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2020 – today
- 2025
[i27]Kunal Handa, Alex Tamkin, Miles McCain, Saffron Huang, Esin Durmus, Sarah Heck, Jared Mueller, Jerry Hong, Stuart Ritchie, Tim Belonax, Kevin K. Troy, Dario Amodei, Jared Kaplan, Jack Clark, Deep Ganguli:
Which Economic Tasks are Performed with AI? Evidence from Millions of Claude Conversations. CoRR abs/2503.04761 (2025)- 2024
- [c17]Saffron Huang
, Divya Siddarth, Liane Lovitt, Thomas I. Liao, Esin Durmus, Alex Tamkin, Deep Ganguli:
Collective Constitutional AI: Aligning a Language Model with Public Input. FAccT 2024: 1395-1417 - [c16]Alex Tamkin, Mohammad Taufeeque, Noah D. Goodman:
Codebook Features: Sparse and Discrete Interpretability for Neural Networks. ICML 2024 - [c15]Cem Anil, Esin Durmus, Nina Panickssery, Mrinank Sharma, Joe Benton, Sandipan Kundu, Joshua Batson, Meg Tong, Jesse Mu, Daniel Ford, Francesco Mosconi, Rajashree Agrawal, Rylan Schaeffer, Naomi Bashkansky, Samuel Svenningsen, Mike Lambert, Ansh Radhakrishnan, Carson Denison, Evan Hubinger, Yuntao Bai, Trenton Bricken, Timothy Maxwell, Nicholas Schiefer, James Sully, Alex Tamkin, Tamera Lanham, Karina Nguyen, Tomek Korbak, Jared Kaplan, Deep Ganguli, Samuel R. Bowman, Ethan Perez, Roger B. Grosse, David Kristjanson Duvenaud:
Many-shot Jailbreaking. NeurIPS 2024 - [i26]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) - [i25]Saffron Huang, Divya Siddarth, Liane Lovitt, Thomas I. Liao, Esin Durmus, Alex Tamkin, Deep Ganguli:
Collective Constitutional AI: Aligning a Language Model with Public Input. CoRR abs/2406.07814 (2024) - [i24]Carson Denison, Monte MacDiarmid, Fazl Barez, David Duvenaud, Shauna Kravec, Samuel Marks, Nicholas Schiefer, Ryan Soklaski, Alex Tamkin, Jared Kaplan, Buck Shlegeris, Samuel R. Bowman, Ethan Perez, Evan Hubinger:
Sycophancy to Subterfuge: Investigating Reward-Tampering in Large Language Models. CoRR abs/2406.10162 (2024) - [i23]Alex Tamkin, Miles McCain, Kunal Handa, Esin Durmus, Liane Lovitt, Ankur Rathi, Saffron Huang, Alfred Mountfield, Jerry Hong, Stuart Ritchie, Michael Stern, Brian Clarke, Landon Goldberg, Theodore R. Sumers, Jared Mueller, William McEachen, Wes Mitchell, Shan Carter, Jack Clark, Jared Kaplan, Deep Ganguli:
Clio: Privacy-Preserving Insights into Real-World AI Use. CoRR abs/2412.13678 (2024) - 2023
- [c14]Jasmine Bayrooti, Noah D. Goodman, Alex Tamkin:
Multispectral Contrastive Learning with Viewmaker Networks. CVPR Workshops 2023: 440-448 - [c13]Zhengxuan Wu, Alex Tamkin, Isabel Papadimitriou:
Oolong: Investigating What Makes Transfer Learning Hard with Controlled Studies. EMNLP 2023: 3280-3289 - [c12]Alex Tamkin, Kunal Handa, Avash Shrestha, Noah D. Goodman:
Task Ambiguity in Humans and Language Models. ICLR 2023 - [c11]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:
Turbulence in Focus: Benchmarking Scaling Behavior of 3D Volumetric Super-Resolution with BLASTNet 2.0 Data. NeurIPS 2023 - [c10]Alex Tamkin, Margalit Glasgow, Xiluo He, Noah D. Goodman:
Feature Dropout: Revisiting the Role of Augmentations in Contrastive Learning. NeurIPS 2023 - [i22]Jasmine Bayrooti, Noah D. Goodman, Alex Tamkin:
Multispectral Self-Supervised Learning with Viewmaker Networks. CoRR abs/2302.05757 (2023) - [i21]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:
BenchMD: A Benchmark for Modality-Agnostic Learning on Medical Images and Sensors. CoRR abs/2304.08486 (2023) - [i20]Risto Uuk, Carlos Ignacio Gutierrez, Alex Tamkin:
Operationalising the Definition of General Purpose AI Systems: Assessing Four Approaches. CoRR abs/2306.02889 (2023) - [i19]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:
Towards Measuring the Representation of Subjective Global Opinions in Language Models. CoRR abs/2306.16388 (2023) - [i18]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:
