Year: 2021

Abstract:
This paper studies few-shot learning via representation learning, where one uses T source tasks with n1 data per task to learn a representation in order to reduce the sample complexity of a target task for which there is only n2( n1) data. Specifically, we focus on the setting where there exists a good common representation between source and target, and our goal is to understand how much a sample size reduction is possible. First, we study the setting where this common representation is low-dimensional and provide a risk bound of Õ( dk n1T + k n2 ) on the target task for the linear representation class; here d is the ambient input dimension and k( d) is the dimension of the representation. This result bypasses the Ω( 1 T ) barrier under the i.i.d. task assumption, and can capture the desired property that all n1T samples from source tasks can be pooled together for representation learning. We further extend this result to handle a general representation function class and obtain a similar result. Next, we consider the setting where the common representation may be high-dimensional but is capacity-constrained (say in norm); here, we again demonstrate the advantage of representation learning in both high-dimensional linear regression and neural networks, and show that representation learning can fully utilize all n1T samples from source tasks.

text: k n2 ) on the target task for the linear representation class; here d is the ambient input dimension and k( d) is the dimension of the representation. This result bypasses the Ω( 1T ) barrier under the i.i.d. task assumption, and can capture the desired property that all n1T samples from source tasks can be pooled together for representation learning. We further extend this result to handle a general representation function class and obtain a similar result. Next, we consider the setting where the common representation may be high-dimensional but is capacity-constrained (say in norm); here, we again demonstrate the advantage of representation learning in both high-dimensional linear regression and neural networks, and show that representation learning can fully utilize all n1T samples from source tasks.

heading: 1 INTRODUCTION
text: A popular scheme for few-shot learning, i.e., learning in a data-scarce environment, is representation learning, where one first learns a feature extractor, or representation, e.g., the last layer of a convolutional neural network, from different but related source tasks, and then uses a simple predictor (usually a linear function) on top of this representation in the target task. The hope is that the learned representation captures the common structure across tasks, which makes a linear predictor sufficient for the target task. If the learned representation is good enough, it is possible that a few samples are sufficient for learning the target task, which can be much smaller than the number of samples required to learn the target task from scratch.
While representation learning has achieved tremendous success in a variety of applications (Bengio et al., 2013), its theoretical studies are limited. In existing theoretical work, the most natural algorithm is to explicitly look for the optimal representation given source data, which when combined with a (different) linear predictor on top for each task can achieve the smallest cumulative training error on the source tasks. Of course, it is not guaranteed that the representation found will be useful for the target task unless one makes some assumptions to characterize the connections between different tasks. Existing work often imposes a probabilistic assumption about the connection between tasks: each task is sampled i.i.d. from an underlying distribution. Under this assumption, Maurer et al. (2016) showed an Õ( 1√
T + 1√n2 ) risk bound on the target task, where T is the number of source
tasks, n1 is the number of samples per source task, and n2 is the number of samples from the target task.1 Unsatisfactorily, this bound necessarily requires the number of tasks T to be large, and it ∗Alphabetical Order. 1We only focus on the dependence on T , n1 and n2 in this paragraph. Note that Maurer et al. (2016) only considered n1 = n2, but their approach does not give a better result even if n1 > n2.
does not improve when the number of samples per source task, n1, increases. Intuitively, one should expect more data to help, and therefore an ideal bound would be 1√
n1T + 1√n2 (or 1 n1T + 1n2 in the realizable case), because n1T is the total number of training data points from source tasks, which can be potentially pooled to learn the representation.
Unfortunately, as pointed out by Maurer et al. (2016), there exists an example that satisfies the i.i.d. task assumption for which Ω( 1√
T ) is unavoidable (or Ω( 1T ) in the realizable setting). This means
that the i.i.d. assumption alone is not sufficient if we want to take advantage of a large amount of samples per task. Therefore, a natural question is:
What connections between tasks enable representation learning to utilize all source data?
In this paper, we obtain the first set of results that fully utilize the n1T data from source tasks. We replace the i.i.d. assumption over tasks with natural structural conditions on the input distributions and linear predictors. These conditions depict that the target task can be in some sense “covered” by the source tasks, which will further give rise to the desirable guarantees.
First, we study the setting where there exists a common well-specified low-dimensional representation in source and target tasks, and obtain an Õ( dkn1T + k n2
) risk bound on the target task where d is the ambient input dimension, k( d) is the dimension of the representation, and n2 is the number of data from the target task. Note that this improves the dn2 rate of just learning the target task without using representation learning. The term dkn1T indicates that we can fully exploit all n1T data in the source tasks to learn the representation. We further extend this result to handle general representation function class and obtain an Õ(C(Φ)n1T + k n2
) risk bound on the target task, where Φ is the representation function class and C (Φ) is a certain complexity measure of Φ. Second, we study the setting where there exists a common linear high-dimensional representation for
source and target tasks, and obtain an Õ ( R̄√Tr(Σ)√
n1T +
R̄ √ ‖Σ‖
2√ n2
) rate where R̄ is a normalized nuclear
norm control over linear predictors, and Σ is the covariance matrix of the raw feature. This also improves over the baseline rate for the case without using representation learning. We further extend this result to two-layer neural networks with ReLU activation. Again, our results indicate that we can fully exploit n1T source data.
A technical insight coming out of our analysis is that any capacity-controlled method that gets low test error on the source tasks must also get low test error on the target task by virtue of being forced to learn a good representation. Our result on high-dimensional representations shows that the capacity control for representation learning does not have to be through explicit low dimensionality.
Organization. The rest of the paper is organized as follows. We review related work in Section 2. In Section 3, we formally describe the setting we consider. In Section 4, we present our main result for low-dimensional linear representation learning. A generalization to nonlinear representation classes is demonstrated in Section 5. In Section 6, we present our main result for high-dimensional linear representation learning. In Section 7, we present our result for representation learning in neural networks. We conclude in Section 8 and leave most of the proofs to appendices.

heading: 2 RELATED WORK
text: The idea of multitask representation learning at least dates back to Caruana (1997); Thrun and Pratt (1998); Baxter (2000). Empirically, representation learning has shown its great power in various domains; see Bengio et al. (2013) for a survey. In particular, representation learning is widely adopted for few-shot learning tasks (Sun et al., 2017; Goyal et al., 2019). Representation learning is also closely connected to meta-learning (Schaul and Schmidhuber, 2010). Recent work Raghu et al. (2019) empirically suggested that the effectiveness of the popular meta-learning algorithm Model Agnostic Meta-Learning (MAML) is due to its ability to learn a useful representation. The scheme we analyze in this paper is closely related to Lee et al. (2019); Bertinetto et al. (2018) for meta-learning.
On the theoretical side, Baxter (2000) performed the first theoretical analysis and gave sample complexity bounds using covering numbers. Maurer et al. (2016) and follow-up work gave analyses on the benefit of representation learning for reducing the sample complexity of the target task. They assumed every task is i.i.d. drawn from an underlying distribution and can obtain an Õ( 1√
T + 1√n2 )
rate. As pointed out in Maurer et al. (2016), the 1√ T dependence is not improvable even if n1 →∞ because 1√
T is the rate of concentration for the distribution over tasks.
The concurrent work of Tripuraneni et al. (2020) studies low-dimensional linear representation learning and obtains a similar result as ours in this case, but they assume isotropic inputs for all tasks, which is a special case of our result. Furthermore, we also provide results for high-dimensional linear representations, general non-linear representations, and two-layer neural networks. Tripuraneni et al. (2020) also give a computationally efficient algorithm for standard Gaussian inputs and a lower bound for subspace recovery in the low-dimensional linear setting.
Another recent line of theoretical work analyzed gradient-based meta-learning methods (Denevi et al., 2019; Finn et al., 2019; Khodak et al., 2019) and showed guarantees for convex losses by using tools from online convex optimization. Lastly, we remark that there are analyses for other representation learning schemes (Arora et al., 2019; McNamara and Balcan, 2017; Galanti et al., 2016; Alquier et al., 2016; Denevi et al., 2018).

heading: 3 NOTATION AND SETUP
text: Notation. Let [n] = {1, 2, . . . , n}. We use ‖·‖ or ‖·‖2 to denote the `2 norm of a vector or the spectral norm of a matrix. Denote by ‖·‖F and ‖·‖∗ the Frobenius norm and the nuclear norm of a matrix, respectively. Let 〈·, ·〉 be the Euclidean inner product between vectors or matrices. Denote by I the identity matrix. Let N (µ, σ2)/N (µ,Σ) be the one-dimensional/multi-dimensional Gaussian distribution, and χ2(m) the chi-squared distribution with m degrees of freedom.
For a matrix A ∈ Rm×n, let σi(A) be its i-th largest singular value. Let span(A) be the subspace of Rm spanned by the columns of A, i.e., span(A) = {Av | v ∈ Rn}. Denote PA = A(A>A)†A> ∈ Rm×m, which is the projection matrix onto span(A). Here † stands for the Moore-Penrose pseudoinverse. Note that 0 PA I and P 2A = PA. We also define P⊥A = I − PA, which is the projection matrix onto span(A)⊥, the orthogonal complement of span(A) in Rm. For a positive semidefinite (psd) matrix B, denote by λmax(B) and λmin(B) its largest and smallest eigenvalues, respectively; let B1/2 be the psd matrix such that (B1/2)2 = B.
We use the standard O(·), Ω(·) and Θ(·) notation to hide universal constant factors. We also use a . b or b & a to indicate a = O(b), and use a b or b a to mean that a ≥ C · b for a sufficiently large universal constant C > 0.
Problem Setup. Suppose that there are T source tasks. Each task t ∈ [T ] is associated with a distribution µt over the joint data space X× Y, where X is the input space and Y is the output space. In this paper we consider X ⊆ Rd and Y ⊆ R. For each source task t ∈ [T ] we have access to n1 i.i.d. samples (xt,1, yt,1), . . . , (xt,n1 , yt,n1) from µt. For convenience, we express these n1 samples collectively as an input matrix Xt ∈ Rn1×d and an output vector yt ∈ Rn1 . Multitask learning tries to learn prediction functions for all the T source tasks simultaneously in the hope of discovering some underlying common property of these tasks. The common property we consider in this paper is a representation, which is a function φ : X→ Z that maps an input to some feature space Z ⊆ Rk. We restrict the representation function to be in some function class Φ, e.g., neural networks. We try to use different linear predictors on top of a common representation function φ to model the input-output relations in different source tasks. Namely, for each task t ∈ [T ], we set the prediction function to be x 7→ 〈wt, φ(x)〉 (wt ∈ Rk). Therefore, using the training samples from T tasks, we can solve the following optimization problem to learn the representation:2
minimize φ∈Φ,w1,...,wT∈Rk
1
2n1T T∑ t=1 n1∑ i=1 (yt,i − 〈wt, φ(xt,i)〉)2 . (1)
We overload the notation to allow φ to apply to all the samples in a data matrix simultaneously, i.e., φ(Xt) = [φ(xt,1), . . . , φ(xt,n1)] > ∈ Rn1×k. Then (1) can be rewritten as
minimize φ∈Φ,w1,...,wT∈Rk
1
2n1T T∑ t=1 ‖yt − φ(Xt)wt‖2 . (2)
2We use the `2 loss throughout this paper.
Let φ̂ ∈ Φ be the representation function obtained by solving (2). Now we retain this representation and apply it to future (target) tasks. For a target task specified by a distribution µT+1 over X× Y, suppose we receive n2 i.i.d. samples XT+1 ∈ Rn2×d,yT+1 ∈ Rn2 . We further train a linear predictor on top of φ̂ for this task:
minimize wT+1∈Rk
1
2n2 ∥∥∥yT+1 − φ̂(XT+1)wT+1∥∥∥2 . (3) Let ŵT+1 be the returned solution. We are interested in whether our learned predictor x 7→ 〈ŵT+1, φ̂(x)〉 works well on average for the target task, i.e., we want the population loss
LµT+1(φ̂, ŵT+1) = E(x,y)∼µT+1 1
2 (y − 〈ŵT+1, φ̂(x)〉)2
to be small. In particular, we are interested in the few-shot learning setting, where the number of samples n2 from the target task is small – much smaller than the number of samples required for learning the target task from scratch.
Data assumption. In order for the above learning procedure to make sense, we assume that there is a ground-truth optimal representation function φ∗ ∈ Φ and specializations w∗1 , . . . ,w∗T+1 ∈ Rk for all the tasks such that for each task t ∈ [T + 1], we have E(x,y)∼µt [y|x] = 〈w∗t , φ∗(x)〉. More specifically, we assume (x, y) ∼ µt can be generated by
y = 〈w∗t , φ∗(x)〉+ z, x ∼ pt, z ∼ N (0, σ2), (4)
where x and z are independent. Our goal is to bound the excess risk of our learned model on the target task, i.e., how much our learned model (φ̂, ŵT+1) performs worse than the optimal model (φ∗,w∗T+1) on the target task:
ER(φ̂, ŵT+1) = LµT+1(φ̂, ŵT+1)− LµT+1(φ∗,w∗T+1)
= 1
2 Ex∼pT+1 [(〈ŵT+1, φ̂(x)〉 − 〈w∗T+1, φ∗(x)〉)2].
(5)
Here we have used the relation (4). Oftentimes we are interested in the average performance on a random target task (i.e., w∗T+1 is random). In such case we look at the expected excess risk Ew∗T+1 [ER(φ̂, ŵT+1)].

