30 citations · 97 across the 13 of their papers we have counts for
12 papers · 1 filter
A Fourier Approach to Mixture Learning
Mingda Qiao, Guru Guruganesh, Ankit Singh Rawat +2
We revisit the problem of learning mixtures of spherical Gaussians. Given samples from mixture , the goal is to estimate the means $…
Decision tree heuristics can fail, even in the smoothed setting
Guy Blanc, Jane Lange, Mingda Qiao +1
Greedy decision tree learning heuristics are mainstays of machine learning practice, but theoretical justification for their empirical success remains elusive. In fact, it has long…
Exponential Weights Algorithms for Selective Learning
Mingda Qiao, Gregory Valiant
We study the selective learning problem introduced by Qiao and Valiant (2019), in which the learner observes labeled data points one at a time. At a time of its choosing, the l…
Stronger Calibration Lower Bounds via Sidestepping
Mingda Qiao, Gregory Valiant
We consider an online binary prediction setting where a forecaster observes a sequence of bits one by one. Before each bit is revealed, the forecaster predicts the probability…
A Theory of Selective Prediction
Mingda Qiao, Gregory Valiant
We consider a model of selective prediction, where the prediction algorithm is given a data sequence in an online fashion and asked to predict a pre-specified statistic of the upco…
On Generalization Error Bounds of Noisy Gradient Methods for Non-Convex Learning
Jian Li, Xuanyuan Luo, Mingda Qiao
Generalization error (also known as the out-of-sample error) measures how well the hypothesis learned from training data generalizes to previously unseen data. Proving tight genera…