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Lionel Wong
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2020 – today
- 2024
- [c5]Gabriel Grand, Lionel Wong, Matthew Bowers, Theo X. Olausson, Muxin Liu, Joshua B. Tenenbaum, Jacob Andreas:
LILO: Learning Interpretable Libraries by Compressing and Documenting Code. ICLR 2024 - [c4]Lionel Wong, Jiayuan Mao, Pratyusha Sharma, Zachary S. Siegel, Jiahai Feng, Noa Korneev, Joshua B. Tenenbaum, Jacob Andreas:
Learning Grounded Action Abstractions from Language. ICLR 2024 - [i11]Lance Ying, Tan Zhi-Xuan, Lionel Wong, Vikash Mansinghka, Joshua B. Tenenbaum:
Grounding Language about Belief in a Bayesian Theory-of-Mind. CoRR abs/2402.10416 (2024) - [i10]Guangyuan Jiang, Matthias Hofer, Jiayuan Mao, Lionel Wong, Joshua B. Tenenbaum, Roger P. Levy:
Finding structure in logographic writing with library learning. CoRR abs/2405.06906 (2024) - [i9]Cedegao E. Zhang, Katherine M. Collins, Lionel Wong, Adrian Weller, Joshua B. Tenenbaum:
People use fast, goal-directed simulation to reason about novel games. CoRR abs/2407.14095 (2024) - [i8]Katherine M. Collins, Ilia Sucholutsky, Umang Bhatt, Kartik Chandra, Lionel Wong, Mina Lee, Cedegao E. Zhang, Tan Zhi-Xuan, Mark K. Ho, Vikash Mansinghka, Adrian Weller, Joshua B. Tenenbaum, Thomas L. Griffiths:
Building Machines that Learn and Think with People. CoRR abs/2408.03943 (2024) - [i7]Lance Ying, Tan Zhi-Xuan, Lionel Wong, Vikash Mansinghka, Joshua B. Tenenbaum:
Understanding Epistemic Language with a Bayesian Theory of Mind. CoRR abs/2408.12022 (2024) - 2023
- [j1]Matthew Bowers, Theo X. Olausson, Lionel Wong, Gabriel Grand, Joshua B. Tenenbaum, Kevin Ellis, Armando Solar-Lezama:
Top-Down Synthesis for Library Learning. Proc. ACM Program. Lang. 7(POPL): 1182-1213 (2023) - [c3]Benjamin Lipkin, Lionel Wong, Gabriel Grand, Josh Tenenbaum:
Evaluating statistical language models as pragmatic reasoners. CogSci 2023 - [c2]Marcelo Gomes Mattar, Judith E. Fan, Wai Keen Vong, Lionel Wong:
How does the mind discover useful abstractions? CogSci 2023 - [c1]Cedegao Zhang, Lionel Wong, Gabriel Grand, Josh Tenenbaum:
Grounded physical language understanding with probabilistic programs and simulated worlds. CogSci 2023 - [i6]Benjamin Lipkin, Lionel Wong, Gabriel Grand, Joshua B. Tenenbaum:
Evaluating statistical language models as pragmatic reasoners. CoRR abs/2305.01020 (2023) - [i5]Katherine M. Collins, Albert Q. Jiang, Simon Frieder, Lionel Wong, Miri Zilka, Umang Bhatt, Thomas Lukasiewicz, Yuhuai Wu, Joshua B. Tenenbaum, William Hart, Timothy Gowers, Wenda Li, Adrian Weller, Mateja Jamnik:
Evaluating Language Models for Mathematics through Interactions. CoRR abs/2306.01694 (2023) - [i4]Lionel Wong, Gabriel Grand, Alexander K. Lew, Noah D. Goodman, Vikash K. Mansinghka, Jacob Andreas, Joshua B. Tenenbaum:
From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of Thought. CoRR abs/2306.12672 (2023) - [i3]Lance Ying, Katherine M. Collins, Megan Wei, Cedegao E. Zhang, Tan Zhi-Xuan, Adrian Weller, Joshua B. Tenenbaum, Lionel Wong:
The Neuro-Symbolic Inverse Planning Engine (NIPE): Modeling Probabilistic Social Inferences from Linguistic Inputs. CoRR abs/2306.14325 (2023) - [i2]Gabriel Grand, Lionel Wong, Matthew Bowers, Theo X. Olausson, Muxin Liu, Joshua B. Tenenbaum, Jacob Andreas:
LILO: Learning Interpretable Libraries by Compressing and Documenting Code. CoRR abs/2310.19791 (2023) - [i1]Lionel Wong, Jiayuan Mao, Pratyusha Sharma, Zachary S. Siegel, Jiahai Feng, Noa Korneev, Joshua B. Tenenbaum, Jacob Andreas:
Learning adaptive planning representations with natural language guidance. CoRR abs/2312.08566 (2023)
Coauthor Index
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