heading: 4 LOW-DIMENSIONAL LINEAR REPRESENTATIONS
text: In this section, we consider the case where the representation is a linear map from the original input space Rd to a low-dimensional space Rk (k d). Namely, we let the representation function class be Φ = {x 7→ B>x | B ∈ Rd×k}. Then the optimization problem (2) for learning the representation can be written as:
(B̂, Ŵ )← arg min B∈Rd×k
W=[w1,...,wT ]∈Rk×T
1
2n1T T∑ t=1 ‖yt −XtBwt‖2 . (6)
The inputs from T source tasks, X1, . . . , XT ∈ Rn1×d, can be written in the form of a linear operator X : Rd×T → Rn1×T , where
X (Θ) = [X1θ1, . . . , XTθT ], ∀Θ = [θ1, . . . ,θT ] ∈ Rd×T .
With this notation, (6) can be rewritten as
(B̂, Ŵ )← arg min B∈Rd×k,W∈Rk×T
1
2n1T ‖Y −X (BW )‖2F , where Y = [y1, . . . ,yT ] ∈ R n1×T . (7)
With the learned representation B̂ from (7), for the target task, we further find a linear function on top of the representation:
ŵT+1 ← arg min w∈Rk
1
2n2
∥∥∥yT+1 −XT+1B̂w∥∥∥2 . (8)
As described in Section 3, we assume that all T +1 tasks share a common ground-truth representation specified by a matrix B∗ ∈ Rd×k such that a sample (x, y) ∼ µt satisfies x ∼ pt and y = (w∗t )
>(B∗)>x + z where z ∼ N (0, σ2) is independent of x. Here w∗t ∈ Rk, and we assume ‖w∗t ‖ = Θ(1) for all t ∈ [T + 1]. Denote W ∗ = [w∗1 , . . . ,w∗T ] ∈ Rk×T . Then we can write Y = X (B∗W ∗) + Z, where the noise matrix Z has i.i.d. N (0, σ2) entries. Assume Ex∼pt [x] = 0 and let Σt = Ex∼pt [xx>] for all t ∈ [T + 1]. Note that a sample x ∼ pt can be generated from x = Σ1/2t x̄ for x̄ ∼ p̄t such that Ex̄∼p̄t [x̄] = 0 and Ex̄∼p̄t [x̄x̄>] = I . (p̄t is called the whitening of pt.) In this section we make the following assumptions on the input distributions p1, . . . , pT+1. Assumption 4.1 (subgaussian input). There exists ρ > 0 such that, for all t ∈ [T + 1], the random vector x̄ ∼ p̄t is ρ2-subgaussian.3 Assumption 4.2 (covariance dominance). There exists c > 0 such that Σt c·ΣT+1 for all t ∈ [T ].4
Assumption 4.1 is a standard assumption in statistical learning to obtain probabilistic tail bounds used in our proof. It may be replaced with other moment or boundedness conditions if we adopt different tail bounds in the analysis.
Assumption 4.2 says that every direction spanned by ΣT+1 should also be spanned by Σt (t ∈ [T ]), and the parameter c quantifies how “easy” it is for Σt to cover ΣT+1. Intuitively, the larger c is, the easier it is to cover the target domain using source domains, and we will indeed see that the risk will be proportional to 1c . We remark that we do not necessarily need Σt c · ΣT+1 for all t ∈ [T ]; as long as this holds for a constant fraction of t’s, our result is valid.
We also make the following assumption that characterizes the diversity of the source tasks. Assumption 4.3 (diverse source tasks). The matrix W ∗ = [w∗1 , . . . ,w∗T ] ∈ Rk×T satisfies σ2k(W ∗) ≥ Ω(Tk ).
Recall that ‖w∗t ‖ = Θ(1), which implies ∑k j=1 σ 2 j (W
∗) = ‖W ∗‖2F = Θ(T ). Thus, Assumption 4.3 is equivalent to saying that σ1(W
∗) σk(W∗) = O(1). Roughly speaking, this means that {w∗t }t∈[T ] can cover all directions in Rk. As an example, Assumption 4.3 is satisfied with high probability when w∗t ’s are sampled i.i.d. from N (0,Σ) with λmax(Σ)λmin(Σ) = O(1).
Finally, we make the following assumption on the distribution of the target task. Assumption 4.4 (distribution of target task). Assume that w∗T+1 follows a distribution ν such that∥∥Ew∼ν [ww>]∥∥ ≤ O ( 1k) . Since we assume
∥∥w∗T+1∥∥ = Θ(1), the assumption ∥∥Ew∼ν [ww>]∥∥ ≤ O( 1k ) means that the distribution of w∗T+1 does not align with any direction significantly more than average. It is useful to think of the uniform distribution on the unit sphere as an example, though we can allow a much more general class of distributions. This is also compatible with Assumption 4.3 which says that w∗t ’s cover all the directions.
Assumption 4.4 can be removed at the cost of a slightly worse risk bound. See Remark 4.1. Our main result in this section is the following theorem. Theorem 4.1 (main theorem for linear representations). Fix a failure probability δ ∈ (0, 1). Under Assumptions 4.1, 4.2, 4.3 and 4.4, we further assume 2k ≤ min{d, T} and that the sample sizes in source and target tasks satisfy n1 ρ4(d + log Tδ ), n2 ρ
4(k + log 1δ ), and cn1 ≥ n2. Define κ = maxt∈[T ] λmax(Σt)
mint∈[T ] λmin(Σt) . Then with probability at least 1 − δ over the samples, the expected excess
risk of the learned predictor x 7→ ŵ>T+1B̂x on the target task satisfies
Ew∗T+1∼ν [ER(B̂, ŵT+1)] . σ 2
( kd log(κn1)
cn1T + k + log 1δ n2
) . (9)
3A random vector x is called ρ2-subgaussian if for any fixed unit vector v of the same dimension, the random variable v>x is ρ2-subgaussian, i.e., E[es·v >(x−E[x])] ≤ es 2ρ2/2 (∀s ∈ R).
4Note that Assumption 4.2 is a significant generalization of the identically distributed isotropic assumption used in concurrent work Tripuraneni et al. (2020): they require Σ1 = Σ2 = · · · = ΣT+1 = I .
The proof of Theorem 4.1 is in Appendix A. Theorem 4.1 shows that it is possible to learn the target task using onlyO(k) samples via learning a good representation from the source tasks, which is better than the baseline O(d) sample complexity for linear regression, thus demonstrating the benefit of representation learning. It also shows that all n1T samples from source tasks can be pooled together, bypassing the Ω( 1T ) barrier under the i.i.d. tasks assumption. Remark 4.1 (deterministic target task). We can drop Assumption 4.4 and easily obtain the following excess risk bound for any deterministic w∗T+1 by slightly modifying the proof of Theorem 4.1:
ER(B̂, ŵT+1) . σ 2
( k2d log(κn1)
cn1T +
k2 cn1 + k + log 1δ n2
) ,
which is only at most k times larger than the bound in (9).

heading: 5 GENERAL LOW-DIMENSIONAL REPRESENTATIONS
text: Now we return to the general case described in Section 3 where we allow a general representation function class Φ. We still assume that the representation is of low dimension k like in Section 4, and we assume that inputs from all the tasks follow the same distribution, i.e., p1 = · · · = pT+1 = p, but each task t still has its own specialization function w∗t (c.f. (4)). We overload the notation from Section 4 and use X to represent the collection of all the training inputs from T source tasks X1, . . . , XT ∈ Rn1×d. We can think of X as a third-order tensor of dimension n1 × d× T . To characterize the complexity of the representation function class Φ, we need the standard definition of Gaussian width. Definition 5.1 (Gaussian width). Given a set K ⊂ Rm, the Gaussian width of K is defined as: G (K) := Ez∼N (0,I) supv∈K〈v, z〉.
We will measure the complexity of Φ using the Gaussian width of the following set that depends on the input data X :
FX (Φ) = { A = [a1, . . . ,aT ] ∈ Rn1×T : ‖A‖F = 1, ∃φ, φ′ ∈ Φ s.t. at ∈ span([φ(Xt), φ′(Xt)]),∀t ∈ [T ] } .
(10)
We also need the following definition. Definition 5.2 (covariance between two representations). Given a distribution q over Rd and two representation functions φ, φ′ ∈ Φ, define the covariance between φ and φ′ with respect to q to be
Σq (φ, φ ′) = Ex∼q [ φ (x)φ′ (x) > ] ∈ Rk×k. Also define the symmetric covariance as
Λq(φ, φ ′) =
[ Σq (φ, φ) Σq (φ, φ
′) Σq (φ ′, φ) Σq (φ ′, φ′)
] ∈ R2k×2k.
It is easy to verify Λq(φ, φ′) 0 for any φ, φ′ and q, as shown in the proof of Lemma B.2. We make the following assumptions on the input distribution p, which ensure concentration properties of the representation covariances. Assumption 5.1 (point-wise concentration of covariance). For δ ∈ (0, 1), there exists a number Npoint (Φ, p, δ) such that if n ≥ Npoint (Φ, p, δ), then for any given φ, φ′ ∈ Φ, n i.i.d. samples of p will with probability at least 1− δ satisfy
0.9Λp(φ, φ ′) Λp̂(φ, φ′) 1.1Λp(φ, φ′),
where p̂ is the empirical distribution over the n samples. Assumption 5.2 (uniform concentration of covariance). For δ ∈ (0, 1), there exists a number Nunif (Φ, p, δ) such that if n ≥ Nunif (Φ, p, δ), then n i.i.d. samples of p will with probability at least 1− δ satisfy
0.9Λp(φ, φ ′) Λp̂(φ, φ′) 1.1Λp(φ, φ′), ∀φ, φ′ ∈ Φ,
where p̂ is the empirical distribution over the n samples.
Assumptions 5.1 and 5.2 are conditions on the representation function class Φ and the input distribution p that ensure concentration of empirical covariances to their population counterparts. Typically, we expect Nunif (Φ, p, δ) Npoint (Φ, p, δ) since uniform concentration is a stronger requirement. In Section 4, we have essentially shown that for linear representations and subgaussian input distributions, Nunif (Φ, p, δ) = Õ (d) and Npoint (Φ, p, δ) = Õ (k) (see Claims A.1 and A.2).
Our main theorem in this section is the following: Theorem 5.1 (main theorem for general representations). Fix a failure probability δ ∈ (0, 1). Suppose n1 ≥ Nunif ( Φ, p, δ3T ) and n2 ≥ Npoint ( Φ, p, δ3 ) . Under Assumptions 4.3 and 4.4, with probability at least 1− δ over the samples, the expected excess risk of the learned predictor x 7→ ŵ>T+1φ̂(x) on the target task satisfies
Ew∗T+1∼ν [ER(φ̂, ŵT+1)] . σ 2 (G(FX (Φ))2 + log 1δ n1T + k + log 1δ n2 ) . (11)
Theorem 5.1 is very similar to Theorem 4.1 in terms of the result and the assumptions made. In the bound (11), the complexity of Φ is captured by the Gaussian width of the data-dependent set FX (Φ) defined in (10). Data-dependent complexity measures are ubiquitous in generalization theory, one of the most notable examples being Rademacher complexity. Similar complexity measure also appeared in existing representation learning theory (Maurer et al., 2016). Usually, for specific examples, we can apply concentration bounds to get rid of the data dependency, such as our result for linear representations (Theorem 4.1).
Our assumptions on the linear specification functions w∗t ’s are the same as in Theorem 4.1. The probabilistic assumption on w∗T+1 can also be removed at the cost of an additional factor of k in the bound – see Remark 4.1. We defer the full proof of Theorem 5.1 to Appendix B.

