4 citations · 4 across the 1 of their papers we have counts for
6 papers
Learning the Truth From Only One Side of the Story
Heinrich Jiang, Qijia Jiang, Aldo Pacchiano
Learning under one-sided feedback (i.e., where we only observe the labels for examples we predicted positively on) is a fundamental problem in machine learning -- applications incl…
Acceleration with a Ball Optimization Oracle
Yair Carmon, Arun Jambulapati, Qijia Jiang +4
Consider an oracle which takes a point and returns the minimizer of a convex function in an ball of radius around . It is straightforward to show that rough…
Optimizing Black-box Metrics with Adaptive Surrogates
Qijia Jiang, Olaoluwa Adigun, Harikrishna Narasimhan +2
We address the problem of training models with black-box and hard-to-optimize metrics by expressing the metric as a monotonic function of a small number of easy-to-optimize surroga…
Complexity of Highly Parallel Non-Smooth Convex Optimization
Sébastien Bubeck, Qijia Jiang, Yin Tat Lee +2
A landmark result of non-smooth convex optimization is that gradient descent is an optimal algorithm whenever the number of computed gradients is smaller than the dimension . In…
Near-optimal method for highly smooth convex optimization
Sébastien Bubeck, Qijia Jiang, Yin Tat Lee +2
We propose a near-optimal method for highly smooth convex optimization. More precisely, in the oracle model where one obtains the order Taylor expansion of a function at t…
Subgradient Descent Learns Orthogonal Dictionaries
Yu Bai, Qijia Jiang, Ju Sun
This paper concerns dictionary learning, i.e., sparse coding, a fundamental representation learning problem. We show that a subgradient descent algorithm, with random initializatio…