Dylan Sam

Publications4
h-index2
Citations29
Highly Influential Citations3
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Adversarial Multi Class Learning under Weak Supervision with Performance Guarantees
TLDR
The learning algorithm provably converges to a model that has minimum empirical risk with respect to an adversarial choice over feasible labelings for a set of unlabeled data, where the feasibility of a labeling is computed through constraints defined by rigorously estimated statistics of the weak supervision sources.
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Semi-Supervised Aggregation of Dependent Weak Supervision Sources With Performance Guarantees
We develop a novel method that provides theoretical guarantees for learning from weak labelers without the (mostly unrealistic) assumption that the errors of the weak labelers are independent or come…
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Automated Data Accountability for Missions in Mars Rover Data
This paper proposes an automated solution system to assist with Real-Time Operations and automatically identify and report on issues with data transfer, archive, and manipulation throughout the…
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Undergraduate Honors Thesis Learning from Dependent Weak Supervision Sources