Stephanie C. Y. Chan

According to our database, Stephanie C. Y. Chan authored at least 26 papers between 2020 and 2025.

Collaborative distances:

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

2025

Representation biases: will we achieve complete understanding by analyzing representations?

Andrew Kyle Lampinen, Stephanie C. Y. Chan, Yuxuan Li, Katherine L. Hermann

CoRR, July, 2025

The emergence of sparse attention: impact of data distribution and benefits of repetition.

Nicolas Zucchet, Francesco D'Angelo, Andrew K. Lampinen, Stephanie C. Y. Chan

CoRR, May, 2025

Predictability Shapes Adaptation: An Evolutionary Perspective on Modes of Learning in Transformers.

Alexander Y. Ku, Thomas L. Griffiths, Stephanie C. Y. Chan

CoRR, May, 2025

On the generalization of language models from in-context learning and finetuning: a controlled study.

Andrew K. Lampinen, Arslan Chaudhry, Stephanie C. Y. Chan, Cody Wild, Diane Wan, Alex Ku, Jörg Bornschein, Razvan Pascanu, Murray Shanahan, James L. McClelland

CoRR, May, 2025

How do language models learn facts? Dynamics, curricula and hallucinations.

Nicolas Zucchet, Jörg Bornschein, Stephanie C. Y. Chan, Andrew K. Lampinen, Razvan Pascanu, Soham De

CoRR, March, 2025

Strategy Coopetition Explains the Emergence and Transience of In-Context Learning.

Aaditya K. Singh, Ted Moskovitz, Sara Dragutinovic, Felix Hill, Stephanie C. Y. Chan, Andrew M. Saxe

CoRR, March, 2025

2024

Learned feature representations are biased by complexity, learning order, position, and more.

Andrew Kyle Lampinen, Stephanie C. Y. Chan, Katherine L. Hermann

Trans. Mach. Learn. Res., 2024

LearnLM: Improving Gemini for Learning.

Abhinit Modi, Aditya Srikanth Veerubhotla, Aliya Rysbek, Andrea Huber, Brett Wiltshire, Brian Veprek, Daniel Gillick, Daniel Kasenberg, Derek Ahmed, Irina Jurenka, James Cohan, Jennifer She, Julia Wilkowski, Kaiz Alarakyia, Kevin R. McKee, Lisa Wang, Markus Kunesch, Mike Schaekermann, Miruna Pîslar, Nikhil Joshi, Parsa Mahmoudieh, Paul Jhun, Sara Wiltberger, Shakir Mohamed, Shashank Agarwal, Shubham Milind Phal, Sun Jae Lee, Theofilos Strinopoulos, Wei-Jen Ko, Amy Wang, Ankit Anand, Avishkar Bhoopchand, Dan Wild, Divya Pandya, Filip Bar, Garth Graham, Holger Winnemoeller, Mahvish Nagda, Prateek Kolhar, Renee Schneider, Shaojian Zhu, Stephanie Chan, Steve Yadlowsky, Viknesh Sounderajah, Yannis Assael

CoRR, 2024

The broader spectrum of in-context learning.

Andrew Kyle Lampinen, Stephanie C. Y. Chan, Aaditya K. Singh, Murray Shanahan

CoRR, 2024

Towards Responsible Development of Generative AI for Education: An Evaluation-Driven Approach.

