Summary and Contributions: This paper studies the meta-learning in the setting of mixture of k linear regressions. Based on an outlier-robust PCA algorithm and a SOS-based algorithm to utilize the higher order moments, it improves the sample complexity required for heavy tasks.
Strengths: The paper is sound. It improves the theoretical guarantees in terms of the sample complexity, and extends the existing work to accommodate adversarial data. The idea of using the SOS-based method to exploit the higher order statistics as well as the robust subspace estimation algorithm may find useful for other problems. And the topic is trendy in learning at the moment.
Weaknesses: It would improve the quality of the paper if more comprehensive numerical experiments can be provided, especially the performance of Algorithm 1.
Correctness: I believe them to be correct.
Clarity: The paper is well written.
Relation to Prior Work: The paper gives a nice review of and comparison with exsiting works.
Summary and Contributions: The paper designs an outlier-robust PCA algorithm and a sum-of-squares algorithm to exploit the information from higher order moments. The proposed approach is robust to adversarial corruption and achieves a statistical trade-off.
Strengths: 1. The motivation is clear and the author provides clear and intuitive analysis. 2. The claims and conclusion are convincing supported by sufficient theoretical analysis and proof. 3. The topic and contribution are suitable for the NeurIPS community.
Weaknesses: 1. No sufficient experimental evaluation for its performance and comparison 2. The claim that “Our main result relies on making each step of Algorithm 1 robust” has no corresponding empirical verification.
Correctness: Correct [but not carefully check for each proof and equation]
Clarity: Basically, it is well written, but there should be suitable trade-offs for the main paper and supplement, theoretical analysis and experimental (if provided more)
Relation to Prior Work: Yes, the paper discusses the relation between this work and previous work, and analyses the limitation of prior work, highlights the contribution of this work.
Additional Feedback: The paper design a novel meta-learning approach which is robust to data scarcity and adversarial corruption. The author provides clear and intuitive analysis for the use of light and heavy tasks. The proposed algorithms and corresponding claims are convincing supported by sufficient theoretical analysis and proof. But there is no sufficient evaluation for its performance and comparison in experiment part, the figure 2 only verifies the impact of the corrupted points. So, the actual improvement and performance of the algo. 1 are not clear; in addition, the claim that “Our main result relies on making each step of Algorithm 1 robust” has no corresponding empirical verification. So, there should be suitable trade-offs for the main paper and supplement, theoretical analysis and experimental (if provided more).
Summary and Contributions: This paper deals with "meta-learning" for the case of mixture of linear regressions. More specifically, suppose we deal with n linear regression data sets after which we are challenged with a final learning task of linear regression, but the parameters of these “tasks” are not completely unrelated. In particular, suppose there is a prior distribution (with at most k possible outcomes) from which parameters of linear regression (i.e., the linear function and noise's variance) are sampled. The general idea here is that by learning from "different" (yet related) tasks the learner aims to do better on the final task, and the paper’s focus is on a theoretically natural setting. (One of the) paper's contribution is to address with the *robust* version of the mentioned problem. Namely, it is shown how to do the meta learning (of mixed linear regressions) when some small (say 1%) fraction of each task (out of n tasks) is corrupted adversarially. The paper also shows how to deal with "small batches" setting in which even the larger batches are << sqrt(k) in size, though then the paper's algorithm needs more batches. E.g., if *only* small tasks are given (of size log k) then the paper's algorithm needs quasi-polynomial(k)=n tasks and running time. The paper does this by looking at more moments (than just the 2nd moment, which was the approached of previous work stuck at requiring batches of size sqrt(k)). The algorithm follows the following recipe of first finding a subspace spanned by the k possible regression vectors (a form of PCA) then a clustering of that space to find the k parameters and then a "classification" step that fine tunes the found parameters. The middle step uses large batches and the other steps use small batches. The paper's contribution is to do all three steps above in a robust way (the 2nd of which uses ideas from some-of-squares methods) such that they can tolerate some fraction of arbitrary points added to it. At the same time, the paper also shows how to manage small batches of way smaller than what previous work could handle (going from sqrt(k) to log(k)). Post rebuttal: Thanks for the clarifications. They answered all of my questions.
Strengths: Addressing an important basic problem in the theory of meta learning. Extension to the robust regime is very interesting. The paper (at least the main body) is written quite well.
Weaknesses: The abstract and intro both start with mentioning practical scenarios (i.e., medical image processing) but I found no more discussions in the paper that points out to such applications of the proved results. I know that this is theoretical work, but still it would be better if applications of such meta learning framework were explored as well.
