can be interpreted as bounding the “effective number” of pieces induced by the set of neighborhood distributions { N mir x | x ∈ X} .
This turns out to be a quantity that lies in [1 , √ m ] (shown formally in Appendix Fact B.1).
To reason more intuitively about this quantity, it is helpful to consider its behavior in extreme scenarios.
First, consider the case where N mir x is the same distribution (say N ) regardless of x ; i.e., neighborhoods are completely overlapping.
Then, ρ S = (cid:82) x (cid:48) ∈X ( p N ( x (cid:48) ))
dx (cid:48)
= 1 .
In the other extreme, consider if neighborhoods centered on the training data are all disjoint with supports X 1 , . . .
, X | S | .
Here, the integral splits into m summands as: ρ S =
(cid:80) mi =1 (cid:82) x (cid:48) ∈X i 1 √ m p N mir x i ( x (cid:48) ) dx (cid:48) =
√ m .
Thus, ρ S grows from 1 to √ m as the level of overlap between the neighborhoods N mir x 1 , . . .
, N mir x | S | reduces.
For intuition at non-extreme values, we show in Appendix B.2 that in a simple setting, ρ = √ m 1 − k
(where 0 ≤ k ≤ 1 ) if every neighborhood is just large enough to encompass a 1 / m 1 − k fraction of the mass of D .
Rademacher complexity of G .
We now use ρ S to bound the empirical Rademacher complexity of
G .
Recall that the empirical Rademacher complexity of a function class H consisting of h : X → R is deﬁned as ˆ R S ( H ) :
= E (cid:126)σ (cid:2) 1 m sup h ∈H σ i h ( x i ) (cid:3) , where the σ i ’s are i.i.d.
and drawn uniformly from {− 1 , 1 } .
Roughly, this captures the complexity of H by measuring how well it can ﬁt random labels.
Standard results allow us to then use ˆ R S ( H ) to bound the generalization error for h ∈ H .
Now, in order to deﬁne the Rademacher complexity of G (which consists of a different kind of functions whose domain is X × X instead of X ), it is useful to think of g as a randomized function .
Speciﬁcally, at any target point x , the output of g is a random variable g x (cid:48)
( x ) where the randomness comes from x (cid:48) ∼ N mir x .
Then, in Lemma 4.1, we take this stochasticity into account to deﬁne and bound the complexity of G .
To keep our statement general, we consider a generic loss function
L : R × R → R (e.g., the squared error loss is L ( y, y (cid:48) )
= ( y − y (cid:48) ) 2 ).
Indeed, whenever L satisﬁes a standard Lipschitz assumption, we can bound the complexity of G (composed with the loss function
L ) in terms of ρ S , the complexity of G local , and the Lipschitzness of L :
Lemma 4.1.
(see Appendix Lemma D.1 for full, precise statement) Let L ( · , y (cid:48) ) be a c -Lipschitz function w.r.t.
y (cid:48) in that for all y 1 , y 2 ∈ [ − B, B ] , | L ( y 1 , y (cid:48) )
− L ( y 2 , y (cid:48) )
| ≤ c | y 1 − y 2 | .
Then, the empirical Rademacher complexity of G under the loss function L is deﬁned and bounded as:
[Equation]
where (cid:126)σ is uniformly distributed over {− 1 , 1 } m .
We note that the proof technique employed here may be of independent theoretical interest as it provides a novel way to bound the complexity of a (particular type of) randomized function.
Although techniques like PAC-Bayes provide ways to bound the complexity of randomized functions, they only apply to functions where the randomization comes from stochasticity in the parameters, which is not the case here.
Main result.
With the above key lemma in hand, we are now ready to prove our main result, which bounds the generalization error of f in terms of the complexity of G , thereby establishing a connection between model generalization and local explainability.
Theorem 1.
(see Appendix Theorem 3 for full, precise statement)
With probability over 1 − δ over the draws of S = { ( x 1 , y 1 ) , . . .
, ( x m , y m ) } ∼ D m , for all f ∈ F and for all g ∈ G , we have (ignoring ln 1 /δ factors):
[Equation]
This result decomposes the test error of f into four quantities.
The ﬁrst quantity corresponds to the training error of f on the training set S .
The second and the third correspond to the MNF of f with respect to g , computed on test and training data respectively.
The fourth and ﬁnal quantity corresponds to a term that bounds the complexity of G in terms of the “disjointedness factor” ρ S and the complexity of the local function class G local .
Takeaway.
A key aspect of this bound is the trade-off that it captures when varying neighborhood widths.
Consider shrinking the neighborhood widths to smaller and smaller values, in turn creating less and less overlap between the neighborhoods of the training examples in S .
Then, on the one hand, we’d observe that the complexity term (the fourth term on the R.H.S) increases.
Speciﬁcally, since ˆ R S ( G local ) typically scales as O (1 / √ m ) , as we go from the one extreme of full overlap to the other extreme of complete disjointedness, the complexity term increases from O (1 / √ m )
to
O (1) .
At this upper extreme, the bound becomes trivial, as such a constant upper bound would directly follow from just the O (1) bounds assumed on Y .
On the other hand, as the widths decrease, the ﬁdelity terms (the second and third terms) would likely decrease – this is because the simple functions in G local would ﬁnd it increasingly easier to approximate f on the shrinking neighborhoods.
This trade-off is intuitive.
A function f that is hardly amenable to being ﬁt by local explanations would require extremely tiny neighborhoods for G local to locally approximate it (i.e. make the MNF terms small).
For example, in an extreme case, when the neighborhoods N mir x are set be point masses at x , it is trivially easy to ﬁnd g x (cid:48) ( · ) ∈ G local with no approximation error.
Thus, the complexity term would be too large in this case, implying that a hard-to-interpret f results in bad generalization.
On the other hand, when f is easier to interpret, then we’d expect it to be well-approximated by G local even with wider neighborhoods.
This allows one to afford smaller values for both the complexity and MNF terms.
In other words, an easy-to-interpret f enjoys better generalization guarantees.
Caveats.
