Ilia Sucholutsky

According to our database, Ilia Sucholutsky authored at least 46 papers between 2019 and 2024.

Collaborative distances:

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

2024

exKidneyBERT: a language model for kidney transplant pathology reports and the crucial role of extended vocabularies.

Tiancheng Yang, Ilia Sucholutsky, Kuang-Yu Jen, Matthias Schonlau

PeerJ Comput. Sci., 2024

Adaptive Language-Guided Abstraction from Contrastive Explanations.

Andi Peng, Belinda Z. Li, Ilia Sucholutsky, Nishanth Kumar, Julie A. Shah, Jacob Andreas, Andreea Bobu

CoRR, 2024

Multilevel Interpretability Of Artificial Neural Networks: Leveraging Framework And Methods From Neuroscience.

Zhonghao He, Jascha Achterberg, Katie Collins, Kevin K. Nejad, Danyal Akarca, Yinzhu Yang, Wes Gurnee, Ilia Sucholutsky, Yuhan Tang, Rebeca Ianov, George Ogden, Chole Li, Kai Sandbrink, Stephen Casper, Anna Ivanova, Grace W. Lindsay

CoRR, 2024

Building Machines that Learn and Think with People.

Katherine M. Collins, Ilia Sucholutsky, Umang Bhatt, Kartik Chandra, Lionel Wong, Mina Lee, Cedegao E. Zhang, Tan Zhi-Xuan, Mark K. Ho, Vikash Mansinghka, Adrian Weller, Joshua B. Tenenbaum, Thomas L. Griffiths

CoRR, 2024

Modulating Language Model Experiences through Frictions.

Katherine M. Collins, Valerie Chen, Ilia Sucholutsky, Hannah Rose Kirk, Malak Sadek, Holli Sargeant, Ameet Talwalkar, Adrian Weller, Umang Bhatt

CoRR, 2024

Quantifying Spuriousness of Biased Datasets Using Partial Information Decomposition.

Barproda Halder, Faisal Hamman, Pasan Dissanayake, Qiuyi Zhang, Ilia Sucholutsky, Sanghamitra Dutta

CoRR, 2024

Large Language Models Assume People are More Rational than We Really are.

Ryan Liu, Jiayi Geng, Joshua C. Peterson, Ilia Sucholutsky, Thomas L. Griffiths

CoRR, 2024

Representational Alignment Supports Effective Machine Teaching.

Ilia Sucholutsky, Katherine M. Collins, Maya Malaviya, Nori Jacoby, Weiyang Liu, Theodore R. Sumers, Michalis Korakakis, Umang Bhatt, Mark K. Ho, Joshua B. Tenenbaum, Bradley C. Love, Zachary A. Pardos, Adrian Weller, Thomas L. Griffiths

CoRR, 2024

Analyzing the Roles of Language and Vision in Learning from Limited Data.

Allison Chen, Ilia Sucholutsky, Olga Russakovsky, Thomas L. Griffiths

CoRR, 2024

Large language models surpass human experts in predicting neuroscience results.

Xiaoliang Luo, Akilles Rechardt, Guangzhi Sun, Kevin K. Nejad, Felipe Yáñez, Bati Yilmaz, Kangjoo Lee, Alexandra O. Cohen, Valentina Borghesani, Anton Pashkov, Daniele Marinazzo, Jonathan Nicholas, Alessandro Salatiello, Ilia Sucholutsky, Pasquale Minervini, Sepehr Razavi, Roberta Rocca, Elkhan Yusifov, Tereza Okalova, Nianlong Gu, Martin Ferianc, Mikail Khona, Kaustubh R. Patil, Pui-Shee Lee, Rui Mata, Nicholas E. Myers, Jennifer K. Bizley, Sebastian Musslick, Isil Poyraz Bilgin, Guiomar Niso, Justin M. Ales, Michael Gaebler, N. Apurva Ratan Murty, Leyla Loued-Khenissi, Anna Behler, Chloe M. Hall, Jessica Dafflon, Sherry Dongqi Bao, Bradley C. Love

CoRR, 2024

A Rational Analysis of the Speech-to-Song Illusion.

