Sr. Computer Scientist, Artificial Intelligence Center
Eric Yeh is an Senior Computer Scientist in the Artificial Intelligence Center at SRI International. He has expertise applying and adapting machine learning methods for a wide variety of domains, such as anomaly detection over cellular base-stations, human guided machine learning, multimodal image and video retrieval, semantic parsing, and textual summarization. Most recently he was principal investigator for a project investigating conditional generative methods. He holds a MS in Computer Science with a Distinction in Research from Stanford University.
Recent publications
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Automatic Measures for Evaluating Generative Design Methods for Architects
We describe the expectations architects have for design proposals from conceptual sketches, and identify corresponding automated metrics from the literature.
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Outcome-Guided Counterfactuals for Reinforcement Learning Agents from a Jointly Trained Generative Latent Space
We present a novel generative method for producing unseen and plausible counterfactual examples for reinforcement learning (RL) agents based upon outcome variables that characterize agent behavior.
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Bridging the Gap: Converting Human Advice into Imagined Examples
We present an approach that converts human advice into synthetic or imagined training experiences, serving to scaffold the low-level representations of simple, reactive learning systems such as reinforcement learners.
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Interestingness Elements for Explainable Reinforcement Learning through Introspection
The framework uses introspective analysis of an agent’s history of interaction with its environment to extract several interestingness elements regarding its behavior.
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Explanation to Avert Surprise
We present an explanation framework based on the notion of explanation drivers —i.e., the intent or purpose behind agent explanations. We focus on explanations meant to reconcile expectation violations and enumerate a set of triggers for proactive explanation.
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An Annotated Corpus and Method for Analysis of Ad-Hoc Structures Embedded in Text
We describe a method for identifying and performing functional analysis of structured regions that are embedded in natural language documents, such as tables or key-value lists.