Our bound has two limitations worth noting.
First, for high-dimensional datasets (like image datasets), practical choices of N mir x can lead to almost no overlap between neighborhoods, rendering the bound trivial in practice.
This potentially poor dimension-dependence is a caveat similarly shared by bounds for non-parametric local regression, whereby increasing d results in an exponential increase in the required sample size (Fan, 1993; Fan & Gijbels, 1996).
Nevertheless, we show later in our experiments that for lower-dimensional datasets and for practical choices of N mir x , there can be sufﬁcient neighborhood overlap to achieve values of ρ S that are o ( √ m ) .
A second caveat is that the second quantity, MNF ( f, g ) , requires unlabeled test data to be computed, which may be limiting if one is interested in numerically computing this bound in practice.
It is however possible to get a bound without this dependence, although only on the test error of g rather than f (see Appendix Theorem 4).
Nevertheless, we believe that the above bound has theoretical value in how it establishes a connection between the interpretability of f and its generalization.
G ENERALIZATION OF L OCAL E XPLAINABILITY
We now turn our attention to a more subtle kind of generalization that is both unstudied yet important.
Typically, the way g x (cid:48) is learned at any source point x (cid:48) is by ﬁtting a ﬁnite set of points sampled near x (cid:48) , with the hope that this ﬁt generalizes to unseen, neighboring target points.
Naturally, we would want to ask: how well do the explanations g x (cid:48) themselves generalize in this sense?
This question is straightforward to answer in settings like for LIME (Ribeiro et al., 2016).
For instance, assume that we learn g x (cid:48) by sampling a set S x (cid:48) of points from a Gaussian centered at x (cid:48) , and that we care about the ﬁt of g x (cid:48) generalizing to the same Gaussian.
Here, we can make a standard arugment based on Rademacher complexity to bound the error of g x (cid:48) on the Gaussian by its training error on S x (cid:48) and ˆ R S x (cid:48) ( G local ) .
We can apply the same argument individually for the explanation at each source point x (cid:48) , because we have a fresh dataset S x (cid:48) to generate each g x (cid:48) .
If one has the resources to sample more points to create larger S x (cid:48) , then these bounds will also naturally tighten.
However, consider ﬁnite sample-based local explanation settings like MAPLE (Plumb et al., 2018) and RL-LIM (Yoon et al., 2019) where the training procedure is vastly different: in these procedures, the goal is to learn local explanations g x (cid:48) in a way that is sensitive to the local structure of the (unknown) underlying data distribution D .
So, instead of ﬁtting the g x (cid:48) to samples drawn from an arbitrarily deﬁned (e.g. Gaussian) distribution, here one ﬁrst draws a ﬁnite sample S from D and then labels it using f .
Then, across all x (cid:48) ∈ X , one reuses the same dataset S , but learns each g x (cid:48) on a correspondingly reweighted version of S (typically, points in S that are nearer to x (cid:48) are weighted more heavily).
Contrast this with the former setting, where for each x (cid:48) , one has access to a fresh dataset (namely, S x (cid:48) ) to learn g x (cid:48) .
This distinction then makes it interesting to wonder when the reuse of a common dataset S could cause the explanations to generalize poorly.
Motivated by this question, we present Theorem 2.
By using Lemma 4.1, we provide a bound on the “test MNF” (which corresponds to the ﬁt of g x (cid:48) on the unseen data averaged across D ) in terms of the “train MNF” (which corresponds to the ﬁt of g x (cid:48) on S ) and the complexity term from Lemma 4.1.
We must however caution the reader that this theorem does not answer the exact question posed in the above paragraph; it only addresses it indirectly as we discuss shortly.
Theorem 2.
(see Appendix Theorem 2-full for full, precise statement)
For a ﬁxed function f , with high probability 1 − δ over the draws of S ∼ D m , for all g ∈ G , we have (ignoring ln 1 /δ factors):
[Equation]
[Table]
[Equation]
[Table]
[Equation]
Understanding the overall bound.
We ﬁrst elaborate on how this bound provides an (indirect) answer to our question about how well explanations generalize.
Consider a procedure like MAPLE that learns g using the ﬁnite dataset S .
For each x (cid:48) ∈ X , we would expect this procedure to have learned a g x (cid:48) that ﬁts well on at least those target points x in S that are near x (cid:48) .
In doing so, it’s reasonable to expect the training procedure to have implicitly controlled the “train MNF ” term.
The reasoning for this is that the train MNF computes the error in the ﬁt of g x (cid:48) on S for different values of x (cid:48) , and sums these up in a way that errors corresponding to nearby values of ( x, x (cid:48) ) are weighted more (i.e., the weight is given by p N mir x
( x (cid:48) ) ).
Now, our bound suggests that when the train MNF is minimized, this carries over to test MNF too, provided the complexity term is not large .
That is, we can say that the ﬁt of g x (cid:48) generalizes well to unseen, nearby target points x that lie outside of S .
The indirectness of our result.
Existing ﬁnite sample-based explainers do not explicitly minimize the train MNF term (e.g., MAPLE minimizes an error based upon NF ).
However, as argued above, they have implicit control over train MNF .
Hence, our bound essentially treats MNF as a surrogate for reasoning about the generalization of the explanations learned by an arbitrary procedure.
As such, our bound does not comment on how well the exact kind of ﬁdelity metric used during training generalizes to test data.
Nevertheless, we hope that this result offers a concrete ﬁrst step towards quantifying the generalization of local explanations.
Furthermore, we also note that one could also imagine a novel explanation-learning procedure that does explicitly minimize the train MNF term to learn g ; in such a case our bound would provide a direct answer to how well its explanations generalize.
Indeed, we derive such a theoretically-principled algorithm in Appendix A.
Takeaway.
While the above bound captures a similar trade-off involving neighborhood width as
Theorem 1, it is worth pausing to appreciate the distinct manner in which this trade-off arises here.
In particular, when the width is too small, we know that the complexity term approaches O (1) and generalization is poor.
