← Authors

Stephen Tu

32 papers · 2014–2025 · 9 conferences · across top CS/AI conferences

Achievements

πŸƒ Academic Marathon (11) πŸŒ‰ Interdisciplinary Bridge 🐝 Cross-Pollinator (9) πŸ—ΊοΈ Taxonomy Completionist (10) 🌍 Conference Polyglot (9) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🌈 Renaissance Researcher (5) 🀝 Dynamic Duo (10) πŸ—ƒοΈ Keyword Collector (130) ⚑ Prolific Year (6) πŸ’Ž Century Club (32) πŸ”₯ Unstoppable (10) πŸ“ˆ Trend Setter

Conferences

NIPS (7) ICML (6) L4DC (6) CORL (4) AISTATS (2) COLT (2) ICLR (2) JMLR (2) OSDI (1)

Top co-authors

Nikolai Matni (10) Benjamin Recht (7) Vikas Sindhwani (4) Jean-Jacques Slotine (4) Ingvar Ziemann (4) Nicholas Boffi (3) Max Simchowitz (3) Michael I. Jordan (3) Sumeet Singh (3) Horia Mania (3)

Research topics

Robotics (1)

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