heading: 6 HIGH-DIMENSIONAL LINEAR REPRESENTATIONS
text: In this section, we consider the case where the representation is a general linear map without an explicit dimensionality constraint, and we will prove a norm-based result by exploiting the intrinsic dimension of the representation. Such a generalization is desirable since in many applications the representation dimension is not restricted.
Without loss of generality, we let the representation function class be Φ = {x 7→ B>x | B ∈ Rd×T }. We note that a dimension-T representation is sufficient for learning T source tasks and any choice of dimension greater than T will not change our argument. We use the same notation from Section 4 unless otherwise specified.
In this section we additionally assume that all tasks have the same input covariance: Assumption 6.1. The input distributions in all tasks satisfy Σ1 = · · · = ΣT+1 = Σ.
Note that each task t still has its own specialization functionw∗t (c.f. (4)). We remark that there are many interesting and nontrivial scenarios under Assumption 6.1 – for example, consider the case where the inputs in each task are all images from ImageNet and each task asks whether the image is from a specific class.
Since we do not have a dimensionality constraint, we modify (7) by adding norm constraints:
(B̂, Ŵ )← arg min B∈Rd×T ,W∈RT×T
1
2n1 ‖Y −X (BW )‖2F +
λ 2 ‖W‖2F + λ 2 ‖B‖2. (12)
For the target task, we also modify (8) by adding a norm constraint:
ŵT+1 ← arg min ‖w‖≤r
1
2n2 ‖XT+1B̂w − yT+1‖2. (13)
We will specify the choices of regularization, i.e., λ and r in Theorem 6.1.
Similar to Section 4, the source task data relation is denoted as Y = X (Θ∗) + Z, where Θ∗ ∈ Rd×T is the ground truth and Z has i.i.d. N (0, σ2) entries. Suppose that the target task data satisfy yT+1 = XT+1θ ∗ T+1 + zT+1 ∈ Rn2 . Similar to the setting in Section 4, we assume the target task data is subgaussian as in Assumption 4.1.
Theorem 6.1 (main theorem for high-dimensional representations). Fix a failure probability δ ∈ (0, 1). Under Assumptions 4.1 and 6.1, we further assume n1 ≥ n2, R = ‖Θ‖∗. Let r = 2 √ R/T , R̄ = R/ √ T and proper λ specified in Lemma C.2. Let the target task model θ∗T+1 be coherent with the source task models Θ∗ in the sense that θ∗T+1 ∼ ν = N (0,Θ∗(Θ∗)>/T ). Then with probability at least 1− δ over the samples, the expected excess risk of the learned predictor x 7→ ŵ>T+1B̂>x on the target task satisfies:
Eθ∗T+1∼ν [ER(B̂, ŵT+1)] ≤ σR̄ · Õ (√ Tr(Σ)√ n1T + √ ‖Σ‖2√ n2 ) + ζn1,n2 , (14)
where ζn1,n2 := ρ 4R̄2Õ ( Tr(Σ) n1 + ‖Σ‖n2 ) is lower-order terms due to randomness of the input data.
Here Õ hides logarithmic factors. The proof of Theorem 6.1 is given in Appendix C. Note that ‖Θ∗‖F = √ T when each θ∗t is of unit
norm. Thus R̄ = ‖Θ∗‖∗ / √ T should generally be regarded as O(1) for a well-behaved Θ∗ that is nearly low-dimensional. In this regime, Theorem 6.1 indicates that we are able to exploit all n1T samples from the source tasks, similar to Theorem 4.1.
With a good representation, the sample complexity on the target task can also improve over learning the target task from scratch. Consider the baseline of regular ridge regression directly applied to the target task data:
θ̂ ← arg min ‖θ‖≤‖θ∗T+1‖
1
2n2 ‖XT+1θ − yT+1‖2. (15)
Its standard excess risk bound in fixed design is ER(θ̂λ) . σ √ ‖θ∗T+1‖22Tr(Σ)
n2 . (See e.g. Hsu et al.
(2012).) Taking expectation over θ∗T+1 ∼ ν = N (0,Θ∗(Θ∗)>/T ), we obtain
Eθ∗T+1∼ν [ER(θ̂ λ)] . σ ‖Θ∗‖F√ T
√ Tr(Σ)
n2 . (16)
Compared with (16), our bound (14) is an improvement as long as ‖Θ ∗‖2∗
‖Θ∗‖2F Tr(Σ)‖Σ‖ . The left hand
side ‖Θ ∗‖2∗
‖Θ∗‖2F is always no more than the rank of Θ∗, and we call it the intrinsic rank. Hence we see that
we can gain from representation learning if the source predictors are intrinsically low dimensional.
To intuitively understand how this is achieved, we note that a representation B is reweighing linear combinations of the features according to their “importance” on the T source tasks. We make an analogy with a simple case of feature selection. Suppose we have learned a representation vector b where bi scales with the importance of the i-th feature, i.e., the representation is φ(x) = x b (entry-wise product). Then ridge regression on the target task data (X,y), minimize‖w‖≤r 12n2 ‖X · diag(b)·w−y‖22, is equivalent to minimize‖diag(b)−1v‖≤r 12n2 ‖Xv−y‖ 2 2. From the above equation, we see that the features with large |bi| (those that were useful on the source tasks) will be more heavily used than the ones with small |bi| due to the reweighed `2 constraint. Thus the important features are learned from the source tasks, and the coefficients are learned from the target task. Remark 6.1 (The non-convex landscape). Although the optimization problem (12) is non-convex, its structure allows us to apply existing landscape analysis of matrix factorization problems (Haeffele et al., 2014) and to show that it has the nice properties of no strict saddles and no bad local minima. Therefore, randomly initialized gradient descent or perturbed gradient descent are guaranteed to converge to a global minimum of (12) (Ge et al., 2015; Lee et al., 2016; Jin et al., 2017). Remark 6.2 (Multi-class problems). When both source and target have multi-class labels instead of independent tasks, using quadratic loss on the one-hot labels, our results apply similarly and will
attain an excess risk of the form σR̄Õ (√
Tr(Σ) √ n1 + √ ‖Σ‖2√ n2 ) plus lower-order terms (see e.g. Lee
et al. (2020)). Notice the result is independent of the number of classes.

heading: 7 NEURAL NETWORKS
text: In this section, we show that we can provably learn good representations in a neural network.
Consider a two-layer ReLU neural network fB,w(x) = w>(B>x)+, where w ∈ Rd, B ∈ Rd0×d and x ∈ Rd0 . Here (·)+ is the ReLU activation (z)+ = max{0, z} defined element-wise. Namely, we let the representation function class be Φ = {x→ (B>x)+|B ∈ Rd0×d}. On the source tasks we use the square loss with weight decay regularizer:5
(B̂, Ŵ )← arg min B∈Rd0×d,W=[w1,···wT ]∈Rd×T
1
2n1T T∑ t=1 ‖yt − (XtB)+wt‖2 + λ 2 ‖B‖2F + λ 2 ‖W‖2F .
(17)
On the target task, we simply re-train the output layer while fixing the hidden layer weights:
ŵT+1 ← arg min ‖w‖≤r
1
2n2 ‖yT+1 − (XT+1B̂)+w‖2. (18)
Assumption 7.1. All tasks share the same input distribution: p1 = · · · = pT+1 = p. We redefine Σ to be the covariance operator of the feature induced by ReLU, i.e., it is a kernel defined by Σ(u,v) = Ex∼p[(u>x)+(v>x)+], for u,v on the unit sphere Sd0−1 ⊂ Rd0 . Assumption 7.2 (teacher network). Assume for the source tasks that yt = (XtB∗)+w∗t + zt is generated by a teacher network with parameters B∗ ∈ Rd0×d,W ∗ = [w∗1 , · · · ,w∗T ] ∈ Rd×T , and noise term zt ∼ N (0, σ2I). A standard lifting of the neural network is: fαt = 〈αt, φ(x)〉 where φ(x) : Sd0−1 → R, φ(x)b = (b>x)+ is the feature map, i.e., for each task, αt(bi/‖bi‖) = Wi,t‖bi‖ and is zero elsewhere. We assume αT+1 that describes the target function to follow a Gaussian process µ with covariance function K(b, b′) = ∑T t=1 αt(b)αt(b
′). Theorem 7.1. Fix a failure probability δ ∈ (0, 1). Under Assumptions 4.1, 7.1 and 7.2, let n1 ≥ n2, R̄ = ( 12‖B ∗‖2F + 12‖W ∗‖2F )/ √ T . Let the target task model fαT+1 = 〈αT+1, φ(x)〉 be coherent with the source task models in the sense that α∗T+1 ∼ ν. Set r2 = (‖B∗‖2F + ‖W ∗‖2F )/T .Then with probability at least 1 − δ over the samples, the expected excess risk of the learned predictor x 7→ ŵ>T+1(B̂>x)+ on the target task satisfies:
EαT+1∼ν [ER(fB̂,ŵT+1)] ≤ σR̄ · Õ (√ Tr(Σ)√ n1T + √ ‖Σ‖2√ n2 ) + ζn1,n2 , (19)
where ζn1,n2 := ρ 4R̄2Õ(Tr(Σ)n1 + ‖Σ‖ n2 ) is lower-order term due to randomness of the input data.
To highlight the advantage of representation learning, we compare to training a neural network with weight decay directly on the target task:
(B̂, ŵ) = arg min B,w,‖Bw‖≤R̄
1
2n n∑ i=1 ‖yt+1 − (XT+1B)+w‖2. (20)
The error of the baseline method in fixed-design is
E[ER(fB̂,ŵ)] . σR̄
√ Tr(Σ)
n2 . (21)
We see that Equation (19) is always smaller than Equation (21) since n1T ≥ n2. See Appendix D for the proof of Theorem 7.1 and the calculation of (21).

heading: 8 CONCLUSION
text: We gave the first statistical analysis showing that representation learning can fully exploit all data points from source tasks to enable few-shot learning on a target task. This type of results were shown for both low-dimensional and high-dimensional representation function classes.
There are many important directions to pursue in representation learning and few-shot learning. Our results in Sections 6 and 7 indicate that explicit low dimensionality is not necessary, and norm-based capacity control also forces the classifier to learn good representations. Further questions include whether this is a general phenomenon in all deep learning models, whether other capacity control can be applied, and how to optimize to attain good representations.
5Wei et al. (2019) show that (17) can be minimized in polynomial iteration complexity using perturbed gradient descent, though potentially exponential width is required.

heading: ACKNOWLEDGMENTS
text: SSD acknowledges support of National Science Foundation (Grant No. DMS-1638352) and the Infosys Membership. JDL acknowledges support of the ARO under MURI Award W911NF-11-10303, the Sloan Research Fellowship, and NSF CCF 2002272. WH is supported by NSF, ONR, Simons Foundation, Schmidt Foundation, Amazon Research, DARPA and SRC. QL is supported by NSF #2030859 and the Computing Research Association for the CIFellows Project. The authors also acknowledge the generous support of the Institute for Advanced Study on the Theoretical Machine Learning program, where SSD, WH, JDL, and QL were participants.

