activity
20132020
most citedLearning with Confident Examples: Rank Pruning for Robust Classification with Noisy Labels

55 citations · 105 across the 6 of their papers we have counts for

collaborators

8 papers

cs.CY2020★ 6 cited

IP Geolocation Underestimates Regressive Economic Patterns in MOOC Usage

Daniela Ganelin, Isaac Chuang

Massive open online courses (MOOCs) promise to make rigorous higher education accessible to everyone, but prior research has shown that registrants tend to come from backgrounds of…

stat.ML2019

Confident Learning: Estimating Uncertainty in Dataset Labels

Curtis G. Northcutt, Lu Jiang, Isaac L. Chuang

Learning exists in the context of data, yet notions of confidence typically focus on model predictions, not label quality. Confident learning (CL) is an alternative approach which…

cs.LG2019★ 13 cited

Learnability for the Information Bottleneck

Tailin Wu, Ian Fischer, Isaac L. Chuang +1

The Information Bottleneck (IB) method (\cite{tishby2000information}) provides an insightful and principled approach for balancing compression and prediction for representation lea…

cs.LG2018★ 12 cited

Meta-learning autoencoders for few-shot prediction

Tailin Wu, John Peurifoy, Isaac L. Chuang +1

Compared to humans, machine learning models generally require significantly more training examples and fail to extrapolate from experience to solve previously unseen challenges. To…

cs.HC2018★ 5 cited

Modelling and Using Response Times in Online Courses

Ilia Rushkin, Isaac Chuang, Dustin Tingley

Each time a learner in a self-paced online course seeks to answer an assessment question, it takes some time for the student to read the question and arrive at an answer to submit.…

stat.ML2017★ 55 cited

Learning with Confident Examples: Rank Pruning for Robust Classification with Noisy Labels

Curtis G. Northcutt, Tailin Wu, Isaac L. Chuang

Noisy PN learning is the problem of binary classification when training examples may be mislabeled (flipped) uniformly with noise rate rho1 for positive examples and rho0 for negat…

cs.CY2015

Detecting and Preventing "Multiple-Account" Cheating in Massive Open Online Courses

Curtis G. Northcutt, Andrew D. Ho, Isaac L. Chuang

We describe a cheating strategy enabled by the features of massive open online courses (MOOCs) and detectable by virtue of the sophisticated data systems that MOOCs provide. The st…

physics.ed-ph2013★ 14 cited

Participation And performance In 8.02x Electricity And Magnetism: The First Physics MOOC From MITx

Saif Rayyan, Daniel T. Seaton, John Belcher +2

Massive Open Online Courses are an exciting new avenue for instruction and research, yet they are full of unknowns. In the Spring of 2013, MITx released its first introductory phys…