← Authors

Noah Goodman

72 papers · 2007–2025 · 14 conferences · across top CS/AI conferences

Achievements

πŸ—ΊοΈ Taxonomy Completionist (22) 🐝 Cross-Pollinator (12) 🐣 Hot Topic Early Bird πŸƒ Academic Marathon (18) 🌍 Conference Polyglot (14) 🌈 Renaissance Researcher (6) πŸŒ‰ Interdisciplinary Bridge 🧭 Keyword Pioneer 🌟 Keyword Trendsetter Combo (8) 🏠 Conference Loyalist (30) 🧬 Topic Evolution πŸ† Grand Slam 🌱 Topic Pioneer πŸ‘‘ Triple Crown πŸ”¬ Deep Specialist (12) πŸ† Keyword Champion ⚑ Prolific Year (6) πŸš€ Conference Pioneer πŸ“ˆ Trend Setter πŸ—ƒοΈ Keyword Collector (63) πŸ’Ž Century Club (72) ❓ The Questioner (2) πŸ”₯ Unstoppable (10)

Conferences

NIPS (30) EMNLP (7) ICLR (7) ACL (6) ICML (5) AISTATS (4) NAACL (4) AAAI (3) CLEAR (1) CONLL (1) EACL (1) ICCV (1) IJCNLP (1) JMLR (1)

Top co-authors

Alex Tamkin (9) Christopher Potts (8) Mike Wu (8) Jesse Mu (7) Zhengxuan Wu (6) Atticus Geiger (6) Robert Hawkins (5) Thomas Icard (5) Dorsa Sadigh (5) Gabriel Poesia (5)

Research topics

Education (3)

Keywords

representation learning (7) bayesian inference (7) probabilistic programming (6) neural network (6) causal abstraction (6) language model (5) pragmatic reasoning (5) amortized inference (5) markov chain monte carlo (5) reference game (4) large language model (4) causal inference (4) generative model (4) reinforcement learning (3) variational autoencoder (3) dialogue system (3) natural language generation (3) bayesian model (3) transfer learning (3) variational inference (3)

