Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

We study linear regression and classification in a setting where the learning algorithm is allowed to access only a limited number of attributes per example, known as the limited attribute observation model. In this well-studied model, we provide the first lower bounds giving a limit on the precision attainable by any algorithm for several variants of regression, notably linear regression with the absolute loss and the squared loss, as well as for classification with the hinge loss. We complement these lower bounds with a general purpose algorithm that gives an upper bound on the achievable precision limit in the setting of learning with missing data.

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

First, as R4 suggested, "symbolic35 tree" was more approachable for people in the ML community. Second, the symbolic tree is declared by the user using36 decorators and serves to represent high-level program constructs, which is different from the AST that represents all37 the syntactic structures for the program. For example, the full Python AST contains information about objects' class38 methods, whereas our symbolic representation does not.39 R4: "Second, most of their tool/language design could be summarized as adding some kind of non determinis-40 tic/parametric choice ... It's extension to ML does not introduce anything particularly new ..."41 We agree with R4 that symbolic programming and non-deterministic programming are well-studied topics in the PL42 community. However, we would like to emphasize that this work is the first to introduce such concepts to AutoML43 to significantly reduce engineering effort, which is a novel and useful contribution. For example, PyGlove leverages44 symbolic manipulation to decouple the search algorithm, search space and child program, which enabled us to unify45 the interface among search methods with and without weight sharing. To enable symbolic programming in Python,46 PyGlove implements an object model for maintaining the consistency of program state during symbolic manipulation.47 R4 "Provide the grammar in the main text"48 We understand the "grammar" here as a reference to the formal definition of the search space specification. We will49 revise current Appendix Table 3 into a formal definition, and add it to the "search space" sub-section.50

The promising zero-shot generalization of vision-language models such as CLIP has led to their adoption using prompt learning for numerous downstream tasks. Previous works have shown test-time prompt tuning using entropy minimization to adapt text prompts for unseen domains. While effective, this overlooks the key cause for performance degradation to unseen domains - distribution shift. In this work, we explicitly handle this problem by aligning the out-of-distribution (OOD) test sample statistics to those of the source data using prompt tuning. We use a single test sample to adapt multi-modal prompts at test time by minimizing the feature distribution shift to bridge the gap in the test domain. Evaluating against the domain generalization benchmark, our method improves zero-shot top1 accuracy beyond existing prompt-learning techniques, with a 3.08%improvement over the baseline MaPLe. In cross-dataset generalization with unseen categories across 10 datasets, our method improves consistently across all datasets compared to the existing state-of-the-art.