Intuitively, this is because in this case, the procedure for learning g x (cid:48) would have been trained to ﬁt very few datapoints from S that happened to have fallen in the small neigbhorhood of x (cid:48) .
On the other hand, when the neighborhoods are large, many datapoints from
S are likely to fall in each neighborhood, thus allowing each g x (cid:48) to be trained on an effectively larger dataset.
However, with large neighborhoods, it may also be hard to ﬁnd functions in G local that ﬁt so many points in S .
Overall, one practical takeaway from this bound is that it is important to not excessively shrink the neighborhood widths if one wants explanations that generalize well for predicting how f behaves at unseen points (see Section 6).
Caveats.
We remark that this particular bound applies only when the dataset S is solely used to learn g ; i.e., both f and the neighborhoods must be learned beforehand with separate data 2 .
This sort of a framework is typical when deriving theoretical results for models like random forests, where it greatly aids analysis to assume that the trees’ splits and their decisions are learned from independent datasets (e.g. two halves of an original dataset) (Arlot & Genuer, 2014).
Now, if one is interested in a bound where S is also used to simultaneously learn f , one would have to introduce a term corresponding to the complexity of F .
Another caveat is that our bound only tells us how well the explanations g x (cid:48) generalize on average over different values of x (cid:48) .
This does not tell us anything about the generalization of the quality of g x (cid:48) for an arbitrary value of x (cid:48) .
That being said, just as average accuracy remains a central metric for performance (despite its lack of sensitivity to discrepancies across inputs), average MNF can still be a useful quantity for evaluating an explainer’s overall performance.
E MPIRICAL R ESULTS
We present two sets of empirical results to illustrate the the usefulness of our bounds.
First, we demonstrate that ρ S grows much slower than O ( √ m ) which, as stated before, establishes that our bounds yield meaningful convergence rates.
Second, we show that Theorem 2 accurately reﬂects the relationship between explanation generalization (Theorem 2) and the width of N mir x used to both generate and evaluate explanations.
Setup.
For both experiments, we use several regression datasets from the UCI collection (Dua &
Graff, 2017) and standardize each feature to have mean 0 and variance 1.
We train neural networks as our “black-box” models with the same setup as in (Plumb et al., 2020), using both their nonregularized and ExpO training procedures.
The latter explicitly regularizes for NF during training, which we ﬁnd also decreases MNF on all datasets.
For generating explanations, we deﬁne G local to be linear models and optimize each g x using the empirical MNF minimizer (see Appendix A).
Finally, we approximate ρ S using a provably accurate sampling-based approach (see Appendix E).
Growth-rate of ρ S .
In Figure 1 (top), we track the sample dependence of ρ S for various neighborhoods of width σ , setting N mir x = N ( x, σI ) .
We speciﬁcally approximate the growth rate as polynomial, estimating the exponent by taking the overall slope of a log-log plot of ρ S over m .
To cover a natural range for each dataset, σ is varied to be between the smallest and half the largest inter-example l 2 distances.
In these plots, while small σ result in a large exponent for ρ S and large σ cause g to intuitively saturate towards a global linear model, we observe that there do exist values of σ , where both these terms are in control i.e., we observe that we can achieve a growth rate of approximately O ( m 0 . 2 ) without causing g to saturate and MNF metrics to rise sharply.
Generalization and neighborhood size.
As per the setting of Theorem 2, we generate all explanations using data not used to learn the black-box model.
Speciﬁcally, we split the original test data into two halves, using only the ﬁrst half for explanation training and the second for explanation testing .
We plot MNF as measured over these two subsets of examples in Figure 1 (bottom).
From the results, it is evident that a generalization gap between train and test MNF exists.
Further, recall that Theorem 2 predicts that this gap decreases as wider neighborhoods are used, a phenomena reﬂected in most of these plots.
As a result, while training MNF monotonically increases with larger neighborhoods, test MNF always decreases at certain ranges of σ .
C ONCLUSION AND F UTURE W ORK
In this work, we have studied two novel connections between learning theory and local explanations.
We believe these results may be of use in guiding the following directions of future work: (1) developing new local explanation algorithms inspired by our theory and the metric of MNF ; (2) resolving caveats or otherwise strengthening the theory presented in this paper; and (3) exploring applications of our techniques beyond interpretability, such as the general problem of deep learning generalization or others that require reasoning about the complexity of randomized functions.
A CKNOWLEDGMENTS
This work was supported in part by DARPA FA875017C0141, the National Science Foundation grants IIS1705121 and IIS1838017, an Okawa Grant, a Google Faculty Award, an Amazon Web
Services Award, a JP Morgan A.I. Research Faculty Award, and a Carnegie Bosch Institute Research
Award.
Vaishnavh Nagarajan was supported by a grant from the Bosch Center for AI.
Any opinions, ﬁndings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reﬂect the views of DARPA, the National Science Foundation, or any other funding agency.
A M ORE ON M IRRORED NEIGHBORHOOD FIDELITY A. A N ALTERNATIVE INTERPRETATION
Here we elaborate on how the expression for MNF can be parsed in the same way as NF after juggling some terms around.
Recall that MNF is deﬁned as:
[Equation]
and with an abuse of notation, we let MNF ( f, g, x ) =
E x (cid:48) ∼ N mir x (cid:2) ( f ( x ) − g x (cid:48) ( x )) 2 (cid:3) .
Here the outer expectation is over the target points x that the explanations try to ﬁt, and the inner expectation is over the source points x (cid:48) which give rise to the explanations g x (cid:48) .
If we can swap these expectations around, we can afford a similar parsing as NF .
To get there, ﬁrst consider the joint distribution over x and x (cid:48) that is induced by generating x ∼ D and then picking x (cid:48) ∼ N mir x .
Under this joint distribution, we need an expression for the marginal distribution of x (cid:48) .
This distribution, which we denote by D † , is given by:
[Equation]
To get a sense of what D † looks like, imagine that N mir x is a Gaussian centered at x .
Then D † corresponds to convolving D with a Gaussian i.e., a smoother version of D .
