NIPS Proceedings
^{β}
Books
Daniel J. Hsu
21 Papers
Benefits of over-parameterization with EM
(2018)
Leveraged volume sampling for linear regression
(2018)
Overfitting or perfect fitting? Risk bounds for classification and regression rules that interpolate
(2018)
Linear regression without correspondence
(2017)
Global Analysis of Expectation Maximization for Mixtures of Two Gaussians
(2016)
Search Improves Label for Active Learning
(2016)
Efficient and Parsimonious Agnostic Active Learning
(2015)
Mixing Time Estimation in Reversible Markov Chains from a Single Sample Path
(2015)
Scalable Non-linear Learning with Adaptive Polynomial Expansions
(2014)
The Large Margin Mechanism for Differentially Private Maximization
(2014)
Contrastive Learning Using Spectral Methods
(2013)
When are Overcomplete Topic Models Identifiable? Uniqueness of Tensor Tucker Decompositions with Structured Sparsity
(2013)
A Spectral Algorithm for Latent Dirichlet Allocation
(2012)
Identifiability and Unmixing of Latent Parse Trees
(2012)
Learning Mixtures of Tree Graphical Models
(2012)
Spectral Methods for Learning Multivariate Latent Tree Structure
(2011)
Stochastic convex optimization with bandit feedback
(2011)
Agnostic Active Learning Without Constraints
(2010)
A Parameter-free Hedging Algorithm
(2009)
Multi-Label Prediction via Compressed Sensing
(2009)
A general agnostic active learning algorithm
(2007)