21 citations · 119 across the 23 of their papers we have counts for
6 papers · 1 filter
Artificial Replay: A Meta-Algorithm for Harnessing Historical Data in Bandits
Siddhartha Banerjee, Sean R. Sinclair, Milind Tambe +2
Most real-world deployments of bandit algorithms exist somewhere in between the offline and online set-up, where some historical data is available upfront and additional data is co…
Exploiting Neighborhood Interference with Low Order Interactions under Unit Randomized Design
Mayleen Cortez-Rodriguez, Matthew Eichhorn, Christina Lee Yu
Network interference, where the outcome of an individual is affected by the treatment assignment of those in their social network, is pervasive in real-world settings. However, it…
Overcoming the Long Horizon Barrier for Sample-Efficient Reinforcement Learning with Latent Low-Rank Structure
Tyler Sam, Yudong Chen, Christina Lee Yu
The practicality of reinforcement learning algorithms has been limited due to poor scaling with respect to the problem size, as the sample complexity of learning an -optimal pol…
Staggered Rollout Designs Enable Causal Inference Under Interference Without Network Knowledge
Mayleen Cortez, Matthew Eichhorn, Christina Lee Yu
Randomized experiments are widely used to estimate causal effects across a variety of domains. However, classical causal inference approaches rely on critical independence assumpti…
Estimating Total Treatment Effect in Randomized Experiments with Unknown Network Structure
Christina Lee Yu, Edoardo M Airoldi, Christian Borgs +1
Randomized experiments are widely used to estimate the causal effects of a proposed treatment in many areas of science, from medicine and healthcare to the physical and biological…
Detection of Small Holes by the Scale-Invariant Robust Density-Aware Distance (RDAD) Filtration
Chunyin Siu, Gennady Samorodnitsky, Christina Lee Yu +1
A novel topological-data-analytical (TDA) method is proposed to distinguish, from noise, small holes surrounded by high-density regions of a probability density function. The propo…