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Upcoming Seminar

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Models and Algorithms for Solving Sequential Decision Problems under Uncertainty

Date: Mar. 21, 2pm

Speaker: Mykel Kochenderfer

Location: Sycamore Conference Room (040-195)

Abstract: Many important problems involve decision making under uncertainty, including aircraft collision avoidance, wildfire management, and disaster response. When designing automated decision support systems, it is important to account for the various sources of uncertainty when making or recommending decisions. Accounting for these sources of uncertainty and carefully balancing the multiple objectives of the system can be very challenging. One way to model such problems is as a partially observable Markov decision process (POMDP). Recent advances in algorithms, memory capacity, and processing power, have allowed us to solve POMDPs for real-world problems. This talk will discuss models for sequential decision making and algorithms for solving them.

Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis

Date: Mar. 28, 2pm

Speaker: Michela Paganini

Location: Sycamore Conference Room (040-195)

Abstract: We provide a bridge between generative modeling in the Machine Learning community and simulated physical processes in High Energy Particle Physics by applying a novel Generative Adversarial Network (GAN) architecture to the production of jet images -- 2D representations of energy depositions from particles interacting with a calorimeter. We propose a simple architecture, the Location-Aware Generative Adversarial Network, that learns to produce realistic radiation patterns from simulated high energy particle collisions. The pixel intensities of GAN-generated images faithfully span over many orders of magnitude and exhibit the desired low-dimensional physical properties (i.e., jet mass, n-subjettiness, etc.). We shed light on limitations, and provide a novel empirical validation of image quality and validity of GAN-produced simulations of the natural world. This work provides a base for further explorations of GANs for use in faster simulation in High Energy Particle Physics.

Past Seminars

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Data Programming: A New Framework for Weakly Supervising Machine Learning Models

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Abstract: Today's state-of-the-art machine learning models require require massive labeled  labeled training sets--which usually do not exist for real-world applications. Instead, I’ll discuss a newly proposed machine learning paradigm--data programming--and a system built around it, Snorkel, in which the developer focuses on writing a set of of labeling functions, which are just scripts that programmatically label data. The resulting labels are noisy, but we model this as a generative process—learning, essentially, which labeling functions are more accurate than others—and then use this to train an end discriminative model (for example, a deep neural network in TensorFlow).  Given certain conditions, we show that this method has the same asymptotic scaling with respect to generalization error as directly-supervised approaches. Empirically, we find that by modeling a noisy training set creation process in this way, we can take potentially low-quality labeling functions from the user, and use these to train high-quality end models. We see this as providing a general framework for many many weak supervision techniques techniques, and at a higher level, as defining a new programming model for weakly-supervised machine learning systems.

Models and Algorithms for Solving Sequential Decision Problems under Uncertainty

Date: Mar. 21, 2pm

Speaker: Mykel Kochenderfer

Location: Sycamore Conference Room (040-195)

Abstract: Many important problems involve decision making under uncertainty, including aircraft collision avoidance, wildfire management, and disaster response. When designing automated decision support systems, it is important to account for the various sources of uncertainty when making or recommending decisions. Accounting for these sources of uncertainty and carefully balancing the multiple objectives of the system can be very challenging. One way to model such problems is as a partially observable Markov decision process (POMDP). Recent advances in algorithms, memory capacity, and processing power, have allowed us to solve POMDPs for real-world problems. This talk will discuss models for sequential decision making and algorithms for solving them.

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ProxImaL: Efficient Image Optimization using Proximal Algorithms

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