Next, under the same joint distribution, we also need an expression for the distribution of x conditioned on x (cid:48) .
This distribution, denoted as N † x (cid:48) , is given by:
[Equation]
Intuitively, N † x (cid:48) is distribution that is centered around x (cid:48) and is also weighted by the distribution D i.e., points that are both close to x (cid:48) and realistic under D have greater weight under N † x (cid:48) .
This is because the term p N mir x ( x (cid:48) ) in the numerator prioritizes points that are near x (cid:48)
(imagine N mir x being a
Gaussian centered at x ), and the term p D ( x ) prioritizes realistic points.
With these deﬁnitions in hand, we can swap the expectations around and get:
[Equation]
This then has the same structure as NF in that the outer expectation is over the source points and the inner distribution over target points, and hence can be interpreted similarly.
The key insight here is that from the perspective of each individual explanation g x (cid:48) , the relevant distribution it needs to ﬁt (to achieve low MNF ) is N † x (cid:48) , which takes into account both the local neighborhoods and the real data distribution D .
In comparison, only the former matters for NF .
A. A LGORITHM FOR MINIMIZING EMPIRICAL MIRRORED NEIGHBORHOOD FIDELITY
We now consider how one might actually ﬁt explanations to minimize MNF .
Recall from the above discussion that from the point of view of each source point x (cid:48) , MNF measures how well g x (cid:48) ﬁts f on the distribution with density p N † x (cid:48)
( x ) = p D
[Equation]
x
[Equation]
X
[Equation]
) dx .
Note that one generally does not have access to samples from this distribution due to the dependence on D .
However as we argue, one can minimize the empirical version of MNF given access to a ﬁnite sample S drawn i.i.d.
from D by solving the following weighted regression problem:
[Equation]
To see what the empirical version of MNF is, we can replace the outer expectation (over x ∼ D ) in the original deﬁnition of MNF with an empirical expectation over S
= { x i } | S | i
=1 , giving us
Empirical MNF
[Equation] [Equation]
S |
[Equation]
[Table]
[Equation]
[Table]
[Equation]
[Table]
[Equation] [Equation]
[Table]
To minimize the overall empirical MNF , one can thus choose a g x (cid:48) for each x (cid:48) such that it minimizes the above integrand, which is akin to performing one weighted least squares regression.
Thus, one notable difference between minimizing empirical MNF and NF is that we need to use real examples to ﬁt g x (cid:48) for MNF but not for NF since the target distribution of interest there can be user-deﬁned (i.e. it may be chosen such that it can be easily sampled from).
A. T RADE - OFFS BETWEEN MNF AND NF
We now discuss in further detail the comparison between MNF and NF , listing both the relative advantages and disadvantages of each.
It should be noted that this discussion is of a somewhat more exploratory nature; we do not aim to make deﬁnitive value judgments (i.e. one metric is always more useful than the other), but rather to provide a better qualitative understanding of how these two metrics might be expected to behave.
We hope that this discussion prompts a more careful consideration of ﬁdelity metrics in future works involving local explanations.
A.3. A DVANTAGES OF MNF
In many practical situations (esp.
for i.i.d. cases), it is reasonable to assume that practitioners will care signiﬁcantly about generating explanations for predictions at realistic on-distribution points and hoping that those (local) models correctly approximate what the model will do at nearby points which are also realistic.
Our core argument for the usefulness of MNF compared to NF is that it can be used to come closer to characterizing performance relative to the second part of this goal (i.e. predicting what the model will do at realistic points).
To reiterate Section 3, this is an especially important concern for modern ML settings, which often involve signiﬁcant feature dependencies (i.e. lower dimensional data manifolds) and models that behave unstably when extrapolating beyond the given task and training data.
As we illustrate below in a toy example, when one uses NF with standard neighborhood choices that don’t respect data dependencies (e.g. N x = N (0 , σI ) ), one may overemphasize the ability of explanations to ﬁt this noisy behavior on regions that are off-manifold.
Toy example.
We compare the abilities of MNF and NF to serve as the basis for generating local explanations.
In what follows, we refer to g NF and g MNF as the linear local explainers that minimize
NF and MNF respectively.
We speciﬁcally consider a simple setup where the full input space has dimension d = 2 but the data exists on a manifold of dimension k = 1 .
Under task-distribution D , let x 1 ∼ N (0 , 1) while x 2 = 0 .
Further consider the learned model f ( x ) = x 1 − βx 1 x 22 , where one may assume β (cid:29) 0 .
As an important note, on the task distribution D , f is functionally equivalent to a fairly simple function, i.e. f ( x ) ≡ x 1 .
Minimizing NF : To learn g NF x , we may simply sample many x (cid:48) ∼ N x and ﬁnd a linear g NF x ( · ) that ﬁts these points well.
Now, we can expect this process to generalize in a way that E x (cid:48) ∼ N x
[( f ( x i ) − g x ( x (cid:48) )) 2 ] is minimized.
In fact, one could consider the ideal scenario where we sample inﬁnitely many unlabeled examples, and thus ﬁnd the best possible linear approximation given this neighborhood distribution.
However, observe that minimizing the above quantity provides absolutely no guarantee whatsoever regarding the error committed on D i.e., E x (cid:48) ∼ D [( f ( x i ) − g x ( x (cid:48) )) 2 ] .
This is because D has zero measure when considering X
= R 2 .
This means that if f is arbitrarily volatile along the irrelevant x 2 direction, g x can also be arbitrarily incorrect on D .
Indeed, this is the case in the setting above.
Letting g x ( x (cid:48) )
=
w 1 x (cid:48) 1 + w 2 x (cid:48) 2 and N x = N (0 , I ) , it can be shown that
NF ( f, g, x ) is minimized by w 1 = 1 − β .
Since β can be arbitrarily large, this explanation can be unboundedly poor at recovering a function equivalent to f ( x ) ≡ x 1 on D .
Indeed, it can even get the sign of the coefﬁcient for the active feature x 1 wrong when β > 1 .
Minimizing MNF : Note that none of the above is a problem when we learn g MNF , because we ﬁt each g MNF x (cid:48) only on target points x that are from the real data manifold.
