Timezone: »
Throughout the cognitive-science literature, there is widespread agreement that decision-making agents operating in the real world do so under limited information-processing capabilities and without access to unbounded cognitive or computational resources. Prior work has drawn inspiration from this fact and leveraged an information-theoretic model of such behaviors or policies as communication channels operating under a bounded rate constraint. Meanwhile, a parallel line of work also capitalizes on the same principles from rate-distortion theory to formalize capacity-limited decision making through the notion of a learning target, which facilitates Bayesian regret bounds for provably-efficient learning algorithms. In this paper, we aim to elucidate this latter perspective by presenting a brief survey of these information-theoretic models of capacity-limited decision making in biological and artificial agents.
Author Information
Dilip Arumugam (Stanford University)
Mark Ho (New York University)
Noah Goodman (Stanford University)
Benjamin Van Roy (Stanford University)
More from the Same Authors
-
2021 : DABS: a Domain-Agnostic Benchmark for Self-Supervised Learning »
Alex Tamkin · Vincent Liu · Rongfei Lu · Daniel Fein · Colin Schultz · Noah Goodman -
2021 : Learning to solve complex tasks by growing knowledge culturally across generations »
Michael Tessler · Jason Madeano · Pedro Tsividis · Noah Goodman · Josh Tenenbaum -
2022 : Lemma: Bootstrapping High-Level Mathematical Reasoning with Learned Symbolic Abstractions »
Zhening Li · Gabriel Poesia Reis e Silva · Omar Costilla Reyes · Noah Goodman · Armando Solar-Lezama -
2022 : How to talk so AI will learn: instructions, descriptions, and pragmatics »
Theodore Sumers · Robert Hawkins · Mark Ho · Tom Griffiths · Dylan Hadfield-Menell -
2022 : In the ZONE: Measuring difficulty and progression in curriculum generation »
Rose Wang · Jesse Mu · Dilip Arumugam · Natasha Jaques · Noah Goodman -
2022 : Fast Adaptation via Human Diagnosis of Task Distribution Shift »
Andi Peng · Mark Ho · Aviv Netanyahu · Julie A Shah · Pulkit Agrawal -
2023 Poster: Why think step by step? Reasoning emerges from the locality of experience »
Benjamin Prystawski · Michael Li · Noah Goodman -
2023 Poster: Parsel🐍: Algorithmic Reasoning with Language Models by Composing Decompositions »
Eric Zelikman · Qian Huang · Gabriel Poesia · Noah Goodman · Nick Haber -
2023 Poster: Interpretability at Scale: Identifying Causal Mechanisms in Alpaca »
Zhengxuan Wu · Atticus Geiger · Christopher Potts · Noah Goodman -
2023 Poster: Feature Dropout: Revisiting the Role of Augmentations in Contrastive Learning »
Alex Tamkin · Margalit Glasgow · Xiluo He · Noah Goodman -
2023 Poster: Learning to Compress Prompts with Gist Tokens »
Jesse Mu · Xiang Li · Noah Goodman -
2022 : MATH-AI: Toward Human-Level Mathematical Reasoning »
Francois Charton · Noah Goodman · Behnam Neyshabur · Talia Ringer · Daniel Selsam -
2022 : Learning Mathematical Reasoning for Education »
Noah Goodman -
2022 : Invited Talk: Noah Goodman »
Noah Goodman -
2022 Poster: Assistive Teaching of Motor Control Tasks to Humans »
Megha Srivastava · Erdem Biyik · Suvir Mirchandani · Noah Goodman · Dorsa Sadigh -
2022 Poster: CLEVRER-Humans: Describing Physical and Causal Events the Human Way »
Jiayuan Mao · Xuelin Yang · Xikun Zhang · Noah Goodman · Jiajun Wu -
2022 Poster: Geoclidean: Few-Shot Generalization in Euclidean Geometry »
Joy Hsu · Jiajun Wu · Noah Goodman -
2022 Poster: Active Learning Helps Pretrained Models Learn the Intended Task »
