From kirthevasankandasamy at gmail.com Mon Sep 24 23:04:42 2018 From: kirthevasankandasamy at gmail.com (Kirthevasan Kandasamy) Date: Mon, 24 Sep 2018 23:04:42 -0400 Subject: Fwd: Thesis Defense - Oct. 3, 2018 - Kirthevasan Kandasamy - Tuning Hyper-parameters without Grad-students: Scaling up Bandit Optimisation In-Reply-To: References: Message-ID: Hi everyone, I am defending next Wednesday. You are welcome to drop by. Thanks! Samy ---------- Forwarded message --------- From: Diane Stidle Date: Mon, Sep 24, 2018 at 4:19 PM Subject: Thesis Defense - Oct. 3, 2018 - Kirthevasan Kandasamy - Tuning Hyper-parameters without Grad-students: Scaling up Bandit Optimisation To: ml-seminar at cs.cmu.edu , zoubin at eng.cam.ac.uk < zoubin at eng.cam.ac.uk>, *Thesis Defense* Date: October 3, 2018 Time: 12:30pm (EDT) Place: 8102 GHC PhD Candidate: Kirthevasan Kandasamy *Title: **Tuning Hyper-parameters without Grad-students: Scaling up Bandit Optimisation* Abstract: This thesis explores scalable methods for adaptive decision making under uncertainty, where the goal of an agent is to design an experiment, observe the outcome, and plan subsequent experiments to achieve a desired goal. Typically, each experiment incurs a large computational or economic cost, and we need to keep the number of experiments to a minimum. Many of such problems fall under the bandit framework, where each experiment evaluates a noisy function and the goal is to find the optimum of this function. A common use case for the bandit framework, pervasive in many industrial and scientific applications, is hyper-parameter tuning, where we need to find the optimal configuration of a black-box system by tuning the several knobs which affect the performance of the system. Some applications include statistical model selection, materials design, optimal policy selection in robotics, and maximum likelihood inference in simulation based scientific models. More generally, bandits are but one class of problems studied under the umbrella of adaptive decision-making under uncertainty. Problems such as active learning and design of experiments are other examples of adaptive decision-making, but unlike bandits, progress towards a desired goal is not made known to the agent via a reward signal. With increasingly expensive function evaluations and demands to optimise over complex input spaces, bandit optimisation tasks face new challenges today. At the same time, there are new opportunities that have not been exploited previously. We study the following questions in this thesis to enable the application of bandits and more broadly adaptive decision-making to modern applications. - Conventional bandit methods work reliably in low dimensional settings, but scale poorly with input dimensionality. Scaling such methods to high dimensional inputs requires addressing several computational and statistical challenges. - In many applications, an expensive function can be cheaply approximated. We study techniques that can use information from these cheap lower fidelity approximations to speed up the overall optimisation process. - Conventional bandit methods are inherently sequential. We study parallelisation techniques so as to deploy several function evaluations at the same time. - Typical methods assume that a design can be characterised by a Euclidean vector. We study bandit methods on graph-structured spaces. As a specific application, we study neural architecture search, which optimises for the structure of the neural network by viewing as a directed graph with node labels and node weights. - Many methods for adaptive decision-making are not competitive with human experts. Incorporating domain knowledge and human intuition about specific problems may significantly improve practical performance. We first study the above topics in the bandit framework and then study how they can be extended to broader decision-making problems. We develop methods with theoretical guarantees which simultaneously enjoy good empirical performance. As part of this thesis, we also develop an open source platform for scalable and robust bandit optimisation. Thesis Committee: Barnab?s P?czos(Co-chair) Jeff Schneider (Co-chair) Aarti Singh Zoubin Ghahramani (University of Cambridge) *Link to draft document: *http://www.cs.cmu.edu/~kkandasa/docs/thesis.pdf -- Diane Stidle Graduate Programs Manager Machine Learning Department Carnegie Mellon Universitystidle at cmu.edu 412-268-1299 -------------- next part -------------- An HTML attachment was scrubbed... URL: