9 papers
From POMDP Theory to Deep RL with Particle Filters
Kevin Tan, Ziping Xu
Model-based reinforcement learning in partially observable environments requires inferring hidden states, learning latent dynamics, and planning under the resulting belief. Practic…
Improving scalable oversight with co-trained monitors
Joseph H. Rudoler, Kevin Tan, Benedict Tessler +2
Worker-monitor setups are a promising approach to AI oversight, but training workers against fixed monitors can incentivize monitor evasion. We study whether this failure mode can…
Estimating Implicit Regularization in Deep Learning
Joseph H. Rudoler, Kevin Tan, Giles Hooker +1
Deep learning systems are known to exhibit implicit regularization (alt. implicit bias), favoring simple solutions instead of merely minimizing the loss function. In some cases, we…
Optimistic Actor-Critic with Parametric Policies for Linear Markov Decision Processes
Max Qiushi Lin, Reza Asad, Kevin Tan +3
Although actor-critic methods have been successful in practice, their theoretical analyses have several limitations. Specifically, existing theoretical work either sidesteps the ex…
Statistical Inference under Adaptive Sampling with LinUCB
Wei Fan, Kevin Tan, Yuting Wei
Adaptively collected data has become ubiquitous within modern practice. However, even seemingly benign adaptive sampling schemes can introduce severe biases, rendering traditional…
Actor-Critics Can Achieve Optimal Sample Efficiency
Kevin Tan, Wei Fan, Yuting Wei
Actor-critic algorithms have become a cornerstone in reinforcement learning (RL), leveraging the strengths of both policy-based and value-based methods. Despite recent progress in…
Hybrid Reinforcement Learning Breaks Sample Size Barriers in Linear MDPs
Kevin Tan, Wei Fan, Yuting Wei
Hybrid Reinforcement Learning (RL), where an agent learns from both an offline dataset and online explorations in an unknown environment, has garnered significant recent interest.…
Distributed Least Squares in Small Space via Sketching and Bias Reduction
Sachin Garg, Kevin Tan, Michał Dereziński
Matrix sketching is a powerful tool for reducing the size of large data matrices. Yet there are fundamental limitations to this size reduction when we want to recover an accurate e…
A Natural Extension To Online Algorithms For Hybrid RL With Limited Coverage
Kevin Tan, Ziping Xu
Hybrid Reinforcement Learning (RL), leveraging both online and offline data, has garnered recent interest, yet research on its provable benefits remains sparse. Additionally, many…