Timezone: »
Author Information
Balaraman Ravindran (Indian Institute of Technology)
Chelsea Finn (Stanford, Google)

Chelsea Finn is an Assistant Professor in Computer Science and Electrical Engineering at Stanford University. Finn's research interests lie in the capability of robots and other agents to develop broadly intelligent behavior through learning and interaction. To this end, her work has included deep learning algorithms for concurrently learning visual perception and control in robotic manipulation skills, inverse reinforcement methods for learning reward functions underlying behavior, and meta-learning algorithms that can enable fast, few-shot adaptation in both visual perception and deep reinforcement learning. Finn received her Bachelor's degree in Electrical Engineering and Computer Science at MIT and her PhD in Computer Science at UC Berkeley. Her research has been recognized through the ACM doctoral dissertation award, the Microsoft Research Faculty Fellowship, the C.V. Ramamoorthy Distinguished Research Award, and the MIT Technology Review 35 under 35 Award, and her work has been covered by various media outlets, including the New York Times, Wired, and Bloomberg. Throughout her career, she has sought to increase the representation of underrepresented minorities within CS and AI by developing an AI outreach camp at Berkeley for underprivileged high school students, a mentoring program for underrepresented undergraduates across four universities, and leading efforts within the WiML and Berkeley WiCSE communities of women researchers.
Alessandro Lazaric (FACEBOOK)
Katja Hofmann (Microsoft)
Marc Bellemare (Google DeepMind)
More from the Same Authors
-
2023 : Suboptimal Data Can Bottleneck Scaling »
Jacob Buckman · Kshitij Gupta · Ethan Caballero · Rishabh Agarwal · Marc Bellemare -
2023 Poster: Bootstrapped Representations in Reinforcement Learning »
Charline Le Lan · Stephen Tu · Mark Rowland · Anna Harutyunyan · Rishabh Agarwal · Marc Bellemare · Will Dabney -
2023 Poster: The Statistical Benefits of Quantile Temporal-Difference Learning for Value Estimation »
Mark Rowland · Yunhao Tang · Clare Lyle · Remi Munos · Marc Bellemare · Will Dabney -
2023 Poster: Bigger, Better, Faster: Human-level Atari with human-level efficiency »
Max Schwarzer · Johan Obando Ceron · Aaron Courville · Marc Bellemare · Rishabh Agarwal · Pablo Samuel Castro -
2022 Poster: Interactively Learning Preference Constraints in Linear Bandits »
David Lindner · Sebastian Tschiatschek · Katja Hofmann · Andreas Krause -
2022 Spotlight: Interactively Learning Preference Constraints in Linear Bandits »
David Lindner · Sebastian Tschiatschek · Katja Hofmann · Andreas Krause -
2022 Poster: Distributional Hamilton-Jacobi-Bellman Equations for Continuous-Time Reinforcement Learning »
Harley Wiltzer · David Meger · Marc Bellemare -
2022 Spotlight: Distributional Hamilton-Jacobi-Bellman Equations for Continuous-Time Reinforcement Learning »
Harley Wiltzer · David Meger · Marc Bellemare -
2021 : Panel Discussion »
Rosemary Nan Ke · Danijar Hafner · Pieter Abbeel · Chelsea Finn · Chelsea Finn -
2021 : Invited Talk by Chelsea Finn »
Chelsea Finn -
2021 : Towards Human-like and Collaborative AI in Video Games »
Katja Hofmann -
2021 Poster: Exploration in Approximate Hyper-State Space for Meta Reinforcement Learning »
Luisa Zintgraf · Leo Feng · Cong Lu · Maximilian Igl · Kristian Hartikainen · Katja Hofmann · Shimon Whiteson -
2021 Spotlight: Exploration in Approximate Hyper-State Space for Meta Reinforcement Learning »
Luisa Zintgraf · Leo Feng · Cong Lu · Maximilian Igl · Kristian Hartikainen · Katja Hofmann · Shimon Whiteson -
2021 Poster: TeachMyAgent: a Benchmark for Automatic Curriculum Learning in Deep RL »
Clément Romac · Rémy Portelas · Katja Hofmann · Pierre-Yves Oudeyer -
2021 Spotlight: TeachMyAgent: a Benchmark for Automatic Curriculum Learning in Deep RL »
Clément Romac · Rémy Portelas · Katja Hofmann · Pierre-Yves Oudeyer -
