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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MIL-BERT: Classification of Arbitrarily Large Text with Performance and Explanatory Guarantees
Abstract Objective/Background Most artificial intelligence systems can analyze only a limited amount of text at once. Shortening a document can remove important evidence, while processing the entire document may require…
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Interpolative Decoding: Exploring the Spectrum of Personality Traits in LLMs
Abstract Recent research has explored using very large language models (LLMs) as proxies for humans in tasks such as simulation, surveys, and studies. While LLMs do not possess a human…
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AI as Collaborative Partner: Rethinking Human-AI Teaming for the Real World
Abstract Much work in human-AI teaming today involves collaboration under fairly constrained settings. Humans supervise AI agents, who are relegated to following orders. The division of tasks is relatively superficial,…
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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.