Irina Jurenka, Markus Kunesch, Kevin R. McKee, Daniel Gillick, Shaojian Zhu, Sara Wiltberger, Shubham Milind Phal, Katherine L. Hermann, Daniel Kasenberg, Avishkar Bhoopchand, Ankit Anand, Miruna Pîslar, Stephanie Chan, Lisa Wang, Jennifer She, Parsa Mahmoudieh, Aliya Rysbek, Wei-Jen Ko, Andrea Huber, Brett Wiltshire, Gal Elidan, Roni Rabin, Jasmin Rubinovitz, Amit Pitaru, Mac McAllister, Julia Wilkowski, David Choi, Roee Engelberg, Lidan Hackmon, Adva Levin, Rachel Griffin, Michael Sears, Filip Bar, Mia Mesar, Mana Jabbour, Arslan Chaudhry, James Cohan, Sridhar Thiagarajan, Nir Levine, Ben Brown, Dilan Görür, Svetlana Grant, Rachel Hashimshoni, Laura Weidinger, Jieru Hu, Dawn Chen, Kuba Dolecki, Canfer Akbulut, Maxwell L. Bileschi, Laura Culp, Wen-Xin Dong, Nahema Marchal, Kelsie Van Deman, Hema Bajaj Misra, Michael Duah, Moran Ambar, Avi Caciularu, Sandra Lefdal, Christopher Summerfield, James An, Pierre-Alexandre Kamienny, Abhinit Mohdi, Theofilos Strinopoulos, Annie Hale, Wayne Anderson, Luis C. Cobo, Niv Efron, Muktha Ananda, Shakir Mohamed, Maureen Heymans, Zoubin Ghahramani, Yossi Matias, Ben Gomes, Lila Ibrahim

CoRR, 2024

Many-Shot In-Context Learning.

Rishabh Agarwal, Avi Singh, Lei M. Zhang, Bernd Bohnet, Stephanie Chan, Ankesh Anand, Zaheer Abbas, Azade Nova, John D. Co-Reyes, Eric Chu, Feryal M. P. Behbahani, Aleksandra Faust, Hugo Larochelle

CoRR, 2024

Scaling Instructable Agents Across Many Simulated Worlds.

SIMA Team, Maria Abi Raad, Arun Ahuja, Catarina Barros, Frederic Besse, Andrew Bolt, Adrian Bolton, Bethanie Brownfield, Gavin Buttimore, Max Cant, Sarah Chakera, Stephanie C. Y. Chan, Jeff Clune, Adrian Collister, Vikki Copeman, Alex Cullum, Ishita Dasgupta, Dario de Cesare, Julia Di Trapani, Yani Donchev, Emma Dunleavy, Martin Engelcke, Ryan Faulkner, Frankie Garcia, Charles Gbadamosi, Zhitao Gong, Lucy Gonzalez, Kshitij Gupta, Karol Gregor, Arne Olav Hallingstad, Tim Harley, Sam Haves, Felix Hill, Ed Hirst, Drew A. Hudson, Jony Hudson, Steph Hughes-Fitt, Danilo J. Rezende, Mimi Jasarevic, Laura Kampis, Nan Rosemary Ke, Thomas Keck, Junkyung Kim, Oscar Knagg, Kavya Kopparapu, Andrew K. Lampinen, Shane Legg, Alexander Lerchner, Marjorie Limont, Yulan Liu, Maria Loks-Thompson, Joseph Marino, Kathryn Martin Cussons, Loic Matthey, Siobhan Mcloughlin, Piermaria Mendolicchio, Hamza Merzic, Anna Mitenkova, Alexandre Moufarek, Valéria Oliveira, Yanko Gitahy Oliveira, Hannah Openshaw, Renke Pan, Aneesh Pappu, Alex Platonov, Ollie Purkiss, David P. Reichert, John Reid, Pierre Harvey Richemond, Tyson Roberts, Giles Ruscoe, Jaume Sanchez Elias, Tasha Sandars, Daniel P. Sawyer, Tim Scholtes, Guy Simmons, Daniel Slater, Hubert Soyer, Heiko Strathmann, Peter Stys, Allison C. Tam, Denis Teplyashin, Tayfun Terzi, Davide Vercelli, Bojan Vujatovic, Marcus Wainwright, Jane X. Wang, Zhengdong Wang, Daan Wierstra, Duncan Williams, Nathaniel Wong, Sarah York, Nick Young

CoRR, 2024

Many-Shot In-Context Learning.

