171 citations · 937 across the 54 of their papers we have counts for
9 papers · 1 filter
Enhancing Stratospheric Weather Analyses and Forecasts by Deploying Sensors from a Weather Balloon
Kiwan Maeng, Iskender Kushan, Brandon Lucia +1
The ability to analyze and forecast stratospheric weather conditions is fundamental to addressing climate change. However, our capacity to collect data in the stratosphere is limit…
Multi-Robot Collision Avoidance under Uncertainty with Probabilistic Safety Barrier Certificates
Wenhao Luo, Wen Sun, Ashish Kapoor
Safety in terms of collision avoidance for multi-robot systems is a difficult challenge under uncertainty, non-determinism and lack of complete information. This paper aims to prop…
Modeling Affect-based Intrinsic Rewards for Exploration and Learning
Dean Zadok, Daniel McDuff, Ashish Kapoor
Positive affect has been linked to increased interest, curiosity and satisfaction in human learning. In reinforcement learning, extrinsic rewards are often sparse and difficult to…
Learning Visuomotor Policies for Aerial Navigation Using Cross-Modal Representations
Rogerio Bonatti, Ratnesh Madaan, Vibhav Vineet +2
Machines are a long way from robustly solving open-world perception-control tasks, such as first-person view (FPV) aerial navigation. While recent advances in end-to-end Machine Le…
Explorations and Lessons Learned in Building an Autonomous Formula SAE Car from Simulations
Dean Zadok, Tom Hirshberg, Amir Biran +2
This paper describes the exploration and learnings during the process of developing a self-driving algorithm in simulation, followed by deployment on a real car. We specifically co…
Bias Correction of Learned Generative Models using Likelihood-Free Importance Weighting
Aditya Grover, Jiaming Song, Alekh Agarwal +4
A learned generative model often produces biased statistics relative to the underlying data distribution. A standard technique to correct this bias is importance sampling, where sa…
Characterizing Bias in Classifiers using Generative Models
Daniel McDuff, Shuang Ma, Yale Song +1
Models that are learned from real-world data are often biased because the data used to train them is biased. This can propagate systemic human biases that exist and ultimately lead…
Synthetic Examples Improve Generalization for Rare Classes
Sara Beery, Yang Liu, Dan Morris +5
The ability to detect and classify rare occurrences in images has important applications - for example, counting rare and endangered species when studying biodiversity, or detectin…
Inverse Optimal Planning for Air Traffic Control
Ekaterina Tolstaya, Alejandro Ribeiro, Vijay Kumar +1
We envision a system that concisely describes the rules of air traffic control, assists human operators and supports dense autonomous air traffic around commercial airports. We dev…