Daniel J. Hsu
28 papers · 2007–2023 · 2 conferences · across top CS/AI conferences
Achievements
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π§ Keyword Pioneer π Renaissance Researcher (5) π Interdisciplinary Bridge πΊοΈ Taxonomy Completionist (22) π£ Hot Topic Early Bird
πΊοΈ
Taxonomy Completionist
(22)
π
Conference Polyglot
(2)
π§
Keyword Pioneer
π
Keyword Trendsetter Combo
(4)
π
Conference Loyalist
(27)
π
The Namer
π±
Topic Pioneer
π¬
Deep Specialist
(10)
π
Keyword Champion
(2)
π
Century Club
(28)
ποΈ
Keyword Collector
(76)
π
Trend Setter
π₯
Unstoppable
(15)
β
The Questioner
(2)
Conferences
NIPS (27)
COLT (1)
Top co-authors
Research topics
Keywords
active learning
(4)
parameter estimation
(3)
tensor decomposition
(3)
spectral method
(3)
latent variable model
(3)
linear regression
(3)
agnostic learning
(3)
expectation maximization
(3)
mixture model
(2)
regret bound
(2)
graphical model
(2)
unsupervised learning
(2)
online learning
(2)
spectral algorithm
(2)
identifiability
(2)
supervised learning
(2)
topic modeling
(2)
generalization bound
(2)
latent dirichlet allocation
(1)
statistical modeling
(1)
Papers
Intrinsic dimensionality and generalization properties of the R-norm inductive bias
COLT 2023
Representational Strengths and Limitations of Transformers
NIPS 2023
Masked Prediction: A Parameter Identifiability View
NIPS 2022
Support vector machines and linear regression coincide with very high-dimensional features
NIPS 2021
Bayesian decision-making under misspecified priors with applications to meta-learning
NIPS 2021
Ensuring Fairness Beyond the Training Data
NIPS 2020
On the number of variables to use in principal component regression
NIPS 2019
Overfitting or perfect fitting? Risk bounds for classification and regression rules that interpolate
NIPS 2018
Benefits of over-parameterization with EM
NIPS 2018
Leveraged volume sampling for linear regression
NIPS 2018
Linear regression without correspondence
NIPS 2017
Global Analysis of Expectation Maximization for Mixtures of Two Gaussians
NIPS 2016
Search Improves Label for Active Learning
NIPS 2016
Mixing Time Estimation in Reversible Markov Chains from a Single Sample Path
NIPS 2015
Efficient and Parsimonious Agnostic Active Learning
NIPS 2015
Scalable Non-linear Learning with Adaptive Polynomial Expansions
NIPS 2014
The Large Margin Mechanism for Differentially Private Maximization
NIPS 2014
Contrastive Learning Using Spectral Methods
NIPS 2013
When are Overcomplete Topic Models Identifiable? Uniqueness of Tensor Tucker Decompositions with Structured Sparsity
NIPS 2013
A Spectral Algorithm for Latent Dirichlet Allocation
NIPS 2012
Identifiability and Unmixing of Latent Parse Trees
NIPS 2012
Learning Mixtures of Tree Graphical Models
NIPS 2012
Spectral Methods for Learning Multivariate Latent Tree Structure
NIPS 2011
Stochastic convex optimization with bandit feedback
NIPS 2011
Agnostic Active Learning Without Constraints
NIPS 2010
A Parameter-free Hedging Algorithm
NIPS 2009
Multi-Label Prediction via Compressed Sensing
NIPS 2009
A general agnostic active learning algorithm
NIPS 2007