9 citations · 21 across the 9 of their papers we have counts for
7 papers · 1 filter
Continual Learning as Computationally Constrained Reinforcement Learning
Saurabh Kumar, Henrik Marklund, Ashish Rao +4
An agent that efficiently accumulates knowledge to develop increasingly sophisticated skills over a long lifetime could advance the frontier of artificial intelligence capabilities…
A Definition of Non-Stationary Bandits
Yueyang Liu, Xu Kuang, Benjamin Van Roy
Despite the subject of non-stationary bandit learning having attracted much recent attention, we have yet to identify a formal definition of non-stationarity that can consistently…
Brain Model State Space Reconstruction Using an LSTM Neural Network
Yueyang Liu, Artemio Soto-Breceda, Yun Zhao +5
Objective Kalman filtering has previously been applied to track neural model states and parameters, particularly at the scale relevant to EEG. However, this approach lacks a reliab…
Non-Stationary Bandit Learning via Predictive Sampling
Yueyang Liu, Xu Kuang, Benjamin Van Roy
Thompson sampling has proven effective across a wide range of stationary bandit environments. However, as we demonstrate in this paper, it can perform poorly when applied to non-st…
Gaussian Imagination in Bandit Learning
Yueyang Liu, Adithya M. Devraj, Benjamin Van Roy +1
Assuming distributions are Gaussian often facilitates computations that are otherwise intractable. We study the performance of an agent that attains a bounded information ratio wit…
Robust Convergence in Federated Learning through Label-wise Clustering
Hunmin Lee, Yueyang Liu, Donghyun Kim +1
Non-IID dataset and heterogeneous environment of the local clients are regarded as a major issue in Federated Learning (FL), causing a downturn in the convergence without achieving…
AED: An black-box NLP classifier model attacker
Yueyang Liu, Yan Huang, Zhipeng Cai
Deep Neural Networks (DNNs) have been successful in solving real-world tasks in domains such as connected and automated vehicles, disease, and job hiring. However, their implicatio…