Question: How does this work relate to Janzing (2019)?
Evidence: Theoretical Analysis of the DG Setting and Algorithms The DG problem setting was first analysed in. Since then there have been some attempts to analyse DG algorithms from a generalisation bound perspective. However these studies have theoretical results that are either restricted to specific model classes, such as kernel machines, or make strong assumptions about how the domains seen during training will resemble those seen at test time-e.g., that all domains are convex combinations of a finite pre-determined set of prototypical domains. In contrast, our Rademacher complexity approach can be applied to a broad range of model classes (including neural networks), and makes comparatively milder assumptions about the relationship between domains-i.e., they are i.i.d. samples from another arbitrary distribution over domains.
The majority of the existing work investigating the theoretical foundations of DG follow the initial formalisation of the domain generalisation problem put forth by, where the goal is to minimise the expected error over unseen domains. However, several recent works have also explored the idea of bounding the error on a single unseen domain with the most pathological distribution shift. This type of analysis is typically rooted in methods from causal inference, rather than statistical learning theory. As a consequence, they are able to make stronger claims for the problems they address, but the scope of their analysis is necessarily limited to the scenarios where their assumptions about the underlying causal structures are valid. For example, Janzing (2019) provides bounds that assume problems conform to a specific class of structural equation models, and the analysis is performed under the assumption that infinite training data is available within each of the observed training domains. Throughout the work we address the stan-dard DG formalisation given by, where one is concerned with the expected performance of a model on domains sampled from some distribution over domains.
Others rely on trying to link between domain adaptation objectives (where target domains are observable for alignment to source domains) and domain generalisation (where target domains are not observable and thus cannot correctly be used in a learning objective). proceed by making assumptions on the structure of the distribution over possible domains (i.e., that it has support determined by the convex hull of a finite set of prototypical domains), which allows them to upper bound the domain alignment metric. provide a bound that depends on an unobservable domain distance quantity, which they then approximate in experiments using kernel density estimates. is another piece of work that theoretically investigates the generalisation of ERM in a DG setting. They deal with online DG, where each time-step corresponds to observing a new domain, and the learner must produce a new model capable of generalising to novel domains. Another point of difference between their work and the standard DG problem setting of is that the domain at each time-step is chosen by an adversary. They analyse this game for a finite number of time-steps, but they assume each domain has an infinite amount of data. They also put some limitations on the adversary: e.g., it must choose a domain that is a convex combination of a finite number of pre-determined domains. In contrast, our theoretical analysis is in the more realistic setting where one has a finite amount of data per domain, and the domains we consider are not limited to convex combinations of a set of prototypical domains. Possibly the most similar work to our theoretical contributions is due to, who also provide learningtheoretic generalisation bounds for DG. However, their analysis only applies to finite hypothesis classes (which does not include, e.g., linear models or neural networks), whereas ours can be applied to any class amenable to analysis with Rademacher complexity.
The main existing empirical analysis on DG is, who compared several state of the art DG methods under a common evaluation and hyper-parameter tuning protocol called DomainBed. They ultimately defend Empirical Risk Minimization (ERM) over more sophisticated alternatives on the grounds that no competitor consistently beats it across the benchmark suite. We also broadly defend ERM, and build on the same benchmark, but differently we provide a much deeper analysis into when and why ERM works. More specifically: (i) We provide a new theoretical analysis of ERM's generalisation quality unlike the prior purely empirical evaluation, (ii) We re-use the DomainBed benchmark to directly corroborate this theory under controlled conditions using linear models where model complexity can be tractably and accurately tuned. (iii) We use our complexity-based analysis to explain the previously erratic results of prior DomainBed competitors in terms of model complexity. (iv) We identify, and empirically validate, the preferred model selection criterion for DG, a point which was inconclusive in.
However, several recent works have also explored the idea of bounding the error on a single unseen domain with the most pathological distribution shift (Janzing, 2019). This type of analysis is typically rooted in methods from causal inference, rather than statistical learning theory. As a consequence, they are able to make stronger claims for the problems they address, but the scope of their analysis is necessarily limited to the scenarios where their assumptions about the underlying causal structures are valid. For example, Janzing (2019) provides bounds that assume problems conform to a specific class of structural equation models, and the analysis is performed under the assumption that infinite training data is available within each of the observed training domains. Throughout the work we address the stan2 Under review as a conference paper at ICLR 2022 dard DG formalisation given by Blanchard et al. (2011), where one is concerned with the expected performance of a model on domains sampled from some distribution over domains.
Answer: There are several significant differences to the scope of our work compared to Janzing. Janzing assumes access to infinite data in each domain (we assume a finite training set), uses a non-standard measure of model complexity (we use Rademacher complexity), and restricts analysis to problems with a specific causal structure, while we make no causal structure assumption. The former means that the wide range of existing tools to measure or constrain Rademacher complexity for diverse model classes can be used with ours, but not Janzing’s. The latter means that our analysis is more generally applicable to diverse problems. Moreover, it is unclear whether the object they bound (expected risk under the interventional distribution) is equivalent to what we bound (expected risk across all domains). We have added discussion of this paper (and others) to our related work section.