Raja Marjieh, Pol van Rijn, Ilia Sucholutsky, Harin Lee, Thomas L. Griffiths, Nori Jacoby

CoRR, 2024

Measuring Implicit Bias in Explicitly Unbiased Large Language Models.

Xuechunzi Bai, Angelina Wang, Ilia Sucholutsky, Thomas L. Griffiths

CoRR, 2024

Concept Alignment.

Sunayana Rane, Polyphony J. Bruna, Ilia Sucholutsky, Christopher T. Kello, Thomas L. Griffiths

CoRR, 2024

Learning with Language-Guided State Abstractions.

Andi Peng, Ilia Sucholutsky, Belinda Z. Li, Theodore R. Sumers, Thomas L. Griffiths, Jacob Andreas, Julie Shah

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Preference-Conditioned Language-Guided Abstraction.

Andi Peng, Andreea Bobu, Belinda Z. Li, Theodore R. Sumers, Ilia Sucholutsky, Nishanth Kumar, Thomas L. Griffiths, Julie A. Shah

Proceedings of the 2024 ACM/IEEE International Conference on Human-Robot Interaction, 2024

Characterizing Similarities and Divergences in Conversational Tones in Humans and LLMs by Sampling with People.

Dun-Ming Huang, Pol van Rijn, Ilia Sucholutsky, Raja Marjieh, Nori Jacoby

Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2024

Pushing the Limits of Learning from Limited Data.

Maya Malaviya, Ilia Sucholutsky, Thomas L. Griffiths

Proceedings of the AAAI 2024 Spring Symposium Series, 2024

2023

Learning Human-like Representations to Enable Learning Human Values.

Andrea Wynn, Ilia Sucholutsky, Thomas L. Griffiths

CoRR, 2023

Concept Alignment as a Prerequisite for Value Alignment.

Sunayana Rane, Mark K. Ho, Ilia Sucholutsky, Thomas L. Griffiths

CoRR, 2023

Getting aligned on representational alignment.

Ilia Sucholutsky, Lukas Muttenthaler, Adrian Weller, Andi Peng, Andreea Bobu, Been Kim, Bradley C. Love, Erin Grant, Jascha Achterberg, Joshua B. Tenenbaum, Katherine M. Collins, Katherine L. Hermann, Kerem Oktar, Klaus Greff, Martin N. Hebart, Nori Jacoby, Qiuyi Zhang, Raja Marjieh, Robert Geirhos, Sherol Chen, Simon Kornblith, Sunayana Rane, Talia Konkle, Thomas P. O'Connell, Thomas Unterthiner, Andrew K. Lampinen, Klaus-Robert Müller, Mariya Toneva, Thomas L. Griffiths

CoRR, 2023

Dimensions of Disagreement: Unpacking Divergence and Misalignment in Cognitive Science and Artificial Intelligence.

Kerem Oktar, Ilia Sucholutsky, Tania Lombrozo, Thomas L. Griffiths

CoRR, 2023

On the informativeness of supervision signals.

Ilia Sucholutsky, Ruairidh M. Battleday, Katherine M. Collins, Raja Marjieh, Joshua C. Peterson, Pulkit Singh, Umang Bhatt, Nori Jacoby, Adrian Weller, Thomas L. Griffiths

Proceedings of the Uncertainty in Artificial Intelligence, 2023

Human-in-the-Loop Mixup.

Katherine M. Collins, Umang Bhatt, Weiyang Liu, Vihari Piratla, Ilia Sucholutsky, Bradley C. Love, Adrian Weller

Proceedings of the Uncertainty in Artificial Intelligence, 2023

Alignment with human representations supports robust few-shot learning.

Ilia Sucholutsky, Tom Griffiths

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

Analyzing Diffusion as Serial Reproduction.

Raja Marjieh, Ilia Sucholutsky, Thomas A. Langlois, Nori Jacoby, Thomas L. Griffiths

Proceedings of the International Conference on Machine Learning, 2023

End-to-End Learnable Masks With Differentiable Indexing.

Dibyanshu Shekhar, Sree Harsha Nelaturu, Ashwath Shetty, Ilia Sucholutsky

Proceedings of the First Tiny Papers Track at ICLR 2023, 2023

Words are all you need? Language as an approximation for human similarity judgments.

