131 citations · 397 across the 37 of their papers we have counts for
37 papers
Regulating Algorithmic Management: A Multi-Stakeholder Study of Challenges in Aligning Software and the Law for Workplace Scheduling
Jonathan Lynn, Rachel Y. Kim, Sicun Gao +3
Algorithmic management (AM)'s impact on worker well-being has led to calls for regulation. However, little is known about the effectiveness and challenges in real-world AM regulati…
Iterative Reachability Estimation for Safe Reinforcement Learning
Milan Ganai, Zheng Gong, Chenning Yu +2
Ensuring safety is important for the practical deployment of reinforcement learning (RL). Various challenges must be addressed, such as handling stochasticity in the environments,…
Sequential Neural Barriers for Scalable Dynamic Obstacle Avoidance
Hongzhan Yu, Chiaki Hirayama, Chenning Yu +2
There are two major challenges for scaling up robot navigation around dynamic obstacles: the complex interaction dynamics of the obstacles can be hard to model analytically, and th…
Smaller Language Models are Better Black-box Machine-Generated Text Detectors
Niloofar Mireshghallah, Justus Mattern, Sicun Gao +2
With the advent of fluent generative language models that can produce convincing utterances very similar to those written by humans, distinguishing whether a piece of text is machi…
Patching Approximately Safe Value Functions Leveraging Local Hamilton-Jacobi Reachability Analysis
Sander Tonkens, Alex Toofanian, Zhizhen Qin +2
Safe value functions, such as control barrier functions, characterize a safe set and synthesize a safety filter, overriding unsafe actions, for a dynamic system. While function app…
Learning Stabilization Control from Observations by Learning Lyapunov-like Proxy Models
Milan Ganai, Chiaki Hirayama, Ya-Chien Chang +1
The deployment of Reinforcement Learning to robotics applications faces the difficulty of reward engineering. Therefore, approaches have focused on creating reward functions by Lea…
Accelerating Multi-Agent Planning Using Graph Transformers with Bounded Suboptimality
Chenning Yu, Qingbiao Li, Sicun Gao +1
Conflict-Based Search is one of the most popular methods for multi-agent path finding. Though it is complete and optimal, it does not scale well. Recent works have been proposed to…
Monte Carlo Tree Descent for Black-Box Optimization
Yaoguang Zhai, Sicun Gao
The key to Black-Box Optimization is to efficiently search through input regions with potentially widely-varying numerical properties, to achieve low-regret descent and fast progre…
Policy Optimization with Advantage Regularization for Long-Term Fairness in Decision Systems
Eric Yang Yu, Zhizhen Qin, Min Kyung Lee +1
Long-term fairness is an important factor of consideration in designing and deploying learning-based decision systems in high-stake decision-making contexts. Recent work has propos…
Learning Control Admissibility Models with Graph Neural Networks for Multi-Agent Navigation
Chenning Yu, Hongzhan Yu, Sicun Gao
Deep reinforcement learning in continuous domains focuses on learning control policies that map states to distributions over actions that ideally concentrate on the optimal choices…
Reducing Collision Checking for Sampling-Based Motion Planning Using Graph Neural Networks
Chenning Yu, Sicun Gao
Sampling-based motion planning is a popular approach in robotics for finding paths in continuous configuration spaces. Checking collision with obstacles is the major computational…
Learning-based Motion Planning in Dynamic Environments Using GNNs and Temporal Encoding
Ruipeng Zhang, Chenning Yu, Jingkai Chen +2
Learning-based methods have shown promising performance for accelerating motion planning, but mostly in the setting of static environments. For the more challenging problem of plan…
Quantifying Safety of Learning-based Self-Driving Control Using Almost-Barrier Functions
Zhizhen Qin, Tsui-Wei Weng, Sicun Gao
Path-tracking control of self-driving vehicles can benefit from deep learning for tackling longstanding challenges such as nonlinearity and uncertainty. However, deep neural contro…
SCALE: Online Self-Supervised Lifelong Learning without Prior Knowledge
Xiaofan Yu, Yunhui Guo, Sicun Gao +1
Unsupervised lifelong learning refers to the ability to learn over time while memorizing previous patterns without supervision. Although great progress has been made in this direct…
Everyone's Preference Changes Differently: Weighted Multi-Interest Retrieval Model
Hui Shi, Yupeng Gu, Yitong Zhou +3
User embeddings (vectorized representations of a user) are essential in recommendation systems. Numerous approaches have been proposed to construct a representation for the user in…
Safe Control with Learned Certificates: A Survey of Neural Lyapunov, Barrier, and Contraction methods
Charles Dawson, Sicun Gao, Chuchu Fan
Learning-enabled control systems have demonstrated impressive empirical performance on challenging control problems in robotics, but this performance comes at the cost of reduced t…
Learning Bounded Context-Free-Grammar via LSTM and the Transformer:Difference and Explanations
Hui Shi, Sicun Gao, Yuandong Tian +2
Long Short-Term Memory (LSTM) and Transformers are two popular neural architectures used for natural language processing tasks. Theoretical results show that both are Turing-comple…
Safe Nonlinear Control Using Robust Neural Lyapunov-Barrier Functions
Charles Dawson, Zengyi Qin, Sicun Gao +1
Safety and stability are common requirements for robotic control systems; however, designing safe, stable controllers remains difficult for nonlinear and uncertain models. We devel…
Stabilizing Neural Control Using Self-Learned Almost Lyapunov Critics
Ya-Chien Chang, Sicun Gao
The lack of stability guarantee restricts the practical use of learning-based methods in core control problems in robotics. We develop new methods for learning neural control polic…
Transient Stability Assessment of Networked Microgrids Using Neural Lyapunov Methods
Tong Huang, Sicun Gao, Le Xie
This paper proposes a novel transient stability assessment tool for networked microgrids based on neural Lyapunov methods. Assessing transient stability is formulated as a problem…
Provably Efficient Model-based Policy Adaptation
Yuda Song, Aditi Mavalankar, Wen Sun +1
The high sample complexity of reinforcement learning challenges its use in practice. A promising approach is to quickly adapt pre-trained policies to new environments. Existing met…
Neural Lyapunov Control
Ya-Chien Chang, Nima Roohi, Sicun Gao
We propose new methods for learning control policies and neural network Lyapunov functions for nonlinear control problems, with provable guarantee of stability. The framework consi…
How to pick the domain randomization parameters for sim-to-real transfer of reinforcement learning policies?
Quan Vuong, Sharad Vikram, Hao Su +2
Recently, reinforcement learning (RL) algorithms have demonstrated remarkable success in learning complicated behaviors from minimally processed input. However, most of this succes…
Tight Continuous-Time Reachtubes for Lagrangian Reachability
Jacek Cyranka, Md. Ariful Islam, Scott A. Smolka +2
We introduce continuous Lagrangian reachability (CLRT), a new algorithm for the computation of a tight and continuous-time reachtube for the solution flows of a nonlinear, time-var…