2 citations · 2 across the 7 of their papers we have counts for
8 papers
Gap-Free Streaming PCA Beyond Rank-One Updates: Near-Optimal Rates and Applications to Differential Privacy
Anming Gu, Syamantak Kumar, Kevin Tian +1
Streaming principal component analysis (PCA) seeks to recover a leading spectral subspace in a single pass over a data stream. We give a new analysis of the ubiquitous Oja's algori…
Optimal Recalibration of an Online Predictor
Lunjia Hu, Kevin Tian, Chutong Yang
We study the problem of recalibrating an online predictor [KE17, OKS24]: given an arbitrary "hint" sequence of forecasts, the learner must output new predictions that are calibrate…
Simultaneous Blackwell Approachability and Applications to Multiclass Omniprediction
Lunjia Hu, Kevin Tian, Chutong Yang
Omniprediction is a learning problem that requires suboptimality bounds for each of a family of losses against a family of comparator predictors . We ini…
Private Geometric Median in Nearly-Linear Time
Syamantak Kumar, Daogao Liu, Kevin Tian +1
Estimating the geometric median of a dataset is a robust counterpart to mean estimation, and is a fundamental problem in computational geometry. Recently, [HSU24] gave an $(\vareps…
Omnipredicting Single-Index Models with Multi-Index Models
Lunjia Hu, Kevin Tian, Chutong Yang
Recent work on supervised learning [GKR+22] defined the notion of omnipredictors, i.e., predictor functions over features that are simultaneously competitive for minimizing a f…
Testing Calibration in Nearly-Linear Time
Lunjia Hu, Arun Jambulapati, Kevin Tian +1
In the recent literature on machine learning and decision making, calibration has emerged as a desirable and widely-studied statistical property of the outputs of binary prediction…
Omnipredictors for Constrained Optimization
Lunjia Hu, Inbal Livni-Navon, Omer Reingold +1
The notion of omnipredictors (Gopalan, Kalai, Reingold, Sharan and Wieder ITCS 2021), suggested a new paradigm for loss minimization. Rather than learning a predictor based on a kn…
Active Learning Polynomial Threshold Functions
Omri Ben-Eliezer, Max Hopkins, Chutong Yang +1
We initiate the study of active learning polynomial threshold functions (PTFs). While traditional lower bounds imply that even univariate quadratics cannot be non-trivially activel…