Andreas, Jacob

60 publications

TMLR 2026 Policy Learning with a Language Bottleneck Megha Srivastava, Cédric Colas, Dorsa Sadigh, Jacob Andreas

TMLR 2026 ThinkPrune: Pruning Long Chain-of-Thought of LLMs via Reinforcement Learning Bairu Hou, Yang Zhang, Jiabao Ji, Yujian Liu, Kaizhi Qian, Jacob Andreas, Shiyu Chang

ICML 2025 (How) Do Language Models Track State? Belinda Z. Li, Zifan Carl Guo, Jacob Andreas

ICML 2025 A Hitchhiker’s Guide to Scaling Law Estimation Leshem Choshen, Yang Zhang, Jacob Andreas

NeurIPS 2025 Automated Detection of Visual Attribute Reliance with a Self-Reflective Agent Christy Li, Josep Lopez Camuñas, Jake Thomas Touchet, Jacob Andreas, Agata Lapedriza, Antonio Torralba, Tamar Rott Shaham

ICLR 2025 Eliciting Human Preferences with Language Models Belinda Z. Li, Alex Tamkin, Noah Goodman, Jacob Andreas

ICLR 2025 Learning How Hard to Think: Input-Adaptive Allocation of LM Computation Mehul Damani, Idan Shenfeld, Andi Peng, Andreea Bobu, Jacob Andreas

NeurIPS 2025 Learning Linear Attention in Polynomial Time Morris Yau, Ekin Akyürek, Jiayuan Mao, Joshua B. Tenenbaum, Stefanie Jegelka, Jacob Andreas

NeurIPS 2025 LoRA vs Full Fine-Tuning: An Illusion of Equivalence Reece S Shuttleworth, Jacob Andreas, Antonio Torralba, Pratyusha Sharma

ICLRW 2025 Self-Steering Language Models Gabriel Grand, Joshua B. Tenenbaum, Vikash Mansinghka, Alexander K. Lew, Jacob Andreas

ICML 2025 The Surprising Effectiveness of Test-Time Training for Few-Shot Learning Ekin Akyürek, Mehul Damani, Adam Zweiger, Linlu Qiu, Han Guo, Jyothish Pari, Yoon Kim, Jacob Andreas

NeurIPSW 2024 A Llama Sunk My Battleship! Asking Rational Questions with LLMs via Bayesian Inference Gabriel Grand, Valerio Pepe, Jacob Andreas, Joshua B. Tenenbaum

ICML 2024 A Multimodal Automated Interpretability Agent Tamar Rott Shaham, Sarah Schwettmann, Franklin Wang, Achyuta Rajaram, Evan Hernandez, Jacob Andreas, Antonio Torralba

CoRL 2024 Adaptive Language-Guided Abstraction from Contrastive Explanations Andi Peng, Belinda Z. Li, Ilia Sucholutsky, Nishanth Kumar, Julie Shah, Jacob Andreas, Andreea Bobu

NeurIPS 2024 Algorithmic Capabilities of Random Transformers Ziqian Zhong, Jacob Andreas

ICLRW 2024 Decision-Oriented Dialogue for Human-AI Collaboration Jessy Lin, Nicholas Tomlin, Jacob Andreas, Jason Eisner

ICML 2024 Decomposing Uncertainty for Large Language Models Through Input Clarification Ensembling Bairu Hou, Yujian Liu, Kaizhi Qian, Jacob Andreas, Shiyu Chang, Yang Zhang

ICML 2024 In-Context Language Learning: Architectures and Algorithms Ekin Akyürek, Bailin Wang, Yoon Kim, Jacob Andreas

ICLR 2024 LILO: Learning Interpretable Libraries by Compressing and Documenting Code Gabriel Grand, Lionel Wong, Matthew Bowers, Theo X. Olausson, Muxin Liu, Joshua B. Tenenbaum, Jacob Andreas

ICLR 2024 Learning Grounded Action Abstractions from Language Lionel Wong, Jiayuan Mao, Pratyusha Sharma, Zachary S Siegel, Jiahai Feng, Noa Korneev, Joshua B. Tenenbaum, Jacob Andreas