heading: A PROOF OF THEOREM 4.1
text: We first prove several claims and then combine them to finish the proof of Theorem 4.1. We will use technical lemmas proved in Section A.1. Claim A.1 (covariance concentration of source tasks). Suppose n1 ρ4(d + log(T/δ)) for δ ∈ (0, 1). Then with probability at least 1− δ10 over the inputs X1, . . . , XT in the source tasks, we have
0.9Σt 1
n1 X>t Xt 1.1Σt, ∀t ∈ [T ]. (22)
Proof. According to our assumption on pt, we can write Xt = X̄tΣ 1/2 t , where X̄t ∈ Rn1×d and the rows of X̄t hold i.i.d. samples of p̄t. Since p̄t satisfies the conditions in Lemma A.6, from Lemma A.6 we know that with probability at least 1− δ10T ,
0.9I 1 n1 X̄>t X̄t 1.1I,
which implies
0.9Σt 1
n1 Σ
1/2 t X̄ > t X̄tΣ 1/2 t =
1
n1 X>t Xt 1.1Σt.
The proof is finished by taking a union bound over all t ∈ [T ].
Claim A.2 (covariance concentration of target task). Suppose n2 ρ4(k+ log(1/δ)) for δ ∈ (0, 1). Then for any given matrix B ∈ Rd×2k that is independent of XT+1, with probability at least 1− δ10 over XT+1 we have
0.9B>ΣT+1B 1
n2 B>X>T+1XT+1B 1.1B>ΣT+1B. (23)
Proof. According to our assumption on pT+1, we can write XT+1 = X̄T+1Σ 1/2 T+1, where X̄T+1 ∈ Rn2×d and the rows of X̄T+1 hold i.i.d. samples of p̄T+1. We take the SVD of Σ1/2T+1B: Σ 1/2 T+1B = UDV >, where U ∈ Rd×2k has orthonormal columns. Now we look at the matrix X̄T+1U ∈ Rn2×2k. It is easy to see that the rows of X̄T+1U are i.i.d. 2k-dimensional random vectors with zero mean, identity covariance, and are ρ2-subgaussian. Therefore, applying Lemma A.6, with probability at least 1− δ10 we have
0.9I 1 n2 U>X̄>T+1X̄T+1U 1.1I,
which implies
0.9V DDV > 1 n2 V DU>X̄>T+1X̄T+1UDV > 1.1V DDV >.
Since 1n2V DU >X̄>T+1X̄T+1UDV > = 1n2B >Σ 1/2 T+1X̄ > T+1X̄T+1Σ 1/2 T+1B = 1 n2 B>X>T+1XT+1B and V DDV > = V DU>UDV > = B>ΣT+1B, the above inequality becomes
0.9B>ΣT+1B 1
n2 B>X>T+1XT+1B 1.1B>ΣT+1B.
Claim A.3 (guarantee on source training data). Under the setting of Theorem 4.1, with probability at least 1− δ5 we have
‖X (B̂Ŵ −B∗W ∗)‖2F . σ2 (kT + kd log(κn1) + log(1/δ)) . (24)
Proof. We assume that (22) is true, which happens with probability at least 1 − δ10 according to Claim A.1.
Let Θ̂ = B̂Ŵ and Θ∗ = B∗W ∗. From the optimality of B̂ and Ŵ for (7) we have ‖Y −X (Θ̂)‖2F ≤ ‖Y −X (Θ∗)‖2F . Plugging in Y = X (Θ∗) + Z, this becomes
‖X (Θ̂−Θ∗)‖2F ≤ 2〈Z,X (Θ̂−Θ∗)〉. (25)
Let ∆ = Θ̂ − Θ∗. Since rank(∆) ≤ 2k, we can write ∆ = V R = [V r1, · · · , V rT ] where V ∈ Od,2k and R = [r1, · · · , rT ] ∈ R2k×T . Here Od1,d2 (d1 ≥ d2) is the set of orthonormal d1× d2 matrices (i.e., the columns are orthonormal). For each t ∈ [T ] we further write XtV = UtQt where Ut ∈ On1,2k and Qt ∈ R2k×2k. Then we have
〈Z,X (∆)〉 = T∑ t=1 z>t XtV rt
= T∑ t=1 z>t UtQtrt
≤ T∑ t=1 ∥∥U>t zt∥∥ · ‖Qtrt‖ ≤
√√√√ T∑ t=1 ∥∥U>t zt∥∥2 · √√√√ T∑ t=1 ‖Qtrt‖2
= √√√√ T∑ t=1 ∥∥U>t zt∥∥2 · √√√√ T∑ t=1 ‖UtQtrt‖2
= √√√√ T∑ t=1 ∥∥U>t zt∥∥2 · √√√√ T∑ t=1 ‖XtV rt‖2
= √√√√ T∑ t=1
∥∥U>t zt∥∥2 · ‖X (∆)‖F . (26) Next we give a high-probability upper bound on ∑T t=1
∥∥U>t zt∥∥2 using the randomness in Z. Since Ut’s depend on V which depends on Z, we will need an -net argument to cover all possible V ∈ Od,2k. First, for any fixed V̄ ∈ Od,2k, we let XtV̄ = ŪtQ̄t where Ūt ∈ On,2k. The Ūt’s defined in this way are independent of Z. Since Z has i.i.d. N (0, σ2) entries, we know that σ−2 ∑T t=1
∥∥Ū>t zt∥∥2 is distributed as χ2(2kT ). Using the standard tail bound for χ2 random variables, we know that with probability at least 1− δ′ over Z,
σ−2 T∑ t=1 ∥∥Ū>t zt∥∥2 . kT + log(1/δ′). Therefore, using the same argument in (26) we know that with probability at least 1− δ′,
〈Z,X (V̄ R)〉 . σ √ kT + log(1/δ′) ∥∥X (V̄ R)∥∥ F .
Now, from Lemma A.5 we know that there exists an -net N of Od,2k in Frobenius norm such that N ⊂ Od,2k and |N | ≤ ( 6 √ 2k )
2kd. Applying a union bound over N , we know that with probability at least 1− δ′|N |,
〈Z,X (V̄ R)〉 . σ √ kT + log(1/δ′) ∥∥X (V̄ R)∥∥ F , ∀V̄ ∈ N . (27)
Choosing δ′ = δ 20( 6 √ 2k ) 2kd , we know that (27) holds with probability at least 1− δ20 .
We will use (22), (25) and (27) to complete the proof of the claim. This is done in the following steps:
1. Upper bounding ‖Z‖F .
Since σ−2 ‖Z‖2F ∼ χ2(n1T ), we know that with probability at least 1− δ 20 ,
‖Z‖2F . σ 2(n1T + log(1/δ)). (28)
2. Upper bounding ‖∆‖F .
From (25) we have ‖X (∆)‖2F ≤ 2 ‖Z‖F ‖X (∆)‖F , which implies ‖X (∆)‖F ≤ 2 ‖Z‖F . σ √ n1T + log(1/δ). On the other hand, letting the t-th column of ∆ be δt, we have
‖X (∆)‖2F = T∑ t=1 ‖Xtδt‖2
= T∑ t=1 δ>t X > t Xtδt
≥ 0.9n1 T∑ t=1 δ>t Σtδt (using (22))
≥ 0.9n1 T∑ t=1 λmin(Σt) ‖δt‖2
≥ 0.9n1λ ‖∆‖2F , where λ = mint∈[T ] λmin(Σt). Hence we obtain
‖∆‖2F . ‖X (∆)‖2F n1λ . σ2(n1T + log(1/δ)) n1λ .
3. Applying the -net N . Let V̄ ∈ N such that ∥∥V − V̄ ∥∥ F ≤ . Then we have∥∥X (V R− V̄ R)∥∥2 F
= T∑ t=1 ∥∥Xt(V − V̄ )rt∥∥2 ≤
T∑ t=1 ‖Xt‖2 ∥∥V − V̄ ∥∥2 ‖rt‖2
≤ T∑ t=1 1.1n1λmax(Σt) 2 ‖rt‖2 (using (22))
≤ 1.1n1λ̄ 2 ‖R‖2F (λ̄ = max t∈[T ] λmax(Σt))
= 1.1n1λ̄ 2 ‖∆‖2F (‖∆‖F = ‖V R‖F = ‖R‖F )
.n1λ̄ 2 · σ
2(n1T + log(1/δ))
n1λ
=κ 2σ2(n1T + log(1/δ)). (29)
4. Finishing the proof.
We have the following chain of inequalities:
1 2 ‖X (∆)‖2F
≤〈Z,X (∆)〉 (using (25)) = 〈Z,X (V̄ R)〉+ 〈Z,X (V R− V̄ R)〉
.σ √ kT + log(1/δ′) ∥∥X (V̄ R)∥∥ F + ‖Z‖F ∥∥X (V R− V̄ R)∥∥ F (using (27))
≤σ √ kT + log(1/δ′) ( ‖X (V R)‖F + ∥∥X (V R− V̄ R)∥∥ F ) + σ √ n1T + log(1/δ) ∥∥X (V R− V̄ R)∥∥ F
(using (28))
.σ √ kT + log(1/δ′) ‖X (V R)‖F + σ √ n1T + log(1/δ′) ∥∥X (V R− V̄ R)∥∥ F
(using k < n1 and δ′ < δ) .σ √ kT + log(1/δ′) ‖X (∆)‖F + σ √ n1T + log(1/δ′) · √ κ 2σ2(n1T + log(1/δ))
(using (29)) ≤σ √ kT + log(1/δ′) ‖X (∆)‖F + σ 2 √ κ(n1T + log(1/δ ′)).
Finally, we let = k√ κn1 , and recall δ′ = δ 20( 6 √ 2k ) 2kd . Then the above inequality implies
‖X (∆)‖F
. max { σ √ kT + log(1/δ′), √ σ2 √ κ(n1T + log(1/δ′)) } = max { σ √ kT + log(1/δ′), σ √ k
n1 (n1T + log(1/δ′)) } ≤ max { σ √ kT + log(1/δ′), σ √ kT + log(1/δ′)) } (using k < n1)
=σ √ kT + log(1/δ′)
.σ √ kT + kd log k + log 1
δ ≤σ √ kT + kd log(κn1) + log 1
δ .
The high-probability events we have used in the proof are (22), (27) and (28). By a union bound, the failure probability is at most δ10 + δ 20 + δ 20 = δ 5 . Therefore the proof is completed.
Claim A.4 (Guarantee on target training data). Under the setting of Theorem 4.1, with probability at least 1− 2δ5 , we have
1
n2
∥∥∥P⊥ XT+1B̂ XT+1B ∗ ∥∥∥2 F . σ2 ( kT + kd log(κn1) + log 1 δ ) cn1 · σ2k(W ∗) .
Proof. We suppose that the high-probability events in Claims A.1, A.2 and A.3 happen, which holds with probability at least 1− 2δ5 . Here we instantiate Claim A.2 using B = [B̂, B ∗] ∈ Rd×2k.
From the optimality of B̂ and Ŵ in (6) we know XtB̂ŵt = PXtB̂yt = PXtB̂(XtB ∗w∗t + zt) for each t ∈ [T ]. Then we have
σ2 (kT + kd log(κn1) + log(1/δ))
& ‖X (B̂Ŵ −B∗W ∗)‖2F (from (24))
= T∑ t=1 ∥∥∥XtB̂ŵt −XtB∗ŵ∗t ∥∥∥2 =
T∑ t=1 ∥∥∥PXtB̂(XtB∗w∗t + zt)−XtB∗ŵ∗t ∥∥∥2 =
T∑ t=1 ∥∥∥−P⊥ XtB̂ XtB ∗w∗t + PXtB̂zt ∥∥∥2 =
T∑ t=1 (∥∥∥−P⊥ XtB̂ XtB ∗w∗t ∥∥∥2 + ∥∥∥PXtB̂zt∥∥∥2) (the cross term is 0) ≥
T∑ t=1 ∥∥∥P⊥ XtB̂ XtB ∗w∗t ∥∥∥2
≥ 0.9n1 T∑ t=1 ∥∥∥P⊥ Σ 1/2 t B̂ Σ 1/2 t B ∗w∗t ∥∥∥2 (using (22) and Lemma A.7) ≥ 0.9cn1
T∑ t=1 ∥∥∥∥P⊥Σ1/2T+1B̂Σ1/2T+1B∗w∗t ∥∥∥∥2 (using Assumption 4.2 and Lemma A.7)
= 0.9cn1 ∥∥∥∥P⊥Σ1/2T+1B̂Σ1/2T+1B∗W ∗ ∥∥∥∥2 F
≥ 0.9cn1 ∥∥∥∥P⊥Σ1/2T+1B̂Σ1/2T+1B∗ ∥∥∥∥2 F · σ2k(W ∗).
Next, we write B̂ = [B̂, B∗] [ I 0 ] =: BA and B∗ = [B̂, B∗] [ 0 I ] =: BC. Recall that we have
1 n2 B>X>T+1XT+1B 1.1B>ΣT+1B from Claim A.2. Then using Lemma A.7 we can obtain
1.1 ∥∥∥∥P⊥Σ1/2T+1BAΣ1/2T+1BC ∥∥∥∥2 F ≥ 1 n2 ∥∥∥P⊥XT+1BAXT+1BC∥∥∥2 F ,
i.e.,
1.1 ∥∥∥∥P⊥Σ1/2T+1B̂Σ1/2T+1B∗ ∥∥∥∥2 F ≥ 1 n2 ∥∥∥P⊥ XT+1B̂ XT+1B ∗ ∥∥∥2 F .
Therefore we get
σ2 (kT + kd log(κn1) + log(1/δ)) & 0.9cn1 1.1n2 ∥∥∥P⊥ XT+1B̂ XT+1B ∗ ∥∥∥2 F · σ2k(W ∗),
completing the proof.
Proof of Theorem 4.1. We will use all the high-probability events in Claims A.1, A.2, A.3 and A.4. Here we instantiate Claim A.2 using B = [B̂, B∗] ∈ Rd×2k. The success probability is at least 1− 4δ5 .
For the target task, the excess risk of our learned linear predictor x 7→ (B̂ŵT+1)>x is
ER(B̂, ŵT+1) = 1
2 Ex∼pT+1
[( x>(B̂ŵT+1 −B∗w∗T+1) )2] = 1
2 (B̂ŵT+1 −B∗w∗T+1)>ΣT+1(B̂ŵT+1 −B∗w∗T+1).
Applying Claim A.2 with B = [B̂, B∗], we have
0.9B>ΣT+1B 1
n2 B>X>T+1XT+1B,
which implies 0.9v>B>ΣT+1Bv ≤ 1n2vB >X>T+1XT+1Bv for v = [ ŵT+1 w∗T+1 ] . This becomes
(B̂ŵT+1 −B∗w∗T+1)>ΣT+1(B̂ŵT+1 −B∗w∗T+1)
≤ 1 0.9n2 (B̂ŵT+1 −B∗w∗T+1)>X>T+1XT+1(B̂ŵT+1 −B∗w∗T+1).
Therefore we have
ER(B̂, ŵT+1) ≤ 1
1.8n2 (B̂ŵT+1 −B∗w∗T+1)>X>T+1XT+1(B̂ŵT+1 −B∗w∗T+1)
= 1
1.8n2
∥∥∥XT+1(B̂ŵT+1 −B∗w∗T+1)∥∥∥2 .
From the optimality of ŵT+1 in (8) we know XT+1B̂ŵT+1 = PXT+1B̂yT+1 = PXT+1B̂(XT+1B ∗w∗T+1 + zT+1). It follows that
ER(B̂, ŵT+1) . 1
n2 ∥∥∥PXT+1B̂(XT+1B∗w∗T+1 + zT+1)−XT+1B∗w∗T+1∥∥∥2F = 1
n2
∥∥∥−P⊥ XT+1B̂ XT+1B ∗w∗T+1 + PXT+1B̂zT+1 ∥∥∥2 F
= 1
n2
∥∥∥P⊥ XT+1B̂ XT+1B ∗w∗T+1 ∥∥∥2 F + 1
n2 ∥∥∥PXT+1B̂zT+1∥∥∥2F . Recall that w∗T+1 ∼ ν and
∥∥Ew∼ν [ww>]∥∥ ≤ O( 1k ). Taking expectation over w∗T+1 ∼ ν and denoting Σ = Ew∼ν [ww>], we obtain
Ew∗T+1∼ν [ER(B̂, ŵT+1)]
. 1
n2 Ew∗T+1∼ν
[∥∥∥P⊥ XT+1B̂ XT+1B ∗w∗T+1 ∥∥∥2 F ] + 1
n2 ∥∥∥PXT+1B̂zT+1∥∥∥2F = 1
n2 Ew∗T+1∼ν
[ Tr [ P⊥ XT+1B̂ XT+1B ∗w∗T+1w ∗ T+1 ( P⊥ XT+1B̂ XT+1B ∗ )>]] + 1
n2 ∥∥∥PXT+1B̂zT+1∥∥∥2F = 1
n2 Tr
[ P⊥ XT+1B̂ XT+1B ∗Σ ( P⊥ XT+1B̂ XT+1B ∗ )>] + 1
n2 ∥∥∥PXT+1B̂zT+1∥∥∥2F = 1
n2
∥∥∥P⊥ XT+1B̂ XT+1B ∗Σ1/2 ∥∥∥2 F + 1
n2 ∥∥∥PXT+1B̂zT+1∥∥∥2F ≤ 1 n2 ∥∥∥P⊥ XT+1B̂ XT+1B ∗ ∥∥∥2 F ∥∥∥Σ1/2∥∥∥2 + 1 n2
∥∥∥PXT+1B̂zT+1∥∥∥2F . 1
n2k
∥∥∥P⊥ XT+1B̂ XT+1B ∗ ∥∥∥2 F + 1
n2 ∥∥∥PXT+1B̂zT+1∥∥∥2F (using ‖Σ‖ . 1k ) . 1
k · σ
2 (kT + kd log(κn1) + log(1/δ))
cn1 · σ2k(W ∗) +
1
n2 ∥∥∥PXT+1B̂zT+1∥∥∥2F (using Claim A.4) . σ2 (kT + kd log(κn1) + log(1/δ))
cn1T +
1
n2 ∥∥∥PXT+1B̂zT+1∥∥∥2F . (using σ2k(W ∗) & Tk ) For the second term above, notice that 1σ2
∥∥∥PXT+1B̂zT+1∥∥∥2F ∼ χ2(k), and thus with probability at least 1− δ5 we have 1 σ2
∥∥∥PXT+1B̂zT+1∥∥∥2F . k + log 1δ . Therefore we obtain the final bound Ew∗T+1∼ν [ER(B̂, ŵT+1)] . σ2 (kT + kd log(κn1) + log(1/δ)) cn1T + σ2(k + log 1δ ) n2
= σ2 ( kd log(κn1)
cn1T +
k
cn1 + log 1δ cn1T + k + log 1δ n2 ) . σ2 ( kd log(κn1)
cn1T + k + log 1δ n2
) ,
where the last inequality is due to cn1 ≥ n2.
A.1 TECHNICAL LEMMAS
Lemma A.5. Let Od1,d2 = {V ∈ Rd1×d2 | V >V = I} (d1 ≥ d2), and ∈ (0, 1). Then there exists a subset N ⊂ Od1,d2 that is an -net of Od1,d2 in Frobenius norm such that |N | ≤ ( 6 √ d2 )
d1d2 , i.e., for any V ∈ Od1,d2 , there exists V ′ ∈ N such that ‖V − V ′‖F ≤ .
Proof. For any V ∈ Od1,d2 , each column of V has unit `2 norm. It is well known that there exists an
2 √ d2
-net (in `2 norm) of the unit sphere in Rd1 with size ( 6 √ d2 ) d1 . Using this net to cover
all the columns, we obtain a set N ′ ⊂ Rd1×d2 that is an 2 -net of Od1,d2 in Frobenius norm and |N ′| ≤ ( 6 √ d2 ) d1d2 .
Finally, we need to transform N ′ into an -net N that is a subset of Od1,d2 . This can be done by projecting each point in N ′ onto Od1,d2 . Namely, for each V̄ ∈ N ′, let P(V̄ ) be its closest point in Od1,d2 (in Frobenium norm); then define N = {P(V̄ ) | V̄ ∈ N ′}. Then we have |N | ≤ |N ′| ≤ ( 6 √ d2 )
d1d2 and N is an -net of Od1,d2 , because for any V ∈ Od1,d2 , there exists V̄ ∈ N ′ such that ∥∥V − V̄ ∥∥ F ≤ 2 , which implies P(V̄ ) ∈ N and ∥∥V − P(V̄ )∥∥ F ≤∥∥V − V̄ ∥∥ F + ∥∥V̄ − P(V̄ )∥∥ F ≤ ∥∥V − V̄ ∥∥ F + ∥∥V̄ − V ∥∥ F = 2 ∥∥V − V̄ ∥∥ F ≤ .
Lemma A.6. Let a1, . . . ,an be i.i.d. d-dimensional random vectors such that E[ai] = 0, E[aia>i ] = I , and ai is ρ2-subgaussian. For δ ∈ (0, 1), suppose n ρ4(d+ log(1/δ)). Then with probability at least 1− δ we have
0.9I 1 n n∑ i=1 aia > i 1.1I.
Proof. Let A = 1n ∑n i=1 aia > i − I . Then it suffices to show ‖A‖ ≤ 0.1 with probability at least 1− δ. We use a standard -net argument for the unit sphere Sd−1 = {v ∈ Rd : ‖v‖ = 1}. First, consider any fixed v ∈ Sd−1. We have v>Av = 1n ∑n i=1[(v >ai) 2 − 1]. From our assumptions on ai we know that v>ai has mean 0 and variance 1 and is ρ2-subgaussian. (Note that we must have ρ ≥ 1.) Therefore (v>ai)2 − 1 is zero-mean and 16ρ2-sub-exponential. By Bernstein inequality for sub-exponential random variables, we have for any > 0,
Pr [ |v>Av| > ] ≤ 2 exp ( −n
2 min
{ 2
(16ρ2)2 , 16ρ2
}) .
Next, take a 15 -netN ⊂ S d−1 of Sd−1 with size |N | ≤ eO(d). By a union bound over all v ∈ N , we have
Pr [ max v∈N |v>Av| > ] ≤ 2|N | exp ( −n 2 min { 2 (16ρ2)2 , 16ρ2 }) ≤ exp ( O(d)− n
2 min
{ 2
(16ρ2)2 , 16ρ2
}) .
Plugging in = 120 and noticing ρ > 1, the above inequality becomes
Pr [ max v∈N |v>Av| > 1 20 ] ≤ exp ( O(d)− n 2 · (1/20) 2 (16ρ2)2 ) ≤ δ,
where the last inequality is due to n ρ4 (d+ log(1/δ)). Therefore, with probability at least 1 − δ we have maxv∈N |v>Av| ≤ 120 . Suppose this indeed happens. Next, for any u ∈ Sd−1, there exists u′ ∈ N such that ‖u− u′‖ ≤ 15 . Then we have∥∥u>Au∥∥ ≤ ∥∥(u′)>Au′∥∥+ 2 ∥∥(u− u′)>Au′∥∥+ ∥∥(u− u′)>A(u− u′)∥∥
≤ 1 20 + 2 ‖u− u′‖ · ‖A‖ · ‖u′‖+ ‖u− u′‖2 · ‖A‖
≤ 1 20 + 2 · 1 5 · ‖A‖ · 1 +
( 1
5
)2 · ‖A‖
≤ 1 20 + 1 2 ‖A‖ .
Taking a supreme over u ∈ Sd−1, we obtain ‖A‖ ≤ 120 + 1 2 ‖A‖, i.e., ‖A‖ ≤ 1 10 .
Lemma A.7. If two matrices A1 and A2 (with the same number of columns) satisfy A>1 A1 A>2 A2, then for any matrix B (of compatible dimensions), we have
A>1 P ⊥ A1BA1 A > 2 P ⊥ A2BA2.
As a consequence, for any matrices B and B′ (of compatible dimensions), we have∥∥P⊥A1BA1B′∥∥2F ≥ ∥∥P⊥A2BA2B′∥∥2F . Proof. For the first part of the lemma, it suffices to show the following for any vector v:
v>A>1 P ⊥ A1BA1v ≥ v >A>2 P ⊥ A2BA2v,
which is equivalent to
min w ‖A1Bw −A1v‖22 ≥ min w ‖A2Bw −A2v‖22.
Let w∗ ∈ arg minw ‖A1Bw −A1v‖22. Then we have
min w ‖A1Bw −A1v‖22 = ‖A1Bw∗ −A1v‖22
= (Bw∗ − v)>A>1 A1(Bw∗ − v) ≥ (Bw∗ − v)>A>2 A2(Bw∗ − v) = ‖A2Bw∗ −A2v‖22 ≥ min
w ‖A2Bw −A2v‖22,
finishing the proof of the first part.
For the second part, from A>1 P ⊥ A1B A1 A>2 P⊥A2BA2 we know
(B′)>A>1 P ⊥ A1BA1B ′ (B′)>A>2 P⊥A2BA2B ′.
Taking trace on both sides, we obtain∥∥P⊥A1BA1B′∥∥2F ≥ ∥∥P⊥A2BA2B′∥∥2F , which finishes the proof.