3 This will ensure that g MNF is in line with a potentially important desiderata for local explanations i.e., that they can faithfully capture a function that is accurate along the task-relevant data directions (of course, only up to a linear approximation).
To illustrate more completely, recall that g MNF is learned as follows: assuming access to S
= { x 1 , . . .
, x m } ∼ D m , we have
[Equation]
Now since S lies on the manifold of D , we have that x 2 = 0 on all those points.
Therefore, for each x , we ﬁnd the solution which minimizes
[Equation]
It is easy to see that with just two distinct datapoints from S , we would recover w 1 = 1 , which leads to perfect predictions for how the function behaves on D .
As a remark, an even more natural version of the above setting would be one where f is non-linear even on the data manifold.
But even here we can still argue that g MNF would be close to the best possible linear function within the manifold up to a 1 / √ m error (e.g., a generlization bound like our
Theorem 2 guarantees this on average over x ).
On the other hand, regardless of how many unlabeled datapoints we ﬁt g NF with, we would learn g NF that can behave arbitrarily poorly on the manifold.
A.3. L IMITATIONS OF MNF
Below, we discuss some limitations of MNF as well as potential future directions for possibly addressing them.
At a high-level, we believe while each represents a legitimate concern, they may arguably be (depending on context) “reasonable prices to pay” for the advantages of MNF compared to NF described previously.
MNF explanations may lose local meaning: Using MNF to evaluate/generate explanations at lowprobability source points x (cid:48) may have little to do with how f actually behaves around x (cid:48) .
Because the target point distribution is x | x (cid:48)
∝ p D ( x ) p N x ( x (cid:48) ) , very little probability mass might be placed in the vicinity around x (cid:48) when p D ( x ) is small.
This would be the case when x (cid:48) is off-manifold or in low-density regions on the support of the real data distribution.
The former might be dismissable if one cares about i.i.d.
settings (which is not always the case) but the latter could be very important in applications where rare cases correspond to high-stakes decisions (e.g. disease diagnostics).
In these scenarios, the explanation might still be too biased towards how the model is behaving at higher density regions.
However, some potential future directions to remedy this are:
• It might help to allow N mir x to have smaller width around lower probability points from D (allowing one to concentrate N mir x around x despite the form of D ).
It remains a challenge to see how one would actually set these widths but it could be of help if a limit can be assumed on how quickly the value p D ( x ) are allowed to change around a given x . • There also could be some utility in considering a more general deﬁnition of MNF that lets one choose an arbitrary outer distribution x ∼ Q other than simply the task distribution D .
That is, if one really cares about mutually consistent explanations in some arbitrary region (which could be on or off-manifold), then this would potentially allow one to able to customize a metric for that purpose.
Less intuitive target point neighborhoods:
Very closely related to the previous limitation, in interpreting MNF -based explanations, an end-user would have to understand that g x (cid:48) are not exactly approximations for the locality around x (cid:48) but rather for the true target distribution that captures in some sense “on-manifold points near x (cid:48) (modulated by the concentration of N mir x ).”
This makes it harder for a user to know the exact window in which their explanation is directly valid for (compared to a user-speciﬁed target neighborhood for NF ).
In practice, this shortcoming could be at least partially mitigated as long is it is carefully communicated to users that this limitation exists, i.e. they should focus on using MNF explanations only at and for predicting what happens at realistic points.
Unnaturalness of source points : While MNF does emphasize realistic target points, it also focuses on explanations generated at potentially off-manifold source points.
Further, one could argue that the advantages of MNF are partly because N x is chosen naively for NF .
For instance if one deﬁned
N x = N † x in the deﬁnition for MNF , then g NF and g MNF would produce the same explanations because the relevant (inner) expectations would be the same (comparing NF and the reversed expectation form of MNF from Appendix A.1).
However, in this proposal, now the average metric for NF seems more natural in an additional sense since it also only reﬂects caring about ensuring realistic source points (as the outer expectation is over x ∼ D ).
[Equation]
Given these two points, might MNF be less interesting on its own?
For the ﬁrst point, we argue that using standard “naive” settings of N x with NF can also be considered “unnatural” in that it takes into account how explanations at on-manifold source points perform at off-manifold target points.
Second, though the speciﬁcation NF proposed above may be more ideal as a metric, it also becomes less clear how to evaluate it computationally, as the inner distribution cannot be sampled from easily.
On the other hand, we can use the original form of writing out MNF (without the expecations ﬂipped) to directly approximate MNF with relevant samples from D .
Inability to reﬂect what the model “causally” depends on: In the second toy-example, it was shown that if f ( x ) = x 1 − βx 1 x 22 but the data manifold is ( x 1 , x 2 ) =
( x 1 , 0) , one could get arbitrarily poor ﬁdelity and feature relevancy (for x 1 ) on this manifold using standard neighborhoods.
But MNF runs into a new problem when the feature set actually includes a highly correlated third feature: for example, consider ( x 1 , x 2 , x 3 ) where the manifold is deﬁned by points ( x 1 , x 2 , x 3 )
=
( x 1 , 0 , x 1 ) .
Thus according to MNF , g ( x ) = x 1 , g ( x ) = x 3 , and indeed g ( x ) = − x 1 + 2 x 3 are all equally good explanations (because MNF only cares about whether g ( x ) =
f ( x ) on manifold).
However, the output of f clearly only “depends” on the input value of x 1 for its decisions (in a causal sense).
On the other hand, because NF samples target points both on and off manifold, it would correctly see that x 3 has no effect.
More broadly, the argument here is that in any conversation involving manifolds, one inherently is speaking about some sort of feature dependencies.
Thus, one may similarly suffer from the issues of explanations not being causal w.r.t.
the output of f and also not being fully identiﬁable.
On the other hand, we note that in the new toy-example, NF is not an ideal ﬁx either because the cost is potentially an arbitrary coefﬁcient for x 1 and extremely poor ﬁdelity on D .