Alex Tamkin · Dat Nguyen · Salil Deshpande · Jesse Mu · Noah Goodman -
2022 Poster: An Information-Theoretic Framework for Deep Learning »
Hong Jun Jeon · Benjamin Van Roy -
2022 Poster: Foundation Posteriors for Approximate Probabilistic Inference »
Mike Wu · Noah Goodman -
2022 Poster: How to talk so AI will learn: Instructions, descriptions, and autonomy »
Theodore Sumers · Robert Hawkins · Mark Ho · Tom Griffiths · Dylan Hadfield-Menell -
2022 Poster: STaR: Bootstrapping Reasoning With Reasoning »
Eric Zelikman · Yuhuai Wu · Jesse Mu · Noah Goodman -
2022 Poster: Planning to the Information Horizon of BAMDPs via Epistemic State Abstraction »
Dilip Arumugam · Satinder Singh -
2022 Poster: DABS 2.0: Improved Datasets and Algorithms for Universal Self-Supervision »
Alex Tamkin · Gaurab Banerjee · Mohamed Owda · Vincent Liu · Shashank Rammoorthy · Noah Goodman -
2022 Poster: Deciding What to Model: Value-Equivalent Sampling for Reinforcement Learning »
Dilip Arumugam · Benjamin Van Roy -
2022 Poster: Improving Intrinsic Exploration with Language Abstractions »
Jesse Mu · Victor Zhong · Roberta Raileanu · Minqi Jiang · Noah Goodman · Tim Rocktäschel · Edward Grefenstette -
2021 : Spotlight Talk: Learning to solve complex tasks by growing knowledge culturally across generations »
Noah Goodman · Josh Tenenbaum · Michael Tessler · Jason Madeano -
2021 : Environment Capacity »
Benjamin Van Roy -
2021 : Reducing the Information Horizon of Bayes-Adaptive Markov Decision Processes via Epistemic State Abstraction »
Dilip Arumugam · Satinder Singh -
2021 : Multi-party referential communication in complex strategic games »
Jessica Mankewitz · Veronica Boyce · Brandon Waldon · Georgia Loukatou · Dhara Yu · Jesse Mu · Noah Goodman · Michael C Frank -
2021 Workshop: Meaning in Context: Pragmatic Communication in Humans and Machines »
Jennifer Hu · Noga Zaslavsky · Aida Nematzadeh · Michael Franke · Roger Levy · Noah Goodman -
2021 : Opening remarks »
Jennifer Hu · Noga Zaslavsky · Aida Nematzadeh · Michael Franke · Roger Levy · Noah Goodman -
2021 Poster: Emergent Communication of Generalizations »
Jesse Mu · Noah Goodman -
2021 Poster: The Value of Information When Deciding What to Learn »
Dilip Arumugam · Benjamin Van Roy -
2021 Poster: Contrastive Reinforcement Learning of Symbolic Reasoning Domains »
Gabriel Poesia · WenXin Dong · Noah Goodman -
2021 Poster: Improving Compositionality of Neural Networks by Decoding Representations to Inputs »
Mike Wu · Noah Goodman · Stefano Ermon -
2021 Panel: The Consequences of Massive Scaling in Machine Learning »
Noah Goodman · Melanie Mitchell · Joelle Pineau · Oriol Vinyals · Jared Kaplan -
2020 Poster: Language Through a Prism: A Spectral Approach for Multiscale Language Representations »
Alex Tamkin · Dan Jurafsky · Noah Goodman -
2019 : Reinforcement Learning Beyond Optimization »
Benjamin Van Roy -
2019 Poster: Information-Theoretic Confidence Bounds for Reinforcement Learning »
Xiuyuan Lu · Benjamin Van Roy -
2019 Poster: Variational Bayesian Optimal Experimental Design »
Adam Foster · Martin Jankowiak · Elias Bingham · Paul Horsfall · Yee Whye Teh · Thomas Rainforth · Noah Goodman -
2019 Spotlight: Variational Bayesian Optimal Experimental Design »
Adam Foster · Martin Jankowiak · Elias Bingham · Paul Horsfall · Yee Whye Teh · Thomas Rainforth · Noah Goodman -
2018 Poster: An Information-Theoretic Analysis for Thompson Sampling with Many Actions »
Shi Dong · Benjamin Van Roy -
2018 Poster: Scalable Coordinated Exploration in Concurrent Reinforcement Learning »
Maria Dimakopoulou · Ian Osband · Benjamin Van Roy -
