75 citations · 897 across the 91 of their papers we have counts for
6 papers · 1 filter
Distributed Submodular Maximization
Baharan Mirzasoleiman, Amin Karbasi, Rik Sarkar +1
Many large-scale machine learning problems--clustering, non-parametric learning, kernel machines, etc.--require selecting a small yet representative subset from a large dataset. Su…
Lazier Than Lazy Greedy
Baharan Mirzasoleiman, Ashwinkumar Badanidiyuru, Amin Karbasi +2
Is it possible to maximize a monotone submodular function faster than the widely used lazy greedy algorithm (also known as accelerated greedy), both in theory and practice? In this…
Convolutional Neural Associative Memories: Massive Capacity with Noise Tolerance
Amin Karbasi, Amir Hesam Salavati, Amin Shokrollahi
The task of a neural associative memory is to retrieve a set of previously memorized patterns from their noisy versions using a network of neurons. An ideal network should have the…
Noise Facilitation in Associative Memories of Exponential Capacity
Amin Karbasi, Amir Hesam Salavati, Amin Shokrollahi +1
Recent advances in associative memory design through structured pattern sets and graph-based inference algorithms have allowed reliable learning and recall of an exponential number…
Near-Optimally Teaching the Crowd to Classify
Adish Singla, Ilija Bogunovic, Gábor Bartók +2
How should we present training examples to learners to teach them classification rules? This is a natural problem when training workers for crowdsourcing labeling tasks, and is als…
Near Optimal Bayesian Active Learning for Decision Making
Shervin Javdani, Yuxin Chen, Amin Karbasi +3
How should we gather information to make effective decisions? We address Bayesian active learning and experimental design problems, where we sequentially select tests to reduce unc…