Andrea Zanette, PhD Candidate
Institute for Computational and Mathematical Engineering
Stanford University
[lastname] at stanford.edu
I am a PhD candidate in the Institute for Computational and Mathematical Engineering at Stanford University advised by prof Emma Brunskill and Mykel J. Kochenderfer. During my PhD, I have also worked with Alessandro Lazaric from Facebook Artificial Intelligence Research and Alekh Agarwal from Microsoft Research.
My research focuses on designing and analyzing provably efficient methods for Reinforcement Learning, a subarea of Artificial Intelligence that deals with sequential decision making under uncertainty.
My research has been partially supported by Total.
Before starting my PhD (2017), I was a master’s student in the same department (2015-2017).
In my former life I was a mechanical engineer. I worked in the civil construction sector and for M3E, developing high performance linear algebra software. I also spent some time at the The von Karman Institute for Fluid Dynamics, a NATO-affiliated international research establishment.
Publications
- Andrea Zanette
Exponential Lower Bounds for Batch Reinforcement Learning:
Batch RL can be Exponentially Harder than Online RL
ICML (International Conference on Machine Learning), 2021, Long Oral, [Paper]
* Csaba Szepesvári teaches this lower bound in detail in his Foundation of RL class in this video, and as always, his explanations are great - Andrea Zanette, Ching-An Cheng, Alekh Agarwal
Cautiously Optimistic Policy Optimization and Exploration with Linear Function Approximation,
COLT (Conference on Learning Theory) 2021 [Paper] - Andrea Zanette, Alessandro Lazaric, Mykel Kochenderfer, Emma Brunskill
Provably Efficient Reward-Agnostic Navigation with Linear Value Iteration,
NeurIPS (Neural Information Processing Systems), 2020 [Paper] - Andrea Zanette, Alessandro Lazaric, Mykel Kochenderfer, Emma Brunskill
Learning Near Optimal Policies with Low Inherent Bellman Error
ICML (International Conference on Machine Learning), 2020 [Paper] - Andrea Zanette*, David Brandfonbrener*, Emma Brunskill, Matteo Pirotta, Alessandro Lazaric
Frequentist Regret Bounds for Randomized Least-Squares Value Iteration
AISTATS (International Conference on Artificial Intelligence and Statistics), 2020 [Paper]
(* denotes equal contribution) - Andrea Zanette, Mykel J. Kochenderfer, Emma Brunskill
Almost Horizon-Free Structure-Aware Best Policy Identification with a Generative Model
NeurIPS (Neural Information Processing Systems), 2019 [Paper] - Andrea Zanette, Alessandro Lazaric, Mykel J. Kochenderfer, Emma Brunskill
Limiting Extrapolation in Linear Approximate Value Iteration
NeurIPS (Neural Information Processing Systems), 2019 [Paper] - Andrea Zanette, Emma Brunskill
Tighter Problem-Dependent Regret Bounds in Reinforcement Learning without Domain Knowledge using Value Function Bounds
ICML (International Conference on Machine Learning), 2019, Oral, [Paper] - Andrea Zanette, Junzi Zhang, Mykel J. Kochenderfer
Robust Super-Level Set Estimation using Gaussian Processes
in ECML-PKDD (European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases), 2018, Oral [Paper] - Andrea Zanette, Emma Brunskill
Problem Dependent Reinforcement Learning Bounds Which Can Identify Bandit Structure in MDPs
in ICML (International Conference on Machine Learning), 2018, Long Oral [Paper] - Andrea Zanette, Massimiliano Ferronato, Carlo Janna
Enriching the finite element method with meshfree techniques in structural mechanics
in IJNME (International Journal for Numerical Methods in Engineering), 2017, [Paper]
Awarded by Advances in Engineering as key scientific article contributing to excellence in science and engineering research [Award] - Andrea Zanette, Massimiliano Ferronato, Carlo Janna
Enriching the Finite Element Method with meshfree particles in structural mechanics
in PAMM (Proceedings in Applied Mathematics and Mechanics), 2015, Oral
Best Poster Award at International CAE Conference 2014 [Award]
Featured in Enginsoft 2014, issue number 4 [Media]
Teaching
- TA for Math of Machine Learning Summer School, University of Washington, August 2019
- Instructor for ICME Workshop on Reinforcement Learning, Stanford University, August 2018
- TA: CS234 (Reinforcement Learning) in 2018, 2019, 2020; CS332 (Advanced Reinforcement Learning) in 2018; CS238 (Decision Making Under Uncertainty) in 2018; CME 200 (Linear Algebra) in 2016-2020; CME 307 (Optimization) in 2017; AA222 (Engineering Design and Optimization) in 2017, 2018
Professional Service
- Area Chair: ICML ’20, ICLR ’21
- Conference Reviewer: COLT ’19, NeurIPS ’19-’20-’21, ICML ’21, AAAI ’20, AISTATS ’20-’21
- Journal Reviewer: Journal of Artificial Intelligence Research (2017-ongoing)
