21 citations · 29 across the 5 of their papers we have counts for
5 papers
Using Adaptive Experiments to Rapidly Help Students
Angela Zavaleta-Bernuy, Qi Yin Zheng, Hammad Shaikh +4
Adaptive experiments can increase the chance that current students obtain better outcomes from a field experiment of an instructional intervention. In such experiments, the probabi…
Algorithms for Adaptive Experiments that Trade-off Statistical Analysis with Reward: Combining Uniform Random Assignment and Reward Maximization
Tong Li, Jacob Nogas, Haochen Song +8
Traditional randomized A/B experiments assign arms with uniform random (UR) probability, such as 50/50 assignment to two versions of a website to discover whether one version engag…
Challenges in Statistical Analysis of Data Collected by a Bandit Algorithm: An Empirical Exploration in Applications to Adaptively Randomized Experiments
Joseph Jay Williams, Jacob Nogas, Nina Deliu +4
Multi-armed bandit algorithms have been argued for decades as useful for adaptively randomized experiments. In such experiments, an algorithm varies which arms (e.g. alternative in…
Spatio-Temporal Adversarial Learning for Detecting Unseen Falls
Shehroz S. Khan, Jacob Nogas, Alex Mihailidis
Fall detection is an important problem from both the health and machine learning perspective. A fall can lead to severe injuries, long term impairments or even death in some cases.…
DeepFall -- Non-invasive Fall Detection with Deep Spatio-Temporal Convolutional Autoencoders
Jacob Nogas, Shehroz S. Khan, Alex Mihailidis
Human falls rarely occur; however, detecting falls is very important from the health and safety perspective. Due to the rarity of falls, it is difficult to employ supervised classi…