Studying Large Language Model Generalization with Influence Functions. CoRR abs/2308.03296 (2023) - [i17]Wai Tong Chung, Bassem Akoush, Pushan Sharma, Alex Tamkin, Ki Sung Jung, Jacqueline H. Chen, Jack Guo, Davy Brouzet, Mohsen Talei, Bruno Savard, Alexei Y. Poludnenko, Matthias Ihme:
Turbulence in Focus: Benchmarking Scaling Behavior of 3D Volumetric Super-Resolution with BLASTNet 2.0 Data. CoRR abs/2309.13457 (2023) - [i16]Belinda Z. Li, Alex Tamkin, Noah D. Goodman, Jacob Andreas:
Eliciting Human Preferences with Language Models. CoRR abs/2310.11589 (2023) - [i15]Alex Tamkin, Mohammad Taufeeque, Noah D. Goodman:
Codebook Features: Sparse and Discrete Interpretability for Neural Networks. CoRR abs/2310.17230 (2023) - [i14]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) - [i13]Alex Tamkin, Amanda Askell, Liane Lovitt, Esin Durmus, Nicholas Joseph, Shauna Kravec, Karina Nguyen, Jared Kaplan, Deep Ganguli:
Evaluating and Mitigating Discrimination in Language Model Decisions. CoRR abs/2312.03689 (2023) - 2022
- [c9]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 - [c8]Alex Tamkin, Dat Nguyen, Salil Deshpande, Jesse Mu, Noah D. Goodman:
Active Learning Helps Pretrained Models Learn the Intended Task. NeurIPS 2022 - [i12]Zhengxuan Wu, Isabel Papadimitriou, Alex Tamkin:
Oolong: Investigating What Makes Crosslingual Transfer Hard with Controlled Studies. CoRR abs/2202.12312 (2022) - [i11]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) - [i10]Alex Tamkin, Margalit Glasgow, Xiluo He, Noah D. Goodman:
Feature Dropout: Revisiting the Role of Augmentations in Contrastive Learning. CoRR abs/2212.08378 (2022) - [i9]Alex Tamkin, Kunal Handa, Avash Shrestha, Noah D. Goodman:
Task Ambiguity in Humans and Language Models. CoRR abs/2212.10711 (2022) - 2021
- [c7]Alex Tamkin, Mike Wu, Noah D. Goodman:
Viewmaker Networks: Learning Views for Unsupervised Representation Learning. ICLR 2021 - [c6]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 - [i8]Alex Tamkin, Miles Brundage, Jack Clark, Deep Ganguli:
Understanding the Capabilities, Limitations, and Societal Impact of Large Language Models. CoRR abs/2102.02503 (2021) - [i7]Daniel Rothchild, Alex Tamkin, Julie Yu, Ujval Misra, Joseph Gonzalez:
C5T5: Controllable Generation of Organic Molecules with Transformers. CoRR abs/2108.10307 (2021) - [i6]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) - [i5]Ananya Karthik, Mike Wu, Noah D. Goodman, Alex Tamkin:
Tradeoffs Between Contrastive and Supervised Learning: An Empirical Study. CoRR abs/2112.05340 (2021) - 2020
- [c5]Ramtin Keramati, Christoph Dann, Alex Tamkin, Emma Brunskill:
Being Optimistic to Be Conservative: Quickly Learning a CVaR Policy. AAAI 2020: 4436-4443 - [c4]Alex Tamkin, Trisha Singh, Davide Giovanardi, Noah D. Goodman:
Investigating Transferability in Pretrained Language Models. EMNLP (Findings) 2020: 1393-1401 - [c3]Alex Tamkin, Dan Jurafsky, Noah D. Goodman:
Language Through a Prism: A Spectral Approach for Multiscale Language Representations. NeurIPS 2020 - [i4]Alex Tamkin, Trisha Singh, Davide Giovanardi, Noah D. Goodman:
Investigating Transferability in Pretrained Language Models. CoRR abs/2004.14975 (2020) - [i3]Alex Tamkin, Mike Wu, Noah D. Goodman:
Viewmaker Networks: Learning Views for Unsupervised Representation Learning. CoRR abs/2010.07432 (2020) - [i2]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
- [c2]Jessica R. Cauchard, Alex Tamkin, Cheng Yao Wang, Luke Vink, Michelle Park, Tommy Fang, James A. Landay:
Drone.io: A Gestural and Visual Interface for Human-Drone Interaction. HRI 2019: 153-162 - [c1]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:
Recursive Routing Networks: Learning to Compose Modules for Language Understanding. NAACL-HLT (1) 2019: 3631-3648 - [i1]Ramtin Keramati, Christoph Dann, Alex Tamkin, Emma Brunskill:
Being Optimistic to Be Conservative: Quickly Learning a CVaR Policy. CoRR abs/1911.01546 (2019)
Coauthor Index
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last updated on 2025-04-14 00:35 CEST by the dblp team
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