Robustness via Cross-Domain Ensembles
Yeo, T., Kar O.*, Zamir, A.
ICCV 2021 (Oral)
Ensembling via a diverse set of middle domains.
Zamir, A.*, Sax, A.*, Yeo, T., Kar O., Cheerla, N., Suri, R., Cao, Z., Malik, J., Guibas, L.
Arxiv 2020, ECCV 2020 (demo)
Given an arbitrary dictionary of tasks, augment the learning objective with constraints for cross-task consistency.
Yeo, T., Kamalaruban, P., Singla, A., Merchant, A., Asselborn, T., Faucon, L., Dillenbourg, P., Cevher, V
AAAI 2019 (Oral), AMLD 2019 (Invited talk)
Finding the optimal sequence of examples to train a class of linear algorithms with applications to teaching to write and to classify.
Kamalaruban P., Devidze R., Yeo, T., Mittal T., Cevher V., Singla A.
NIPS 2018 Learning by Instruction Workshop
Finding the optimal sequence of examples to train an Inverse Reinforcement Learning agent.
PhD in Machine Learning and Computer Vision
MPhil in Machine Learning and Machine Intelligence
Thesis: Bayesian Optimization for Natural Language Processing
MSc in Financial Engineering
BSc (Hons) in Quantitative Finance
Thesis: High Order Numerical Methods for Solving PDE with Optimal Stopping Times