Jason (jcreed) wrote,
Jason
jcreed

Open-ended machine learning/regression question:

Imagine the kind of situation where you have a pile of training data that consists of pairs

ALICE > BOB
BOB > CAROL
CAROL > BOB
BOB > CAROL

whose intended meaning is what you'd expect for a system that computes chess scores and the like: Alice beat Bob at some competitive task, say, chess, then Bob beat Carol, then Carol beat Bob, then Bob beat Carol again. So I'm aware of there being lots of work on designing scoring systems like this, so that score reflects how likely Alice is to beat Carol in the future, even though Alice has never played Carol before.

But suppose you have lots of other features that you can extract from the entities being compared, (e.g., I have age and hair color and average amount of chess books read per month, for Alice and Bob and Carol and Doug and Emily) and you want to predict the outcome of a match between Doug and Emily --- who are not featured in the training data at all.

Is there a name for this kind of task? Is there a good principled answer/algorithm/approach/whatever? It's weird because you have training data, but it's only indirectly about the function you seem to want to compute, which is some kind of function from person to real numbered score.
Tags: machine learning, math, ranking
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  • (no subject)

    Lunch at the goog with wjl, also ran into cgarrod and kelli and tomczak and gopi and agoode. Attended sigbovik, was traditional and glonous and…

  • (no subject)

    The eighth sigbovik sigbovicked successfully. A few really funny talks, some farther on the meh end of the spectrum, as is not uncommon. I really…

  • (no subject)

    Sigbovik happened! It was a good and fine sigbovik. I am mostly impressed that it has simply continued to happen for 7 consecutive years.