2018 Poster: Bias and Generalization in Deep Generative Models: An Empirical Study »
Shengjia Zhao · Hongyu Ren · Arianna Yuan · Jiaming Song · Noah Goodman · Stefano Ermon -
2018 Spotlight: Bias and Generalization in Deep Generative Models: An Empirical Study »
Shengjia Zhao · Hongyu Ren · Arianna Yuan · Jiaming Song · Noah Goodman · Stefano Ermon -
2018 Poster: Multimodal Generative Models for Scalable Weakly-Supervised Learning »
Mike Wu · Noah Goodman -
2017 : Morning panel discussion »
Jürgen Schmidhuber · Noah Goodman · Anca Dragan · Pushmeet Kohli · Dhruv Batra -
2017 : "Language in context" »
Noah Goodman -
2017 Poster: Ensemble Sampling »
Xiuyuan Lu · Benjamin Van Roy -
2017 Poster: Conservative Contextual Linear Bandits »
Abbas Kazerouni · Mohammad Ghavamzadeh · Yasin Abbasi · Benjamin Van Roy -
2017 Poster: Learning Disentangled Representations with Semi-Supervised Deep Generative Models »
Siddharth Narayanaswamy · Brooks Paige · Jan-Willem van de Meent · Alban Desmaison · Noah Goodman · Pushmeet Kohli · Frank Wood · Philip Torr -
2016 Poster: Deep Exploration via Bootstrapped DQN »
Ian Osband · Charles Blundell · Alexander Pritzel · Benjamin Van Roy -
2016 Poster: Neurally-Guided Procedural Models: Amortized Inference for Procedural Graphics Programs using Neural Networks »
Daniel Ritchie · Anna Thomas · Pat Hanrahan · Noah Goodman -
2015 Workshop: Bounded Optimality and Rational Metareasoning »
Samuel J Gershman · Falk Lieder · Tom Griffiths · Noah Goodman -
2014 Workshop: Large-scale reinforcement learning and Markov decision problems »
Benjamin Van Roy · Mohammad Ghavamzadeh · Peter Bartlett · Yasin Abbasi Yadkori · Ambuj Tewari -
2014 Poster: Near-optimal Reinforcement Learning in Factored MDPs »
Ian Osband · Benjamin Van Roy -
2014 Poster: Learning to Optimize via Information-Directed Sampling »
Daniel Russo · Benjamin Van Roy -
2014 Spotlight: Near-optimal Reinforcement Learning in Factored MDPs »
Ian Osband · Benjamin Van Roy -
2014 Poster: Model-based Reinforcement Learning and the Eluder Dimension »
Ian Osband · Benjamin Van Roy -
2013 Poster: (More) Efficient Reinforcement Learning via Posterior Sampling »
Ian Osband · Daniel Russo · Benjamin Van Roy -
2013 Poster: Eluder Dimension and the Sample Complexity of Optimistic Exploration »
Daniel Russo · Benjamin Van Roy -
2013 Oral: Eluder Dimension and the Sample Complexity of Optimistic Exploration »
Daniel Russo · Benjamin Van Roy -
2013 Poster: Efficient Exploration and Value Function Generalization in Deterministic Systems »
Zheng Wen · Benjamin Van Roy -
2013 Poster: Learning and using language via recursive pragmatic reasoning about other agents »
Nathaniel J Smith · Noah Goodman · Michael C Frank -
2013 Poster: Learning Stochastic Inverses »
Andreas Stuhlmüller · Jacob Taylor · Noah Goodman -
2012 Workshop: Probabilistic Programming: Foundations and Applications (2 day) »
Vikash Mansinghka · Daniel Roy · Noah Goodman -
2012 Workshop: Probabilistic Programming: Foundations and Applications (2 day) »
Vikash Mansinghka · Daniel Roy · Noah Goodman -
2012 Poster: Burn-in, bias, and the rationality of anchoring »
Falk Lieder · Tom Griffiths · Noah Goodman -
2012 Poster: Efficient Reinforcement Learning for High Dimensional Linear Quadratic Systems »
Morteza Ibrahimi · Adel Javanmard · Benjamin Van Roy -
2011 Poster: Nonstandard Interpretations of Probabilistic Programs for Efficient Inference »
David Wingate · Noah Goodman · Andreas Stuhlmueller · Jeffrey Siskind -
2009 Poster: Directed Regression »
Yi-Hao Kao · Benjamin Van Roy · Xiang Yan