Overall, the intended usage of an explanation plays a large role in whether MNF or NF is indeed appropriate.
An argument could be made that ﬁnding “what the model uses for its decision”, (while perhaps an important goal) is simply not what MNF explanations are trying capture.
What one could describe MNF as actually looking at is “can I build a simpler local model relevant to the actual task at hand?”
B M ORE ON THE DISJOINTEDNESS FACTOR B. B OUNDS
Recall that the disjointedness factor is deﬁned as ρ S :
= (cid:82) x (cid:48) ∈X (cid:114) (cid:80) mj
=1 ( p N mir xi
( x (cid:48) ))m dx (cid:48) .
Here, we show that it is bounded between 1 and √ m .
Fact B.1.
The disjointedness factor ρ S satisﬁes 1 ≤ ρ S ≤ m .
Proof.
For the lower bound
[Equation]
we note that since the arithmetic mean lower bounds the quadratic mean, we have:
[Equation]
For the upper bound
[Equation]
we make use of the fact that the (cid:96) 2 norm of a vector is smaller than its (cid:96) 1 norm to get:
[Equation]
[Table]
[Equation]
B. V ALUES OF ρ S IN - BETWEEN 1 AND √ m
We know that the disjointedness factor ρ S takes the values 1 and √ m in the two extreme cases where the neighborhoods are completely overlapping or disjoint respectively.
We also know from Fact B. that the only other values it takes lie in between 1 and √ m .
But when does it take these values?
To get a sense of how these in-between values can be realized, we present a toy example.
Specifically, we can show that under some simplistic assumptions, ρ S = √ m 1 − k (where 0 ≤ k ≤ 1 ) if every neighborhood is just large enough to encompass a
[Equation]
fraction of mass of the distribution D .
Our main assumption is that N mir x i is a uniform distribution over whatever support it covers.
Further, to simplify the discussion, we assume that X is a discrete set containing M datapoints in total (think of M as very, very large).
Our analysis should extend easily to settings where these assumptions are violated, but we choose to keep these assumptions for readability.
Then, if every neighborhood contains 1 m 1 − k fraction of mass of the distribution D , this implies it contains Mm 1 − k points.
Therefore, since N mir x i is a uniform distribution, we have that the probability mass of N mir x i on any point x (cid:48) in its support is 1 Mm k − 1 .
Plugging this in the deﬁnition of ρ S , we get:
[Equation]
To further simplify this, we need to compute the innermost summation, which essentially corresponds to the number of mirrored neighborhoods that each point belongs to.
For simplicity, let’s assume that every point belongs to n neighborhoods.
To estimate n , observe that for each of the m neighborhoods to contain Mm 1 − k points, and for each of the M points to be in n neighborhoods, we must have:
[Equation]
Thus, n = m k .
Plugging this back in, we get ρ S
= m 1 − k 2 .
C P IECE - WISE G LOBAL A PPROXIMATION C.0. G ENERALIZATION BOUND ASSUMING PIECEWISENESS
We now discuss the Rademacher complexity of a simpler class of local-approximation functions: a class of piecewise-simple functions g ∈ G with K pieces.
In particular, one can show that the complexity of these functions grows with K as √ K .
To see why, ﬁrst let us call the K disjoint regions that g is deﬁned over as R 1 , . .
, R K .
Correspondingly, the original training set S
= { x i } mi can be divided into the subsets S 1 = { x 1 ,i } m 1 i =1 , . . .
, S k
=
{ x K,i } m K i
=1 that happen to fall into the respective regions and the pieces of g are g 1 , . .
. , g K ∈ G local are simple functions.
Then, one can split the Rademacher complexity over the whole dataset in terms of these subsets, to get:
[Equation]
Now, assuming each ˆ R S k
( G local ) is O (cid:16) 1 √ m k (cid:17) , in the worst-case, each subset has the same number of points m k
= m/K , the sum in the last expression can be bounded as O (cid:16)(cid:113) Km (cid:17) .
D P ROOFS
Below, we present the full statement and proof of Lemma 4.1 which bounds the Rademacher complexity of G .
The main difference between this statement and the version in the main paper is that we replace the Rademacher complexity of G local with a slightly more carefully deﬁned version of it deﬁned below:
[Equation]
This quantity is essentially a bound on the empirical Rademacher complexity of G local on all possible subsets of S , with an appropriate scaling factor.
We note that although this quantity is technically larger than the original quantity namely ˆ R S ( G local ) , for all practical purposes, it is reasonable to think of ˆ R ∗ S ( G local ) as being identical to ˆ R S ( G local ) modulo some constant factor.
For example, if we have that for all h ∈ G local , h ( x ) = w · x where (cid:107) w (cid:107) 2 ≤ α , then one would typically bound ˆ R S ( G local ) by O
[Equation]
The bound on ˆ R ∗ S ( G local ) however would resolve to O
[Equation]
Now, as long as we assume that (cid:107) x i (cid:107) are all bounded by some constant, both these bounds are asymptotically the same, and have the same 1 / √ m dependence on m .
Additionally, we also remark that that it is possible to write our results in terms of tighter deﬁnitions of ˆ R ∗ S ( G local ) , however our statements read much cleaner with the above deﬁnition.
Lemma D.1.
(full, precise statement of Lemma 4.1) Let L ( · , y (cid:48) ) be a c -Lipschitz function w.r.t.
y (cid:48) in that for all y 1 , y 2 ∈ [ − B, B ] , | L ( y 1 , y (cid:48) )
− L ( y 2 , y (cid:48) )
| ≤ c | y 1 − y 2 | .
Let S = { ( x 1 , y 1 ) , . . .
, ( x m , y m ) } ∈ ( X × Y ) m .
Then, the empirical Rademacher complexity of G under the loss function L is deﬁned and bounded as:
[Equation]
where recall that ρ S :
= (cid:82) x (cid:48) ∈X
[Equation]
m dx
[Equation]
is the disjointedness factor.
Our high level proof idea is to ﬁrst construct a distribution ˜ D over X such that each of the inner expectations over N mir x i (for each i ) can be rewritten as an expectation over x (cid:48) ∼ ˜ D .
