Cyril Zhang will present his Pre FPO "Optimization and Regret Minimization in Stateful Environments" on May 19, 2020 at 2pm via Zoom
Cyril Zhang will present his Pre FPO "Optimization and Regret Minimization in Stateful Environments" on May 19, 2020 at 2pm. Zoom link: [ https://princeton.zoom.us/j/5554928263?pwd=Zys1NUY5RldIWjZVZVhJYVJCVHcydz09 | https://princeton.zoom.us/j/5554928263?pwd=Zys1NUY5RldIWjZVZVhJYVJCVHcydz09 ] The members of his committee are as follows: Advisor: Elad Hazan Examiners: Sanjeev Arora, Mark Braverman Readers: Yoram Singer, Karthik Narasimhan Abstract follows below. Temporally correlated data and non-convex programs are two core challenges at the frontier of reconciling theory and practice in machine learning. I will present varied perspectives on algorithms and provable learning guarantees in stateful environments, such as those encountered in reinforcement learning, control theory, and time-series analysis. Unified by the framework of online convex optimization, this thesis will consist of two main sections: - Learning dynamical systems: a line of theoretical work on learning to predict, control, and plan in the presence of time-invariant state transitions, beyond the linear-quadratic-Gaussian assumptions of classical control theory. The regret framework gives us robust algorithms which bypass the usual distributional assumptions and system identification pipelines. - Optimization in deep learning: a line of empirical work on understanding the behavior of adaptive gradient methods in state-of-the-art deep learning. These poorly-understood heuristics were borne from the online convex world; I will discuss some of the intuitions we can carry over, as well as some of the challenges and insufficiencies. Finally, I will tie these together with some recent work applying these ideas to my favorite non-convex time series problem: statistical language modeling with deep neural networks.
Hi all, due to technical difficulties, new zoom link:
https://princeton.zoom.us/j/91035968494
On Wed, May 13, 2020 at 5:00 PM Nicki Mahler
Cyril Zhang will present his Pre FPO "Optimization and Regret Minimization in Stateful Environments" on May 19, 2020 at 2pm.
Zoom link: https://princeton.zoom.us/j/5554928263?pwd=Zys1NUY5RldIWjZVZVhJYVJCVHcydz09
The members of his committee are as follows: Advisor: Elad Hazan Examiners: Sanjeev Arora, Mark Braverman Readers: Yoram Singer, Karthik Narasimhan
Abstract follows below.
Temporally correlated data and non-convex programs are two core challenges at the frontier of reconciling theory and practice in machine learning. I will present varied perspectives on algorithms and provable learning guarantees in stateful environments, such as those encountered in reinforcement learning, control theory, and time-series analysis. Unified by the framework of online convex optimization, this thesis will consist of two main sections:
- Learning dynamical systems: a line of theoretical work on learning to predict, control, and plan in the presence of time-invariant state transitions, beyond the linear-quadratic-Gaussian assumptions of classical control theory. The regret framework gives us robust algorithms which bypass the usual distributional assumptions and system identification pipelines.
- Optimization in deep learning: a line of empirical work on understanding the behavior of adaptive gradient methods in state-of-the-art deep learning. These poorly-understood heuristics were borne from the online convex world; I will discuss some of the intuitions we can carry over, as well as some of the challenges and insufficiencies.
Finally, I will tie these together with some recent work applying these ideas to my favorite non-convex time series problem: statistical language modeling with deep neural networks.
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participants (2)
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Elad Hazan -
Nicki Mahler