3 citations · 6 across the 6 of their papers we have counts for
6 papers
A Survey on Active Feature Acquisition Strategies
Linus Aronsson, Arman Rahbar, Morteza Haghir Chehreghani
Active feature acquisition (AFA) studies how to sequentially acquire features for each data instance to trade off predictive performance against acquisition cost. This survey offer…
Cost-Efficient Online Decision Making: A Combinatorial Multi-Armed Bandit Approach
Arman Rahbar, Niklas Åkerblom, Morteza Haghir Chehreghani
Online decision making plays a crucial role in numerous real-world applications. In many scenarios, the decision is made based on performing a sequence of tests on the incoming dat…
Efficient Online Decision Tree Learning with Active Feature Acquisition
Arman Rahbar, Ziyu Ye, Yuxin Chen +1
Constructing decision trees online is a classical machine learning problem. Existing works often assume that features are readily available for each incoming data point. However, i…
Analysis of Knowledge Transfer in Kernel Regime
Arman Rahbar, Ashkan Panahi, Chiranjib Bhattacharyya +2
Knowledge transfer is shown to be a very successful technique for training neural classifiers: together with the ground truth data, it uses the "privileged information" (PI) obtain…
Do Kernel and Neural Embeddings Help in Training and Generalization?
Arman Rahbar, Emilio Jorge, Devdatt Dubhashi +1
Recent results on optimization and generalization properties of neural networks showed that in a simple two-layer network, the alignment of the labels to the eigenvectors of the co…
Recovery Bounds on Class-Based Optimal Transport: A Sum-of-Norms Regularization Framework
Arman Rahbar, Ashkan Panahi, Morteza Haghir Chehreghani +2
We develop a novel theoretical framework for understating OT schemes respecting a class structure. For this purpose, we propose a convex OT program with a sum-of-norms regularizati…