169 citations · 169 across the 12 of their papers we have counts for
5 papers · 1 filter
Active Learning for Stochastic Contextual Linear Bandits
Emma Brunskill, Ishani Karmarkar, Zhaoqi Li
A key goal in stochastic contextual linear bandits is to efficiently learn a near-optimal policy. Prior algorithms for this problem learn a policy by strategically sampling actions…
Subsampling Graphs with GNN Performance Guarantees
Mika Sarkin Jain, Stefanie Jegelka, Ishani Karmarkar +2
How can we subsample graph data so that a graph neural network (GNN) trained on the subsample achieves performance comparable to training on the full dataset? This question is of f…
Mean-Field Sampling for Cooperative Multi-Agent Reinforcement Learning
Emile Anand, Ishani Karmarkar, Guannan Qu
Designing efficient algorithms for multi-agent reinforcement learning (MARL) is fundamentally challenging because the size of the joint state and action spaces grows exponentially…
Truncated Variance Reduced Value Iteration
Yujia Jin, Ishani Karmarkar, Aaron Sidford +1
We provide faster randomized algorithms for computing an -optimal policy in a discounted Markov decision process with -state-action pairs, bounded rewards, and d…
Accelerating data-driven algorithm selection for combinatorial partitioning problems
Vaggos Chatziafratis, Ishani Karmarkar, Yingxi Li +1
Data-driven algorithm selection is a powerful approach for choosing effective heuristics for computational problems. It operates by evaluating a set of candidate algorithms on a co…