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ORCIDNoah D. Goodman
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- affiliation: Stanford University, Department of Psychology, USA
- affiliation: Massachusetts Institute of Technology, Department of Brain and Cognitive Sciences, USA
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2020 – today
- 2025
[c157]Joy Hsu, Jiayuan Mao, Joshua B. Tenenbaum, Noah D. Goodman, Jiajun Wu:
What Makes a Maze Look Like a Maze? ICLR 2025- [c156]Belinda Z. Li, Alex Tamkin, Noah D. Goodman, Jacob Andreas:
Eliciting Human Preferences with Language Models. ICLR 2025 - [i134]Kanishk Gandhi, Michael Y. Li, Lyle Goodyear, Louise Li, Aditi Bhaskar, Mohammed Zaman, Noah D. Goodman:
BoxingGym: Benchmarking Progress in Automated Experimental Design and Model Discovery. CoRR abs/2501.01540 (2025) - [i133]Satchel Grant, Noah D. Goodman, James L. McClelland:
Emergent Symbol-like Number Variables in Artificial Neural Networks. CoRR abs/2501.06141 (2025) - [i132]Polina Tsvilodub, Kanishk Gandhi, Haoran Zhao, Jan-Philipp Fränken, Michael Franke, Noah D. Goodman:
Non-literal Understanding of Number Words by Language Models. CoRR abs/2502.06204 (2025) - [i131]Kanishk Gandhi, Ayush Chakravarthy, Anikait Singh, Nathan Lile, Noah D. Goodman:
Cognitive Behaviors that Enable Self-Improving Reasoners, or, Four Habits of Highly Effective STaRs. CoRR abs/2503.01307 (2025) - [i130]Taylor Sorensen, Pushkar Mishra, Roma Patel, Michael Henry Tessler, Michiel A. Bakker, Georgina Evans, Iason Gabriel, Noah D. Goodman, Verena Rieser:
Value Profiles for Encoding Human Variation. CoRR abs/2503.15484 (2025) - [i129]Yutong Xin, Jimmy Xin, Gabriel Poesia, Noah D. Goodman, Qiaochu Chen, Isil Dillig:
Automated Discovery of Tactic Libraries for Interactive Theorem Proving. CoRR abs/2503.24036 (2025) - 2024
- [j25]Gabriel Poesia, Kanishk Gandhi, Eric Zelikman, Noah D. Goodman:
Certified Deductive Reasoning with Language Models. Trans. Mach. Learn. Res. 2024 (2024) - [c155]Atticus Geiger, Zhengxuan Wu, Christopher Potts, Thomas Icard, Noah D. Goodman:
Finding Alignments Between Interpretable Causal Variables and Distributed Neural Representations. CLeaR 2024: 160-187 - [c154]Rose E. Wang, Pawan Wirawarn, Omar Khattab, Noah D. Goodman, Dorottya Demszky:
Backtracing: Retrieving the Cause of the Query. EACL (Findings) 2024: 722-735 - [c153]Joy He-Yueya, Noah D. Goodman, Emma Brunskill:
Evaluating and Optimizing Educational Content with Large Language Model Judgments. EDM 2024 - [c152]Steven Y. Feng, Noah D. Goodman, Michael Frank:
Is Child-Directed Speech Effective Training Data for Language Models? EMNLP 2024: 22055-22071 - [c151]Ruocheng Wang, Eric Zelikman, Gabriel Poesia, Yewen Pu, Nick Haber, Noah D. Goodman:
Hypothesis Search: Inductive Reasoning with Language Models. ICLR 2024 - [c150]Michael Y. Li, Emily B. Fox, Noah D. Goodman:
Automated Statistical Model Discovery with Language Models. ICML 2024 - [c149]Alex Tamkin, Mohammad Taufeeque, Noah D. Goodman:
Codebook Features: Sparse and Discrete Interpretability for Neural Networks. ICML 2024 - [c148]Zhengxuan Wu, Atticus Geiger, Aryaman Arora, Jing Huang
, Zheng Wang, Noah D. Goodman, Christopher D. Manning, Christopher Potts:
pyvene: A Library for Understanding and Improving PyTorch Models via Interventions. NAACL (Demonstrations) 2024: 158-165 - [c147]Jan-Philipp Fränken, Eric Zelikman, Rafael Rafailov, Kanishk Gandhi, Tobias Gerstenberg, Noah D. Goodman:
Self-Supervised Alignment with Mutual Information: Learning to Follow Principles without Preference Labels. NeurIPS 2024 - [c146]Zachary Kenton, Noah Y. Siegel, János Kramár, Jonah Brown-Cohen, Samuel Albanie, Jannis Bulian, Rishabh Agarwal, David Lindner, Yunhao Tang, Noah D. Goodman, Rohin Shah:
On scalable oversight with weak LLMs judging strong LLMs. NeurIPS 2024 - [c145]Gabriel Poesia, David Broman, Nick Haber, Noah D. Goodman:
Learning Formal Mathematics From Intrinsic Motivation. NeurIPS 2024 - [i128]Zhengxuan Wu, Atticus Geiger, Jing Huang, Aryaman Arora, Thomas Icard, Christopher Potts, Noah D. Goodman:
A Reply to Makelov et al. (2023)'s "Interpretability Illusion" Arguments. CoRR abs/2401.12631 (2024) - [i127]Michael Y. Li, Emily B. Fox, Noah D. Goodman:
Automated Statistical Model Discovery with Language Models. CoRR abs/2402.17879 (2024) - [i126]Joy He-Yueya, Noah D. Goodman, Emma Brunskill:
Evaluating and Optimizing Educational Content with Large Language Model Judgments. CoRR abs/2403.02795 (2024) - [i125]Rose E. Wang, Pawan Wirawarn, Omar Khattab, Noah D. Goodman, Dorottya Demszky:
Backtracing: Retrieving the Cause of the Query. CoRR abs/2403.03956 (2024) - [i124]Kunal Handa, Yarin Gal, Ellie Pavlick, Noah D. Goodman, Jacob Andreas, Alex Tamkin, Belinda Z. Li:
Bayesian Preference Elicitation with Language Models. CoRR abs/2403.05534 (2024) - [i123]Zhengxuan Wu, Atticus Geiger, Aryaman Arora, Jing Huang, Zheng Wang, Noah D. Goodman, Christopher D. Manning, Christopher Potts:
pyvene: A Library for Understanding and Improving PyTorch Models via Interventions. CoRR abs/2403.07809 (2024) - [i122]Eric Zelikman, Georges Harik, Yijia Shao, Varuna Jayasiri, Nick Haber, Noah D. Goodman:
Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking. CoRR abs/2403.09629 (2024) - [i121]Chinmaya Andukuri, Jan-Philipp Fränken, Tobias Gerstenberg, Noah D. Goodman:
STaR-GATE: Teaching Language Models to Ask Clarifying Questions. CoRR abs/2403.19154 (2024) - [i120]Kanishk Gandhi, Denise Lee, Gabriel Grand, Muxin Liu, Winson Cheng, Archit Sharma, Noah D. Goodman:
Stream of Search (SoS): Learning to Search in Language. CoRR abs/2404.03683 (2024) - [i119]Jan-Philipp Fränken, Kanishk Gandhi, Tori Qiu, Ayesha Khawaja, Noah D. Goodman, Tobias Gerstenberg:
Procedural Dilemma Generation for Evaluating Moral Reasoning in Humans and Language Models. CoRR abs/2404.10975 (2024) - [i118]Jan-Philipp Fränken, Eric Zelikman, Rafael Rafailov, Kanishk Gandhi, Tobias Gerstenberg, Noah D. Goodman:
Self-Supervised Alignment with Mutual Information: Learning to Follow Principles without Preference Labels. CoRR abs/2404.14313 (2024) - [i117]Gabriel Poesia, David Broman, Nick Haber, Noah D. Goodman:
Learning Formal Mathematics From Intrinsic Motivation. CoRR abs/2407.00695 (2024) - [i116]Shubhra Mishra, Gabriel Poesia, Belinda Mo, Noah D. Goodman:
MathCAMPS: Fine-grained Synthesis of Mathematical Problems From Human Curricula. CoRR abs/2407.00900 (2024) - [i115]Zachary Kenton, Noah Y. Siegel, János Kramár, Jonah Brown-Cohen, Samuel Albanie, Jannis Bulian, Rishabh Agarwal, David Lindner, Yunhao Tang, Noah D. Goodman, Rohin Shah:
On scalable oversight with weak LLMs judging strong LLMs. CoRR abs/2407.04622 (2024) - [i114]Joy He-Yueya, Wanjing Anya Ma, Kanishk Gandhi, Benjamin W. Domingue, Emma Brunskill, Noah D. Goodman:
Psychometric Alignment: Capturing Human Knowledge Distributions via Language Models. CoRR abs/2407.15645 (2024) - [i113]Steven Y. Feng, Noah D. Goodman, Michael C. Frank:
Is Child-Directed Speech Effective Training Data for Language Models? CoRR abs/2408.03617 (2024) - [i112]Joy Hsu, Jiayuan Mao, Joshua B. Tenenbaum, Noah D. Goodman, Jiajun Wu:
What Makes a Maze Look Like a Maze? CoRR abs/2409.08202 (2024) - [i111]Kanishk Gandhi, Zoe Lynch, Jan-Philipp Fränken, Kayla Patterson, Sharon Wambu, Tobias Gerstenberg, Desmond C. Ong, Noah D. Goodman:
Human-like Affective Cognition in Foundation Models. CoRR abs/2409.11733 (2024) - [i110]Aryaman Arora, Dan Jurafsky, Christopher Potts, Noah D. Goodman:
Bayesian scaling laws for in-context learning. CoRR abs/2410.16531 (2024) - [i109]Michael Y. Li, Vivek Vajipey, Noah D. Goodman, Emily B. Fox:
CriticAL: Critic Automation with Language Models. CoRR abs/2411.06590 (2024) - 2023
- [c144]Rose E. Wang, Pawan Wirawarn, Noah D. Goodman, Dorottya Demszky:
SIGHT: A Large Annotated Dataset on Student Insights Gathered from Higher Education Transcripts. BEA@ACL 2023: 315-351 - [c143]Ben Prystawski, Dilip Arumugam, Noah D. Goodman:
Cultural reinforcement learning: a framework for modeling cumulative culture on a limited channel. CogSci 2023 - [c142]Ben Prystawski, Paul H. Thibodeau, Christopher Potts, Noah D. Goodman:
Psychologically-informed chain-of-thought prompts for metaphor understanding in large language models. CogSci 2023 - [c141]Polina Tsvilodub, Michael Franke, Robert D. Hawkins, Noah D. Goodman:
Overinformative Question Answering by Humans and Machines. CogSci 2023 - [c140]Dhara Yu, Noah D. Goodman, Jesse Mu:
Characterizing tradeoffs between teaching via language and demonstrations in multi-agent systems. CogSci 2023 - [c139]Jasmine Bayrooti, Noah D. Goodman, Alex Tamkin:
Multispectral Contrastive Learning with Viewmaker Networks. CVPR Workshops 2023: 440-448 - [c138]Joy Hsu, Gabriel Poesia, Jiajun Wu, Noah D. Goodman:
Can Visual Scratchpads With Diagrammatic Abstractions Augment LLM Reasoning? ICBINB 2023: 21-28 - [c137]Alex Tamkin, Kunal Handa, Avash Shrestha, Noah D. Goodman:
Task Ambiguity in Humans and Language Models. ICLR 2023 - [c136]Megha Srivastava, Noah D. Goodman, Dorsa Sadigh:
Generating Language Corrections for Teaching Physical Control Tasks. ICML 2023: 32561-32574 - [c135]Kanishk Gandhi, Jan-Philipp Fränken, Tobias Gerstenberg, Noah D. Goodman:
Understanding Social Reasoning in Language Models with Language Models. NeurIPS 2023 - [c134]Jesse Mu, Xiang Li, Noah D. Goodman:
Learning to Compress Prompts with Gist Tokens. NeurIPS 2023 - [c133]Ben Prystawski, Michael Li, Noah D. Goodman:
Why think step by step? Reasoning emerges from the locality of experience. NeurIPS 2023 - [c132]Alex Tamkin, Margalit Glasgow, Xiluo He, Noah D. Goodman:
Feature Dropout: Revisiting the Role of Augmentations in Contrastive Learning. NeurIPS 2023 - [c131]Zhengxuan Wu, Atticus Geiger, Thomas Icard, Christopher Potts, Noah D. Goodman:
Interpretability at Scale: Identifying Causal Mechanisms in Alpaca. NeurIPS 2023 - [c130]Eric Zelikman, Qian Huang, Gabriel Poesia, Noah D. Goodman, Nick Haber:
Parsel🦆: Algorithmic Reasoning with Language Models by Composing Decompositions. NeurIPS 2023 - [i108]Jasmine Bayrooti, Noah D. Goodman, Alex Tamkin:
Multispectral Self-Supervised Learning with Viewmaker Networks. CoRR abs/2302.05757 (2023) - [i107]Atticus Geiger, Zhengxuan Wu, Christopher Potts, Thomas Icard, Noah D. Goodman:
Finding Alignments Between Interpretable Causal Variables and Distributed Neural Representations. CoRR abs/2303.02536 (2023) - [i106]Ben Prystawski, Noah D. Goodman:
Why think step-by-step? Reasoning emerges from the locality of experience. CoRR abs/2304.03843 (2023) - [i105]Jesse Mu, Xiang Lisa Li, Noah D. Goodman:
Learning to Compress Prompts with Gist Tokens. CoRR abs/2304.08467 (2023) - [i104]Joy He-Yueya, Gabriel Poesia, Rose E. Wang, Noah D. Goodman:
Solving Math Word Problems by Combining Language Models With Symbolic Solvers. CoRR abs/2304.09102 (2023) - [i103]Dilip Arumugam, Mark K. Ho, Noah D. Goodman, Benjamin Van Roy:
Bayesian Reinforcement Learning with Limited Cognitive Load. CoRR abs/2305.03263 (2023) - [i102]Polina Tsvilodub, Michael Franke, Robert D. Hawkins, Noah D. Goodman:
Overinformative Question Answering by Humans and Machines. CoRR abs/2305.07151 (2023) - [i101]Zhengxuan Wu, Atticus Geiger, Christopher Potts, Noah D. Goodman:
Interpretability at Scale: Identifying Causal Mechanisms in Alpaca. CoRR abs/2305.08809 (2023) - [i100]Dhara Yu, Noah D. Goodman, Jesse Mu:
Characterizing tradeoffs between teaching via language and demonstrations in multi-agent systems. CoRR abs/2305.11374 (2023) - [i99]Kanishk Gandhi, Dorsa Sadigh, Noah D. Goodman:
Strategic Reasoning with Language Models. CoRR abs/2305.19165 (2023) - [i98]Gabriel Poesia, Kanishk Gandhi, Eric Zelikman, Noah D. Goodman:
Certified Reasoning with Language Models. CoRR abs/2306.04031 (2023) - [i97]Megha Srivastava, Noah D. Goodman, Dorsa Sadigh:
Generating Language Corrections for Teaching Physical Control Tasks. CoRR abs/2306.07012 (2023) - [i96]Rose E. Wang, Pawan Wirawarn, Noah D. Goodman, Dorottya Demszky:
SIGHT: A Large Annotated Dataset on Student Insights Gathered from Higher Education Transcripts. CoRR abs/2306.09343 (2023) - [i95]Eric Zelikman, Qian Huang, Percy Liang, Nick Haber, Noah D. Goodman:
Just One Byte (per gradient): A Note on Low-Bandwidth Decentralized Language Model Finetuning Using Shared Randomness. CoRR abs/2306.10015 (2023) - [i94]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) - [i93]Kanishk Gandhi, Jan-Philipp Fränken, Tobias Gerstenberg, Noah D. Goodman:
Understanding Social Reasoning in Language Models with Language Models. CoRR abs/2306.15448 (2023) - [i92]Ruocheng Wang, Eric Zelikman, Gabriel Poesia, Yewen Pu, Nick Haber, Noah D. Goodman:
Hypothesis Search: Inductive Reasoning with Language Models. CoRR abs/2309.05660 (2023) - [i91]Jiayuan Mao, Xuelin Yang, Xikun Zhang, Noah D. Goodman, Jiajun Wu:
CLEVRER-Humans: Describing Physical and Causal Events the Human Way. CoRR abs/2310.03635 (2023) - [i90]Belinda Z. Li, Alex Tamkin, Noah D. Goodman, Jacob Andreas:
Eliciting Human Preferences with Language Models. CoRR abs/2310.11589 (2023) - [i89]Alex Tamkin, Mohammad Taufeeque, Noah D. Goodman:
Codebook Features: Sparse and Discrete Interpretability for Neural Networks. CoRR abs/2310.17230 (2023) - [i88]Jan-Philipp Fränken, Sam Kwok, Peixuan Ye, Kanishk Gandhi, Dilip Arumugam, Jared Moore, Alex Tamkin, Tobias Gerstenberg, Noah D. Goodman:
Social Contract AI: Aligning AI Assistants with Implicit Group Norms. CoRR abs/2310.17769 (2023) - 2022
- [j24]Michael Henry Tessler, Noah D. Goodman:
Warm (for Winter): Inferring Comparison Classes in Communication. Cogn. Sci. 46(3) (2022) - [j23]Michael Henry Tessler, Joshua B. Tenenbaum, Noah D. Goodman:
Logic, Probability, and Pragmatics in Syllogistic Reasoning. Top. Cogn. Sci. 14(3): 574-601 (2022) - [c129]Julia White, Amy Burkhardt, Jason D. Yeatman, Noah D. Goodman:
Automated generation of sentence reading fluency test items. CogSci 2022 - [c128]Fei Fang, Kunal Sinha, Noah D. Goodman, Christopher Potts, Elisa Kreiss:
Color Overmodification Emerges from Data-Driven Learning and Pragmatic Reasoning. CogSci 2022 - [c127]Veronica Boyce, Robert D. Hawkins, Noah D. Goodman, Michael C. Frank:
Two's company but six is a crowd: emergence of conventions in multiparty communication games. CogSci 2022 - [c126]Gabriel Poesia Reis e Silva, Noah D. Goodman:
Left to the Reader: Abstracting Solutions in Mathematical Reasoning. CogSci 2022 - [c125]Julia White, Noah D. Goodman, Robert X. D. Hawkins:
Mixed-effects transformers for hierarchical adaptation. EMNLP 2022: 3944-3954 - [c124]Elisa Kreiss, Fei Fang, Noah D. Goodman, Christopher Potts:
Concadia: Towards Image-Based Text Generation with a Purpose. EMNLP 2022: 4667-4684 - [c123]Rose E. Wang, Esin Durmus, Noah D. Goodman, Tatsunori Hashimoto:
Language modeling via stochastic processes. ICLR 2022 - [c122]Atticus Geiger, Zhengxuan Wu, Hanson Lu, Josh Rozner, Elisa Kreiss, Thomas Icard, Noah D. Goodman, Christopher Potts:
Inducing Causal Structure for Interpretable Neural Networks. ICML 2022: 7324-7338 - [c121]Zhengxuan Wu, Atticus Geiger, Joshua Rozner, Elisa Kreiss, Hanson Lu, Thomas Icard, Christopher Potts, Noah D. Goodman:
Causal Distillation for Language Models. NAACL-HLT 2022: 4288-4295 - [c120]Joy Hsu, Jiajun Wu, Noah D. Goodman:
Geoclidean: Few-Shot Generalization in Euclidean Geometry. NeurIPS 2022 - [c119]Jiayuan Mao, Xuelin Yang, Xikun Zhang, Noah D. Goodman, Jiajun Wu:
CLEVRER-Humans: Describing Physical and Causal Events the Human Way. NeurIPS 2022 - [c118]Jesse Mu, Victor Zhong, Roberta Raileanu, Minqi Jiang, Noah D. Goodman, Tim Rocktäschel, Edward Grefenstette:
Improving Intrinsic Exploration with Language Abstractions. NeurIPS 2022 - [c117]Megha Srivastava, Erdem Biyik, Suvir Mirchandani, Noah D. Goodman, Dorsa Sadigh:
Assistive Teaching of Motor Control Tasks to Humans. NeurIPS 2022 - [c116]Alex Tamkin, Gaurab Banerjee, Mohamed Owda, Vincent Liu, Shashank Rammoorthy, Noah D. Goodman:
DABS 2.0: Improved Datasets and Algorithms for Universal Self-Supervision. NeurIPS 2022 - [c115]Alex Tamkin, Dat Nguyen, Salil Deshpande, Jesse Mu, Noah D. Goodman:
Active Learning Helps Pretrained Models Learn the Intended Task. NeurIPS 2022 - [c114]Mike Wu, Noah D. Goodman:
Foundation Posteriors for Approximate Probabilistic Inference. NeurIPS 2022 - [c113]Eric Zelikman, Yuhuai Wu, Jesse Mu, Noah D. Goodman:
STaR: Bootstrapping Reasoning With Reasoning. NeurIPS 2022 - [i87]Jesse Mu, Victor Zhong, Roberta Raileanu, Minqi Jiang, Noah D. Goodman, Tim Rocktäschel, Edward Grefenstette:
Improving Intrinsic Exploration with Language Abstractions. CoRR abs/2202.08938 (2022) - [i86]Rose E. Wang, Esin Durmus, Noah D. Goodman, Tatsunori Hashimoto:
Language modeling via stochastic processes. CoRR abs/2203.11370 (2022) - [i85]Eric Zelikman, Yuhuai Wu, Noah D. Goodman:
STaR: Bootstrapping Reasoning With Reasoning. CoRR abs/2203.14465 (2022) - [i84]Alex Tamkin, Dat Nguyen, Salil Deshpande, Jesse Mu, Noah D. Goodman:
Active Learning Helps Pretrained Models Learn the Intended Task. CoRR abs/2204.08491 (2022) - [i83]Rose E. Wang, Mike Wu, Noah D. Goodman:
Know Thy Student: Interactive Learning with Gaussian Processes. CoRR abs/2204.12072 (2022) - [i82]Julia White, Noah D. Goodman, Robert X. D. Hawkins:
Mixed-effects transformers for hierarchical adaptation. CoRR abs/2205.01749 (2022) - [i81]Fei Fang, Kunal Sinha, Noah D. Goodman, Christopher Potts, Elisa Kreiss:
Color Overmodification Emerges from Data-Driven Learning and Pragmatic Reasoning. CoRR abs/2205.09172 (2022) - [i80]Mike Wu, Noah D. Goodman:
Foundation Posteriors for Approximate Probabilistic Inference. CoRR abs/2205.09735 (2022) - [i79]Ben Prystawski, Paul H. Thibodeau, Noah D. Goodman:
Psychologically-informed chain-of-thought prompts for metaphor understanding in large language models. CoRR abs/2209.08141 (2022) - [i78]Dilip Arumugam, Mark K. Ho, Noah D. Goodman, Benjamin Van Roy:
On Rate-Distortion Theory in Capacity-Limited Cognition & Reinforcement Learning. CoRR abs/2210.16877 (2022) - [i77]Zhening Li, Gabriel Poesia, Omar Costilla-Reyes, Noah D. Goodman, Armando Solar-Lezama:
LEMMA: Bootstrapping High-Level Mathematical Reasoning with Learned Symbolic Abstractions. CoRR abs/2211.08671 (2022) - [i76]Megha Srivastava, Erdem Biyik, Suvir Mirchandani, Noah D. Goodman, Dorsa Sadigh:
Assistive Teaching of Motor Control Tasks to Humans. CoRR abs/2211.14003 (2022) - [i75]Gabriel Poesia, Noah D. Goodman:
Peano: Learning Formal Mathematical Reasoning. CoRR abs/2211.15864 (2022) - [i74]Joy Hsu, Jiajun Wu, Noah D. Goodman:
Geoclidean: Few-Shot Generalization in Euclidean Geometry. CoRR abs/2211.16663 (2022) - [i73]Robert D. Hawkins, Andrew M. Berdahl, Alex 'Sandy' Pentland, Joshua B. Tenenbaum, Noah D. Goodman, P. M. Krafft:
Flexible social inference facilitates targeted social learning when rewards are not observable. CoRR abs/2212.00869 (2022) - [i72]Alex Tamkin, Margalit Glasgow, Xiluo He, Noah D. Goodman:
Feature Dropout: Revisiting the Role of Augmentations in Contrastive Learning. CoRR abs/2212.08378 (2022) - [i71]Eric Zelikman, Qian Huang, Gabriel Poesia, Noah D. Goodman, Nick Haber:
Parsel: A Unified Natural Language Framework for Algorithmic Reasoning. CoRR abs/2212.10561 (2022) - [i70]Alex Tamkin, Kunal Handa, Avash Shrestha, Noah D. Goodman:
Task Ambiguity in Humans and Language Models. CoRR abs/2212.10711 (2022) - 2021
- [j22]Robert X. D. Hawkins, Hyowon Gweon, Noah D. Goodman:
The Division of Labor in Communication: Speakers Help Listeners Account for Asymmetries in Visual Perspective. Cogn. Sci. 45(3) (2021) - [j21]Shyamal Buch, Li Fei-Fei, Noah D. Goodman:
Neural Event Semantics for Grounded Language Understanding. Trans. Assoc. Comput. Linguistics 9: 875-890 (2021) - [j20]Desmond C. Ong, Harold Soh, Jamil Zaki, Noah D. Goodman:
Applying Probabilistic Programming to Affective Computing. IEEE Trans. Affect. Comput. 12(2): 306-317 (2021) - [c112]Gabriel Poesia, Noah D. Goodman:
Pragmatic Code Autocomplete. AAAI 2021: 445-452 - [c111]Megha Srivastava, Noah D. Goodman:
Question Generation for Adaptive Education. ACL/IJCNLP (2) 2021: 692-701 - [c110]Ali Malik, Mike Wu, Vrinda Vasavada, Jinpeng Song, Madison Coots, John Mitchell, Noah D. Goodman, Chris Piech:
Generative Grading: Near Human-level Accuracy for Automated Feedback on Richly Structured Problems. EDM 2021 - [c109]Julia White, Gabriel Poesia, Robert X. D. Hawkins, Dorsa Sadigh, Noah D. Goodman:
Open-domain clarification question generation without question examples. EMNLP (1) 2021: 563-570 - [c108]Rose E. Wang, Julia White, Jesse Mu, Noah D. Goodman:
Calibrate your listeners! Robust communication-based training for pragmatic speakers. EMNLP (Findings) 2021: 977-984 - [c107]Alex Tamkin, Mike Wu, Noah D. Goodman:
Viewmaker Networks: Learning Views for Unsupervised Representation Learning. ICLR 2021 - [c106]Mike Wu, Milan Mosse, Chengxu Zhuang, Daniel Yamins, Noah D. Goodman:
Conditional Negative Sampling for Contrastive Learning of Visual Representations. ICLR 2021 - [c105]Gabriel Poesia, Wenxin Dong, Noah D. Goodman:
Contrastive Reinforcement Learning of Symbolic Reasoning Domains. NeurIPS 2021: 15946-15956 - [c104]Jesse Mu, Noah D. Goodman:
Emergent Communication of Generalizations. NeurIPS 2021: 17994-18007 - [c103]Alex Tamkin, Vincent Liu, Rongfei Lu, Daniel Fein, Colin Schultz, Noah D. Goodman:
DABS: a Domain-Agnostic Benchmark for Self-Supervised Learning. NeurIPS Datasets and Benchmarks 2021 - [c102]Mike Wu, Noah D. Goodman, Stefano Ermon:
Improving Compositionality of Neural Networks by Decoding Representations to Inputs. NeurIPS 2021: 26689-26700 - [i69]Robert X. D. Hawkins, Michael Franke, Michael C. Frank, Kenny Smith, Thomas L. Griffiths, Noah D. Goodman:
From partners to populations: A hierarchical Bayesian account of coordination and convention. CoRR abs/2104.05857 (2021) - [i68]Elisa Kreiss, Noah D. Goodman, Christopher Potts:
Concadia: Tackling image accessibility with context. CoRR abs/2104.08376 (2021) - [i67]Mike Wu, Noah D. Goodman, Stefano Ermon:
Improving Compositionality of Neural Networks by Decoding Representations to Inputs. CoRR abs/2106.00769 (2021) - [i66]Jesse Mu, Noah D. Goodman:
Emergent Communication of Generalizations. CoRR abs/2106.02668 (2021) - [i65]Megha Srivastava, Noah D. Goodman:
Question Generation for Adaptive Education. CoRR abs/2106.04262 (2021) - [i64]Gabriel Poesia, Wenxin Dong, Noah D. Goodman:
Contrastive Reinforcement Learning of Symbolic Reasoning Domains. CoRR abs/2106.09146 (2021) - [i63]Mike Wu, Noah D. Goodman, Chris Piech, Chelsea Finn:
ProtoTransformer: A Meta-Learning Approach to Providing Student Feedback. CoRR abs/2107.14035 (2021) - [i62]Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, Russ B. Altman, Simran Arora, Sydney von Arx, Michael S. Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, Erik Brynjolfsson, Shyamal Buch, Dallas Card, Rodrigo Castellon, Niladri S. Chatterji, Annie S. Chen, Kathleen Creel, Jared Quincy Davis, Dorottya Demszky, Chris Donahue, Moussa Doumbouya, Esin Durmus, Stefano Ermon, John Etchemendy, Kawin Ethayarajh, Li Fei-Fei, Chelsea Finn, Trevor Gale, Lauren E. Gillespie, Karan Goel, Noah D. Goodman, Shelby Grossman, Neel Guha, Tatsunori Hashimoto, Peter Henderson, John Hewitt, Daniel E. Ho, Jenny Hong, Kyle Hsu, Jing Huang, Thomas Icard, Saahil Jain, Dan Jurafsky, Pratyusha Kalluri, Siddharth Karamcheti, Geoff Keeling, Fereshte Khani, Omar Khattab, Pang Wei Koh, Mark S. Krass, Ranjay Krishna, Rohith Kuditipudi, et al.:
On the Opportunities and Risks of Foundation Models. CoRR abs/2108.07258 (2021) - [i61]Mike Wu, Richard Lee Davis, Benjamin W. Domingue, Chris Piech, Noah D. Goodman:
Modeling Item Response Theory with Stochastic Variational Inference. CoRR abs/2108.11579 (2021) - [i60]Robert D. Hawkins, Megumi Sano, Noah D. Goodman, Judith E. Fan:
Visual resemblance and communicative context constrain the emergence of graphical conventions. CoRR abs/2109.13861 (2021) - [i59]Oliver Zhang, Mike Wu, Jasmine Bayrooti, Noah D. Goodman:
Temperature as Uncertainty in Contrastive Learning. CoRR abs/2110.04403 (2021) - [i58]Rose E. Wang, Julia White, Jesse Mu, Noah D. Goodman:
Calibrate your listeners! Robust communication-based training for pragmatic speakers. CoRR abs/2110.05422 (2021) - [i57]Julia White, Gabriel Poesia, Robert X. D. Hawkins, Dorsa Sadigh, Noah D. Goodman:
Open-domain clarification question generation without question examples. CoRR abs/2110.09779 (2021) - [i56]Alex Tamkin, Vincent Liu, Rongfei Lu, Daniel Fein, Colin Schultz, Noah D. Goodman:
DABS: A Domain-Agnostic Benchmark for Self-Supervised Learning. CoRR abs/2111.12062 (2021) - [i55]Atticus Geiger, Zhengxuan Wu, Hanson Lu, Josh Rozner, Elisa Kreiss, Thomas Icard, Noah D. Goodman, Christopher Potts:
Inducing Causal Structure for Interpretable Neural Networks. CoRR abs/2112.00826 (2021) - [i54]Zhengxuan Wu, Atticus Geiger, Josh Rozner, Elisa Kreiss, Hanson Lu, Thomas Icard, Christopher Potts, Noah D. Goodman:
Causal Distillation for Language Models. CoRR abs/2112.02505 (2021) - [i53]Ananya Karthik, Mike Wu, Noah D. Goodman, Alex Tamkin:
Tradeoffs Between Contrastive and Supervised Learning: An Empirical Study. CoRR abs/2112.05340 (2021) - 2020
- [j19]Robert X. D. Hawkins, Michael C. Frank, Noah D. Goodman:
Characterizing the Dynamics of Learning in Repeated Reference Games. Cogn. Sci. 44(6) (2020) - [j18]Ishita Dasgupta, Demi Guo, Samuel J. Gershman, Noah D. Goodman:
Analyzing Machine-Learned Representations: A Natural Language Case Study. Cogn. Sci. 44(12) (2020) - [j17]Benjamin N. Peloquin, Noah D. Goodman, Michael C. Frank:
The Interactions of Rational, Pragmatic Agents Lead to Efficient Language Structure and Use. Top. Cogn. Sci. 12(1): 433-445 (2020) - [c101]Mike Wu, Kristy Choi, Noah D. Goodman, Stefano Ermon:
Meta-Amortized Variational Inference and Learning. AAAI 2020: 6404-6412 - [c100]Jesse Mu, Percy Liang, Noah D. Goodman:
Shaping Visual Representations with Language for Few-Shot Classification. ACL 2020: 4823-4830 - [c99]Robert X. D. Hawkins, Noah D. Goodman, Adele E. Goldberg, Tom Griffiths:
Generalizing meanings from partners to populations: Hierarchical inference supports convention formation on networks. CogSci 2020 - [c98]Julia White, Jesse Mu, Noah D. Goodman:
