18 citations · 39 across the 7 of their papers we have counts for
7 papers
A Normative Theory of Decision Making from Multiple Stimuli: The Contextual Diffusion Decision Model
Michael Shvartsman, Vaibhav Srivastava, Narayanan Sundaram +1
The dynamics of simple two-alternative forced-choice (2AFC) decisions are well-modeled by a class of random walk models (e.g. Laming, 1968; Ratcliff, 1978; Usher & McClelland, 2001…
Response Time Improves Choice Prediction and Function Estimation for Gaussian Process Models of Perception and Preferences
Michael Shvartsman, Benjamin Letham, Stephen Keeley
Models for human choice prediction in preference learning and psychophysics often consider only binary response data, requiring many samples to accurately learn preferences or perc…
Look-Ahead Acquisition Functions for Bernoulli Level Set Estimation
Benjamin Letham, Phillip Guan, Chase Tymms +2
Level set estimation (LSE) is the problem of identifying regions where an unknown function takes values above or below a specified threshold. Active sampling strategies for efficie…
Adaptive Nonparametric Psychophysics
Lucy Owen, Jonathan Browder, Benjamin Letham +3
We introduce a new set of models and adaptive psychometric testing methods for multidimensional psychophysics. In contrast to traditional adaptive staircase methods like PEST and Q…
Incorporating structured assumptions with probabilistic graphical models in fMRI data analysis
Ming Bo Cai, Michael Shvartsman, Anqi Wu +2
With the wide adoption of functional magnetic resonance imaging (fMRI) by cognitive neuroscience researchers, large volumes of brain imaging data have been accumulated in recent ye…
Unifying Few- and Zero-Shot Egocentric Action Recognition
Tyler R. Scott, Michael Shvartsman, Karl Ridgeway
Although there has been significant research in egocentric action recognition, most methods and tasks, including EPIC-KITCHENS, suppose a fixed set of action classes. Fixed-set cla…
Matrix-normal models for fMRI analysis
Michael Shvartsman, Narayanan Sundaram, Mikio C. Aoi +3
Multivariate analysis of fMRI data has benefited substantially from advances in machine learning. Most recently, a range of probabilistic latent variable models applied to fMRI dat…