Search Results for author: Sicun Gao

Found 15 papers, 6 papers with code

Monte Carlo Tree Descent for Black-Box Optimization

no code implementations1 Nov 2022 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 progress toward the optima.

Gaussian Processes

Policy Optimization with Advantage Regularization for Long-Term Fairness in Decision Systems

1 code implementation22 Oct 2022 Eric Yang Yu, Zhizhen Qin, Min Kyung Lee, Sicun Gao

Long-term fairness is an important factor of consideration in designing and deploying learning-based decision systems in high-stake decision-making contexts.

Decision Making Fairness

Reducing Collision Checking for Sampling-Based Motion Planning Using Graph Neural Networks

1 code implementation NeurIPS 2021 Chenning Yu, Sicun Gao

We propose new learning-based methods for reducing collision checking to accelerate motion planning by training graph neural networks (GNNs) that perform path exploration and path smoothing.

Motion Planning

Learning-based Motion Planning in Dynamic Environments Using GNNs and Temporal Encoding

no code implementations16 Oct 2022 Ruipeng Zhang, Chenning Yu, Jingkai Chen, Chuchu Fan, Sicun Gao

Learning-based methods have shown promising performance for accelerating motion planning, but mostly in the setting of static environments.

Imitation Learning Motion Planning

SCALE: Online Self-Supervised Lifelong Learning without Prior Knowledge

no code implementations24 Aug 2022 Xiaofan Yu, Yunhui Guo, Sicun Gao, Tajana Rosing

To address the challenges, we propose Self-Supervised ContrAstive Lifelong LEarning without Prior Knowledge (SCALE) which can extract and memorize representations on-the-fly purely from the data continuum.

Everyone's Preference Changes Differently: Weighted Multi-Interest Retrieval Model

1 code implementation14 Jul 2022 Hui Shi, Yupeng Gu, Yitong Zhou, Bo Zhao, Sicun Gao, Jishen Zhao

In this paper, we propose the Multi-Interest Preference (MIP) model, an approach that not only produces multi-interest for users by using the user's sequential engagement more effectively but also automatically learns a set of weights to represent the preference over each embedding so that the candidates can be retrieved from each interest proportionally.

Recommendation Systems Retrieval

Learning Bounded Context-Free-Grammar via LSTM and the Transformer:Difference and Explanations

1 code implementation16 Dec 2021 Hui Shi, Sicun Gao, Yuandong Tian, Xinyun Chen, Jishen Zhao

With the forced decomposition, we show that the performance upper bounds of LSTM and Transformer in learning CFL are close: both of them can simulate a stack and perform stack operation along with state transitions.

Safe Nonlinear Control Using Robust Neural Lyapunov-Barrier Functions

no code implementations14 Sep 2021 Charles Dawson, Zengyi Qin, Sicun Gao, Chuchu Fan

Safety and stability are common requirements for robotic control systems; however, designing safe, stable controllers remains difficult for nonlinear and uncertain models.

Transient Stability Assessment of Networked Microgrids Using Neural Lyapunov Methods

no code implementations2 Dec 2020 Tong Huang, Sicun Gao, Le Xie

Case studies suggest that the proposed method can address networked microgrids with heterogeneous interface dynamics, and in comparison with conventional methods that are based on quadratic Lyapunov functions, can characterize the security regions with much less conservativeness.

Neural Lyapunov Control

1 code implementation NeurIPS 2019 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.

Learning Latent Representations for Inverse Dynamics using Generalized Experiences

no code implementations25 Sep 2019 Aditi Mavalankar, Sicun Gao

Many practical robot locomotion tasks require agents to use control policies that can be parameterized by goals.


How to pick the domain randomization parameters for sim-to-real transfer of reinforcement learning policies?

1 code implementation28 Mar 2019 Quan Vuong, Sharad Vikram, Hao Su, Sicun Gao, Henrik I. Christensen

A human-specified design choice in domain randomization is the form and parameters of the distribution of simulated environments.

REAS: Combining Numerical Optimization with SAT Solving

no code implementations13 Feb 2018 Jeevana Priya Inala, Sicun Gao, Soonho Kong, Armando Solar-Lezama

In this paper, we present ReaS, a technique that combines numerical optimization with SAT solving to synthesize unknowns in a program that involves discrete and floating point computation.

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