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- Jason Lee Homepage
- Jason Lee Home
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Sep 19, 2016 Lee made the revelation while giving an update on his life to a local news site, The Dentonite. The actor moved to Denton, Texas, for a quieter lifestyle with his wife, Ceren Alkac, and their. Willamette Heritage, where the Jason Lee house is open to the public with admission to the museum, is likely to play an education and preservation role. It just recently re-opened and is operating. With my intimate knowledge in the field, i provide my clients with personalised solutions to connect them to their home.
Jason D. Lee |
About Me
I am an assistant professor of Electrical Engineering and Computer Science (secondary) in Princeton University and a member of the Theoretical Machine Learning Group. Previously, I was a member of the IAS and an assistant professor at USC for three years. Before that, I was a postdoc in the Computer Science Department at UC Berkeley working with Michael I. Jordan, and also collaborated with Ben Recht. I received my PhD in Applied Math advised by Trevor Hastie and Jonathan Taylor. I received a BS in Mathematics from Duke University advised by Mauro Maggioni.I am a native of Cupertino, CA.
UPCOMING PERFORMANCES: Cinderella: Der Prinz Eduard von Winterstein Theater, Annaberg-Buchholz, DE 04., 07., 10., 24., 31. Oktober 2020 20., 22. Email Jason Lee BA (hons) Music with Visual Practice, MA Fine Art - University of Brighton I love teaching Media Studies and am very proud of the creative work that previous students have produced.
My research interests are broadly in
Foundations of Deep Learning (slides)(video)
Representation Learning (slides)(video)
Foundations of Deep Reinforcement Learning (slides)(video 1)(video 2)
Princeton PhD students interested in machine learning, statistics, or optimization research, please contact me; I advise students in Computer Science, Electrical Engineering, Math, ORFE, and PACM. I am recruiting PhD students and postdoctoral scholars starting in 2021 at Princeton University, please email me a CV apply.
Jason Lee Home
My current focus is on machine learning with a focus on foundations of deep learning, reprsentation learning, and deep reinforcement learning. See my talk at MIT slides and video or my tutorial at the Simons Institute: tutorial slides and video.
I am also happy to host remote visitors. Summer visitors please contact me around February to schedule your visit. See a list of past visitors at here.
Awards
Sloan Research Fellow in Computer Science, Alfred P. Sloan Foundation
Finalist for Best Paper Prize for Young Researchers in Continuous Optimization
ICML 2018 Workshop on Nonconvex Optimization for MLBest Paper Award for ‘‘Algorithmic Regularization in Learning Deep Homogeneous Models: Layers are Automatically Balanced'
NIPS 2016 Best Student Paper Award for ‘‘Matrix Completion has no Spurious Local Minima'.
Selected Publications
Jason Lee Homepage
Jason Lee Home
Sanity-Checking Pruning Methods: Random Tickets can Win the Jackpot.
Jingtong Su, Yihang Chen, Tianle Cai, Tianhao Wu, Ruiqi Gao, Liwei Wang, and Jason D. Lee.
NeurIPS 2020.
Predicting What You Already Know Helps: Provable Self-Supervised Learning.
Jason D. Lee, Qi Lei, Nikunj Saunshi, and Jiacheng Zhuo.
Towards Understanding Hierarchical Learning: Benefits of Neural Representations.
Minshuo Chen, Yu Bai, Jason D. Lee, Tuo Zhao, Huan Wang, Caiming Xiong, and Richard Socher.
NeurIPS 2020.
Few-Shot Learning via Learning the Representation, Provably.
Simon S. Du, Wei Hu, Sham M. Free piano music sheets home. Kakade, Jason D. Lee, and Qi Lei.
Shape Matters: Understanding the Implicit Bias of the Noise Covariance.
Jeff Z. HaoChen, Colin Wei, Jason D. Lee, and Tengyu Ma.
Beyond Linearization: On Quadratic and Higher-Order Approximation of Wide Neural Networks .