2021 Poster: Navigation Turing Test (NTT): Learning to Evaluate Human-Like Navigation »
Sam Devlin · Raluca Georgescu · Ida Momennejad · Jaroslaw Rzepecki · Evelyn Zuniga · Gavin Costello · Guy Leroy · Ali Shaw · Katja Hofmann -
2021 Spotlight: Navigation Turing Test (NTT): Learning to Evaluate Human-Like Navigation »
Sam Devlin · Raluca Georgescu · Ida Momennejad · Jaroslaw Rzepecki · Evelyn Zuniga · Gavin Costello · Guy Leroy · Ali Shaw · Katja Hofmann -
2020 : Panel discussion »
Kavya Srinet · Katja Hofmann · Yoav Artzi · Alex Kearney · Julia Hockenmaier -
2020 : Open-ended environments for advancing RL Q&A »
Max Jaderberg · Katja Hofmann -
2020 : The NetHack Learning Environment Q&A »
Tim Rocktäschel · Katja Hofmann -
2020 Workshop: Workshop on Learning in Artificial Open Worlds »
Arthur Szlam · Katja Hofmann · Ruslan Salakhutdinov · Noboru Kuno · William Guss · Kavya Srinet · Brandon Houghton -
2020 : Opening remarks »
Katja Hofmann -
2020 : Q&A with Katja Hoffman »
Katja Hofmann · Luisa Zintgraf · Rika Antonova · Sarath Chandar · Shagun Sodhani -
2020 : Challenges & Opportunities in Lifelong Reinforcement Learning by Katja Hoffman »
Katja Hofmann · Rika Antonova · Luisa Zintgraf -
2020 Workshop: 4th Lifelong Learning Workshop »
Shagun Sodhani · Sarath Chandar · Balaraman Ravindran · Doina Precup -
2020 Poster: Representations for Stable Off-Policy Reinforcement Learning »
Dibya Ghosh · Marc Bellemare -
2019 : Chelsea Finn: "A Practical View on Generalization and Autonomy in the Real World" »
Chelsea Finn -
2019 : Meta-Learning: Challenges and Frontiers (Chelsea Finn) »
Chelsea Finn -
2019 Workshop: ICML Workshop on Imitation, Intent, and Interaction (I3) »
Nicholas Rhinehart · Sergey Levine · Chelsea Finn · He He · Ilya Kostrikov · Justin Fu · Siddharth Reddy -
2019 Workshop: Workshop on Multi-Task and Lifelong Reinforcement Learning »
Sarath Chandar · Shagun Sodhani · Khimya Khetarpal · Tom Zahavy · Daniel J. Mankowitz · Shie Mannor · Balaraman Ravindran · Doina Precup · Chelsea Finn · Abhishek Gupta · Amy Zhang · Kyunghyun Cho · Andrei A Rusu · Facebook Rob Fergus -
2019 : Panel Discussion (Nati Srebro, Dan Roy, Chelsea Finn, Mikhail Belkin, Aleksander Mądry, Jason Lee) »
Nati Srebro · Daniel Roy · Chelsea Finn · Mikhail Belkin · Aleksander Madry · Jason Lee -
2019 : panel discussion with Craig Boutilier (Google Research), Emma Brunskill (Stanford), Chelsea Finn (Google Brain, Stanford, UC Berkeley), Mohammad Ghavamzadeh (Facebook AI), John Langford (Microsoft Research) and David Silver (Deepmind) »
Peter Stone · Craig Boutilier · Emma Brunskill · Chelsea Finn · John Langford · David Silver · Mohammad Ghavamzadeh -
2019 : Keynote by Chelsea Finn: Training for Generalization »
Chelsea Finn -
2019 Poster: Statistics and Samples in Distributional Reinforcement Learning »
Mark Rowland · Robert Dadashi · Saurabh Kumar · Remi Munos · Marc Bellemare · Will Dabney -
2019 Poster: Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables »
Kate Rakelly · Aurick Zhou · Chelsea Finn · Sergey Levine · Deirdre Quillen -
2019 Poster: Fast Context Adaptation via Meta-Learning »
Luisa Zintgraf · Kyriacos Shiarlis · Vitaly Kurin · Katja Hofmann · Shimon Whiteson -
2019 Oral: Statistics and Samples in Distributional Reinforcement Learning »
Mark Rowland · Robert Dadashi · Saurabh Kumar · Remi Munos · Marc Bellemare · Will Dabney -
2019 Oral: Fast Context Adaptation via Meta-Learning »
Luisa Zintgraf · Kyriacos Shiarlis · Vitaly Kurin · Katja Hofmann · Shimon Whiteson -
2019 Oral: Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables »
Kate Rakelly · Aurick Zhou · Chelsea Finn · Sergey Levine · Deirdre Quillen -
2019 Poster: Learning a Prior over Intent via Meta-Inverse Reinforcement Learning »
Kelvin Xu · Ellis Ratner · Anca Dragan · Sergey Levine · Chelsea Finn -
2019 Poster: The Value Function Polytope in Reinforcement Learning »
Robert Dadashi · Marc Bellemare · Adrien Ali Taiga · Nicolas Le Roux · Dale Schuurmans -
