Christopher Liaw
14 papers · 2017–2025 · 7 conferences · across top CS/AI conferences
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(21)
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Conference Polyglot
(7)
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Century Club
(14)
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Trend Setter
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Keyword Collector
(58)
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Unstoppable
(9)
Conferences
COLT (3)
NIPS (3)
ALT (2)
ICML (2)
JMLR (2)
AAAI (1)
ICLR (1)
Top co-authors
Research topics
Keywords
differential privacy
(4)
learning theory
(3)
sample complexity
(3)
online learning
(2)
distribution learning
(2)
neural network
(2)
gaussian distribution
(2)
multi-armed bandit
(2)
total variation distance
(2)
mixture of gaussian
(2)
regret bound
(2)
gaussian mixture
(2)
strongly convex
(1)
machine learning
(1)
parameter estimation
(1)
sample compression
(1)
statistical estimation
(1)
regret analysis
(1)
piecewise linear
(1)
no-regret learning
(1)
Papers
Agnostic Private Density Estimation for GMMs via List Global Stability
ALT 2025
Continuous Prediction with Experts' Advice
JMLR 2024
Mixtures of Gaussians are Privately Learnable with a Polynomial Number of Samples
ALT 2024
Polynomial Time and Private Learning of Unbounded Gaussian Mixture Models
ICML 2023
Improved Online Learning Algorithms for CTR Prediction in Ad Auctions
ICML 2023
Private and polynomial time algorithms for learning Gaussians and beyond
COLT 2022
Convergence Analysis of No-Regret Bidding Algorithms in Repeated Auctions
AAAI 2021
Privately Learning Mixtures of Axis-Aligned Gaussians
NIPS 2021
Improved Algorithms for Online Submodular Maximization via First-order Regret Bounds
NIPS 2020
Tight analyses for non-smooth stochastic gradient descent
COLT 2019
A new dog learns old tricks: RL finds classic optimization algorithms
ICLR 2019
Nearly-tight VC-dimension and Pseudodimension Bounds for Piecewise Linear Neural Networks
JMLR 2019
Nearly tight sample complexity bounds for learning mixtures of Gaussians via sample compression schemes
NIPS 2018
Nearly-tight VC-dimension bounds for piecewise linear neural networks
COLT 2017