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Author Information
Nati Srebro (Toyota Technological Institute at Chicago)
Daniel Roy (Univ of Toronto | Toronto)
Chelsea Finn (Stanford, Google, UC Berkeley)

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.
Mikhail Belkin (Ohio State University)
Aleksander Madry (MIT)
Jason Lee (University of Southern California)
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2021 : Invited Talk #4 »
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2021 : Panel Discussion »
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2021 Poster: Fast margin maximization via dual acceleration »
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2021 Poster: On the Implicit Bias of Initialization Shape: Beyond Infinitesimal Mirror Descent »
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2021 Oral: On the Implicit Bias of Initialization Shape: Beyond Infinitesimal Mirror Descent »
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2020 Poster: Generalization via Derandomization »
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2020 Poster: From ImageNet to Image Classification: Contextualizing Progress on Benchmarks »
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2019 : Chelsea Finn: "A Practical View on Generalization and Autonomy in the Real World" »
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2019 Workshop: Identifying and Understanding Deep Learning Phenomena »
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2019 Workshop: ICML Workshop on Imitation, Intent, and Interaction (I3) »
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2019 Workshop: Workshop on Multi-Task and Lifelong Reinforcement Learning »
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2019 : Keynote by Jason Lee: On the Foundations of Deep Learning: SGD, Overparametrization, and Generalization »
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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) »
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2019 : Keynote by Chelsea Finn: Training for Generalization »
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2019 : Keynote by Dan Roy: Progress on Nonvacuous Generalization Bounds »
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2019 Poster: Semi-Cyclic Stochastic Gradient Descent »
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2019 Poster: Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables »
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2019 Poster: Exploring the Landscape of Spatial Robustness »
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2019 Oral: Exploring the Landscape of Spatial Robustness »
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2019 Oral: Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables »
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2019 Poster: Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints »
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2019 Poster: Gradient Descent Finds Global Minima of Deep Neural Networks »
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2019 Poster: Learning a Prior over Intent via Meta-Inverse Reinforcement Learning »
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2019 Poster: Online Meta-Learning »
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2019 Poster: Lexicographic and Depth-Sensitive Margins in Homogeneous and Non-Homogeneous Deep Models »
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2019 Oral: Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints »
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2019 Oral: Gradient Descent Finds Global Minima of Deep Neural Networks »
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2019 Oral: Learning a Prior over Intent via Meta-Inverse Reinforcement Learning »
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2019 Oral: Online Meta-Learning »
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2018 Poster: On the Power of Over-parametrization in Neural Networks with Quadratic Activation »
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2018 Poster: Gradient Primal-Dual Algorithm Converges to Second-Order Stationary Solution for Nonconvex Distributed Optimization Over Networks »
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2018 Poster: Entropy-SGD optimizes the prior of a PAC-Bayes bound: Generalization properties of Entropy-SGD and data-dependent priors »
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2018 Poster: On the Limitations of First-Order Approximation in GAN Dynamics »
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2018 Oral: On the Limitations of First-Order Approximation in GAN Dynamics »
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2018 Oral: Entropy-SGD optimizes the prior of a PAC-Bayes bound: Generalization properties of Entropy-SGD and data-dependent priors »
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2018 Oral: On the Power of Over-parametrization in Neural Networks with Quadratic Activation »
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2018 Oral: Gradient Primal-Dual Algorithm Converges to Second-Order Stationary Solution for Nonconvex Distributed Optimization Over Networks »
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2018 Poster: Gradient Descent Learns One-hidden-layer CNN: Don't be Afraid of Spurious Local Minima »
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2018 Poster: To Understand Deep Learning We Need to Understand Kernel Learning »
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2018 Poster: Characterizing Implicit Bias in Terms of Optimization Geometry »
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2018 Oral: To Understand Deep Learning We Need to Understand Kernel Learning »
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2018 Oral: Characterizing Implicit Bias in Terms of Optimization Geometry »
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2018 Oral: Gradient Descent Learns One-hidden-layer CNN: Don't be Afraid of Spurious Local Minima »
Simon Du · Jason Lee · Yuandong Tian · Aarti Singh · Barnabás Póczos -
2018 Poster: The Power of Interpolation: Understanding the Effectiveness of SGD in Modern Over-parametrized Learning »
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2018 Poster: Universal Planning Networks: Learning Generalizable Representations for Visuomotor Control »
Aravind Srinivas · Allan Jabri · Pieter Abbeel · Sergey Levine · Chelsea Finn -
2018 Poster: A Classification-Based Study of Covariate Shift in GAN Distributions »
Shibani Santurkar · Ludwig Schmidt · Aleksander Madry -
2018 Oral: The Power of Interpolation: Understanding the Effectiveness of SGD in Modern Over-parametrized Learning »
Siyuan Ma · Raef Bassily · Mikhail Belkin -
2018 Oral: A Classification-Based Study of Covariate Shift in GAN Distributions »
Shibani Santurkar · Ludwig Schmidt · Aleksander Madry -
2018 Oral: Universal Planning Networks: Learning Generalizable Representations for Visuomotor Control »
Aravind Srinivas · Allan Jabri · Pieter Abbeel · Sergey Levine · Chelsea Finn -
2017 : Panel Discussion »
Balaraman Ravindran · Chelsea Finn · Alessandro Lazaric · Katja Hofmann · Marc Bellemare -
2017 : Talk »
Chelsea Finn -
2017 Poster: Efficient Distributed Learning with Sparsity »
Jialei Wang · Mladen Kolar · Nati Srebro · Tong Zhang -
2017 Talk: Efficient Distributed Learning with Sparsity »
Jialei Wang · Mladen Kolar · Nati Srebro · Tong Zhang -
2017 Poster: Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks »
Chelsea Finn · Pieter Abbeel · Sergey Levine -
2017 Poster: Communication-efficient Algorithms for Distributed Stochastic Principal Component Analysis »
Dan Garber · Ohad Shamir · Nati Srebro -
2017 Talk: Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks »
Chelsea Finn · Pieter Abbeel · Sergey Levine -
2017 Talk: Communication-efficient Algorithms for Distributed Stochastic Principal Component Analysis »
Dan Garber · Ohad Shamir · Nati Srebro -
2017 Tutorial: Deep Reinforcement Learning, Decision Making, and Control »
Sergey Levine · Chelsea Finn