Timothy T. Rogers

ICML Conference 2016 Conference Paper

Representational Similarity Learning with Application to Brain Networks

• Urvashi Oswal
• Christopher R. Cox
• Matthew A. Lambon Ralph
• Timothy T. Rogers
• Robert D. Nowak

Representational Similarity Learning (RSL) aims to discover features that are important in representing (human-judged) similarities among objects. RSL can be posed as a sparsity-regularized multi-task regression problem. Standard methods, like group lasso, may not select important features if they are strongly correlated with others. To address this shortcoming we present a new regularizer for multitask regression called Group Ordered Weighted \ell_1 (GrOWL). Another key contribution of our paper is a novel application to fMRI brain imaging. Representational Similarity Analysis (RSA) is a tool for testing whether localized brain regions encode perceptual similarities. Using GrOWL, we propose a new approach called Network RSA that can discover arbitrarily structured brain networks (possibly widely distributed and non-local) that encode similarity information. We show, in theory and fMRI experiments, how GrOWL deals with strongly correlated covariates.

ICML Conference 2013 Conference Paper

Learning from Human-Generated Lists

• Kwang-Sung Jun
• Xiaojin Zhu 0001
• Burr Settles
• Timothy T. Rogers

Human-generated lists are a form of non-iid data with important applications in machine learning and cognitive psychology. We propose a generative model - sampling with reduced replacement (SWIRL) - for such lists. We discuss SWIRL’s relation to standard sampling paradigms, provide the maximum likelihood estimate for learning, and demonstrate its value with two real-world applications: (i) In a ""feature volunteering"" task where non-experts spontaneously generate feature=>label pairs for text classification, SWIRL improves the accuracy of state-of-the-art feature-learning frameworks. (ii) In a ""verbal fluency"" task where brain-damaged patients generate word lists when prompted with a category, SWIRL parameters align well with existing psychological theories, and our model can classify healthy people vs. patients from the lists they generate.

TIST Journal 2012 Journal Article

Metric Learning for Estimating Psychological Similarities

• Jun-Ming Xu
• Xiaojin Zhu
• Timothy T. Rogers

An important problem in cognitive psychology is to quantify the perceived similarities between stimuli. Previous work attempted to address this problem with multidimensional scaling (MDS) and its variants. However, there are several shortcomings of the MDS approaches. We propose Yada, a novel general metric-learning procedure based on two-alternative forced-choice behavioral experiments. Our method learns forward and backward nonlinear mappings between an objective space in which the stimuli are defined by the standard feature vector representation and a subjective space in which the distance between a pair of stimuli corresponds to their perceived similarity. We conduct experiments on both synthetic and real human behavioral datasets to assess the effectiveness of Yada. The results show that Yada outperforms several standard embedding and metric-learning algorithms, both in terms of likelihood and recovery error.

ICML Conference 2010 Conference Paper

Cognitive Models of Test-Item Effects in Human Category Learning

• Xiaojin Zhu 0001
• Bryan R. Gibson
• Kwang-Sung Jun
• Timothy T. Rogers
• Joseph Harrison
• Chuck Kalish