Learning to refer informatively by amortizing pragmatic reasoning. CogSci 2020 - [c97]Robert X. D. Hawkins, Minae Kwon, Dorsa Sadigh, Noah D. Goodman:
Continual Adaptation for Efficient Machine Communication. CoNLL 2020: 408-419 - [c96]Mike Wu, Richard Lee Davis, Benjamin W. Domingue, Chris Piech, Noah D. Goodman:
Variational Item Response Theory: Fast, Accurate, and Expressive. EDM 2020 - [c95]Alex Tamkin, Trisha Singh, Davide Giovanardi, Noah D. Goodman:
Investigating Transferability in Pretrained Language Models. EMNLP (Findings) 2020: 1393-1401 - [c94]Alex Tamkin, Dan Jurafsky, Noah D. Goodman:
Language Through a Prism: A Spectral Approach for Multiscale Language Representations. NeurIPS 2020 - [i52]Mike Wu, Richard Lee Davis, Benjamin W. Domingue, Chris Piech, Noah D. Goodman:
Variational Item Response Theory: Fast, Accurate, and Expressive. CoRR abs/2002.00276 (2020) - [i51]Robert X. D. Hawkins, Noah D. Goodman, Adele E. Goldberg, Thomas L. Griffiths:
Generalizing meanings from partners to populations: Hierarchical inference supports convention formation on networks. CoRR abs/2002.01510 (2020) - [i50]Alex Tamkin, Trisha Singh, Davide Giovanardi, Noah D. Goodman:
Investigating Transferability in Pretrained Language Models. CoRR abs/2004.14975 (2020) - [i49]Mike Wu, Chengxu Zhuang, Milan Mosse, Daniel Yamins, Noah D. Goodman:
On Mutual Information in Contrastive Learning for Visual Representations. CoRR abs/2005.13149 (2020) - [i48]Julia White, Jesse Mu, Noah D. Goodman:
Learning to refer informatively by amortizing pragmatic reasoning. CoRR abs/2006.00418 (2020) - [i47]Mike Wu, Milan Mosse, Chengxu Zhuang, Daniel Yamins, Noah D. Goodman:
Conditional Negative Sampling for Contrastive Learning of Visual Representations. CoRR abs/2010.02037 (2020) - [i46]Mike Wu, Noah D. Goodman:
A Simple Framework for Uncertainty in Contrastive Learning. CoRR abs/2010.02038 (2020) - [i45]Alex Tamkin, Mike Wu, Noah D. Goodman:
Viewmaker Networks: Learning Views for Unsupervised Representation Learning. CoRR abs/2010.07432 (2020) - [i44]Alex Tamkin, Dan Jurafsky, Noah D. Goodman:
Language Through a Prism: A Spectral Approach for Multiscale Language Representations. CoRR abs/2011.04823 (2020)
2010 – 2019
- 2019
- [j16]Eli Bingham, Jonathan P. Chen, Martin Jankowiak, Fritz Obermeyer, Neeraj Pradhan, Theofanis Karaletsos, Rohit Singh, Paul A. Szerlip, Paul Horsfall, Noah D. Goodman:
Pyro: Deep Universal Probabilistic Programming. J. Mach. Learn. Res. 20: 28:1-28:6 (2019) - [j15]Desmond C. Ong, Jamil Zaki, Noah D. Goodman:
Computational Models of Emotion Inference in Theory of Mind: A Review and Roadmap. Top. Cogn. Sci. 11(2): 338-357 (2019) - [c93]Mike Wu, Milan Mosse, Noah D. Goodman, Chris Piech:
Zero Shot Learning for Code Education: Rubric Sampling with Deep Learning Inference. AAAI 2019: 782-790 - [c92]Bill McDowell, Noah D. Goodman:
Learning from Omission. ACL (1) 2019: 619-628 - [c91]Allen Nie, Erin Bennett, Noah D. Goodman:
DisSent: Learning Sentence Representations from Explicit Discourse Relations. ACL (1) 2019: 4497-4510 - [c90]Mike Wu, Noah D. Goodman, Stefano Ermon:
Differentiable Antithetic Sampling for Variance Reduction in Stochastic Variational Inference. AISTATS 2019: 2877-2886 - [c89]Emmanuel M. Pothos, Jerome R. Busemeyer, Timothy J. Pleskac, James M. Yearsley, Josh Tenenbaum, Noah D. Goodman, Michael Henry Tessler, Tom Griffiths, Falk Lieder, Ralph Hertwig, Thorsten Pachur, Christina Leuker, Richard M. Shiffrin:
Extending Rationality. CogSci 2019: 39-40 - [c88]Sahil Chopra, Michael Henry Tessler, Noah D. Goodman:
The first crank of the cultural ratchet: Learning and transmitting concepts through language. CogSci 2019: 226-232 - [c87]Robert X. D. Hawkins, Megumi Sano, Noah D. Goodman, Judith W. Fan:
Disentangling contributions of visual information and interaction history in the formation of graphical conventions. CogSci 2019: 415-421 - [c86]Benjamin N. Peloquin, Noah D. Goodman, Michael C. Frank:
The interactions of rational, pragmatic agents lead to efficient language structure and use. CogSci 2019: 912-917 - [c85]Panos Achlioptas, Leonidas J. Guibas, Noah D. Goodman, Judy Fan, Robert X. D. Hawkins:
Shapeglot: Learning Language for Shape Differentiation. ICCV 2019: 8937-8946 - [c84]Fritz Obermeyer, Eli Bingham, Martin Jankowiak, Neeraj Pradhan, Justin T. Chiu, Alexander M. Rush, Noah D. Goodman:
Tensor Variable Elimination for Plated Factor Graphs. ICML 2019: 4871-4880 - [c83]Reuben Cohn-Gordon, Noah D. Goodman:
Lost in Machine Translation: A Method to Reduce Meaning Loss. NAACL-HLT (1) 2019: 437-441 - [c82]Adam Foster, Martin Jankowiak, Eli Bingham, Paul Horsfall, Yee Whye Teh, Tom Rainforth, Noah D. Goodman:
Variational Bayesian Optimal Experimental Design. NeurIPS 2019: 14036-14047 - [i43]Jesse Mu, Percy Liang, Noah D. Goodman:
Shaping Visual Representations with Language for Few-shot Classification. ViGIL@NeurIPS 2019 - [i42]Kristy Choi, Mike Wu, Noah D. Goodman, Stefano Ermon:
Meta-Amortized Variational Inference and Learning. CoRR abs/1902.01950 (2019) - [i41]Fritz Obermeyer, Eli Bingham, Martin Jankowiak, Justin T. Chiu, Neeraj Pradhan, Alexander M. Rush, Noah D. Goodman:
Tensor Variable Elimination for Plated Factor Graphs. CoRR abs/1902.03210 (2019) - [i40]Reuben Cohn-Gordon, Noah D. Goodman:
Lost in Machine Translation: A Method to Reduce Meaning Loss. CoRR abs/1902.09514 (2019) - [i39]Judith W. Fan, Robert X. D. Hawkins, Mike Wu, Noah D. Goodman:
Pragmatic inference and visual abstraction enable contextual flexibility during visual communication. CoRR abs/1903.04448 (2019) - [i38]Adam Foster, Martin Jankowiak, Eli Bingham, Paul Horsfall, Yee Whye Teh, Tom Rainforth, Noah D. Goodman:
Variational Estimators for Bayesian Optimal Experimental Design. CoRR abs/1903.05480 (2019) - [i37]Desmond C. Ong, Harold Soh, Jamil Zaki, Noah D. Goodman:
Applying Probabilistic Programming to Affective Computing. CoRR abs/1903.06445 (2019) - [i36]Judith Degen, Robert X. D. Hawkins, Caroline Graf, Elisa Kreiss, Noah D. Goodman:
When redundancy is rational: A Bayesian approach to 'overinformative' referring expressions. CoRR abs/1903.08237 (2019) - [i35]Panos Achlioptas, Judy Fan, Robert X. D. Hawkins, Noah D. Goodman, Leonidas J. Guibas:
ShapeGlot: Learning Language for Shape Differentiation. CoRR abs/1905.02925 (2019) - [i34]Ali Malik, Mike Wu, Vrinda Vasavada, Jinpeng Song, John Mitchell, Noah D. Goodman, Chris Piech:
Generative Grading: Neural Approximate Parsing for Automated Student Feedback. CoRR abs/1905.09916 (2019) - [i33]Allen Nie, Erin D. Bennett, Noah D. Goodman:
Learning to Explain: Answering Why-Questions via Rephrasing. CoRR abs/1906.01243 (2019) - [i32]Ishita Dasgupta, Demi Guo, Samuel J. Gershman, Noah D. Goodman:
Analyzing machine-learned representations: A natural language case study. CoRR abs/1909.05885 (2019) - [i31]Jesse Mu, Percy Liang, Noah D. Goodman:
Shaping Visual Representations with Language for Few-shot Classification. CoRR abs/1911.02683 (2019) - [i30]Robert X. D. Hawkins, Minae Kwon, Dorsa Sadigh, Noah D. Goodman:
Continual adaptation for efficient machine communication. CoRR abs/1911.09896 (2019) - [i29]Mike Wu, Noah D. Goodman:
Multimodal Generative Models for Compositional Representation Learning. CoRR abs/1912.05075 (2019) - [i28]Robert X. D. Hawkins, Michael C. Frank, Noah D. Goodman:
Characterizing the dynamics of learning in repeated reference games. CoRR abs/1912.07199 (2019) - 2018
- [j14]Fereshte Khani, Noah D. Goodman, Percy Liang:
Planning, Inference, and Pragmatics in Sequential Language Games. Trans. Assoc. Comput. Linguistics 6: 543-555 (2018) - [c81]Michael Hahn, Judith Degen, Noah D. Goodman, Dan Jurafsky, Richard Futrell:
An Information-Theoretic Explanation of Adjective Ordering Preferences. CogSci 2018 - [c80]Ishita Dasgupta, Demi Guo, Andreas Stuhlmüller, Samuel Gershman, Noah D. Goodman:
Evaluating Compositionality in Sentence Embeddings. CogSci 2018 - [c79]Robert X. D. Hawkins, Michael Franke, Kenny Smith, Noah D. Goodman:
Emerging abstractions: Lexical conventions are shaped by communicative context. CogSci 2018 - [c78]Long Ouyang, Michael Henry Tessler, Daniel Ly, Noah D. Goodman:
webppl-oed: A practical optimal experiment design system. CogSci 2018 - [c77]Benjamin N. Peloquin, Noah D. Goodman, Michael C. Frank:
Deriving uniform information density behavior in pragmatic agents. CogSci 2018 - [c76]Michael Henry Tessler, Noah D. Goodman:
Statistics as Pottery: Bayesian Data Analysis using Probabilistic Programs. CogSci 2018 - [c75]Michael Henry Tessler, Noah D. Goodman, David Danks, Emily Foster-Hanson, Marjorie Rhodes, Greg Carlson:
Generalizations, from representation to transmission. CogSci 2018 - [c74]Reuben Cohn-Gordon, Noah D. Goodman, Christopher Potts:
Pragmatically Informative Image Captioning with Character-Level Inference. NAACL-HLT (2) 2018: 439-443 - [c73]Mike Wu, Noah D. Goodman:
Multimodal Generative Models for Scalable Weakly-Supervised Learning. NeurIPS 2018: 5580-5590 - [c72]Shengjia Zhao, Hongyu Ren, Arianna Yuan, Jiaming Song, Noah D. Goodman, Stefano Ermon:
Bias and Generalization in Deep Generative Models: An Empirical Study. NeurIPS 2018: 10815-10824 - [i27]Ishita Dasgupta, Demi Guo, Andreas Stuhlmüller, Samuel J. Gershman, Noah D. Goodman:
Evaluating Compositionality in Sentence Embeddings. CoRR abs/1802.04302 (2018) - [i26]Mike Wu, Noah D. Goodman:
Multimodal Generative Models for Scalable Weakly-Supervised Learning. CoRR abs/1802.05335 (2018) - [i25]Reuben Cohn-Gordon, Noah D. Goodman, Christopher Potts:
Pragmatically Informative Image Captioning with Character-Level Reference. CoRR abs/1804.05417 (2018) - [i24]Fereshte Khani, Noah D. Goodman, Percy Liang:
Planning, Inference and Pragmatics in Sequential Language Games. CoRR abs/1805.11774 (2018) - [i23]Robert X. D. Hawkins, Hyowon Gweon, Noah D. Goodman:
Speakers account for asymmetries in visual perspective so listeners don't have to. CoRR abs/1807.09000 (2018) - [i22]Mike Wu, Milan Mosse, Noah D. Goodman, Chris Piech:
Zero Shot Learning for Code Education: Rubric Sampling with Deep Learning Inference. CoRR abs/1809.01357 (2018) - [i21]Reuben Cohn-Gordon, Noah D. Goodman, Christopher Potts:
An Incremental Iterated Response Model of Pragmatics. CoRR abs/1810.00367 (2018) - [i20]Mike Wu, Noah D. Goodman, Stefano Ermon:
Differentiable Antithetic Sampling for Variance Reduction in Stochastic Variational Inference. CoRR abs/1810.02555 (2018) - [i19]Eli Bingham, Jonathan P. Chen, Martin Jankowiak, Fritz Obermeyer, Neeraj Pradhan, Theofanis Karaletsos, Rohit Singh, Paul A. Szerlip, Paul Horsfall, Noah D. Goodman:
Pyro: Deep Universal Probabilistic Programming. CoRR abs/1810.09538 (2018) - [i18]Shengjia Zhao, Hongyu Ren, Arianna Yuan, Jiaming Song, Noah D. Goodman, Stefano Ermon:
Bias and Generalization in Deep Generative Models: An Empirical Study. CoRR abs/1811.03259 (2018) - [i17]Jonathan P. Chen, Fritz Obermeyer, Vladimir Lyapunov, Lionel Gueguen, Noah D. Goodman:
Joint Mapping and Calibration via Differentiable Sensor Fusion. CoRR abs/1812.00880 (2018) - 2017
- [j13]Daniel Lassiter, Noah D. Goodman:
Adjectival vagueness in a Bayesian model of interpretation. Synth. 194(10): 3801-3836 (2017) - [j12]Will Monroe, Robert X. D. Hawkins, Noah D. Goodman, Christopher Potts:
Colors in Context: A Pragmatic Neural Model for Grounded Language Understanding. Trans. Assoc. Comput. Linguistics 5: 325-338 (2017) - [c71]Ishita Dasgupta, Eric Schulz, Noah D. Goodman, Samuel J. Gershman:
Amortized Hypothesis Generation. CogSci 2017 - [c70]Robert X. D. Hawkins, Mike Frank, Noah D. Goodman:
Convention-formation in iterated reference games. CogSci 2017 - [c69]Elisa Kreiss, Robert X. D. Hawkins, Judith Degen, Noah D. Goodman:
Mentioning atypical properties of objects is communicatively efficient. CogSci 2017 - [c68]Michael Henry Tessler, Michael Lopez-Brau, Noah D. Goodman:
Warm (for winter): Comparison class understanding in vague language. CogSci 2017 - [c67]Erica J. Yoon, Michael Henry Tessler, Noah D. Goodman, Michael C. Frank:
"I won't lie, it wasn't amazing": Modeling polite indirect speech. CogSci 2017 - [c66]Siddharth Narayanaswamy, Brooks Paige, Jan-Willem van de Meent, Alban Desmaison, Noah D. Goodman, Pushmeet Kohli, Frank D. Wood, Philip H. S. Torr:
Learning Disentangled Representations with Semi-Supervised Deep Generative Models. NIPS 2017: 5925-5935 - [i16]Will Monroe, Robert X. D. Hawkins, Noah D. Goodman, Christopher Potts:
Colors in Context: A Pragmatic Neural Model for Grounded Language Understanding. CoRR abs/1703.10186 (2017) - [i15]N. Siddharth, Brooks Paige, Jan-Willem van de Meent, Alban Desmaison, Frank D. Wood, Noah D. Goodman, Pushmeet Kohli, Philip H. S. Torr:
Learning Disentangled Representations with Semi-Supervised Deep Generative Models. CoRR abs/1706.00400 (2017) - [i14]Allen Nie, Erin D. Bennett, Noah D. Goodman:
DisSent: Sentence Representation Learning from Explicit Discourse Relations. CoRR abs/1710.04334 (2017) - 2016
- [j11]Justine T. Kao, Roger Levy, Noah D. Goodman:
A Computational Model of Linguistic Humor in Puns. Cogn. Sci. 40(5): 1270-1285 (2016) - [c65]Owain Evans, Andreas Stuhlmüller, Noah D. Goodman:
Learning the Preferences of Ignorant, Inconsistent Agents. AAAI 2016: 323-329 - [c64]Daniel Ritchie, Andreas Stuhlmüller, Noah D. Goodman:
C3: Lightweight Incrementalized MCMC for Probabilistic Programs using Continuations and Callsite Caching. AISTATS 2016: 28-37 - [c63]Michael Franke, Fabian Dablander, Anthea Schöller, Erin Bennett, Judith Degen, Michael Henry Tessler, Justine T. Kao, Noah D. Goodman:
What does the crowd believe? A hierarchical approach to estimating subjective beliefs from empirical data. CogSci 2016 - [c62]Caroline Graf, Judith Degen, Robert X. D. Hawkins, Noah D. Goodman:
Animal, dog, or dalmatian? Level of abstraction in nominal referring expressions. CogSci 2016 - [c61]Robert X. D. Hawkins, Noah D. Goodman:
Conversational expectations account for apparent limits on theory of mind use. CogSci 2016 - [c60]Robert X. D. Hawkins, Noah D. Goodman, Olga Feher, Kenny Smith, Robert L. Goldstone, Tom Griffiths:
The Emergence of Conventions. CogSci 2016 - [c59]Justine T. Kao, Noah D. Goodman:
Empirical and Computational Approaches to Metaphor and Figurative Meaning. CogSci 2016 - [c58]Desmond C. Ong, Jamil Zaki, Noah D. Goodman:
Emotions in lay explanations of behavior. CogSci 2016 - [c57]Ciyang Qing, Noah D. Goodman, Daniel Lassiter:
A rational speech-act model of projective content. CogSci 2016 - [c56]Michael Henry Tessler, Noah D. Goodman:
Communicating generalizations about events. CogSci 2016 - [c55]Tomer D. Ullman, Yang Xu, Noah D. Goodman:
The Pragmatics of Spatial Language. CogSci 2016 - [c54]Erica J. Yoon, Michael Henry Tessler, Noah D. Goodman, Michael C. Frank:
Talking with tact: Polite language as a balance between informativity and kindness. CogSci 2016 - [c53]Will Monroe, Noah D. Goodman, Christopher Potts:
Learning to Generate Compositional Color Descriptions. EMNLP 2016: 2243-2248 - [c52]Daniel Ritchie, Anna Thomas, Pat Hanrahan, Noah D. Goodman:
Neurally-Guided Procedural Models: Amortized Inference for Procedural Graphics Programs using Neural Networks. NIPS 2016: 622-630 - [i13]Daniel Ritchie, Anna Thomas, Pat Hanrahan, Noah D. Goodman:
Neurally-Guided Procedural Models: Learning to Guide Procedural Models with Deep Neural Networks. CoRR abs/1603.06143 (2016) - [i12]Will Monroe, Noah D. Goodman, Christopher Potts:
Learning to Generate Compositional Color Descriptions. CoRR abs/1606.03821 (2016) - [i11]Michael Henry Tessler, Noah D. Goodman:
A pragmatic theory of generic language. CoRR abs/1608.02926 (2016) - [i10]Long Ouyang, Michael Henry Tessler, Daniel Ly, Noah D. Goodman:
Practical optimal experiment design with probabilistic programs. CoRR abs/1608.05046 (2016) - [i9]Daniel Ritchie, Paul Horsfall, Noah D. Goodman:
Deep Amortized Inference for Probabilistic Programs. CoRR abs/1610.05735 (2016) - [i8]N. Siddharth, Brooks Paige, Alban Desmaison, Jan-Willem van de Meent, Frank D. Wood, Noah D. Goodman, Pushmeet Kohli, Philip H. S. Torr:
Inducing Interpretable Representations with Variational Autoencoders. CoRR abs/1611.07492 (2016) - 2015
- [j10]Daniel Ritchie, Sharon Lin, Noah D. Goodman, Pat Hanrahan:
Generating Design Suggestions under Tight Constraints with Gradient-based Probabilistic Programming. Comput. Graph. Forum 34(2): 515-526 (2015) - [j9]Daniel Ritchie, Ben Mildenhall, Noah D. Goodman, Pat Hanrahan:
Controlling procedural modeling programs with stochastically-ordered sequential Monte Carlo. ACM Trans. Graph. 34(4): 105:1-105:11 (2015) - [j8]Thomas L. Griffiths, Falk Lieder, Noah D. Goodman:
Rational Use of Cognitive Resources: Levels of Analysis Between the Computational and the Algorithmic. Top. Cogn. Sci. 7(2): 217-229 (2015) - [j7]Leon Bergen, Noah D. Goodman:
The Strategic Use of Noise in Pragmatic Reasoning. Top. Cogn. Sci. 7(2): 336-350 (2015) - [c51]Ilona Bass, Daniel Hawthorne, Noah D. Goodman, Hyowon Gweon:
Not by number alone: The effect of teachers' knowledge and its value in evaluating "sins of omission". CogSci 2015 - [c50]Erin Bennett, Noah D. Goodman:
Extremely costly intensifiers are stronger than quite costly ones. CogSci 2015 - [c49]Judith Degen, Michael Henry Tessler, Noah D. Goodman:
Wonky worlds: Listeners revise world knowledge when utterances are odd. CogSci 2015 - [c48]Tobias Gerstenberg, Noah D. Goodman, David A. Lagnado, Joshua B. Tenenbaum:
How, whether, why: Causal judgments as counterfactual contrasts. CogSci 2015 - [c47]Robert X. D. Hawkins, Andreas Stuhlmüller, Judith Degen, Noah D. Goodman:
Why do you ask? Good questions provoke informative answers. CogSci 2015 - [c46]Daniel Hawthorne-Madell, Noah D. Goodman:
So good it has to be true: Wishful thinking in theory of mind. CogSci 2015 - [c45]Thomas Icard, Noah D. Goodman:
A Resource-Rational Approach to the Causal Frame Problem. CogSci 2015 - [c44]Justine T. Kao, Noah D. Goodman:
Let's talk (ironically) about the weather: Modeling verbal irony. CogSci 2015 - [c43]Peter M. Krafft, Robert X. D. Hawkins, Alex Pentland, Noah D. Goodman, Joshua B. Tenenbaum:
Emergent Collective Sensing in Human Groups. CogSci 2015 - [c42]Desmond C. Ong, Noah D. Goodman, Jamil Zaki:
Near-misses sting even when they are uncontrollable. CogSci 2015 - [c41]Emily Sumner, Erika DeAngelis, Mara Hyatt, Noah D. Goodman, Celeste Kidd:
Toddlers Always Get the Last Word: Recency biases in early verbal behavior. CogSci 2015 - [i7]Daniel Ritchie, Andreas Stuhlmüller, Noah D. Goodman:
C3: Lightweight Incrementalized MCMC for Probabilistic Programs using Continuations and Callsite Caching. CoRR abs/1509.02151 (2015) - [i6]Andreas Stuhlmüller, Robert X. D. Hawkins, N. Siddharth, Noah D. Goodman:
Coarse-to-Fine Sequential Monte Carlo for Probabilistic Programs. CoRR abs/1509.02962 (2015) - [i5]Owain Evans, Andreas Stuhlmüller, Noah D. Goodman:
Learning the Preferences of Ignorant, Inconsistent Agents. CoRR abs/1512.05832 (2015) - 2014
- [j6]Edward Vul, Noah D. Goodman, Thomas L. Griffiths, Joshua B. Tenenbaum:
One and Done? Optimal Decisions From Very Few Samples. Cogn. Sci. 38(4): 599-637 (2014) - [j5]Andreas Stuhlmüller, Noah D. Goodman:
Reasoning about reasoning by nested conditioning: Modeling theory of mind with probabilistic programs. Cogn. Syst. Res. 28: 80-99 (2014) - [c40]Lingfeng Yang, Pat Hanrahan, Noah D. Goodman:
Generating Efficient MCMC Kernels from Probabilistic Programs. AISTATS 2014: 1068-1076 - [c39]Leon Bergen, Noah D. Goodman:
The strategic use of noise in pragmatic reasoning. CogSci 2014 - [c38]Judith Degen, Noah D. Goodman:
Lost your marbles? The puzzle of dependent measures in experimental pragmatics. CogSci 2014 - [c37]Judith Degen, Noah D. Goodman, Roni Katzir, David Barner, Albert Gatt:
Symposium: The Role of Alternatives in Pragmatic Inference. CogSci 2014 - [c36]Samuel Gershman, Noah D. Goodman:
Amortized Inference in Probabilistic Reasoning. CogSci 2014 - [c35]Tobias Gerstenberg, Noah D. Goodman, David A. Lagnado, Joshua B. Tenenbaum:
From counterfactual simulation to causal judgment. CogSci 2014 - [c34]Noah D. Goodman, Joshua B. Tenenbaum:
Probability, programs, and the mind: Building structured Bayesian models of cognition. CogSci 2014 - [c33]Justine T. Kao, Leon Bergen, Noah D. Goodman:
Formalizing the Pragmatics of Metaphor Understanding. CogSci 2014 - [c32]Desmond C. Ong, Jamil Zaki, Noah D. Goodman:
Understanding Affective Cognition: Frontiers in modeling reasoning about others' emotions. CogSci 2014 - [c31]Michael Henry Tessler, Noah D. Goodman:
Some arguments are probably valid: Syllogistic reasoning as communication. CogSci 2014 - [c30]Tomer D. Ullman, Andreas Stuhlmüller, Noah D. Goodman, Joshua B. Tenenbaum:
Learning physical theories from dynamical scenes. CogSci 2014 - 2013
- [j4]Noah D. Goodman, Andreas Stuhlmüller:
Knowledge and Implicature: Modeling Language Understanding as Social Cognition. Top. Cogn. Sci. 5(1): 173-184 (2013) - [c29]Justine T. Kao, Roger Levy, Noah D. Goodman:
The Funny Thing About Incongruity: A Computational Model of Humor in Puns. CogSci 2013 - [c28]Falk Lieder, Noah D. Goodman, Quentin J. M. Huys:
Learned helplessness and generalization. CogSci 2013 - [c27]Tomer D. Ullman, Joshua B. Tenenbaum, Noah D. Goodman, Shimon Ullman, Elizabeth S. Spelke:
Minimal Nativism: How does cognitive development get off the ground? CogSci 2013 - [c26]Nathaniel J. Smith, Noah D. Goodman, Michael C. Frank:
Learning and using language via recursive pragmatic reasoning about other agents. NIPS 2013: 3039-3047 - [c25]Andreas Stuhlmüller, Jessica Taylor, Noah D. Goodman:
Learning Stochastic Inverses. NIPS 2013: 3048-3056 - [c24]Noah D. Goodman:
The principles and practice of probabilistic programming. POPL 2013: 399-402 - 2012
- [j3]Yi-Ting Yeh, Lingfeng Yang, Matthew Watson, Noah D. Goodman, Pat Hanrahan:
Synthesizing open worlds with constraints using locally annealed reversible jump MCMC. ACM Trans. Graph. 31(4): 56:1-56:11 (2012) - [c23]Leon Bergen, Noah D. Goodman, Roger Levy:
That's what she (could have) said: How alternative utterances affect language use. CogSci 2012 - [c22]Tobias Gerstenberg, Noah D. Goodman:
Ping Pong in Church: Productive use of concepts in human probabilistic inference. CogSci 2012 - [c21]Tobias Gerstenberg, Noah D. Goodman, David A. Lagnado, Joshua B. Tenenbaum:
Noisy Newtons: Unifying process and dependency accounts of causal attribution. CogSci 2012 - [c20]Noah D. Goodman, Andreas Stuhlmüller:
Knowledge and implicature: Modeling language understanding as social cognition. CogSci 2012 - [c19]Noah D. Goodman, Joshua B. Tenenbaum:
Probability, programs, and the mind: Building structured Bayesian models of cognition. CogSci 2012 - [c18]Daniel Lassiter, Noah D. Goodman:
How many kinds of reasoning? Inference, probability, and natural language semantics. CogSci 2012 - [c17]Falk Lieder, Thomas L. Griffiths, Noah D. Goodman:
"Burn-in, bias, and the rationality of anchoring". NIPS 2012: 2699-2707 - [c16]Andreas Stuhlmüller, Noah D. Goodman:
A Dynamic Programming Algorithm for Inference in Recursive Probabilistic Programs. StarAI@UAI 2012 - [c15]Jerry O. Talton, Lingfeng Yang, Ranjitha Kumar, Maxine Lim, Noah D. Goodman, Radomír Mech:
Learning design patterns with bayesian grammar induction. UIST 2012: 63-74 - [i4]David Wingate, Noah D. Goodman, Daniel M. Roy, Joshua B. Tenenbaum:
The Infinite Latent Events Model. CoRR abs/1205.2604 (2012) - [i3]Noah D. Goodman, Vikash Mansinghka, Daniel M. Roy, Kallista A. Bonawitz, Joshua B. Tenenbaum:
Church: a language for generative models. CoRR abs/1206.3255 (2012) - [i2]Andreas Stuhlmüller, Noah D. Goodman:
A Dynamic Programming Algorithm for Inference in Recursive Probabilistic Programs. CoRR abs/1206.3555 (2012) - 2011
- [c14]Timothy O'Donnell, Jesse Snedeker, Joshua B. Tenenbaum, Noah D. Goodman:
Productivity and Reuse in Language. CogSci 2011 - [c13]Timothy O'Donnell, Jesse Snedeker, Joshua B. Tenenbaum, Noah D. Goodman:
Productivity and Reuse in Language: a Developmental Study. CogSci 2011 - [c12]Alex Stiller, Noah D. Goodman, Michael C. Frank:
Ad-hoc scalar implicature in adults and children. CogSci 2011 - [c11]Tomer D. Ullman, Noah D. Goodman, Josh Tenenbaum:
Forward Physics: How people learn and generalize novel dynamical models. CogSci 2011 - [c10]Noah D. Goodman:
Learning and the language of thought. ICCV Workshops 2011: 694 - [c9]David Wingate, Noah D. Goodman, Daniel M. Roy, Leslie Pack Kaelbling, Joshua B. Tenenbaum:
Bayesian Policy Search with Policy Priors. IJCAI 2011: 1565-1570 - [c8]David Wingate, Noah D. Goodman, Andreas Stuhlmüller, Jeffrey Mark Siskind:
Nonstandard Interpretations of Probabilistic Programs for Efficient Inference. NIPS 2011: 1152-1160 - [c7]David Wingate, Andreas Stuhlmüller, Noah D. Goodman:
Lightweight Implementations of Probabilistic Programming Languages Via Transformational Compilation. AISTATS 2011: 770-778 - [i1]Irvin Hwang, Andreas Stuhlmüller, Noah D. Goodman:
Inducing Probabilistic Programs by Bayesian Program Merging. CoRR abs/1110.5667 (2011) - 2010
- [j2]Charles Kemp, Noah D. Goodman, Joshua B. Tenenbaum:
Learning to Learn Causal Models. Cogn. Sci. 34(7): 1185-1243 (2010)
2000 – 2009
- 2009
- [c6]Tomer D. Ullman, Chris L. Baker, Owen Macindoe, Owain Evans, Noah D. Goodman, Joshua B. Tenenbaum:
Help or Hinder: Bayesian Models of Social Goal Inference. NIPS 2009: 1874-1882 - [c5]David Wingate, Noah D. Goodman, Daniel M. Roy, Joshua B. Tenenbaum:
The Infinite Latent Events Model. UAI 2009: 607-614 - 2008
- [j1]Noah D. Goodman, Joshua B. Tenenbaum, Jacob Feldman, Thomas L. Griffiths:
A Rational Analysis of Rule-Based Concept Learning. Cogn. Sci. 32(1): 108-154 (2008) - [c4]Patrick Shafto, Noah D. Goodman:
A Bayesian Model of Pedagogical Reasoning. AAAI Fall Symposium: Naturally-Inspired Artificial Intelligence 2008: 101-102 - [c3]Noah D. Goodman, Vikash K. Mansinghka, Daniel M. Roy, Kallista A. Bonawitz, Joshua B. Tenenbaum:
Church: a language for generative models. UAI 2008: 220-229 - 2007
- [c2]Michael C. Frank, Noah D. Goodman, Joshua B. Tenenbaum:
A Bayesian Framework for Cross-Situational Word-Learning. NIPS 2007: 457-464 - [c1]Charles Kemp, Noah D. Goodman, Joshua B. Tenenbaum:
Learning and using relational theories. NIPS 2007: 753-760
Coauthor Index
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