Daniel J. Hsu
28 papers · 2007–2023 · 2 venues · across top CS/AI venues
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Achievements
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πΊοΈ Taxonomy Completionist (76) π Conference Polyglot Β· Bronze π Academic Marathon Β· Full π Interdisciplinary Bridge Β· Silver π£ Hot Topic Early Bird
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Conference Loyalist
(27)
π
The Namer
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Deep Specialist
(13)
π
Keyword Trendsetter Combo
(4)
π
Keyword Champion
π§
Keyword Pioneer
π
Trend Setter
β
The Questioner
(2)
ποΈ
Keyword Collector
(186)
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Century Club Β· Bronze
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Renaissance Researcher
(7)
π₯
Unstoppable Β· Gold
Venues
Top co-authors
Research topics
Machine Learning
(28)
Optimization & Theory
(24)
Core Methods
(16)
Learning Types Β· Machine Learning
(13)
Mathematics & Optimization
(11)
Optimization
(10)
Artificial Intelligence
(9)
Bayesian & Probabilistic Β· Machine Learning
(6)
Deep Learning
(5)
Learning Paradigms
(5)
Application Areas
(3)
Bayesian & Probabilistic Β· Artificial Intelligence
(3)
Core AI
(3)
Learning Types Β· Deep Learning
(3)
Mathematics
(3)
Keywords
active learning
(4)
spectral method
(4)
sample complexity
(4)
latent variable model
(3)
regret bound
(3)
parameter estimation
(3)
expectation maximization
(3)
tensor decomposition
(3)
linear regression
(3)
agnostic learning
(3)
mixture model
(2)
generalization bound
(2)
hidden markov model
(2)
graphical model
(2)
spectral algorithm
(2)
online learning
(2)
unsupervised learning
(2)
topic modeling
(2)
supervised learning
(2)
identifiability
(2)
Papers
Intrinsic dimensionality and generalization properties of the R-norm inductive bias
COLT 2023
Representational Strengths and Limitations of Transformers
NeurIPS 2023
Masked Prediction: A Parameter Identifiability View
NeurIPS 2022
Bayesian decision-making under misspecified priors with applications to meta-learning
NeurIPS 2021
Support vector machines and linear regression coincide with very high-dimensional features
NeurIPS 2021
Ensuring Fairness Beyond the Training Data
NeurIPS 2020
On the number of variables to use in principal component regression
NeurIPS 2019
Benefits of over-parameterization with EM
NeurIPS 2018
Leveraged volume sampling for linear regression
NeurIPS 2018
Overfitting or perfect fitting? Risk bounds for classification and regression rules that interpolate
NeurIPS 2018
Linear regression without correspondence
NeurIPS 2017
Global Analysis of Expectation Maximization for Mixtures of Two Gaussians
NeurIPS 2016
Search Improves Label for Active Learning
NeurIPS 2016
Efficient and Parsimonious Agnostic Active Learning
NeurIPS 2015
Mixing Time Estimation in Reversible Markov Chains from a Single Sample Path
NeurIPS 2015
Scalable Non-linear Learning with Adaptive Polynomial Expansions
NeurIPS 2014
The Large Margin Mechanism for Differentially Private Maximization
NeurIPS 2014
Contrastive Learning Using Spectral Methods
NeurIPS 2013
When are Overcomplete Topic Models Identifiable? Uniqueness of Tensor Tucker Decompositions with Structured Sparsity
NeurIPS 2013
A Spectral Algorithm for Latent Dirichlet Allocation
NeurIPS 2012
Identifiability and Unmixing of Latent Parse Trees
NeurIPS 2012
Learning Mixtures of Tree Graphical Models
NeurIPS 2012
Spectral Methods for Learning Multivariate Latent Tree Structure
NeurIPS 2011
Stochastic convex optimization with bandit feedback
NeurIPS 2011
Agnostic Active Learning Without Constraints
NeurIPS 2010
A Parameter-free Hedging Algorithm
NeurIPS 2009
Multi-Label Prediction via Compressed Sensing
NeurIPS 2009
A general agnostic active learning algorithm
NeurIPS 2007