2019 Poster: DeepMDP: Learning Continuous Latent Space Models for Representation Learning »
Carles Gelada · Saurabh Kumar · Jacob Buckman · Ofir Nachum · Marc Bellemare -
2019 Poster: Online Meta-Learning »
Chelsea Finn · Aravind Rajeswaran · Sham Kakade · Sergey Levine -
2019 Oral: The Value Function Polytope in Reinforcement Learning »
Robert Dadashi · Marc Bellemare · Adrien Ali Taiga · Nicolas Le Roux · Dale Schuurmans -
2019 Oral: DeepMDP: Learning Continuous Latent Space Models for Representation Learning »
Carles Gelada · Saurabh Kumar · Jacob Buckman · Ofir Nachum · Marc Bellemare -
2019 Oral: Learning a Prior over Intent via Meta-Inverse Reinforcement Learning »
Kelvin Xu · Ellis Ratner · Anca Dragan · Sergey Levine · Chelsea Finn -
2019 Oral: Online Meta-Learning »
Chelsea Finn · Aravind Rajeswaran · Sham Kakade · Sergey Levine -
2019 Tutorial: Meta-Learning: from Few-Shot Learning to Rapid Reinforcement Learning »
Chelsea Finn · Sergey Levine -
2018 Poster: Universal Planning Networks: Learning Generalizable Representations for Visuomotor Control »
Aravind Srinivas · Allan Jabri · Pieter Abbeel · Sergey Levine · Chelsea Finn -
2018 Oral: Universal Planning Networks: Learning Generalizable Representations for Visuomotor Control »
Aravind Srinivas · Allan Jabri · Pieter Abbeel · Sergey Levine · Chelsea Finn -
2017 : Explorartion methods for options »
Alessandro Lazaric -
2017 : Talk »
Chelsea Finn -
2017 : Some experiments with learning hyperparameters, transfer, and multi-task leaning »
Balaraman Ravindran -
2017 Workshop: Reinforcement Learning Workshop »
Doina Precup · Balaraman Ravindran · Pierre-Luc Bacon -
2017 : Lifelong Learning - Panel Discussion »
Sergey Levine · Joelle Pineau · Balaraman Ravindran · Andrei A Rusu -
2017 : Marc G. Bellemare: The role of density models in reinforcement learning »
Marc Bellemare -
2017 Workshop: Lifelong Learning: A Reinforcement Learning Approach »
Sarath Chandar · Balaraman Ravindran · Daniel J. Mankowitz · Shie Mannor · Tom Zahavy -
2017 Poster: Count-Based Exploration with Neural Density Models »
Georg Ostrovski · Marc Bellemare · Aäron van den Oord · Remi Munos -
2017 Talk: Count-Based Exploration with Neural Density Models »
Georg Ostrovski · Marc Bellemare · Aäron van den Oord · Remi Munos -
2017 Poster: Active Learning for Accurate Estimation of Linear Models »
Carlos Riquelme Ruiz · Mohammad Ghavamzadeh · Alessandro Lazaric -
2017 Poster: A Laplacian Framework for Option Discovery in Reinforcement Learning »
Marlos C. Machado · Marc Bellemare · Michael Bowling -
2017 Poster: A Distributional Perspective on Reinforcement Learning »
Marc Bellemare · Will Dabney · Remi Munos -
2017 Poster: Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks »
Chelsea Finn · Pieter Abbeel · Sergey Levine -
2017 Poster: Automated Curriculum Learning for Neural Networks »
Alex Graves · Marc Bellemare · Jacob Menick · Remi Munos · Koray Kavukcuoglu -
2017 Poster: Second-Order Kernel Online Convex Optimization with Adaptive Sketching »
Daniele Calandriello · Alessandro Lazaric · Michal Valko -
2017 Talk: A Laplacian Framework for Option Discovery in Reinforcement Learning »
Marlos C. Machado · Marc Bellemare · Michael Bowling -
2017 Talk: A Distributional Perspective on Reinforcement Learning »
Marc Bellemare · Will Dabney · Remi Munos -
2017 Talk: Active Learning for Accurate Estimation of Linear Models »
Carlos Riquelme Ruiz · Mohammad Ghavamzadeh · Alessandro Lazaric -
2017 Talk: Automated Curriculum Learning for Neural Networks »
Alex Graves · Marc Bellemare · Jacob Menick · Remi Munos · Koray Kavukcuoglu -
2017 Talk: Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks »
Chelsea Finn · Pieter Abbeel · Sergey Levine -
2017 Talk: Second-Order Kernel Online Convex Optimization with Adaptive Sketching »
Daniele Calandriello · Alessandro Lazaric · Michal Valko -
2017 Tutorial: Deep Reinforcement Learning, Decision Making, and Control »
Sergey Levine · Chelsea Finn