ICLR 2024 Learning with Language-Guided State Abstractions Andi Peng, Ilia Sucholutsky, Belinda Z. Li, Theodore Sumers, Thomas L. Griffiths, Jacob Andreas, Julie Shah

ICLR 2024 Linearity of Relation Decoding in Transformer Language Models Evan Hernandez, Arnab Sen Sharma, Tal Haklay, Kevin Meng, Martin Wattenberg, Jacob Andreas, Yonatan Belinkov, David Bau

ICLR 2024 Modeling Boundedly Rational Agents with Latent Inference Budgets Athul Paul Jacob, Abhishek Gupta, Jacob Andreas

IJCAI 2024 Natural Language Decomposition and Interpretation of Complex Utterances Harsh Jhamtani, Hao Fang, Patrick Xia, Eran Levy, Jacob Andreas, Benjamin Van Durme

ICLR 2024 The Consensus Game: Language Model Generation via Equilibrium Search Athul Paul Jacob, Yikang Shen, Gabriele Farina, Jacob Andreas

ICMLW 2024 The Consensus Game: Language Model Generation via Equilibrium Search Athul Paul Jacob, Yikang Shen, Gabriele Farina, Jacob Andreas

ICLR 2023 Characterizing Intrinsic Compositionality in Transformers with Tree Projections Shikhar Murty, Pratyusha Sharma, Jacob Andreas, Christopher D Manning

NeurIPS 2023 FIND: A Function Description Benchmark for Evaluating Interpretability Methods Sarah Schwettmann, Tamar Shaham, Joanna Materzynska, Neil Chowdhury, Shuang Li, Jacob Andreas, David Bau, Antonio Torralba

ICML 2023 Guiding Pretraining in Reinforcement Learning with Large Language Models Yuqing Du, Olivia Watkins, Zihan Wang, Cédric Colas, Trevor Darrell, Pieter Abbeel, Abhishek Gupta, Jacob Andreas

NeurIPSW 2023 Learning Interpretable Libraries by Compressing and Documenting Code Gabriel Grand, Lionel Wong, Matthew Bowers, Theo X. Olausson, Muxin Liu, Joshua B. Tenenbaum, Jacob Andreas

NeurIPSW 2023 Modeling Boundedly Rational Agents with Latent Inference Budgets Athul Jacob, Abhishek Gupta, Jacob Andreas

ICML 2023 PromptBoosting: Black-Box Text Classification with Ten Forward Passes Bairu Hou, Joe O’Connor, Jacob Andreas, Shiyu Chang, Yang Zhang

NeurIPS 2023 The Clock and the Pizza: Two Stories in Mechanistic Explanation of Neural Networks Ziqian Zhong, Ziming Liu, Max Tegmark, Jacob Andreas

NeurIPSW 2023 The Consensus Game: Language Model Generation via Equilibrium Search Athul Paul Jacob, Yikang Shen, Gabriele Farina, Jacob Andreas

ICLR 2023 What Learning Algorithm Is In-Context Learning? Investigations with Linear Models Ekin Akyürek, Dale Schuurmans, Jacob Andreas, Tengyu Ma, Denny Zhou

ICML 2022 Modeling Strong and Human-like Gameplay with KL-Regularized Search Athul Paul Jacob, David J Wu, Gabriele Farina, Adam Lerer, Hengyuan Hu, Anton Bakhtin, Jacob Andreas, Noam Brown

ICLRW 2022 Modeling Strong and Human-like Gameplay with KL-Regularized Search Athul Paul Jacob, David J Wu, Gabriele Farina, Adam Lerer, Hengyuan Hu, Anton Bakhtin, Jacob Andreas, Noam Brown

ICLR 2022 Natural Language Descriptions of Deep Visual Features Evan Hernandez, Sarah Schwettmann, David Bau, Teona Bagashvili, Antonio Torralba, Jacob Andreas

NeurIPS 2022 Pre-Trained Language Models for Interactive Decision-Making Shuang Li, Xavier Puig, Chris Paxton, Yilun Du, Clinton Wang, Linxi Fan, Tao Chen, De-An Huang, Ekin Akyürek, Anima Anandkumar, Jacob Andreas, Igor Mordatch, Antonio Torralba, Yuke Zhu

