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. I also work closely with Alessandro Lazaric from Facebook Artificial Intelligence Research. My research focuses on provably efficient methods for Reinforcement Learning, in particular, I develop agents capable of autonomous exploration. My research has been partially supported by Total.

Before starting my PhD, I was a master’s student in the same department, and prior to joining Stanford I worked in the civil construction sector and for M3E, developing high performance linear algebra software. My bachelor is in Mechanical Engineering from the University of Padova.

Preprints

• Andrea Zanette
  Exponential Lower Bounds for Batch Reinforcement Learning:
  Batch RL can be Exponentially Harder than Online RL, [Paper]

Publications

• 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, AAAI ’20, AISTATS ’20-’21
• Journal Reviewer: Journal of Artificial Intelligence Research (2017-ongoing)