93 citations · 153 across the 18 of their papers we have counts for
21 papers
Alignment of visual brain encoding models with individual perceptual similarity judgments
Daniel Chong, Haomiao Chen, Keith W Jamison +4
Encoding models that predict brain activation responses to images have traditionally been trained to maximize accuracy when predicting observed brain data. However, their internal…
Alignment Forecasting: Predicting Misalignment From Training Data
Chen Yueh-Han, Bruce W. Lee, Ilia Sucholutsky +1
Training a language model on data with a narrow flaw can sometimes make the model broadly misaligned. Inspecting the data at face value often does not settle whether it will emerge…
Selective QA over Conflicting Multi-Source Personal Memory: A Diagnostic Testbed and Method Comparison
Tiancheng Yang, Matthias Schonlau, Ilia Sucholutsky
Emerging personal AI agents are moving toward persistent, multi-source memory. This creates an evaluation problem: systems must decide how to use conflicting or incomplete evidence…
Human-AI Synergy Supports Collective Creative Search
Chenyi Li, Raja Marjieh, Haoyu Hu +4
Generative AI is increasingly transforming creativity into a hybrid human-artificial process, but its impact on the quality and diversity of creative output remains unclear. We stu…
Using LLMs to Advance the Cognitive Science of Collectives
Ilia Sucholutsky, Katherine M. Collins, Nori Jacoby +2
LLMs are already transforming the study of individual cognition, but their application to studying collective cognition has been underexplored. We lay out how LLMs may be able to a…
Human Uncertainty in Concept-Based AI Systems
Katherine M. Collins, Matthew Barker, Mateo Espinosa Zarlenga +6
Placing a human in the loop may abate the risks of deploying AI systems in safety-critical settings (e.g., a clinician working with a medical AI system). However, mitigating risks…
Around the world in 60 words: A generative vocabulary test for online research
Pol van Rijn, Yue Sun, Harin Lee +5
Conducting experiments with diverse participants in their native languages can uncover insights into culture, cognition, and language that may not be revealed otherwise. However, c…
Large language models predict human sensory judgments across six modalities
Raja Marjieh, Ilia Sucholutsky, Pol van Rijn +2
Determining the extent to which the perceptual world can be recovered from language is a longstanding problem in philosophy and cognitive science. We show that state-of-the-art lar…
Alignment with human representations supports robust few-shot learning
Ilia Sucholutsky, Thomas L. Griffiths
Should we care whether AI systems have representations of the world that are similar to those of humans? We provide an information-theoretic analysis that suggests that there shoul…
On the Informativeness of Supervision Signals
Ilia Sucholutsky, Ruairidh M. Battleday, Katherine M. Collins +7
Supervised learning typically focuses on learning transferable representations from training examples annotated by humans. While rich annotations (like soft labels) carry more info…
Human-in-the-Loop Mixup
Katherine M. Collins, Umang Bhatt, Weiyang Liu +4
Aligning model representations to humans has been found to improve robustness and generalization. However, such methods often focus on standard observational data. Synthetic data i…
Analyzing Diffusion as Serial Reproduction
Raja Marjieh, Ilia Sucholutsky, Thomas A. Langlois +2
Diffusion models are a class of generative models that learn to synthesize samples by inverting a diffusion process that gradually maps data into noise. While these models have enj…
Words are all you need? Language as an approximation for human similarity judgments
Raja Marjieh, Pol van Rijn, Ilia Sucholutsky +4
Human similarity judgments are a powerful supervision signal for machine learning applications based on techniques such as contrastive learning, information retrieval, and model al…
Predicting Human Similarity Judgments Using Large Language Models
Raja Marjieh, Ilia Sucholutsky, Theodore R. Sumers +2
Similarity judgments provide a well-established method for accessing mental representations, with applications in psychology, neuroscience and machine learning. However, collecting…
Can Humans Do Less-Than-One-Shot Learning?
Maya Malaviya, Ilia Sucholutsky, Kerem Oktar +1
Being able to learn from small amounts of data is a key characteristic of human intelligence, but exactly {\em how} small? In this paper, we introduce a novel experimental paradigm…
One Line To Rule Them All: Generating LO-Shot Soft-Label Prototypes
Ilia Sucholutsky, Nam-Hwui Kim, Ryan P. Browne +1
Increasingly large datasets are rapidly driving up the computational costs of machine learning. Prototype generation methods aim to create a small set of synthetic observations tha…
Optimal 1-NN Prototypes for Pathological Geometries
Ilia Sucholutsky, Matthias Schonlau
Using prototype methods to reduce the size of training datasets can drastically reduce the computational cost of classification with instance-based learning algorithms like the k-N…
SecDD: Efficient and Secure Method for Remotely Training Neural Networks
Ilia Sucholutsky, Matthias Schonlau
We leverage what are typically considered the worst qualities of deep learning algorithms - high computational cost, requirement for large data, no explainability, high dependence…
'Less Than One'-Shot Learning: Learning N Classes From M<N Samples
Ilia Sucholutsky, Matthias Schonlau
Deep neural networks require large training sets but suffer from high computational cost and long training times. Training on much smaller training sets while maintaining nearly th…
Soft-Label Dataset Distillation and Text Dataset Distillation
Ilia Sucholutsky, Matthias Schonlau
Dataset distillation is a method for reducing dataset sizes by learning a small number of synthetic samples containing all the information of a large dataset. This has several bene…
Deep Learning for System Trace Restoration
Ilia Sucholutsky, Apurva Narayan, Matthias Schonlau +1
Most real-world datasets, and particularly those collected from physical systems, are full of noise, packet loss, and other imperfections. However, most specification mining, anoma…