Rishabh Agarwal, Avi Singh, Lei Zhang, Bernd Bohnet, Luis Rosias, Stephanie C. Y. Chan, Biao Zhang, Ankesh Anand, Zaheer Abbas, Azade Nova, John D. Co-Reyes, Eric Chu, Feryal M. P. Behbahani, Aleksandra Faust, Hugo Larochelle

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

What needs to go right for an induction head? A mechanistic study of in-context learning circuits and their formation.

Aaditya K. Singh, Ted Moskovitz, Felix Hill, Stephanie C. Y. Chan, Andrew M. Saxe

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Genie: Generative Interactive Environments.

Jake Bruce, Michael D. Dennis, Ashley Edwards, Jack Parker-Holder, Yuge Shi, Edward Hughes, Matthew Lai, Aditi Mavalankar, Richie Steigerwald, Chris Apps, Yusuf Aytar, Sarah Bechtle, Feryal M. P. Behbahani, Stephanie C. Y. Chan, Nicolas Heess, Lucy Gonzalez, Simon Osindero, Sherjil Ozair, Scott E. Reed, Jingwei Zhang, Konrad Zolna, Jeff Clune, Nando de Freitas, Satinder Singh, Tim Rocktäschel

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

The Transient Nature of Emergent In-Context Learning in Transformers.

Aaditya K. Singh, Stephanie C. Y. Chan, Ted Moskovitz, Erin Grant, Andrew M. Saxe, Felix Hill

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

Passive learning of active causal strategies in agents and language models.

Andrew K. Lampinen, Stephanie C. Y. Chan, Ishita Dasgupta, Andrew J. Nam, Jane X. Wang

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

2022

Transformers generalize differently from information stored in context vs in weights.

Stephanie C. Y. Chan, Ishita Dasgupta, Junkyung Kim, Dharshan Kumaran, Andrew K. Lampinen, Felix Hill

CoRR, 2022

Language models show human-like content effects on reasoning.

Ishita Dasgupta, Andrew K. Lampinen, Stephanie C. Y. Chan, Antonia Creswell, Dharshan Kumaran, James L. McClelland, Felix Hill

CoRR, 2022

Semantic Exploration from Language Abstractions and Pretrained Representations.

Allison C. Tam, Neil C. Rabinowitz, Andrew K. Lampinen, Nicholas A. Roy, Stephanie C. Y. Chan, DJ Strouse, Jane Wang, Andrea Banino, Felix Hill

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

Data Distributional Properties Drive Emergent In-Context Learning in Transformers.

Stephanie C. Y. Chan, Adam Santoro, Andrew K. Lampinen, Jane X. Wang, Aaditya K. Singh, Pierre H. Richemond, James L. McClelland, Felix Hill

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

Tell me why! Explanations support learning relational and causal structure.

Andrew K. Lampinen, Nicholas A. Roy, Ishita Dasgupta, Stephanie C. Y. Chan, Allison C. Tam, James L. McClelland, Chen Yan, Adam Santoro, Neil C. Rabinowitz, Jane X. Wang, Felix Hill

Proceedings of the International Conference on Machine Learning, 2022

Can language models learn from explanations in context?

Andrew K. Lampinen, Ishita Dasgupta, Stephanie C. Y. Chan, Kory W. Mathewson, Michael Henry Tessler, Antonia Creswell, James L. McClelland, Jane Wang, Felix Hill

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2022, 2022

Zipfian Environments for Reinforcement Learning.

Stephanie C. Y. Chan, Andrew Kyle Lampinen, Pierre Harvey Richemond, Felix Hill

Proceedings of the Conference on Lifelong Learning Agents, 2022

2021

Towards mental time travel: a hierarchical memory for reinforcement learning agents.

Andrew K. Lampinen, Stephanie C. Y. Chan, Andrea Banino, Felix Hill

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

Measuring the Reliability of Reinforcement Learning Algorithms.

Stephanie C. Y. Chan, Samuel Fishman, Anoop Korattikara, John F. Canny, Sergio Guadarrama

Proceedings of the 8th International Conference on Learning Representations, 2020