Yu Bai and Jason D. Lee.
ICLR 2020.
Jason Lee Hometown
On the Theory of Policy Gradient Methods: Optimality, Approximation, and Distribution Shift
Alekh Agarwal, Sham M. Kakade, Jason D. Lee, and Gaurav Mahajan.
JMLR.
Gradient Descent Finds Global Minima of Deep Neural Networks
Simon S. Du, Jason D. Lee, Haochuan Li, Liwei Wang, and Xiyu Zhai.
ICML 2019.
Jason Lee Home Inspector
Gradient Descent Converges to Minimizers.
Jason D. Lee, Max Simchowitz, Michael I. Jordan, and Benjamin Recht.
COLT 2016
I am an assistant professor of Electrical Engineering and Computer Science (secondary) in Princeton University and a member of the Theoretical Machine Learning Group. Previously, I was a member of the IAS and an assistant professor at USC for three years. Before that, I was a postdoc in the Computer Science Department at UC Berkeley working with Michael I. Jordan, and also collaborated with Ben Recht. I received my PhD in Applied Math advised by Trevor Hastie and Jonathan Taylor. I received a BS in Mathematics from Duke University advised by Mauro Maggioni.I am a native of Cupertino, CA.
UPCOMING PERFORMANCES: Cinderella: Der Prinz Eduard von Winterstein Theater, Annaberg-Buchholz, DE 04., 07., 10., 24., 31. Oktober 2020 20., 22. Email Jason Lee BA (hons) Music with Visual Practice, MA Fine Art - University of Brighton I love teaching Media Studies and am very proud of the creative work that previous students have produced.
My research interests are broadly in
Foundations of Deep Learning (slides)(video)
Representation Learning (slides)(video)
Foundations of Deep Reinforcement Learning (slides)(video 1)(video 2)
Princeton PhD students interested in machine learning, statistics, or optimization research, please contact me; I advise students in Computer Science, Electrical Engineering, Math, ORFE, and PACM. I am recruiting PhD students and postdoctoral scholars starting in 2021 at Princeton University, please email me a CV apply.
Jason Lee Home
My current focus is on machine learning with a focus on foundations of deep learning, reprsentation learning, and deep reinforcement learning. See my talk at MIT slides and video or my tutorial at the Simons Institute: tutorial slides and video.
I am also happy to host remote visitors. Summer visitors please contact me around February to schedule your visit. See a list of past visitors at here.
Awards
Sloan Research Fellow in Computer Science, Alfred P. Sloan Foundation
Finalist for Best Paper Prize for Young Researchers in Continuous Optimization
ICML 2018 Workshop on Nonconvex Optimization for MLBest Paper Award for ‘‘Algorithmic Regularization in Learning Deep Homogeneous Models: Layers are Automatically Balanced'
NIPS 2016 Best Student Paper Award for ‘‘Matrix Completion has no Spurious Local Minima'.
Selected Publications
Jason Lee Homepage
Jason Lee Home
Sanity-Checking Pruning Methods: Random Tickets can Win the Jackpot.
Jingtong Su, Yihang Chen, Tianle Cai, Tianhao Wu, Ruiqi Gao, Liwei Wang, and Jason D. Lee.
NeurIPS 2020.
Predicting What You Already Know Helps: Provable Self-Supervised Learning.
Jason D. Lee, Qi Lei, Nikunj Saunshi, and Jiacheng Zhuo.
Towards Understanding Hierarchical Learning: Benefits of Neural Representations.
Minshuo Chen, Yu Bai, Jason D. Lee, Tuo Zhao, Huan Wang, Caiming Xiong, and Richard Socher.
NeurIPS 2020.
Few-Shot Learning via Learning the Representation, Provably.
Simon S. Du, Wei Hu, Sham M. Free piano music sheets home. Kakade, Jason D. Lee, and Qi Lei.
Shape Matters: Understanding the Implicit Bias of the Noise Covariance.
