Search Results for author: Jacob Nogas

Found 5 papers, 1 papers with code

Using Adaptive Experiments to Rapidly Help Students

no code implementations10 Aug 2022 Angela Zavaleta-Bernuy, Qi Yin Zheng, Hammad Shaikh, Jacob Nogas, Anna Rafferty, Andrew Petersen, Joseph Jay Williams

Adaptive experiments can increase the chance that current students obtain better outcomes from a field experiment of an instructional intervention.

Algorithms for Adaptive Experiments that Trade-off Statistical Analysis with Reward: Combining Uniform Random Assignment and Reward Maximization

no code implementations15 Dec 2021 Jacob Nogas, Tong Li, Fernando J. Yanez, Arghavan Modiri, Nina Deliu, Ben Prystawski, Sofia S. Villar, Anna Rafferty, Joseph J. Williams

TS PostDiff takes a Bayesian approach to mixing TS and UR: the probability a participant is assigned using UR allocation is the posterior probability that the difference between two arms is `small' (below a certain threshold), allowing for more UR exploration when there is little or no reward to be gained.

Challenges in Statistical Analysis of Data Collected by a Bandit Algorithm: An Empirical Exploration in Applications to Adaptively Randomized Experiments

no code implementations22 Mar 2021 Joseph Jay Williams, Jacob Nogas, Nina Deliu, Hammad Shaikh, Sofia S. Villar, Audrey Durand, Anna Rafferty

We therefore use our case study of the ubiquitous two-arm binary reward setting to empirically investigate the impact of using Thompson Sampling instead of uniform random assignment.

Spatio-Temporal Adversarial Learning for Detecting Unseen Falls

no code implementations19 May 2019 Shehroz S. Khan, Jacob Nogas, Alex Mihailidis

In this paper, we take an alternate philosophy to detect falls in the absence of their training data, by training the classifier on only the normal activities (that are available in abundance) and identifying a fall as an anomaly.

BIG-bench Machine Learning Philosophy

DeepFall -- Non-invasive Fall Detection with Deep Spatio-Temporal Convolutional Autoencoders

1 code implementation30 Aug 2018 Jacob Nogas, Shehroz S. Khan, Alex Mihailidis

Human falls rarely occur; however, detecting falls is very important from the health and safety perspective.

Anomaly Detection

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