heading: B PROOF OF THEOREM 5.1
text: Here we first prove an important intermediate result on the in-sample risk, which explains how the Gaussian width of FX (Φ) arises.
Claim B.1 (analogue of Claim A.3). Let φ̂ and ŵ1, . . . , ŵT be the optimal solution to (2). Then with probability at least 1− δ we have
T∑ t=1 ∥∥∥φ̂(Xt)ŵt − φ∗(Xt)w∗t ∥∥∥2 . σ2(G(FX (Φ))2 + log 1δ ) .
Proof. By the optimality of φ̂ and ŵ1, . . . , ŵT for (2), we know
T∑ t=1 ∥∥∥yt − φ̂(Xt)ŵt∥∥∥2 ≤ T∑ t=1 ‖yt − φ∗(Xt)w∗t ‖ 2 .
Plugging in yt = φ∗(Xt)w∗t + zt (zt ∼ N (0, I) is independent of Xt), we get T∑ t=1 ∥∥∥φ∗(Xt)w∗t + zt − φ̂(Xt)ŵt∥∥∥2 ≤ T∑ t=1 ‖zt‖2 ,
which gives
T∑ t=1 ∥∥∥φ̂(Xt)ŵt − φ∗(Xt)w∗t ∥∥∥2 ≤ 2 t∑ t=1 〈zt, φ̂(Xt)ŵt − φ∗(Xt)w∗t 〉.
Denote Z = [z1, · · · , zT ] ∈ Rn1×T and A = [a1, · · · ,aT ] ∈ Rn1×T where at = φ̂(Xt)ŵt − φ∗(Xt)w ∗ t . Then the above inequality reads ‖A‖ 2 F ≤ 2〈Z,A〉. Notice that
A ‖A‖F ∈ FX (Φ) (c.f. (10)). It follows that
‖A‖F ≤ 2 〈 Z, A
‖A‖F
〉 ≤ 2 sup
Ā∈FX (Φ) 〈Z, Ā〉. (30)
By definition, we have EZ [ supĀ∈FX (Φ)〈σ −1Z, Ā〉 ]
= G(FX (Φ)). Furthermore, since the function Z 7→ supĀ∈FX (Φ)〈Z, Ā〉 is 1-Lipschitz in Frobenius norm, by the standard Gaussian concentration inequality, we have with probability at least 1− δ,
sup Ā∈FX (Φ)
〈σ−1Z, Ā〉 ≤ E [ sup
Ā∈FX (Φ) 〈σ−1Z, Ā〉
] + √ log 1
δ = G(FX (Φ)) +
√ log 1
δ .
Then the proof is completed using (30).
The proof is conditioned on several high-probability events, each happening with probability at least 1−Ω(δ). By a union bound at the end, the final success probability is also at least 1−Ω(δ). We can always rescale δ by a constant factor such that the final probability is at least 1− δ. Therefore, we will not carefully track the constants before δ in the proof. All the δ’s should be understood as Ω(δ).
We use the following notion of representation divergence.
Definition B.1 (divergence between two representations). Given a distribution q over Rd and two representation functions φ, φ′ ∈ Φ, the divergence between φ and φ′ with respect to q is defined as
Dq (φ, φ ′) = Σq (φ ′, φ′)− Σq (φ′, φ) (Σq (φ, φ))†Σq (φ, φ′) ∈ Rk×k.
It is easy to verify Dq(φ, φ′) 0, Dq(φ, φ) = 0 for any φ, φ′ and q. See Lemma B.2’s proof. The next lemma shows a relation between (symmetric) covariance and divergence.
Lemma B.2. Suppose that two representation functions φ, φ′ ∈ Φ and two distributions q, q′ over Rd satisfy Λq(φ, φ′) α ·Λq′(φ, φ′) for some α > 0. Then it must hold that Dq(φ, φ′) α ·Dq′(φ, φ′).
Proof. Fix any v ∈ Rk. We will prove v>Dq(φ, φ′)v ≥ α · v>Dq′(φ, φ′)v, which will complete the proof of the lemma.
We define a quadratic function f : Rk → R as f(w) = [w>,−v>]Λq(φ, φ′) [ w −v ] . According to
Definition 5.2, we can write
f(w) = w>Σq(φ, φ)w − 2w>Σq(φ, φ′)v + v>Σq(φ′, φ′)v = Ex∼q [( w>φ(x)− v>φ′(x) )2] .
Therefore we have f(w) ≥ 0 for any w ∈ Rk.6 This means that f must have a global minimizer in Rk. Since f is convex, taking its gradient∇f(w) = 2Σq(φ, φ)w − 2Σq(φ, φ′)v and setting the gradient to 0, we obtain a global minimzer w∗ = (Σq(φ, φ))†Σq(φ, φ′)v. Plugging this into the definition of f , we obtain7
min w∈Rk
f(w) = f(w∗) = v>Dq(φ, φ ′)v. (31)
Similarly, letting g(w) = [w>,−v>]Λq′(φ, φ′) [ w −v ] , we have
min w∈Rk
g(w) = v>Dq′(φ, φ ′)v.
6Note that we have proved Λq(φ, φ′) 0. 7Note that (31) implies Dq(φ, φ′) 0.
From Λq(φ, φ′) α · Λq′(φ, φ′) we know f(w) ≥ αg(w) for any w ∈ Rk. Recall that w∗ ∈ arg minw∈Rk f(w). We have
αv>Dq′(φ, φ ′)v = α min w∈Rk g(w) ≤ αg(w∗) ≤ f(w∗)
= min w∈Rk
f(w) = v>Dq(φ, φ ′)v.
This finishes the proof.
Claim B.3 (analogue of Claim A.4). Under the setting of Theorem 5.1, with probability at least 1− δ we have
1
n2
∥∥∥P⊥ φ̂(XT+1) φ∗(XT+1) ∥∥∥2 F . σ2 ( G(FX (Φ))2 + log 1δ ) n1σ2k(W ∗) .
Proof. We continue to use the notation from Claim B.1 and its proof.
Let p̂t be the empirical distribution over the samples inXt (t ∈ [T+1]). According to Assumptions 5.1 and 5.2 as well as the setting in Theorem 5.1, we know that the followings are satisfied with probability at least 1− δ:
0.9Λp(φ, φ ′) Λp̂t(φ, φ′) 1.1Λp(φ, φ′), ∀φ, φ′ ∈ Φ,∀t ∈ [T ], 0.9Λp(φ̂, φ ∗) Λp̂T+1(φ̂, φ∗) 1.1Λp(φ̂, φ∗).
(32)
Notice that φ̂ and φ∗ are independent of the samples from the target task, so n2 ≥ Npoint(Φ, p, δ3 ) is sufficient for the second inequality above to hold with high probability. Using Lemma B.2, we know that (32) implies
0.9Dp(φ, φ ′) Dp̂t(φ, φ′) 1.1Dp(φ, φ′), ∀φ, φ′ ∈ Φ,∀t ∈ [T ], 0.9Dp(φ̂, φ ∗) Dp̂T+1(φ̂, φ∗) 1.1Dp(φ̂, φ∗).
(33)
By the optimality of φ̂ and ŵ1, . . . , ŵT for (2), we know φ̂(Xt)ŵt = Pφ̂(Xt)yt = Pφ̂(Xt)(φ ∗(Xt)w ∗ t + zt). Then we have the following chain of inequalities:
σ2 ( G(FX (Φ))2 + log 1
δ ) &
T∑ t=1 ∥∥∥φ̂(Xt)ŵt − φ∗(Xt)w∗t ∥∥∥2 (Claim B.1) =
T∑ t=1 ∥∥∥Pφ̂(Xt)(φ∗(Xt)w∗t + zt)− φ∗(Xt)w∗t ∥∥∥2 =
T∑ t=1 ∥∥∥−P⊥ φ̂(Xt) φ∗(Xt)w ∗ t + Pφ̂(Xt)zt ∥∥∥2 =
T∑ t=1 (∥∥∥P⊥ φ̂(Xt) φ∗(Xt)w ∗ t ∥∥∥2 + ∥∥∥Pφ̂(Xt)zt∥∥∥2) (cross term is 0) ≥
T∑ t=1 ∥∥∥P⊥ φ̂(Xt) φ∗(Xt)w ∗ t ∥∥∥2 =
T∑ t=1 (w∗t ) >φ∗(Xt) > ( I − φ̂(Xt) ( φ̂(Xt) >φ̂(Xt) )† φ̂(Xt) > ) φ∗(Xt)w ∗ t
=n1 T∑ t=1 (w∗t ) >Dp̂t(φ̂, φ ∗)w∗t
≥ 0.9n1 T∑ t=1 (w∗t ) >Dp(φ̂, φ ∗)w∗t ((33))
= 0.9n1 ∥∥∥∥(Dp(φ̂, φ∗))1/2W ∗∥∥∥∥2 F
≥ 0.9n1 ∥∥∥∥(Dp(φ̂, φ∗))1/2∥∥∥∥2
F
σ2k(W ∗)
= 0.9n1Tr [ Dp(φ̂, φ ∗) ] σ2k(W ∗)
≥ 0.9n1 1.1
Tr [ Dp̂T+1(φ̂, φ ∗) ] σ2k(W ∗) ((33))
= 0.9n1 1.1n2 ∥∥∥P⊥ φ̂(XT+1) φ∗(XT+1) ∥∥∥2 F σ2k(W ∗),
completing the proof.
Now we can finish the proof of Theorem 5.1.
Proof of Theorem 5.1. The excess risk is bounded as
ER(φ̂, ŵT+1)
= 1
2 Ex∼p
[( ŵ>T+1φ̂(x)− (w∗T+1)>φ∗(x) )2] = 1
2 [ ŵT+1 −w∗T+1 ]> Λp(φ̂, φ ∗) [ ŵT+1 −w∗T+1 ] . [ ŵT+1 −w∗T+1 ]> Λp̂T+1(φ̂, φ ∗) [ ŵT+1 −w∗T+1 ] ((32))
= 1
n2 ∥∥∥φ̂ (XT+1) ŵT+1 − φ∗ (XT+1)w∗T+1∥∥∥2 = 1
n2
∥∥∥−P⊥ φ̂(XT+1) φ∗ (XT+1)w ∗ T+1 + Pφ̂(XT+1)zT+1 ∥∥∥2 = 1
n2
(∥∥∥P⊥ φ̂(XT+1) φ∗ (XT+1)w ∗ T+1 ∥∥∥2 + ∥∥∥Pφ̂(XT+1)zT+1∥∥∥2) . 1
n2
∥∥∥P⊥ φ̂(XT+1) φ∗ (XT+1)w ∗ T+1 ∥∥∥2 + σ2(k + log 1δ ) n2 . (using χ2 tail bound)
Taking expectation over w∗T+1 ∼ ν, we get
Ew∗T+1∼ν [ER(φ̂, ŵT+1)]
. 1
n2
∥∥∥P⊥ φ̂(XT+1) φ∗ (XT+1) ∥∥∥2 F ∥∥Ew∼ν [ww>]∥∥+ σ2(k + log 1δ ) n2
. 1
kn2
∥∥∥P⊥ φ̂(XT+1) φ∗ (XT+1) ∥∥∥2 F + σ2(k + log 1δ )
n2
. 1 k · σ2 ( G(FX (Φ))2 + log 1δ ) n1σ2k(W ∗) + σ2(k + log 1δ ) n2 (Claim B.3)
. σ2 ( G(FX (Φ))2 + log 1δ ) n1T + σ2(k + log 1δ ) n2 , (σk(W ∗) & Tk )
finishing the proof.

