511 citations · 3.1k across the 65 of their papers we have counts for
70 papers
TeraHAC: Hierarchical Agglomerative Clustering of Trillion-Edge Graphs
Laxman Dhulipala, Jason Lee, Jakub Łącki +1
We introduce TeraHAC, a -approximate hierarchical agglomerative clustering (HAC) algorithm which scales to trillion-edge graphs. Our algorithm is based on a new approach to…
Efficient displacement convex optimization with particle gradient descent
Hadi Daneshmand, Jason D. Lee, Chi Jin
Particle gradient descent, which uses particles to represent a probability measure and performs gradient descent on particles in parallel, is widely used to optimize functions of p…
MUSBO: Model-based Uncertainty Regularized and Sample Efficient Batch Optimization for Deployment Constrained Reinforcement Learning
DiJia Su, Jason D. Lee, John M. Mulvey +1
In many contemporary applications such as healthcare, finance, robotics, and recommendation systems, continuous deployment of new policies for data collection and online learning i…
A Theory of Label Propagation for Subpopulation Shift
Tianle Cai, Ruiqi Gao, Jason D. Lee +1
One of the central problems in machine learning is domain adaptation. Unlike past theoretical work, we consider a new model for subpopulation shift in the input or representation s…
Beyond Lazy Training for Over-parameterized Tensor Decomposition
Xiang Wang, Chenwei Wu, Jason D. Lee +2
Over-parametrization is an important technique in training neural networks. In both theory and practice, training a larger network allows the optimization algorithm to avoid bad lo…
Sanity-Checking Pruning Methods: Random Tickets can Win the Jackpot
Jingtong Su, Yihang Chen, Tianle Cai +4
Network pruning is a method for reducing test-time computational resource requirements with minimal performance degradation. Conventional wisdom of pruning algorithms suggests that…