Jeff Z. HaoChen, Colin Wei, Jason D. Lee, and Tengyu Ma.
Beyond Linearization: On Quadratic and Higher-Order Approximation of Wide Neural Networks .
Yu Bai and Jason D. Lee.
ICLR 2020.
Jason Lee Hometown
On the Theory of Policy Gradient Methods: Optimality, Approximation, and Distribution Shift
Alekh Agarwal, Sham M. Kakade, Jason D. Lee, and Gaurav Mahajan.
JMLR.
Gradient Descent Finds Global Minima of Deep Neural Networks
Simon S. Du, Jason D. Lee, Haochuan Li, Liwei Wang, and Xiyu Zhai.
ICML 2019.
Jason Lee Home Inspector
Gradient Descent Converges to Minimizers.
Jason D. Lee, Max Simchowitz, Michael I. Jordan, and Benjamin Recht.
COLT 2016
Matrix Completion has No Spurious Local Minimum.
Rong Ge, Jason D. Lee, and Tengyu Ma.
Best Student Paper Award at NeurIPS 2016.
Exact Post-Selection Inference with the Lasso.
Jason D. Lee, Dennis L Sun, Yuekai Sun, and Jonathan Taylor.
Annals of Statistics 2016.
We automate the generation of legal documents as well as related workflows, such as tracking the status of a project, assigning responsible person based on the status, sending out automated emails or reminder texts to customers, etc. We've been in business since 2015 and after years of hard work and negative cashflow, we started to generate revenue in 2018 and became profitable in 2019. Our typical customers are companies and law firms that deal with a large number of contracts and are concentrated in what-we-call the three 'L' verticals: leasing, lending and legal.
What drives you to be a business owner?
I love the fact that I can create and shape an environment designed to make me most productive. If you are into psychology, I'm an INTJ in MBTI, so I prefer to work in small groups of highly skilled and technical people and leave no room for politics, and need to have time and space to think things through and run experiments. I was an attorney for 8 years, and it is very 'left brain' centric, with lots of analysis, critical thinking and judgment. Now that I'm in entrepreneurship, I feel as though I can do more of what I am best at, which is to strategize, make connections between distant fields of study, drawing what the solution might look like, etc. As a result, I'm happier and believe I am making a bigger impact to society by creating solutions that did not previously exist.
How has the Entrepreneurship Center at WCC helped with your business goals?
Jason Lee Home Improvement
[email protected] and the related workshops helped me get exposure to pitching and summarizing our value proposition succinctly. Getting your pitch right is important because you will tell that story a million times over, and your story is what people remember. Even though we did not win any awards at WCC's Pitch, the skills we honed in connection to the preparation helped us win an investment in another competition, so we are grateful to have been part of [email protected] The Entrepreneurship Center also connected us with a mentor, who has helped us make several important decisions with his experience and wisdom throughout our journey.
If you could offer one piece of advice for fellow entrepreneurs or prospective entrepreneurs, what would it be?
I thought long and hard about the main difference between working at a large law firm vs. starting your own business, and I believe the defining difference is the level of entropy (or roughly speaking, the level of chaos) in a given environment. Entrepreneurship is chaotic and is like surviving in the wild: you need to get water, food, start a fire, build a shelter, etc. And if you fail at any, then you die. The same way, in a startup you need a good product, team, marketing, sales, customer service – and if you fail at any, you're doomed. In essence, you will go as far as your weakest link and you need to do it with very limited resources. In a more orderly environment though, there is less survival pressure so the optimum strategy perhaps is to focus on few things that you are good at, be a standout among many to be useful to the society, and rely on others to fill the gaps. So maybe we can say: In order, specialize; in chaos, generalize. But the ultimate point of entrepreneurship is to bring order to chaos, like turning wilderness into farms, so as you survive (i.e. spending resources wisely on your critical weaknesses) and then thrive (finding a niche where your company excels), think about what you need to do and who you need to be as your company evolves and more order is injected through the actions of yourself and others.
