← Authors

Daniel J. Hsu

28 papers · 2007–2023 · 2 conferences · across top CS/AI conferences

Achievements

🐣 Hot Topic Early Bird 🐝 Cross-Pollinator (15) 🌈 Renaissance Researcher (5) πŸŒ‰ Interdisciplinary Bridge πŸƒ Academic Marathon (16) πŸ—ΊοΈ Taxonomy Completionist (22) 🌍 Conference Polyglot (2) 🧭 Keyword Pioneer 🌟 Keyword Trendsetter Combo (4) 🏠 Conference Loyalist (27) πŸ“› The Namer 🌱 Topic Pioneer πŸ”¬ Deep Specialist (10) πŸ† Keyword Champion (2) πŸ’Ž Century Club (28) πŸ—ƒοΈ Keyword Collector (76) πŸ“ˆ Trend Setter πŸ”₯ Unstoppable (15) ❓ The Questioner (2)

Conferences

NIPS (27) COLT (1)

Top co-authors

Sham M. Kakade (7) John Langford (5) Kamalika Chaudhuri (3) Alina Beygelzimer (3) Alekh Agarwal (3) Anima Anandkumar (3) Tong Zhang (3) Ji Xu (3) Navid Ardeshir (2) Arian Maleki (2)

Research topics

Differential Privacy (1)

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