Papers

Causal Abstraction: A Theoretical Foundation for Mechanistic Interpretability JMLR 2025
Value Profiles for Encoding Human Variation EMNLP 2025
What Makes a Maze Look Like a Maze? ICLR 2025
Eliciting Human Preferences with Language Models ICLR 2025
Automated Statistical Model Discovery with Language Models ICML 2024
Hypothesis Search: Inductive Reasoning with Language Models ICLR 2024
Backtracing: Retrieving the Cause of the Query EACL 2024
Finding Alignments Between Interpretable Causal Variables and Distributed Neural Representations CLEAR 2024
pyvene: A Library for Understanding and Improving PyTorch Models via Interventions NAACL 2024
Codebook Features: Sparse and Discrete Interpretability for Neural Networks ICML 2024
Understanding Social Reasoning in Language Models with Language Models NIPS 2023
Why think step by step? Reasoning emerges from the locality of experience NIPS 2023
Generating Language Corrections for Teaching Physical Control Tasks ICML 2023
SIGHT: A Large Annotated Dataset on Student Insights Gathered from Higher Education Transcripts ACL 2023
Interpretability at Scale: Identifying Causal Mechanisms in Alpaca NIPS 2023
Learning to Compress Prompts with Gist Tokens NIPS 2023
Parsel🐍: Algorithmic Reasoning with Language Models by Composing Decompositions NIPS 2023
Feature Dropout: Revisiting the Role of Augmentations in Contrastive Learning NIPS 2023
Task Ambiguity in Humans and Language Models ICLR 2023
Inducing Causal Structure for Interpretable Neural Networks ICML 2022
Foundation Posteriors for Approximate Probabilistic Inference NIPS 2022
CLEVRER-Humans: Describing Physical and Causal Events the Human Way NIPS 2022
STaR: Bootstrapping Reasoning With Reasoning NIPS 2022
Active Learning Helps Pretrained Models Learn the Intended Task NIPS 2022
Assistive Teaching of Motor Control Tasks to Humans NIPS 2022
Improving Intrinsic Exploration with Language Abstractions NIPS 2022
DABS 2.0: Improved Datasets and Algorithms for Universal Self-Supervision NIPS 2022
Geoclidean: Few-Shot Generalization in Euclidean Geometry NIPS 2022
Mixed-effects transformers for hierarchical adaptation EMNLP 2022
Concadia: Towards Image-Based Text Generation with a Purpose EMNLP 2022
Language modeling via stochastic processes ICLR 2022
Causal Distillation for Language Models NAACL 2022
Calibrate your listeners! Robust communication-based training for pragmatic speakers EMNLP 2021
Emergent Communication of Generalizations NIPS 2021
Improving Compositionality of Neural Networks by Decoding Representations to Inputs NIPS 2021
Open-domain clarification question generation without question examples EMNLP 2021
Pragmatic Code Autocomplete AAAI 2021
Question Generation for Adaptive Education ACL 2021
Contrastive Reinforcement Learning of Symbolic Reasoning Domains NIPS 2021
Question Generation for Adaptive Education IJCNLP 2021
Conditional Negative Sampling for Contrastive Learning of Visual Representations ICLR 2021
Viewmaker Networks: Learning Views for Unsupervised Representation Learning ICLR 2021
Meta-Amortized Variational Inference and Learning AAAI 2020
Shaping Visual Representations with Language for Few-Shot Classification ACL 2020
Language Through a Prism: A Spectral Approach for Multiscale Language Representations NIPS 2020
Continual Adaptation for Efficient Machine Communication CONLL 2020
Investigating Transferability in Pretrained Language Models EMNLP 2020
Continual Adaptation for Efficient Machine Communication EMNLP 2020
Variational Bayesian Optimal Experimental Design NIPS 2019
Differentiable Antithetic Sampling for Variance Reduction in Stochastic Variational Inference AISTATS 2019
Lost in Machine Translation: A Method to Reduce Meaning Loss NAACL 2019
Shapeglot: Learning Language for Shape Differentiation ICCV 2019
Tensor Variable Elimination for Plated Factor Graphs ICML 2019
DisSent: Learning Sentence Representations from Explicit Discourse Relations ACL 2019
Learning from Omission ACL 2019
Zero Shot Learning for Code Education: Rubric Sampling with Deep Learning Inference AAAI 2019
Learning to Explain: Answering Why-Questions via Rephrasing ACL 2019
Bias and Generalization in Deep Generative Models: An Empirical Study NIPS 2018
Multimodal Generative Models for Scalable Weakly-Supervised Learning NIPS 2018
Pragmatically Informative Image Captioning with Character-Level Inference NAACL 2018
Learning Disentangled Representations with Semi-Supervised Deep Generative Models NIPS 2017
C3: Lightweight Incrementalized MCMC for Probabilistic Programs using Continuations and Callsite Caching AISTATS 2016
Neurally-Guided Procedural Models: Amortized Inference for Procedural Graphics Programs using Neural Networks NIPS 2016
Generating Efficient MCMC Kernels from Probabilistic Programs AISTATS 2014
Learning Stochastic Inverses NIPS 2013
Learning and using language via recursive pragmatic reasoning about other agents NIPS 2013
Burn-in, bias, and the rationality of anchoring NIPS 2012
Nonstandard Interpretations of Probabilistic Programs for Efficient Inference NIPS 2011
Lightweight Implementations of Probabilistic Programming Languages Via Transformational Compilation AISTATS 2011
Help or Hinder: Bayesian Models of Social Goal Inference NIPS 2009
Learning and using relational theories NIPS 2007
A Bayesian Framework for Cross-Situational Word-Learning NIPS 2007