Correctness: I did not check the proofs in detail, but the discussions, intuition, and citations for technical tools seem solid to me.
Clarity: For someone who is not working on this area, I still found the paper accessible (partly due to formally stated setting and definitions of the problems).
Relation to Prior Work: It seems so.
Additional Feedback: Assumption 2 needs clarification: Do you mean the adversary's knowledge in tampering with each of the three groups of batches is oblivious to the other two groups of batches? or could the adversary first look at *all* batches and then pick the corruptions? minor comments/typos: please define the notation \Tilde\Omega(.). I guessed that means hiding polylog(k) factors, but that is not quite as standard yet, so needs clarification. line 51: "it is critical tailor" -> to tailor? line 78: please say what epsilon is (it becomes clear later). line 89: "but focusing on a simple but canonical" -> yet canonical (repeating but) Line 96: you first describe the general goal of meta learning as learning a final task from previously related tasks. But then the recipe for doing so is split into two steps: first approximating the meta parameter and then doing the final learning. This seems like a very natural approach, but could there be any other more *direct* approach that combines the two steps? E.g, one can define a maximum likelihood formulation that directly aims to maximize the chance of a label y for a given query x for the final task given ALL the data so far. I guess this would be hard to do algorithmically, but just commenting that the way these paragraphs are written it seems like doing as discussed is the only way to do it, because you say in line 105 that "the meta-learning problem refers to solving the following:...". line 107-8: Our goal is solve this -> is to solve (or, is solving) page 4: "As k << d in typical settings" Why? also, maybe good to bring this up earlier as helps getting the right intuition. In algorithm 2: Define k_SVD (k first singular values?) line 250: the sentence needs to be rewritten.
Summary and Contributions: I have read the author feedback. This paper looks at learning mixed linear regressions which were observed across a collection of data sets ranging from small to larger. Additionally, the datasets are allowed to be corrupted by adversarial noise of some predetermined size. The authors present an algorithm to robustly estimate the model parameters by (1) estimating the subspace within which the linear regression parameters live (2) grouping "large" regression tasks in this subspace then learning a regression coefficient for each; and finally (3) assigning "small" regression tasks to these clusters and using their information to refine the parameter estimates. The main result of the paper is Theorem 1 which gives estimation bounds on the various model parameters as a function of the amount adversarial noise and other inputs. Some very small experiments are provided.
Strengths: The main contribution are the theoretic guarantees on the estimation accuracy of a meta-learning algorithm in terms of adversarial corruption rates.
Weaknesses: As the length of the appendix indicates this is clearly a substantial topic with great depth. However, overall, I found the paper very hard to read. For a start, the motivation for the adversarial setup is weak. The authors should make a more convincing case why these types of extensions are necessary in practice. E.g., why are the existing methods they mention brittle and why is this the right adversarial model? In what way does this improve over other method's performance in practice? The first part of the paper seems to be aimed at building early intuition. However, I found it somewhat unhelpful as it presents results before we've even seen what the model in question is. For example, the adversarial model is only explained in Assumption 2 in Section 2, i.e., 5 pages into the paper. The generative model for the data is only introduced on page 3, making it hard to interpret the various corollaries that come before. Additionally, Section 2 mentions much prior context in passing which isn't very well explained (e.g., SOS, Poincar\'e distributions). I would encourage the authors to further streamline the presentation and to clarify context. The only part of the paper that is experimentally verified is the subspace estimation method, which forms only one small part of Algorithm 1 (and the description of the experimental setup is in the Appendix). I would encourage the authors to provide a more comprehensive suite of experiments, which contrasts to existing outlier methods to demonstrate the effectiveness of this approach.
Correctness: I was not able to verify all theoretical details. Most of Algorithm 1 is not empirically verified.
Clarity: As mentioned above, I found the paper hard to read. Instead of giving key intuitions, the authors focus a lot the space on discussing sample rates and complexities. There are almost no experiments given to underline key conclusions.
Relation to Prior Work: The authors discuss related work, which is helpful, though I would have liked to see more justification why this particular adversarial model is the "right" one. Much more context should be given on SOS methods.
Additional Feedback: Corollary 1.1: I am a bit surprised that there is no dependence shown on the constant hidden in the O(1) notation. How does this impact the required sample/time complexity? L74: What is the dependence on k in the O(d^2) notation? Thm. 1: Why is the only dependence on delta in t_L2? Shouldn't the clustering step also have a bearing on overall quality of the estimation?