Yin Tat Lee
32 papers · 2016–2024 · 5 conferences · across top CS/AI conferences
Achievements
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(34)
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(13)
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(2)
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Keyword Collector
(127)
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The Questioner
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(5)
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(32)
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(9)
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Conferences
NIPS (14)
COLT (11)
ICML (4)
ICLR (2)
JMLR (1)
Top co-authors
Research topics
Keywords
convex optimization
(11)
mixing time
(5)
differential privacy
(5)
markov chain monte carlo
(5)
convergence rate
(3)
distributed optimization
(3)
stochastic method
(3)
gradient descent
(3)
network optimization
(3)
riemannian hamiltonian monte carlo
(2)
interior point method
(2)
stochastic gradient descent
(2)
neural network
(2)
empirical risk minimization
(2)
oracle complexity
(2)
gradient oracle
(2)
hamiltonian monte carlo
(2)
primal-dual algorithm
(2)
semidefinite programming
(1)
privacy-preserving learning
(1)
Papers
Differentially Private Synthetic Data via Foundation Model APIs 2: Text
ICML 2024
Exploring the Limits of Differentially Private Deep Learning with Group-wise Clipping
ICLR 2023
Algorithmic Aspects of the Log-Laplace Transform and a Non-Euclidean Proximal Sampler
COLT 2023
Condition-number-independent Convergence Rate of Riemannian Hamiltonian Monte Carlo with Numerical Integrators
COLT 2023
Learning threshold neurons via edge of stability
NIPS 2023
Decomposable Non-Smooth Convex Optimization with Nearly-Linear Gradient Oracle Complexity
NIPS 2022
When Does Differentially Private Learning Not Suffer in High Dimensions?
NIPS 2022
Sampling with Riemannian Hamiltonian Monte Carlo in a Constrained Space
NIPS 2022
Private Convex Optimization via Exponential Mechanism
COLT 2022
Differentially Private Fine-tuning of Language Models
ICLR 2022
A gradient sampling method with complexity guarantees for Lipschitz functions in high and low dimensions
NIPS 2022
Structured Logconcave Sampling with a Restricted Gaussian Oracle
COLT 2021
Private Non-smooth ERM and SCO in Subquadratic Steps
NIPS 2021
Numerical Composition of Differential Privacy
NIPS 2021
Lower Bounds on Metropolized Sampling Methods for Well-Conditioned Distributions
NIPS 2021
Fast and Memory Efficient Differentially Private-SGD via JL Projections
NIPS 2021
Logsmooth Gradient Concentration and Tighter Runtimes for Metropolized Hamiltonian Monte Carlo
COLT 2020
Network size and size of the weights in memorization with two-layers neural networks
NIPS 2020
Acceleration with a Ball Optimization Oracle
NIPS 2020
An $\widetilde\mathcal{O}(m/\varepsilon^3.5)$-Cost Algorithm for Semidefinite Programs with Diagonal Constraints
COLT 2020
Optimal Convergence Rates for Convex Distributed Optimization in Networks
JMLR 2019
Complexity of Highly Parallel Non-Smooth Convex Optimization
NIPS 2019
The Randomized Midpoint Method for Log-Concave Sampling
NIPS 2019
Near-optimal method for highly smooth convex optimization
COLT 2019
A near-optimal algorithm for approximating the John Ellipsoid
COLT 2019
Near Optimal Methods for Minimizing Convex Functions with Lipschitz $p$-th Derivatives
COLT 2019
Solving Empirical Risk Minimization in the Current Matrix Multiplication Time
COLT 2019
Adversarial examples from computational constraints
ICML 2019
Optimal Algorithms for Non-Smooth Distributed Optimization in Networks
NIPS 2018
Efficient Convex Optimization with Membership Oracles
COLT 2018
Optimal Algorithms for Smooth and Strongly Convex Distributed Optimization in Networks
ICML 2017
Black-box Optimization with a Politician
ICML 2016