ICLR 2022 Subspace Regularizers for Few-Shot Class Incremental Learning Afra Feyza Akyürek, Ekin Akyürek, Derry Wijaya, Jacob Andreas

ICLR 2021 Learning to Recombine and Resample Data for Compositional Generalization Ekin Akyürek, Afra Feyza Akyürek, Jacob Andreas

ICML 2021 Leveraging Language to Learn Program Abstractions and Search Heuristics Lionel Wong, Kevin M Ellis, Joshua Tenenbaum, Jacob Andreas

ICLR 2021 Representing Partial Programs with Blended Abstract Semantics Maxwell Nye, Yewen Pu, Matthew Bowers, Jacob Andreas, Joshua B. Tenenbaum, Armando Solar-Lezama

NeurIPS 2021 Teachable Reinforcement Learning via Advice Distillation Olivia Watkins, Abhishek Gupta, Trevor Darrell, Pieter Abbeel, Jacob Andreas

ICCV 2021 Toward a Visual Concept Vocabulary for GAN Latent Space Sarah Schwettmann, Evan Hernandez, David Bau, Samuel Klein, Jacob Andreas, Antonio Torralba

NeurIPS 2020 A Benchmark for Systematic Generalization in Grounded Language Understanding Laura Ruis, Jacob Andreas, Marco Baroni, Diane Bouchacourt, Brenden M Lake

NeurIPS 2020 Compositional Explanations of Neurons Jesse Mu, Jacob Andreas

NeurIPSW 2020 Representing Partial Programs with Blended Abstract Semantics Maxwell Nye, Yewen Pu, Matthew Bowers, Jacob Andreas, Joshua B. Tenenbaum, Armando Solar-Lezama

IJCAI 2019 A Survey of Reinforcement Learning Informed by Natural Language Jelena Luketina, Nantas Nardelli, Gregory Farquhar, Jakob N. Foerster, Jacob Andreas, Edward Grefenstette, Shimon Whiteson, Tim Rocktäschel

ICLR 2019 Guiding Policies with Language via Meta-Learning John D. Co-Reyes, Abhishek Gupta, Suvansh Sanjeev, Nick Altieri, Jacob Andreas, John DeNero, Pieter Abbeel, Sergey Levine

ICLR 2019 Measuring Compositionality in Representation Learning Jacob Andreas

ICML 2018 Can Deep Reinforcement Learning Solve Erdos-Selfridge-Spencer Games? Maithra Raghu, Alex Irpan, Jacob Andreas, Bobby Kleinberg, Quoc Le, Jon Kleinberg

ECCV 2018 Explainable Neural Computation via Stack Neural Module Networks Ronghang Hu, Jacob Andreas, Trevor Darrell, Kate Saenko

NeurIPS 2018 Speaker-Follower Models for Vision-and-Language Navigation Daniel Fried, Ronghang Hu, Volkan Cirik, Anna Rohrbach, Jacob Andreas, Louis-Philippe Morency, Taylor Berg-Kirkpatrick, Kate Saenko, Dan Klein, Trevor Darrell

ICCV 2017 Learning to Reason: End-to-End Module Networks for Visual Question Answering Ronghang Hu, Jacob Andreas, Marcus Rohrbach, Trevor Darrell, Kate Saenko

CVPR 2017 Modeling Relationships in Referential Expressions with Compositional Modular Networks Ronghang Hu, Marcus Rohrbach, Jacob Andreas, Trevor Darrell, Kate Saenko

ICML 2017 Modular Multitask Reinforcement Learning with Policy Sketches Jacob Andreas, Dan Klein, Sergey Levine

CVPR 2016 Neural Module Networks Jacob Andreas, Marcus Rohrbach, Trevor Darrell, Dan Klein

NeurIPS 2015 On the Accuracy of Self-Normalized Log-Linear Models Jacob Andreas, Maxim Rabinovich, Michael I Jordan, Dan Klein

NeurIPS 2014 Unsupervised Transcription of Piano Music Taylor Berg-Kirkpatrick, Jacob Andreas, Dan Klein