Eric Hsiung — accepted AI-conference papers

2026

Learning Quantitative Automata Modulo Theories

Eric Hsiung, Nathan Tsoi, Swarat Chaudhuri, Joydeep Biswas

IJCAI 2026

We introduce QUINTIC, a general algorithm for actively learning quantitative automata from preferences. Quantitative automata evaluate input sequences by applying a valuation function---such as sum, product, or average---to the output labels of the states visited. Such models naturally arise in use

Cited by 0 Scholar ↗

2022

Generalizing to New Domains by Mapping Natural Language to Lifted LTL

Eric Hsiung, Hiloni Mehta, Junchi Chu, Xinyu Liu, Roma Patel, Stefanie Tellex +1

ICRA 2022poster

Recent work on using natural language to specify commands to robots has grounded that language to LTL. However, mapping natural language task specifications to LTL task specifications using language models require probability distributions over finite vocabulary. Existing state-of-the-art methods ha…

Cited by 16 Source ↗Scholar ↗