Alex Tamkin

According to our database, Alex Tamkin authored at least 28 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Bayesian Preference Elicitation with Language Models.

Kunal Handa, Yarin Gal, Ellie Pavlick, Noah D. Goodman, Jacob Andreas, Alex Tamkin, Belinda Z. Li

CoRR, 2024

2023

Evaluating and Mitigating Discrimination in Language Model Decisions.

Alex Tamkin, Amanda Askell, Liane Lovitt, Esin Durmus, Nicholas Joseph, Shauna Kravec, Karina Nguyen, Jared Kaplan, Deep Ganguli

CoRR, 2023

Social Contract AI: Aligning AI Assistants with Implicit Group Norms.

Jan-Philipp Fränken, Sam Kwok, Peixuan Ye, Kanishk Gandhi, Dilip Arumugam, Jared Moore, Alex Tamkin, Tobias Gerstenberg, Noah D. Goodman

CoRR, 2023

Codebook Features: Sparse and Discrete Interpretability for Neural Networks.

Alex Tamkin, Mohammad Taufeeque, Noah D. Goodman

CoRR, 2023

Eliciting Human Preferences with Language Models.

Belinda Z. Li, Alex Tamkin, Noah D. Goodman, Jacob Andreas

CoRR, 2023

Studying Large Language Model Generalization with Influence Functions.

Roger B. Grosse, Juhan Bae, Cem Anil, Nelson Elhage, Alex Tamkin, Amirhossein Tajdini, Benoit Steiner, Dustin Li, Esin Durmus, Ethan Perez, Evan Hubinger, Kamile Lukosiute, Karina Nguyen, Nicholas Joseph, Sam McCandlish, Jared Kaplan, Samuel R. Bowman

CoRR, 2023

Towards Measuring the Representation of Subjective Global Opinions in Language Models.

Esin Durmus, Karina Nyugen, Thomas I. Liao, Nicholas Schiefer, Amanda Askell, Anton Bakhtin, Carol Chen, Zac Hatfield-Dodds, Danny Hernandez, Nicholas Joseph, Liane Lovitt, Sam McCandlish, Orowa Sikder, Alex Tamkin, Janel Thamkul, Jared Kaplan, Jack Clark, Deep Ganguli

CoRR, 2023

Operationalising the Definition of General Purpose AI Systems: Assessing Four Approaches.

Risto Uuk, Carlos Ignacio Gutierrez, Alex Tamkin

CoRR, 2023

BenchMD: A Benchmark for Modality-Agnostic Learning on Medical Images and Sensors.

Kathryn Wantlin, Chenwei Wu, Shih-Cheng Huang, Oishi Banerjee, Farah Dadabhoy, Veeral Vipin Mehta, Ryan Wonhee Han, Fang Cao, Raja R. Narayan, Errol Colak, Adewole S. Adamson, Laura Heacock, Geoffrey H. Tison, Alex Tamkin, Pranav Rajpurkar

CoRR, 2023

Multispectral Self-Supervised Learning with Viewmaker Networks.

Jasmine Bayrooti, Noah D. Goodman, Alex Tamkin

CoRR, 2023

Feature Dropout: Revisiting the Role of Augmentations in Contrastive Learning.

Alex Tamkin, Margalit Glasgow, Xiluo He, Noah D. Goodman

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Turbulence in Focus: Benchmarking Scaling Behavior of 3D Volumetric Super-Resolution with BLASTNet 2.0 Data.

Wai Tong Chung, Bassem Akoush, Pushan Sharma, Alex Tamkin, Ki Sung Jung, Jacqueline Chen, Jack Guo, Davy Brouzet, Mohsen Talei, Bruno Savard, Alexei Y. Poludnenko, Matthias Ihme

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Task Ambiguity in Humans and Language Models.

Alex Tamkin, Kunal Handa, Avash Shrestha, Noah D. Goodman

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Oolong: Investigating What Makes Transfer Learning Hard with Controlled Studies.

Zhengxuan Wu, Alex Tamkin, Isabel Papadimitriou

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023

Multispectral Contrastive Learning with Viewmaker Networks.

Jasmine Bayrooti, Noah D. Goodman, Alex Tamkin

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022

Oolong: Investigating What Makes Crosslingual Transfer Hard with Controlled Studies.

Zhengxuan Wu, Isabel Papadimitriou, Alex Tamkin

CoRR, 2022

Active Learning Helps Pretrained Models Learn the Intended Task.

Alex Tamkin, Dat Nguyen, Salil Deshpande, Jesse Mu, Noah D. Goodman

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

DABS 2.0: Improved Datasets and Algorithms for Universal Self-Supervision.

Alex Tamkin, Gaurab Banerjee, Mohamed Owda, Vincent Liu, Shashank Rammoorthy, Noah D. Goodman

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

Tradeoffs Between Contrastive and Supervised Learning: An Empirical Study.

Ananya Karthik, Mike Wu, Noah D. Goodman, Alex Tamkin

CoRR, 2021

C5T5: Controllable Generation of Organic Molecules with Transformers.

Daniel Rothchild, Alex Tamkin, Julie Yu, Ujval Misra, Joseph Gonzalez

CoRR, 2021

Understanding the Capabilities, Limitations, and Societal Impact of Large Language Models.

Alex Tamkin, Miles Brundage, Jack Clark, Deep Ganguli

CoRR, 2021

DABS: a Domain-Agnostic Benchmark for Self-Supervised Learning.

Alex Tamkin, Vincent Liu, Rongfei Lu, Daniel Fein, Colin Schultz, Noah D. Goodman

Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

Viewmaker Networks: Learning Views for Unsupervised Representation Learning.

Alex Tamkin, Mike Wu, Noah D. Goodman

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

Language Through a Prism: A Spectral Approach for Multiscale Language Representations.

Alex Tamkin, Dan Jurafsky, Noah D. Goodman

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Investigating Transferability in Pretrained Language Models.

Alex Tamkin, Trisha Singh, Davide Giovanardi, Noah D. Goodman

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2020, 2020

Being Optimistic to Be Conservative: Quickly Learning a CVaR Policy.

Ramtin Keramati, Christoph Dann, Alex Tamkin, Emma Brunskill

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

Recursive Routing Networks: Learning to Compose Modules for Language Understanding.

Ignacio Cases, Clemens Rosenbaum, Matthew Riemer, Atticus Geiger, Tim Klinger, Alex Tamkin, Olivia Li, Sandhini Agarwal, Joshua D. Greene, Dan Jurafsky, Christopher Potts, Lauri Karttunen

Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2019

Drone.io: A Gestural and Visual Interface for Human-Drone Interaction.

Jessica R. Cauchard, Alex Tamkin, Cheng Yao Wang, Luke Vink, Michelle Park, Tommy Fang, James A. Landay

Proceedings of the 14th ACM/IEEE International Conference on Human-Robot Interaction, 2019