6 papers
Federated Hierarchical Reinforcement Learning for Adaptive Traffic Signal Control
Yongjie Fu, Lingyun Zhong, Zifan Li +1
Multi-agent reinforcement learning (MARL) has shown promise for adaptive traffic signal control (ATSC), enabling multiple intersections to coordinate signal timings in real time. H…
From seeing to remembering: Images with harder-to-reconstruct representations leave stronger memory traces
Qi Lin, Zifan Li, John Lafferty +1
Much of what we remember is not due to intentional selection, but simply a by-product of perceiving. This raises a foundational question about the architecture of the mind: How doe…
Lasso Guarantees for -Mixing Heavy Tailed Time Series
Kam Chung Wong, Zifan Li, Ambuj Tewari
Many theoretical results for the lasso require the samples to be iid. Recent work has provided guarantees for the lasso assuming that the time series is generated by a sparse Vecto…
Beyond the Hazard Rate: More Perturbation Algorithms for Adversarial Multi-armed Bandits
Zifan Li, Ambuj Tewari
Recent work on follow the perturbed leader (FTPL) algorithms for the adversarial multi-armed bandit problem has highlighted the role of the hazard rate of the distribution generati…
Sampled Fictitious Play is Hannan Consistent
Zifan Li, Ambuj Tewari
Fictitious play is a simple and widely studied adaptive heuristic for playing repeated games. It is well known that fictitious play fails to be Hannan consistent. Several variants…
Lasso Guarantees for Time Series Estimation Under Subgaussian Tails and -Mixing
Kam Chung Wong, Zifan Li, Ambuj Tewari
Many theoretical results on estimation of high dimensional time series require specifying an underlying data generating model (DGM). Instead, along the footsteps of~\cite{wong2017l…