Armed with some Python and a white-hot sense of injustice, one medical student spent six months trying to figure out whether an algorithm trashed his job application. It was mid-October, peak leaf-peeping season in Hanover, New Hampshire, and Chad Markey was on a rare break between clinical rotations during his last year of medical school. He should have been inhaling Green Mountain air and gossiping with his Dartmouth classmates about life after graduation. In a few months, they'd all be going their separate ways to start residency training at hospitals around the country. Instead, Markey was alone in his apartment, deep down a rabbit hole, preparing to go to war. He'd wake each morning, eat breakfast, open his laptop at the kitchen table or settle into the tan armchair with the good back support, and start coding . Some days, he wouldn't notice the sun had gone down until one of his roommates came home and asked why the lights weren't on. For days, Markey had been scrolling through a Discord group about medical residency, a font of crowdsourced knowledge where students report back to their peers on every stage of the application and selection process. He'd watched as other students, lots of them, posted about the interview invitations they'd received.

I Have a Warning for Everyone. Consider this my open admission. When I graduated from UC-Berkeley with my "useless" comparative literature degree, into one of the bleakest job markets in recent American memory, I thought to myself, . That was what brought me to marketing myself as an "academic editor," and an "admissions essay advisor," on various freelancing websites last fall. I figured I had done my fair share of editing for friends throughout the years, and I needed another gig to supplement my inconsistent substitute-teaching paychecks.

Inspired by real immigrant stories, H1B.Life captures the uncertainty, trade-offs, and pure luck that shape the lives of people trying to build a future in the US. When Allison Yang moved to the US from China two years ago, she noticed that immigrants often talked about their visa status like they were playing cards. The former Chinese journalist and founder of the game studio Reality Reload was at an event in New York when she heard fellow Chinese immigrants talking in confusing terminologies, like playing a Queen, Knight, or Ace. Everyone introduced themselves by words like H-1B, OPT, L-1, O-1, NIW--names of legal immigration categories in the US. With their cards on the table, they could start talking in greater depth about each person's immigration journey.

This gave birth to learning-augmented algorithms, which use these predictions to go beyond the standard long-standing worst-case limitations. The design of such algorithms requires establishing good tradeoffs between consistency and robustness, i.e. having improved performance when the predictions are accurate, and not behaving poorly

With more than 4.5 million patent documents, HUPD is two to three times larger than comparable corpora.

However, the exact formula for front-door adjustment depends on the structure of the graph, which is difficult to learn in practice.

Inrecentyears, there has been a significant increase of publicly available unstructured data resources for computer vision and NLP tasks.