heading: C PROOF OF THEOREM 6.1
text: C.1 PROOF SKETCH OF THEOREM 6.1
Let R = ‖Θ∗‖∗. Recall B̂ and Ŵ are derived from Eqn. (12) and let Θ̂ := B̂Ŵ . We first note that the constraint set {‖w‖2i ≤ R/T, ‖B‖2F ≤ R} ensures ‖W‖2F ≤ R and ‖WB‖∗ ≤ R at global minimum. On the other hand, our constraint for W,B is also expressive enough to attain any Θ̂ that satisfies ‖Θ̂‖∗ ≤ R. See reference e.g. Srebro and Shraibman (2005). Therefore at global minimum ‖Ŵ‖F ≤ √ R, ‖B̂‖F ≤ √ R and ‖Θ̂‖∗ ≤ R.
For the ease of proof, we introduce the following auxiliary functions and parameters. Write
L1(W ) = 1
2 ∥∥∥Σ1/2Θ∗ − Σ1/2B̂W∥∥∥2 F ,
Lλ1 (W ) =L1(W ) + λ
2 ‖W‖2F , Wλ1 ← arg min W {Lλ1 (W )},
L2(w) = 1
2 ∥∥∥Σ1/2θ∗T+1 − Σ1/2B̂w∥∥∥2 , wλ2 ← arg min w {L2(w) + λ/2‖w‖2},
L̂1(W ) = 1
2n1 ∥∥∥X (Θ∗ − B̂W )∥∥∥2 , L̂λ1 (W ) =L̂1(W ) + λ
2 ‖W‖2F W̄λ1 ← arg min W {L̂λ1 (W )},
L̂2(w) = 1
2n2 ∥∥∥XT+1θ∗T+1 −XT+1B̂w∥∥∥2 , w̄2 ← arg min w≤r {L̂2(w)}.
We define terms ic,1 and ic,2 that will be used to bound intrinsic dimension concentration error in the input signal. Namely with high probability, ‖Σ1/2Θ‖− √ 1/n1 ∑T t=1 ‖Xtθt‖2 ≤ ic,1‖Θ‖∗, and
similarly ‖Σ1/2B̂v‖ − √
1 n2 ‖XB̂v‖2 ≤ ic,2‖v‖2. Additionally we use ee,i, i ∈ {1, 2} to bound
the estimation error (for fixed design) incurred when using noisy label yT+1 and Y .
The choice of ee,i, and ic,i are respectively justified in Lemma C.5, Claim C.4, Lemma C.10 and Claim C.11, along with some more detailed descriptions.
Proof of Theorem 6.1.
Eθ∗∼ν ER(B̂, ŵT+1) =Eθ∗∼ν L2(ŵT+1)
.Eθ∗∼ν L̂2(ŵT+1) + 2ic,2r2 (Claim C.11)
.Eθ∗∼ν L̂2(w̄2) + 2ee,2r + 2ic,2r2 (Lemma C.4)
≤Eθ∗∼ν L̂2(wλ2 ) + 2ee,2r + 2ic,2r2 (Definition of w̄2) .Eθ∗∼ν L2(wλ2 ) + 2ee,2r + 2ic,2r2 (Claim C.11)
= 1
T L1(W
λ 1 ) + 2 ee,2r + 2 ic,2r 2 (Claim C.3)
. λR
T + 2ee,2r + 2 ic,2r 2 (Lemma C.2)
. 2ee,1R+ 2 ic,1R 2
T + 2ee,2r + 2 ic,2r 2. (Choices of λ)
Each step is with high probability 1− δ/10 over the randomness of X or XT+1. Therefore overall by union bound, with probability 1− δ, by plugging in the values of ic,i and ee,i we have:
Eθ∗∼ν ER(B̂, ŵT+1) ≤ σR√ T Õ (√ Tr(Σ)√ Tn1 + √ ‖Σ‖ √ n2 ) + ρ4R2 T Õ ( TrΣ n1 + ‖Σ‖ n2 ) .
Notice a term ‖Σ‖/n1 is absorbed by ‖Σ‖/n2 since we assume n1 ≥ n2.
Claim C.1 (guarantee with source regularization). 1
n1 ‖X (Θ∗ − Θ̂)‖2F + λ‖Θ̂‖∗ ≤ 3λ‖Θ∗‖∗ ≤ 3λR,
and ‖B̂‖2F ≤ 3R, ‖Ŵ‖2F ≤ 3R for any λ ≥ 2n‖X ∗(Z)‖2.
Here X ∗ is the adjoint operator of X such that X ∗(Z) = ∑T i=1X > t zte > t .
Proof. With the optimality of Θ̂ we have: 1
2n1 ‖X (Θ̂−Θ∗)− Z‖2F + λ‖Θ̂‖∗ ≤
1
2n1 ‖Z‖2F + λ‖Θ∗‖∗,
Let ∆ = Θ̂−Θ∗. Therefore 1
2n1 ‖X (∆)‖2F
≤λ(‖Θ∗‖∗ − ‖Θ̂‖∗) + 1
n1 〈∆,X ∗(Z)〉
≤λ‖Θ∗‖∗ + 1
n1 ‖Θ∗‖∗ · ‖X ∗(Z)‖+
1
n1 ‖Θ̂‖∗ · ‖X ∗(Z)‖ − λ‖Θ̂‖∗
≤λ‖Θ∗‖∗ + λ/2‖Θ∗‖∗ + λ/2‖Θ̂‖∗ − λ‖Θ̂‖∗
(Let λ ≥ 2 n1 ‖X ∗(Z)‖)
= 3
2 λ‖Θ∗‖∗ −
1 2 λ‖Θ̂‖∗.
Therefore 12n1 ‖X (∆)‖ 2 F + λ 2 ‖Θ̂‖∗ ≤ 3 2λ‖Θ ∗‖∗, and clearly both terms satisfy 1n1 ‖X (∆)‖ 2 F ≤ 3λ‖Θ∗‖∗ and ‖Θ̂‖∗ ≤ 3‖Θ∗‖∗.
Lemma C.2 (source task concentration). For a fixed δ > 0, let λ = 2ee,1 + 2ic,1R, we have
Lλ1 (W λ 1 ) .λR
‖Wλ1 ‖F . √ R.
with probability 1− δ/10.
Proof of Lemma C.2.
‖Wλ1 ‖2F < 2
λ Lλ1 (W λ 1 )
≤ 2 λ Lλ1 (Ŵ ) (Definition of W λ 1 )
= 2
λ
{ 1
2 ‖Σ1/2(Θ∗ − Θ̂)‖2F +
λ 2 ‖Ŵ‖2F } ≤ 2 λ { 1 2 ( 1 √ n1 ‖X (Θ∗ − Θ̂)‖F +O( ic,1)R)2 + λ 2 ‖Ŵ‖2F
} ≤ 2 λ { 1 n1 ‖X (Θ∗ −BW̄λ)‖2F + λ 2 ‖W̄λ‖2F +O( 2ic,1R2)
} ≤ 2 λ { 6λR+O( 2ic,1R 2) } (from Claim C.1) = 2
λ O(λR)
=O(R).
Thus both results have been shown.
Claim C.3 (Source and Target Connections).
Eθ∗∼ν L2(wλ2 ) =L1(Wλ1 )
Proof of Claim C.3.
wλ2 = (B̂ >ΣB̂ + λI)−1B̂>Σθ∗T+1 =: Sλθ ∗ T+1,
where Sλ := (B̂>ΣB̂ + λI)−1B̂>Σ.
Eθ∗∼ν L2(wλ2 ) =Eθ∗∼ν ‖Σ2(I − Sλ)θ∗T+1‖2
= 1
T ‖Σ2(I − Sλ)Θ∗T+1‖2
= 1
T L1(W
λ).
Lemma C.4 (Estimation Error for Target Task).
L̂2(ŵ)− L̂2(w̄)
≤ R√ Tn2
σ(log 1/δ)3/2 log(n2) √ ‖Σ‖ =: 2ee,2r.
Proof of Lemma C.4. With the definition of ŵ we write the basic inequality:
1
2n2 ‖XT+1(B̂ŵ − θ∗)− zT+1‖2F ≤
1
2n2 ‖XT+1(B̂w̄ − θ∗)− zT+1‖2F ,
Therefore by rearranging we get:
1
2n2 ‖XT+1B̂(ŵ − w̄)‖2F ≤
1
n2 〈ŵ − w̄, B̂>X>T+1zT+1〉 ≤ √ R/T
n2 ‖B̂>X>T+1zT+1‖2F
. R√ Tn2
σ log2/3(1/δ) log(n2) √ ‖Σ‖ (Claim C.6)
=O(r 2ee,2)
C.2 TECHNICAL LEMMAS
This section includes the technical details for several parts: bounding the noise term from basic inequality; and intrinsic dimension concentration for both source and target tasks. Lemma C.5 (Regularizer Estimation). For X ∈ Rn×d drawn from distribution p with covariance matrix Σ, and noise Z ∼ N (0, σ2In), with high probability 1− δ, we have
2ee,1 := 1
n ‖X>Z‖2 ≤ 1√ n σ
( log 1
δ
)3/2 log(T + n) √ T‖Σ‖+ Tr(Σ).
Proof. We use matrix Bernstein with intrinsic dimension to bound λ (See Theorem 7.3.1 in Tropp et al. (2015)).
Write A = 1√ n X>Z = 1√ n ∑T t=1X >zte > t =: ∑T t=1 St.
EX,Z [AA>] =EX [ T∑ t=1 1 n1 X> EZ [ztz>t ]X ] =σ2TΣ
EX,Z [A>A] = T∑ t=1 1 n eEX,Z [ z>t XX >zte > t ] =
T∑ t=1 1 n EX,Z [z>t XX>zt]ete>t
=σ2Tr(Σ)In.
Therefore the matrix variance statistic of the sum v(A) satisfies: v(A) = σ2 max{T‖Σ‖,Tr(Σ)}. Denote V = diag([TΣ,Tr(Σ)I]) and its intrinsic dimension dΣ = tr(V )/‖V ‖. Tr(V ) = σ2(T + n)Tr(Σ), and ‖V ‖2 ≥ σ2Tr(Σ). Therefore dΣ ≤ T + n. Finally from Hanson-Wright inequality, the upper bound on each term is ‖St‖2 ≤ ‖Xzt‖2 ≤ σ2Tr(Σ) + σ2‖Σ‖ log 1δ + σ 2‖Σ‖F √ log 1δ with probability 1− δ. Thus using ‖Σ‖F ≤ Tr(Σ),
‖St‖ ≤ σ √ (1 + √ log 1
δ )Tr(Σ) + ‖Σ‖ log 1 δ =: L.
Then from intrinsic matrix bernstein (Theorem 7.3.1 in Tropp et al. (2015)), with probability 1− δ we have, ‖A‖ ≤ O(σ √ log 1δ v log(dΣ) + σ log 1 δL log(dΣ)), which gives
‖A‖ ≤ σ √ log 1
δ T‖Σ‖ log(T + n) + log 1 δ Tr(Σ) log(T + n) + log 1 δ σL log(T + n)
. σ ( log 1
δ
)3/2 log(T + n) √ T‖Σ‖+ Tr(Σ).
Claim C.6 (target noise concentration). For a fixed δ > 0, with probability 1 − δ/10, 2ee,2 := 1√ n2 ‖B̂>X>T+1z‖2 ≤ O(log 2/3(1/δ) log(n2) √ Tr(B̂>ΣB̂)) ≤ Õ( √ ‖Σ‖2R).
Proof. The first inequality directly follows from Lemma C.5. Meanwhile Tr(B̂>ΣB̂) = 〈Σ, B̂B̂>〉 ≤ ‖Σ‖2‖B̂B̂>‖∗ . ‖Σ‖2R. This finishes the proof.
Definition C.7. The sub-gaussian norm of some vector y is defined as:
‖y‖ψ2 := sup x∈Sn−1 ‖〈y,x〉‖ψ2 , (34)
where Sn−1 denotes the unit Euclidean sphere in Rn. Definition C.8. Let T ⊂ Rd be a bounded set, and g be a standard normal random vector in Rd, i.e., g ∼ N (0, Id).Then the quantities
w(T ) := E sup x∈T 〈g,x〉, and γ(T ) := E sup x∈T |〈g,x〉| (35)
are called the Gaussian width of T and the Gaussian complexity of T , respectively. Theorem C.9 (Restated Matrix deviation inequality from Vershynin (2017)). Let A be an m × n matrix whose rows ai are independent, isotropic and sub-gaussian random vectors in Rn. Let T ⊂ Rn be a fixed bounded set. Then
E sup x∈T |‖Ax‖2 −
√ m‖x‖2| ≤ Cρ2γ(T ), (36)
where K = maxi ‖Ai‖ψ2 is the maximal sub-gaussian norm of the rows of A. A high-probability version states as follows. With probability 1− δ,
sup x∈T |‖Ax‖2 −
√ m‖x‖2| ≤ Cρ2[γ(T ) + √ log(2/δ)r(T )], (37)
where the radius r(T ) := supx∈T ‖x‖2.
Lemma C.10 (intrinsic dimension concentration). Let X,Xt, t ∈ [T ] be n× d matrix whose rows x are independent, isotropic and sub-gaussian random vectors in Rd that satisfy Assumption 4.1, and the whitening distribution is with sub-gaussian norm C1ρ, where E[x] = 0 and E[xx>] = Σ. For a fixed δ > 0, and any v ∈ Rd, we have
‖Σ1/2v‖2 ≤ 1√ n ‖Xv‖2 + Cρ2√ n
(√ Tr(Σ) + √ log(2/δ)‖Σ‖ ) ‖v‖2.
For any Θ ∈ Rd×T , we further have
‖Σ1/2Θ‖F ≤ 1√ n √√√√ T∑ t=1 ‖Xtθt‖22 + ic,1‖Θ‖∗, (38)
where ic,1 := 2Cρ 2
√ n
(√ Tr(Σ) + √ log(2/δ)‖Σ‖ ) , with probability 1− δ.
Proof. We use Theorem C.9. Let T = {v : |Σ−1/2v|2 ≤ 1}. Let x = Σ1/2z, X = ZΣ1/2. Then γ(T ) = √ Tr(Σ), r(T ) = ‖Σ‖1/2. We note with probability 1− δ,
sup ‖v‖=1 ∣∣∣∣ 1√n‖Xv‖2 − ‖Σ1/2v‖2 ∣∣∣∣
= sup v̄∈T ∣∣∣∣ 1√n‖Zv̄‖2 − ‖v̄‖2 ∣∣∣∣
≤Cρ 2
√ n
( γ(T ) + √ log(2/δ)r(T ) ) = Cρ2√ n (√ Tr(Σ) + √ log(2/δ)‖Σ‖ ) .
Therefore ∣∣∣ 1√n‖Xv‖2 − ‖Σ1/2v‖2∣∣∣ ≤ Cρ2√n (√Tr(Σ) +√log(2/δ)‖Σ‖) ,∀‖v‖ = 1. Then by homogeneity of v, for arbitrary v, we have∣∣∣∣ 1√n‖Xv‖2 − ‖Σ1/2v‖2 ∣∣∣∣ ≤ ‖v‖2 Cρ2√n (√Tr(Σ) +√log(2/δ)‖Σ‖)︸ ︷︷ ︸
term I
.
Notice when n C2ρ4(Tr(Σ) + ‖Σ‖ log 1/δ), term I ≤ 0.1 √ λ. Therefore |‖Σ1/2v‖2 − 1√ n ‖Xv‖2| ≤ 0.1 √ λ‖v‖.
Write Θ = UDV >, where D = diag(σ1, σ2, · · · , σT ).
1
n T∑ t=1 ‖Xtθt‖22 = 1 n T∑ t=1 σ2t ‖Xtut‖2
≥ T∑ t=1 σ2t ( ‖Σ1/2ut‖2 − ‖ut‖2 Cρ2√ n (√ Tr(Σ) + √ log(2/δ)‖Σ‖ ))2
> T∑ t=1 σ2t ( ‖Σ1/2ut‖22 − 2‖Σ1/2ut‖‖ut‖2 Cρ2√ n (√ Tr(Σ) + √ log(2/δ)‖Σ‖ )) =‖Σ1/2Θ‖2F −
2Cρ2√ n
(√ Tr(Σ) + √ log(2/δ)‖Σ‖ )∑ t σt(σt‖Σ1/2ut‖2)
≥‖Σ1/2Θ‖2F − 2Cρ2√ n
(√ Tr(Σ) + √ log(2/δ)‖Σ‖ )∑ t σt(max t σt‖Σ1/2ut‖2)
≥‖Σ1/2Θ‖2F − 2Cρ2√ n
(√ Tr(Σ) + √ log(2/δ)‖Σ‖ ) ‖Θ‖∗‖Σ1/2Θ‖F .
Therefore ‖Σ1/2Θ‖F ≤ 1√n √∑T t=1 |Xtθt‖22 + 2Cρ2√ n (√ Tr(Σ) + √ log(2/δ)‖Σ‖ ) ‖Θ‖∗︸ ︷︷ ︸
term II
.
Claim C.11. Let X be n2 × d matrix whose rows x are independent, isotropic and sub-gaussian random vectors in Rd that satisfy Assumption 4.1, where E[x] = 0 and E[xx>] = Σ. Let ΣB = B>ΣB for some matrix B that satisfies ‖BB>‖∗ . R. Then for a fixed δ > 0, and any v ∈ Rd we have: ‖Σ1/2B v‖ ≤ 1 n2 ‖XB̂v‖ + ic,2‖v‖, where ic,2 := Cρ 2 √ n2 √ R‖Σ‖ log(1/δ), and C is a universal constant.
Proof. This result directly uses Lemma C.10 when replacing X by XB̂. Notice now the subgaussian norm for the whitening distribution for B̂>x remains the same as C1ρ. Therefore ‖Σ1/2B̂v‖2 ≤ 1√ n2 ‖XB̂v‖2 + S‖v‖2 ≤ ‖Σ1/2B̂v‖2 ≤ 1√n2 ‖Xv‖2 + S‖v‖. Here S = Cρ 2/ √ n2( √
Tr(ΣB) +√ log(2/δ)‖ΣB‖) ≤ C ′ρ2/ √ n2 √ ‖Σ‖R log(1/δ) =: ic,2.

