William L. Hamilton

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Probabilistic latent-variable models are a powerful tool for modelling structured data. However, traditional expectation-maximization methods of learning such models are both computationally expensive and prone to local-minima. In contrast to these traditional methods, recently developed learning algorithms based upon the method of moments are both(More)
Predictive state representations (PSRs) offer an expressive framework for modelling partially observable systems. By compactly representing systems as functions of observable quantities, the PSR learning approach avoids using local-minima prone expectation-maximization and instead employs a globally optimal moment-based algorithm. Moreover, since PSRs do(More)
Efficiently learning accurate models of dy-namical systems is of central importance for developing rational agents that can succeed in a wide range of challenging domains. The difficulty of this learning problem is particularly acute in settings with large observation spaces and partial observability. We present a new algorithm, called Compressed Predictive(More)
The most polymorphic gene family in P. falciparum is the ∼60 var genes distributed across parasite chromosomes, both in the subtelomeres and in internal regions. They encode hypervariable surface proteins known as P. falciparum erythrocyte membrane protein 1 (PfEMP1) that are critical for pathogenesis and immune evasion in Plasmodium falciparum. How var(More)
Understanding how words change their meanings over time is key to models of language and cultural evolution, but historical data on meaning is scarce, making theories hard to develop and test. Word embeddings show promise as a di-achronic tool, but have not been carefully evaluated. We develop a robust methodology for quantifying semantic change by(More)
A word's sentiment depends on the domain in which it is used. Computational social science research thus requires sentiment lexicons that are specific to the domains being studied. We combine domain-specific word embeddings with a label propagation framework to induce accurate domain-specific sentiment lexicons using small sets of seed words. We show that(More)
Words shift in meaning for many reasons, including cultural factors like new technologies and regular linguistic processes like sub-jectification. Understanding the evolution of language and culture requires disentangling these underlying causes. Here we show how two different distributional measures can be used to detect two different types of semantic(More)
Computationally modeling the evolution of science by tracking how scientific topics rise and fall over time has important implications for research funding and public policy. However, little is known about the mechanisms underlying topic growth and decline. We investigate the role of rhetorical framing: whether the rhetorical role or function that authors(More)
Pathogen genome sequencing directly from clinical samples is quickly gaining importance in genetic and medical research studies. However, low DNA yield from blood-borne pathogens is often a limiting factor. The problem worsens in extremely base-biased genomes such as the AT-rich Plasmodium falciparum. We present a strategy for whole-genome amplification(More)
Let A = h↵ 0 , ↵ 1 , T, {O } 2⌃ i be a HMM with n  |⌃| states. To prove the consistency of the algorithm described in Section 3.3, we will show that the when given access to data computed from f = f A , the algorithm returns a HMM identical to A modulo a permutation on the states. Suppose that we are given sets of prefixes and suxes P, S ⇢ ⌃ ? like in the(More)
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