Search for: All records

Training with an emphasis on “hard-to-learn” components of the data has been proven as an effective method to improve the generalization of machine learning models, especially in the settings where robustness (e.g., generalization across distributions) is valued. Existing literature discussing this “hard-to-learn” concept are mainly expanded either along the dimension of the samples or the dimension of the features. In this paper, we aim to introduce a simple view merging these two dimensions, leading to a new, simple yet effective, heuristic to train machine learning models by emphasizing the worst-cases on both the sample and the feature dimensions. We name our method W2D following the concept of “Worst-case along Two Dimensions”. We validate the idea and demonstrate its empirical strength over standard benchmarks. 

Machine learning has demonstrated remarkable prediction accuracy over i.i.d data, but the accuracy often drops when tested with data from another distribution. In this paper, we aim to offer another view of this problem in a perspective assuming the reason behind this accuracy drop is the reliance of models on the features that are not aligned well with how a data annotator considers similar across these two datasets. We refer to these features as misaligned features. We extend the conventional generalization error bound to a new one for this setup with the knowledge of how the misaligned features are associated with the label. Our analysis offers a set of techniques for this problem, and these techniques are naturally linked to many previous methods in robust machine learning literature. We also compared the empirical strength of these methods demonstrated the performance when these previous techniques are combined, with implementation available here 

Abstract Heterologous tRNAs used for noncanonical amino acid (ncAA) mutagenesis in mammalian cells typically show poor activity. We recently introduced a virus‐assisted directed evolution strategy (VADER) that can enrich improved tRNA mutants from naïve libraries in mammalian cells. However, VADER was limited to processing only a few thousand mutants; the inability to screen a larger sequence space precluded the identification of highly active variants with distal synergistic mutations. Here, we report VADER2.0, which can process significantly larger mutant libraries. It also employs a novel library design, which maintains base‐pairing between distant residues in the stem regions, allowing us to pack a higher density of functional mutants within a fixed sequence space. VADER2.0 enabled simultaneous engineering of the entire acceptor stem ofM. mazeipyrrolysyl tRNA (tRNA^Pyl), leading to a remarkably improved variant, which facilitates more efficient incorporation of a wider range of ncAAs, and enables facile development of viral vectors and stable cell‐lines for ncAA mutagenesis.