Raja Marjieh, Pol van Rijn, Ilia Sucholutsky, Theodore R. Sumers, Harin Lee, Thomas L. Griffiths, Nori Jacoby

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Around the world in 60 words: A generative vocabulary test for online research.

Pol van Rijn, Yue Sun, Harin Lee, Raja Marjieh, Ilia Sucholutsky, Francesca Lanzarini, Elisabeth André, Nori Jacoby

Proceedings of the 45th Annual Meeting of the Cognitive Science Society, 2023

What Language Reveals about Perception: Distilling Psychophysical Knowledge from Large Language Models.

Raja Marjieh, Ilia Sucholutsky, Pol van Rijn, Nori Jacoby, Tom Griffiths

Proceedings of the 45th Annual Meeting of the Cognitive Science Society, 2023

Large language models meet cognitive science: LLMs as tools, models, and participants.

Mathew D. Hardy, Ilia Sucholutsky, Bill Thompson, Tom Griffiths

Proceedings of the 45th Annual Meeting of the Cognitive Science Society, 2023

Human Uncertainty in Concept-Based AI Systems.

Katherine Maeve Collins, Matthew Barker, Mateo Espinosa Zarlenga, Naveen Raman, Umang Bhatt, Mateja Jamnik, Ilia Sucholutsky, Adrian Weller, Krishnamurthy Dvijotham

Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society, 2023

2022

On the Informativeness of Supervision Signals.

Ilia Sucholutsky, Raja Marjieh, Nori Jacoby, Thomas L. Griffiths

CoRR, 2022

Words are all you need? Capturing human sensory similarity with textual descriptors.

Raja Marjieh, Pol van Rijn, Ilia Sucholutsky, Theodore R. Sumers, Harin Lee, Thomas L. Griffiths, Nori Jacoby

CoRR, 2022

Predicting Human Similarity Judgments Using Large Language Models.

Raja Marjieh, Ilia Sucholutsky, Theodore R. Sumers, Nori Jacoby, Tom Griffiths

Proceedings of the 44th Annual Meeting of the Cognitive Science Society, 2022

Can Humans Do Less-Than-One-Shot Learning?

Maya Malaviya, Ilia Sucholutsky, Kerem Oktar, Tom Griffiths

Proceedings of the 44th Annual Meeting of the Cognitive Science Society, 2022

Playing the Lottery of a Lifetime: The Effect of Socially Induced Aspiration on Q-Learning Agents.

Yosi Hatekar, Rachit Dubey, Theodore R. Sumers, Ilia Sucholutsky

Proceedings of the 44th Annual Meeting of the Cognitive Science Society, 2022

2021

Learning From Almost No Data.

Ilia Sucholutsky

PhD thesis, 2021

Optimal 1-NN prototypes for pathological geometries.

Ilia Sucholutsky, Matthias Schonlau

PeerJ Comput. Sci., 2021

Soft-Label Dataset Distillation and Text Dataset Distillation.

Ilia Sucholutsky, Matthias Schonlau

Proceedings of the International Joint Conference on Neural Networks, 2021

One Line To Rule Them All: Generating LO-Shot Soft-Label Prototypes.

Ilia Sucholutsky, Nam-Hwui Kim, Ryan P. Browne, Matthias Schonlau

Proceedings of the International Joint Conference on Neural Networks, 2021

SecDD: Efficient and Secure Method for Remotely Training Neural Networks (Student Abstract).

Ilia Sucholutsky, Matthias Schonlau

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

'Less Than One'-Shot Learning: Learning N Classes From M < N Samples.

Ilia Sucholutsky, Matthias Schonlau

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

SecDD: Efficient and Secure Method for Remotely Training Neural Networks.

Ilia Sucholutsky, Matthias Schonlau

CoRR, 2020

2019

Pay attention and you won't lose it: a deep learning approach to sequence imputation.

Ilia Sucholutsky, Apurva Narayan, Matthias Schonlau, Sebastian Fischmeister

PeerJ Comput. Sci., 2019

Improving Dataset Distillation.

Ilia Sucholutsky, Matthias Schonlau

CoRR, 2019

Deep Learning for System Trace Restoration.

Ilia Sucholutsky, Apurva Narayan, Matthias Schonlau, Sebastian Fischmeister

Proceedings of the International Joint Conference on Neural Networks, 2019