4 citations · 12 across the 12 of their papers we have counts for
13 papers
Order-Consistent Latent Geometry for Reliable Short-Horizon Planning in World Models
Junlin Wu, Wen Fan, Dandan Zhang
Latent planners often rank candidate action sequences by terminal Euclidean distance to a goal latent. Exact agreement with transition distance would make this score ideal, but is…
Conformal Reachability for Safe Control in Unknown Environments
Xinhang Ma, Junlin Wu, Yiannis Kantaros +1
Designing provably safe control is a core problem in trustworthy autonomy. However, most prior work in this regard assumes either that the system dynamics are known or deterministi…
Learning Vision-Based Neural Network Controllers with Semi-Probabilistic Safety Guarantees
Xinhang Ma, Junlin Wu, Hussein Sibai +2
Ensuring safety in autonomous systems with vision-based control remains a critical challenge due to the high dimensionality of image inputs and the fact that the relationship betwe…
Verified Safe Reinforcement Learning for Neural Network Dynamic Models
Junlin Wu, Huan Zhang, Yevgeniy Vorobeychik
Learning reliably safe autonomous control is one of the core problems in trustworthy autonomy. However, training a controller that can be formally verified to be safe remains a maj…
Axioms for AI Alignment from Human Feedback
Luise Ge, Daniel Halpern, Evi Micha +4
In the context of reinforcement learning from human feedback (RLHF), the reward function is generally derived from maximum likelihood estimation of a random utility model based on…
Preference Poisoning Attacks on Reward Model Learning
Junlin Wu, Jiongxiao Wang, Chaowei Xiao +3
Learning reward models from pairwise comparisons is a fundamental component in a number of domains, including autonomous control, conversational agents, and recommendation systems,…
RLHFPoison: Reward Poisoning Attack for Reinforcement Learning with Human Feedback in Large Language Models
Jiongxiao Wang, Junlin Wu, Muhao Chen +2
Reinforcement Learning with Human Feedback (RLHF) is a methodology designed to align Large Language Models (LLMs) with human preferences, playing an important role in LLMs alignmen…
Exact Verification of ReLU Neural Control Barrier Functions
Hongchao Zhang, Junlin Wu, Yevgeniy Vorobeychik +1
Control Barrier Functions (CBFs) are a popular approach for safe control of nonlinear systems. In CBF-based control, the desired safety properties of the system are mapped to nonne…
Neural Lyapunov Control for Discrete-Time Systems
Junlin Wu, Andrew Clark, Yiannis Kantaros +1
While ensuring stability for linear systems is well understood, it remains a major challenge for nonlinear systems. A general approach in such cases is to compute a combination of…
Certifying Safety in Reinforcement Learning under Adversarial Perturbation Attacks
Junlin Wu, Hussein Sibai, Yevgeniy Vorobeychik
Function approximation has enabled remarkable advances in applying reinforcement learning (RL) techniques in environments with high-dimensional inputs, such as images, in an end-to…
Manipulating Elections by Changing Voter Perceptions
Junlin Wu, Andrew Estornell, Lecheng Kong +1
The integrity of elections is central to democratic systems. However, a myriad of malicious actors aspire to influence election outcomes for financial or political benefit. A commo…
Robust Deep Reinforcement Learning through Bootstrapped Opportunistic Curriculum
Junlin Wu, Yevgeniy Vorobeychik
Despite considerable advances in deep reinforcement learning, it has been shown to be highly vulnerable to adversarial perturbations to state observations. Recent efforts that have…
Learning Generative Deception Strategies in Combinatorial Masking Games
Junlin Wu, Charles Kamhoua, Murat Kantarcioglu +1
Deception is a crucial tool in the cyberdefence repertoire, enabling defenders to leverage their informational advantage to reduce the likelihood of successful attacks. One way dec…