118 citations · 610 across the 55 of their papers we have counts for
9 papers · 2 filters
Learning Discrete Distributions from Untrusted Batches
Mingda Qiao, Gregory Valiant
We consider the problem of learning a discrete distribution in the presence of an fraction of malicious data sources. Specifically, we consider the setting where there is some…
Learning Overcomplete HMMs
Vatsal Sharan, Sham Kakade, Percy Liang +1
We study the problem of learning overcomplete HMMs---those that have many hidden states but a small output alphabet. Despite having significant practical importance, such HMMs are…
Sketching Linear Classifiers over Data Streams
Kai Sheng Tai, Vatsal Sharan, Peter Bailis +1
We introduce a new sub-linear space sketch---the Weight-Median Sketch---for learning compressed linear classifiers over data streams while supporting the efficient recovery of larg…
Learning Populations of Parameters
Kevin Tian, Weihao Kong, Gregory Valiant
Consider the following estimation problem: there are entities, each with an unknown parameter , and we observe independent random variables, …
A Data Prism: Semi-Verified Learning in the Small-Alpha Regime
Michela Meister, Gregory Valiant
We consider a model of unreliable or crowdsourced data where there is an underlying set of binary variables, each evaluator contributes a (possibly unreliable or adversarial) e…
Estimating the unseen from multiple populations
Aditi Raghunathan, Greg Valiant, James Zou
Given samples from a distribution, how many new elements should we expect to find if we continue sampling this distribution? This is an important and actively studied problem, with…