This removes the dependence on i from this expectation, which then allows us to pull this expectation all the way out.
This further allows us to take each x (cid:48) and compute a Rademacher complexity corresponding to the loss of a single local explanation function g x (cid:48) , and then ﬁnally average that complexity over x (cid:48) ∼ ˜ D .
Proof.
We begin by noting that the inner expectations in the Rademacher complexity are over m unique distributions
N mir x i .
our ﬁrst step is to rewrite these expectations in a way that they all apply on the same distribution.
Let us call this distribution ˜ D and deﬁne what it is later.
As long as ˜ D has a support that contains the supports of the above m distributions
[Equation]
we can write:
[Equation]
this allows us to pull the inner expectation out in front of the summation and then the supremum (which now results in an inequality):
[Equation]
which further allows us rewrite the supremum to be over G local instead of G :
[Equation]
and ﬁnally
[Equation]
let us simply interchange the two outer expectations to rewrite the expression as:
[Equation]
What we now have is an inner expectation which boils down to an empirical Rademacher complexity for a ﬁxed x (cid:48)
[Equation]
and an outer expectation that averages this over x (cid:48) ∼ ˜ D .
For the rest of the discussion, we will ﬁx x (cid:48) and focus on bounding the inner term.
For convenience, let us deﬁne w i :=
p N
[Equation] [Equation]
Without loss of generality, assume that w 1 ≤ w 2 ≤ . . .
≤ w m .
Also deﬁne w 0 := 0 .
We then begin by expanding w i into a telescopic summation:
[Equation]
then, we interchange the two summations while adjusting their limits appropriately:
[Equation]
and we pull the outer summation out in front of the supremum and expectation, making it an upper bound:
[Equation]
Intuitively, the above steps have executed the following idea.
The Rademacher complexity on the LHS can be thought of as involving a dataset with weights w 1 , w 2 , . . .
, w m given to the losses on each of the m datapoints.
We then imagine decomposing this “weighted” dataset into multiple weighted datasets while ensuring that the weights summed across these datasets equal w 1 , w 2 , . . .
, w m on the respective datapoints.
Then, we could compute the Rademacher complexity for each of these datasets, and then sum them up to get an upper bound on the complexity corresponding to the original dataset.
The way we decomposed the datasets is as follows:
ﬁrst we extract a w 1 weight out of all the m data points (which is possible since it’s the smallest weight), giving rise to a dataset of m points all with equal weights w 1 .
What remains is a dataset with weights 0 , w 2 − w 1 , w 3 − w 1 , . . .
, w m − w 1 .
From this, we’ll extract a w 2 − w 1 weight out of all but the ﬁrst data point to create a dataset of m − 1 datapoints all equally weighted as w 2 − w 1 .
By proceeding similarly, we can generate m such datasets of cardinality m , m − 1 , . .
, 1 respectively, such that all datasets have equally weighted points, and weights that follow the sequence w 1 − w 0 , w 2 − w 1 , . . .
and so on.
As stated before, we will eventually sum up Rademacher complexity terms computed with respect to each of these datasets.
Now, we continue simplifying the above term by pulling out ( w j − w j − 1 ) since it is only a constant:
[Equation]
next, we apply the standard contraction lemma (Lemma D.2) to make use of the fact h ( x i ) is composed with a c -Lipschitz function to get:
[Equation]
using S j : m to denote the datapoints indexed from j to m , we can rewrite this in short as:
[Equation]
and ﬁnally, we make use of the deﬁnition of R ∗ S ( G local ) in Equation 1 to get:
[Equation]
What remains now is to simplify the summation over w ’s. To do this, we rearrange the telescopic summation as follows:
[Equation]
Note that in the penultimate step we’ve used the Cauchy-Schwartz inequality and in the last step, we have made use of the standard logarithmic upper bound on the m -th harmonic number.
Plugging this back on the Rademacher complexity bound, we get:
[Equation]
plugging in the values of w j , we get:
[Equation]
Now we ﬁnally set ˜ D
such that p ˜ D
( x
[Equation]
) =
[Equation]
where ρ S is a normalization constant such that ρ S =
[Equation]
dx (cid:48) .
Then, the above term would simplify as:
[Equation]
Next, we state and prove the full version of Theorem 1 which provided a generalization guarantee for the test error of f in terms of its local interpretability.
This result follows by applying the previous lemma for the squared error loss.
For this we need to show that the squared error loss is Lipschitz, which follows from our assumption that the range of the functions in F and G local and also the labels y are bounded in [ − B, B ] .
Theorem 3.
(full, precise version of Theorem 1) With probability over 1 − δ over the draws of
S = { ( x 1 , y 1 ) , . . .
, ( x m , y m ) } ∼ D m , for all f ∈ F and for all g ∈ G , we have (ignoring ln 1 /δ factors):
[Equation]
where ρ S denotes the disjointedness factor deﬁned as ρ S :
= (cid:82) x (cid:48) ∈X (cid:113) 1 m (cid:80) mi =1 ( p N mir xi
( x (cid:48) ))
2 dx (cid:48) and ˆ R ∗ S ( G local ) is deﬁned in Equation 1.
Proof.
First, we split the test error into two terms by introducing the g function as follows:
[Equation]
In the ﬁrst step, we have introduced a dummy expectation over x (cid:48) , and in the next step, we have used the following inequality: for any a, b, c ∈ R , ( a − b ) 2 ≤ ( | a − c | + | c − b | ) 2 ≤ 2( | a − c | 2 + | c − b | 2 ) (the ﬁrst inequality in this line is the triangle inequality and the second inequality is the root mean square inequality).
The ﬁrst term on the RHS above is MNF ( f, g ) .
To simplify the second term, we ﬁrst apply a generalization bound based on Rademacher complexity.
Speciﬁcally, we have that w.h.p 1 − δ over the draws of S , for all g ∈ G ,
[Equation]
Now, ˆ R S ( G ) can be bounded using Lemma 4.1 under Lipschitzness of the squared error loss.