heading: D PROOF OF THEOREM 7.1
text: First, we describe a standard lifting of neural networks to infinite dimension linear regression Wei et al. (2019); Rosset et al. (2007); Bengio et al. (2006). Define the infinite feature vector with coordinates φ(x)b = (b
>x)+ for every b ∈ Sd0−1. Let αt be a signed measure on Sd0−1. The inner product notation denotes integration: α>φ(x) , ∫ Sd0−1 φ(x)bdα(b). The t
th output of the infinite-width neural network is fαt(x) = 〈αt, φ(x)〉. Consider the least-squares problem
min α1,...,αt:|supp(αt)|≤d
‖ᾱ‖2,1≤R
1
2n ∑ i,t (yit − α>t φ(xi))2, (39)
whereα(u) = [α1(u), . . . , αT (u)], and ‖α‖2,1 = ∫ Sd0−1 ‖α(b̄)‖2d(b̄). The regularizer corresponds to a group `1 regularizer on the vector measure α.
Proposition D.1. Let γd be the value of Equation (17) when the network has d neurons and γ?d be the value of Equation (39). Then
γd = γ ? d . (40)
Proof. Let B,W be solutions to Equation (17). Let B̄ = BD−1β and Dβ be a diagonal matrix whose entries are βj = ‖B>ej‖2. The network fB,W (x) = W>Dβ(B̄>x)+ and it satisfies
‖β‖2 = ‖B‖F .
We first show that γ?d ≤ γd. Define αt( bj ‖bj‖ ) = Wtjβj . We verify that
α>t φ(x) = d∑ j=1 αt(b̄j)φ(x)b̄j = d∑ j=1 Wtjβj(b̄ > j x)+ = w > t (B >x)+ = fB,wt(x).
Due to the regularizer, and using the AM-GM inequality, at optimality βj = ‖Wej‖2. Next, we verify that the two regularizer values are the same. Let w̄j be the j-th row vector of W . We have
‖α‖2,1 = d∑ j=1 βj‖w̄j‖
≤ d∑ j=1 β2j /2 + ‖w̄j‖2/2
= 1 2 ‖β‖2 + 1 2 ‖W‖2F ≤ R.
Thus the network given by α>t φ(x) has the same network outputs and regularizer values. Thus γ? ≤ γd. Finally, we show that γd ≤ γ?d . Let b̄j for j ∈ [d] be the support of the optimal measure of (39). Define βj = √ ‖α(b̄j)‖2, B = B̄Dβ where B̄ is a matrix whose rows are b̄j , and W such that
Wjt = αt(b̄j)/ √ ‖α(b̄j)‖.
We verify that the network values agree
e>t fB,W (x) = e > t W >Dβ(B̄ >x)+ = ∑ j Wjtβj(b̄ > j x)+ = α >φ(x).
Finally by our construction βj = ‖Wej‖, so the regularizer values agree. Thus γd = γ?d .
Finally, we note that the regularizer can be expressed in a variational form as8
‖α‖2,1 = min b,W :αt(b̄)=β(b̄)wt(b̄) ‖β‖22 + ‖W‖2F ,
where ‖β‖22 = ∫ β(b̄)2d(b̄) and ‖W‖2F = ∑ t ∫ wt(b̄)
2d(b̄). With these in place, we note that Equation (39) can be expressed as Equation (12) with B constrained to be a diagonal operator and xit as the lifted features φ(xit).
Proof of Theorem 7.1. The global minimizer of Equation (39) with d =∞may have infinite support, so the corresponding value may not be achieved by minimizing (17). However, Theorem 6.1 only requires that the we obtain a learner network with regularized loss less than the regularized loss of the teacher network. Since the teacher network has d neurons, this value is attainable by (17). Thus the finite-size network does not need to attain the global minimum of (39) for Claim C.1 to apply.
Since Theorem 6.1 has no dependence (even in the logarithmic terms) on the input dimension of the data, it can be applied when the input features the infinite-dimensional feature vector φ(x). The only part of the proof of Theorem 6.1 specific to the nuclear norm is that the dual norm is the operator norm. In Lemma C.5 we had an upper bound on 1n‖X
>Z‖2. Since we use the ‖ · ‖2,1 norm, we must upper bound 1n‖X
>Z‖2,∞ , the dual of the (2, 1)-norm. Note that ‖A‖2,∞ ≤ ‖A‖2, so the upper bound in Lemma C.5 still applies. Thus, Theorem 7.1 follows from Theorem 6.1.
Proof of (21). The test error of (20) is given by
E[ER(fB̂,ŵ)] . σ 1
2 √ n (‖B∗T+1‖2 + ‖w∗T+1‖22)E xin iid∼ p, z∼N(0,σ2I) [‖Φ(X)>z‖∞], (41)
via the basic inequality (c.f. proof of Claim C.2 and C.4). By the matrix Bernstein inequality (c.f. Lemma C.5 or Wei et al. (2019)), E
xi n iid∼ p,z∼N(0,I)
[‖Φ(X)>z‖∞] . √ tr(Σ). When B∗T+1,w ∗ T+1
are sampled from the same distribution as the source tasks, then 12 (‖B ∗ T+1‖2 + ‖w∗T+1‖22) ≥ R√T . Thus we conclude
E[ER(fB̂,ŵ)] . σ R√ T
√ tr(Σ)
n2 .
8Informally if α ∈ RD×T with D potentially infinite, ‖α‖2,1 = minα=diag(b)W 12‖b‖ 2 2 + 1 2 ‖W‖2F .

