Stephen Tu
32 papers · 2014–2025 · 9 conferences · across top CS/AI conferences
Achievements
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π§ Keyword Pioneer π Renaissance Researcher (5) π Interdisciplinary Bridge πΊοΈ Taxonomy Completionist (10) π£ Hot Topic Early Bird
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Keyword Pioneer
π£
Hot Topic Early Bird
π
Renaissance Researcher
(5)
π€
Dynamic Duo
(10)
ποΈ
Keyword Collector
(130)
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Prolific Year
(6)
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Century Club
(32)
π₯
Unstoppable
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Trend Setter
Conferences
NIPS (7)
ICML (6)
L4DC (6)
CORL (4)
AISTATS (2)
COLT (2)
ICLR (2)
JMLR (2)
OSDI (1)
Top co-authors
Research topics
Keywords
sample complexity
(6)
linear quadratic regulator
(5)
imitation learning
(4)
reinforcement learning
(4)
adaptive control
(4)
policy optimization
(3)
policy evaluation
(3)
continuous control
(3)
dynamical system
(3)
optimal control
(3)
linear regression
(2)
non-convex optimization
(2)
temporal difference learning
(2)
generalization error
(2)
representation learning
(2)
state representation
(2)
system identification
(2)
model predictive control
(2)
trajectory optimization
(2)
stochastic process
(2)
Papers
Shallow diffusion networks provably learn hidden low-dimensional structure
ICLR 2025
Sharp Rates in Dependent Learning Theory: Avoiding Sample Size Deflation for the Square Loss
ICML 2024
Learning from many trajectories
JMLR 2024
Multi-Task Imitation Learning for Linear Dynamical Systems
L4DC 2023
Bootstrapped Representations in Reinforcement Learning
ICML 2023
The noise level in linear regression with dependent data
NIPS 2023
The Power of Learned Locally Linear Models for Nonlinear Policy Optimization
ICML 2023
Robots That Ask For Help: Uncertainty Alignment for Large Language Model Planners
CORL 2023
Agile Catching with Whole-Body MPC and Blackbox Policy Learning
L4DC 2023
Adversarially Robust Stability Certificates can be Sample-Efficient
L4DC 2022
Learning with little mixing
NIPS 2022
TaSIL: Taylor Series Imitation Learning
NIPS 2022
Learning Model Predictive Controllers with Real-Time Attention for Real-World Navigation
CORL 2022
On the Generalization of Representations in Reinforcement Learning
AISTATS 2022
The role of optimization geometry in single neuron learning
AISTATS 2022
Nonparametric adaptive control and prediction: theory and randomized algorithms
JMLR 2022
On the Sample Complexity of Stability Constrained Imitation Learning
L4DC 2022
Safely Learning Dynamical Systems from Short Trajectories
L4DC 2021
Regret Bounds for Adaptive Nonlinear Control
L4DC 2021
Learning Stability Certificates from Data
CORL 2020
Observational Overfitting in Reinforcement Learning
ICLR 2020
Learning Hybrid Control Barrier Functions from Data
CORL 2020
The Gap Between Model-Based and Model-Free Methods on the Linear Quadratic Regulator: An Asymptotic Viewpoint
COLT 2019
Certainty Equivalence is Efficient for Linear Quadratic Control
NIPS 2019
Finite-time Analysis of Approximate Policy Iteration for the Linear Quadratic Regulator
NIPS 2019
Learning Without Mixing: Towards A Sharp Analysis of Linear System Identification
COLT 2018
Least-Squares Temporal Difference Learning for the Linear Quadratic Regulator
ICML 2018
Regret Bounds for Robust Adaptive Control of the Linear Quadratic Regulator
NIPS 2018
Breaking Locality Accelerates Block Gauss-Seidel
ICML 2017
Cyclades: Conflict-free Asynchronous Machine Learning
NIPS 2016
Low-rank Solutions of Linear Matrix Equations via Procrustes Flow
ICML 2016
Fast Databases with Fast Durability and Recovery Through Multicore Parallelism
OSDI 2014