Specifically, we have that for h, h (cid:48) ∈ G local , and for all y ∈ [ − B, B ] , | ( h ( x ) − y ) 2 − ( h
[Equation]
( x ) − y ) 2 | ≤
4 B | h ( x ) − h (cid:48)
( x ) | , since all of h ( x ) , h (cid:48) ( x ) and y lie in [ − B, B ] .
Therefore, from Lemma 4.1 we have that:
[Equation]
The only term that remains to be bounded is the ﬁrst term on the RHS.
This can bounded again using the inequality that for any a, b, c ∈ R , ( a − b ) 2 ≤ ( | a − c | + | c − b | ) 2 ≤ 2( | a − c | 2 + | c − b | 2 ) :
[Equation]
By combining the above three chains of inequalities, we get the ﬁnal bound.
Below, we present an alternative version of Theorem 1 where the generalization bound does not involve the test MNF and hence does not require any unlabeled data from D ; however the bound is not on the test error of f but the test error of g .
Theorem 4.
(an alternative version of Theorem 1) With probability over 1 − δ over the draws of
S = { ( x 1 , y 1 ) , . . .
, ( x m , y m ) } ∼ D m , for all f ∈ F and for all g ∈ G , we have:
[Equation]
Proof.
The proof follows directly from the proof of Theorem 3 starting from Equation
[Equation]
We now state and prove the full version of Theorem 2 which provided a generalization guarantee for the quality of explanations.
Theorem 5.
(full, precise statement of Theorem 2) For a ﬁxed function f , with high probability 1 − δ over the draws of S ∼ D m , for all g ∈ G , we have:
[Equation]
where ˆ R ∗ S ( G local ) is deﬁned in Equation 1.
Proof.
For this result
[Equation]
we need to think of f as a ﬁxed labeling function since it is independent of the dataset S that is used to train g .
Then, one can apply a standard Rademacher complexity bound and invoke Lemma 4.1 to get the ﬁnal result (as invoked in Equation 4).
Below, we state the standard contraction lemma for Rademacher complexity (e.g., Shalev-Shwartz & Ben-David (2014) Lemma 26.9).
The lemma states that composing a function class with a cLipschitz function can scale up its Rademacher complexity by a multiplicative factor of at most c .
Lemma D.2.
(Contraction lemma)
For each i = 1 , 2 , . . .
, m , let φ
i : R → R be a c -Lipschitz function in that for all t, t (cid:48) ∈ B ⊆ R , | φ i ( t ) − φ i ( t (cid:48) )
| ≤ | t − t (cid:48) | .
Then, for any class H of functions h : R → B , we have:
[Equation]
E E XPERIMENT D ETAILS E. P ROCEDURE FOR CALCULATING ρ S
As a reminder, we deﬁne ρ S to be an integral over X , which is not trivial to evaluate in practice, especially when dealing with higher dimensional variables.
[Equation]
Common numerical integration techniques usually incur signiﬁcant computational costs due to the dimension of x .
Though a variety of methods exist, one can intuit the inherent difﬁculty that causes this blow-up by considering the naive approach of simply constructing a Riemann sum across a rectangular meshgrid of points for a d -dimensional X .
If one wants to create a grid of c points per dimension, then c d points (and thus evaluations of the integrand) must be processed.
Instead, we can apply Monte-Carlo Integration to evaluate ρ S .
As we will see, a key feature of this approach is that error will not scale with data dimension and can be bounded probabilistically via a basic Hoeffding bound.
Currently, the integral does not take the form of an expectation so we must introduce a dummy distribution q ( x (cid:48) ) as follows
[Equation]
Now, we can estimate ρ S with n independent samples from q .
[Equation]
This is obviously an unbiased estimate of ρ S , but that in itself is not sufﬁcient.
It is only a feasible approach if we can choose q such that (1) we can actually sample from it, (2) we can calculate q ( x (cid:48) ) for arbitrary x (cid:48) and (3) we can control the variance of
[Equation]
It can be shown by choosing q to be a uniform mixture of the m training set neighborhoods (one for each x i ∈ S ), we can satisfy all 3 properties.
(1) and (2) are trivial if those same properties being satisﬁed by N mir x (which is the case for the Gaussian neighborhoods we consider).
If N mir x can be sampled from, the mixture over m such distributions can obviously be sampled from.
The same goes for calculating the density, which in this case is:
[Equation]
We observe that (3) can also be shown because we can upper and lower bound the quantity in question.
To show this, we ﬁrst re-write it as
[Equation]
where p S ( x (cid:48) ) is a m -dimensional vector of densities each evaluated at x (cid:48)
(i.e. one for each of the m training points).
Since for any vector v , || v || 2 ≤
||
v
|| 1 ≤ √ m ||
v || 2 , the upper and lower bounds for this quantity in question are √ m and 1 respectively.
Thus we can bound the variance of this quantity by 14 ( √ m − 1) 2 ≤ m 4 and Var (ˆ ρ S,n ) ≤ m 4 n .
This does not scale with dimension but only the number of training points!
To be even more concrete, for a given m and n , we can now apply a Hoeffding bound to control the error.
[Equation]
In our experiments we choose n to be 10 m , meaning that the probability that ρ S is off by more than 0.5 is capped at about 1% (recall that ρ S scales from [1 , √ m ] , which means this is a fairly reasonable degree of accuracy).
E. F ULL SET OF RESULTS
Note that for the Day and Music datasets, the test MNF curves behave differently than for the other three datasets.
Here, test MNF rises initially for small neighborhood widths but then drops and plateaus as neighborhoods get larger.
As noted by Plumb et al.
(2020), Day exhibits (globally) a fairly linear relationship between inputs and outputs.
Thus, we hypothesize our results here can be explained on the basis that a global linear model (which is what the explanations saturate towards as σ increases) actually can do quite well at approximating f .
That is, in the terms of the bound, the train MNF does not rise too sharply when saturation occurs but the complexity terms become smaller as neighborhoods get wider.
We hypothesize a similar effect is at play for Music .