HEPML workshop at NIPS14

Session

Session 3

13 déc. 2014, 15:00

Level 5, room 511 c (Palais des Congrès de Montréal)

7. Machine Learning for Ultra-High-Energy Physics (invited talk)

Daniel Whiteson

13/12/2014 15:00

I will describe the computational and machine learning challenges of the CRAYFIS project: a distributed cosmic ray telescope consisting of consumer smartphones and geared for the detection of ultra-high-energy cosmic rays. For more info: http://crayfis.ps.uci.edu/

9. Weighted Classification Cascades for Optimizing Discovery Significance in the HiggsML Challenge

Lester Mackey

13/12/2014 15:40

We introduce a minorization-maximization approach to optimizing common measures of discovery significance in high energy physics. The approach alternates between solving a weighted binary classification problem and updating class weights in a simple, closed-form manner. Moreover, an argument based on convex duality shows that an improvement in weighted classification error on any round...

8. Consistent optimization of AMS by logistic loss minimization

Wojciech Kotlowski

13/12/2014 16:00

In this paper, we theoretically justify an approach popular among participants of the Higgs Boson Machine Learning Challenge to optimize approximate median significance (AMS). The approach is based on the following two-stage procedure. First, a real-valued function is learned by minimizing a surrogate loss for binary classification, such as logistic loss, on the training sample. Then, a...