Large pretrained language models (LMs) like BERT have improved performance in many disparate natural language processing (NLP) tasks. However, fine tuning such models requires a large number of training examples for each target task. Simultaneously, many realistic NLP problems are "few shot", without a sufficiently large training set. In this work, we propose a novel conditional neural process-based approach for few-shot text classification that learns to transfer from other diverse tasks with rich annotation. Our key idea is to represent each task using gradient information from a base model and to train an adaptation network that modulates a text classifier conditioned on the task representation. While previous task-aware few-shot learners represent tasks by input encoding, our novel task representation is more powerful, as the gradient captures input-output relationships of a task. Experimental results show that our approach outperforms traditional fine-tuning, sequential transfer learning, and state-of-the-art meta learning approaches on a collection of diverse few-shot tasks. We further conducted analysis and ablations to justify our design choices.

Although the fairness community has recognized the importance of data, re-searchers in the area primarily rely on UCIAdult when it comes to tabular data. Derived from a 1994 USCensus survey, this dataset has appeared in hundreds of research papers where it served as the basis for the development and comparison of many algorithmic fairness interventions. We reconstruct a superset of the UCI Adult data from available USCensus sources and reveal idiosyncrasies of the UCIAdult dataset that limit its external validity. Our primary contribution is asuite of new datasets derived from USCensus surveys that extend the existing data ecosystem for research on fair machine learning. We create prediction tasks relating to income, employment, health, transportation, and housing. The data span multiple years and all states of the United States, allowing researchers to studytemporal shift and geographic variation. We highlight a broad initial sweep of new empirical insights relating to trade-offs between fairness criteria, performance of algorithmic interventions, and the role of distribution shift based on our new datasets. Our findings inform ongoing debates, challenge some existing narratives, and point to future research directions.

Fans want to reclaim the music and myth of Michael Jackson in the new biopic while critics call for accountability. Still from, which opened April 24. Is truth determined by the size of the audience it reaches? If so, --a new film about the pop singer Michael Jackson that is on track to have the biggest-ever opening for a music biopic, with projected earnings of $70 million at the US box office, despite critics saying it sanitizes the reality of who Jackson actually was--intends to supplant the King of Pop as the apotheosis of artistic virtue. The film's release has sparked a familiar but newly intensified civil war online, between those eager to reclaim the music and myth of Jackson, and those who see any celebration of him as a failure of accountability.