Review summaries:
This paper studies the benefit of few-shot learning for sample complexity, when all the tasks (both source and target task) share the same underline representation. Under some assumptions on the data and tasks, this paper improves the previous result based on the iid task assumption and shows that they can utilize all source data. The considered models include linear model (both low dimensional and high dimensional representation) and two-layer neural network.

The paper aims at justifying the success of few shot learning methods that work based on finding a shared representation among a number of tasks. A serious theoretical challenge is that, even if we assume such a representation exists (and belongs to a predefined class of functions with controlled capacity), we would still need to assume something that connects the source tasks with the target task. Previous work has considered "i.i.d. tasks", however, the obtained bounds were not natural in the sense that we don't have the usual decrease in the error as we increase the size of the training set of the source tasks. Under a different set of assumptions, the authors show that, in a sense, one can "fully" exploit the training data from the source tasks. 

This paper presents some new theoretical insights into a two-layer (linear or non-linear) network based meta-learning framework for dimension reduction and few-shot linear regression. In the considered problem setting, the hidden layer for feature extraction is assumed to be shared across the training and test tasks, and the output layer is optimized in a task-specific way with quadratic loss. For well-specified low-dimensional linear representation learning models, statistical analysis shows that when the tasks are sufficiently divergent, the excess risk of the target task estimator has a near-optimal rate of convergence, up to a near-optimal statistical error of meta-training. The corresponding results for well-specified high-dimensional linear representation and neural networks have also been derived under additional regularization conditions.