Search Results for author: Ziyi Chen

Found 24 papers, 4 papers with code

ARO: Large Language Model Supervised Robotics Text2Skill Autonomous Learning

no code implementations23 Mar 2024 YiWen Chen, Yuyao Ye, Ziyi Chen, Chuheng Zhang, Marcelo H. Ang

Robotics learning highly relies on human expertise and efforts, such as demonstrations, design of reward functions in reinforcement learning, performance evaluation using human feedback, etc.

Language Modelling Large Language Model

Enhancing RAW-to-sRGB with Decoupled Style Structure in Fourier Domain

1 code implementation4 Jan 2024 Xuanhua He, Tao Hu, Guoli Wang, Zejin Wang, Run Wang, Qian Zhang, Keyu Yan, Ziyi Chen, Rui Li, Chenjun Xie, Jie Zhang, Man Zhou

However, current methods often ignore the difference between cell phone RAW images and DSLR camera RGB images, a difference that goes beyond the color matrix and extends to spatial structure due to resolution variations.

Image Restoration

Efficient Title Reranker for Fast and Improved Knowledge-Intense NLP

no code implementations19 Dec 2023 Ziyi Chen, Jize Jiang, Daqian Zuo, Heyi Tao, Jun Yang, Yuxiang Wei

This results in high computational costs and limits the number of retrieved text, hindering accuracy.

Retrieval

Cascade Speculative Drafting for Even Faster LLM Inference

1 code implementation18 Dec 2023 Ziyi Chen, Xiaocong Yang, Jiacheng Lin, Chenkai Sun, Kevin Chen-Chuan Chang, Jie Huang

Introduced to enhance the efficiency of large language model (LLM) inference, speculative decoding operates by having a smaller model generate a draft.

Language Modelling Large Language Model

Detecting Reddit Users with Depression Using a Hybrid Neural Network SBERT-CNN

no code implementations3 Feb 2023 Ziyi Chen, Ren Yang, Sunyang Fu, Nansu Zong, Hongfang Liu, Ming Huang

In this work, we propose a hybrid deep learning model which combines a pretrained sentence BERT (SBERT) and convolutional neural network (CNN) to detect individuals with depression with their Reddit posts.

Sentence text-classification +1

Task-oriented Self-supervised Learning for Anomaly Detection in Electroencephalography

1 code implementation4 Jul 2022 Yaojia Zheng, Zhouwu Liu, Rong Mo, Ziyi Chen, Wei-Shi Zheng, Ruixuan Wang

Compared to supervised learning with labelled disease EEG data which can train a model to analyze specific diseases but would fail to monitor previously unseen statuses, anomaly detection based on only normal EEGs can detect any potential anomaly in new EEGs.

Anomaly Detection EEG +2

Data Sampling Affects the Complexity of Online SGD over Dependent Data

no code implementations31 Mar 2022 Shaocong Ma, Ziyi Chen, Yi Zhou, Kaiyi Ji, Yingbin Liang

Moreover, we show that online SGD with mini-batch sampling can further substantially improve the sample complexity over online SGD with periodic data-subsampling over highly dependent data.

Stochastic Optimization

A Fast and Convergent Proximal Algorithm for Regularized Nonconvex and Nonsmooth Bi-level Optimization

no code implementations30 Mar 2022 Ziyi Chen, Bhavya Kailkhura, Yi Zhou

In this work, we study a proximal gradient-type algorithm that adopts the approximate implicit differentiation (AID) scheme for nonconvex bi-level optimization with possibly nonconvex and nonsmooth regularizers.

Accelerated Proximal Alternating Gradient-Descent-Ascent for Nonconvex Minimax Machine Learning

no code implementations22 Dec 2021 Ziyi Chen, Shaocong Ma, Yi Zhou

Alternating gradient-descent-ascent (AltGDA) is an optimization algorithm that has been widely used for model training in various machine learning applications, which aims to solve a nonconvex minimax optimization problem.

BIG-bench Machine Learning

A Cubic Regularization Approach for Finding Local Minimax Points in Nonconvex Minimax Optimization

no code implementations14 Oct 2021 Ziyi Chen, Zhengyang Hu, Qunwei Li, Zhe Wang, Yi Zhou

However, GDA has been proved to converge to stationary points for nonconvex minimax optimization, which are suboptimal compared with local minimax points.

Sample Efficient Stochastic Policy Extragradient Algorithm for Zero-Sum Markov Game

no code implementations ICLR 2022 Ziyi Chen, Shaocong Ma, Yi Zhou

Two-player zero-sum Markov game is a fundamental problem in reinforcement learning and game theory.

Escaping Saddle Points in Nonconvex Minimax Optimization via Cubic-Regularized Gradient Descent-Ascent

no code implementations29 Sep 2021 Ziyi Chen, Qunwei Li, Yi Zhou

Our result shows that Cubic-GDA achieves an orderwise faster convergence rate than the standard GDA for a wide spectrum of gradient dominant geometry.

How to Improve Sample Complexity of SGD over Highly Dependent Data?

no code implementations29 Sep 2021 Shaocong Ma, Ziyi Chen, Yi Zhou, Kaiyi Ji, Yingbin Liang

Specifically, with a $\phi$-mixing model that captures both exponential and polynomial decay of the data dependence over time, we show that SGD with periodic data-subsampling achieves an improved sample complexity over the standard SGD in the full spectrum of the $\phi$-mixing data dependence.

Stochastic Optimization

Sample and Communication-Efficient Decentralized Actor-Critic Algorithms with Finite-Time Analysis

no code implementations8 Sep 2021 Ziyi Chen, Yi Zhou, Rongrong Chen, Shaofeng Zou

Actor-critic (AC) algorithms have been widely adopted in decentralized multi-agent systems to learn the optimal joint control policy.

Multi-Agent Off-Policy TD Learning: Finite-Time Analysis with Near-Optimal Sample Complexity and Communication Complexity

no code implementations24 Mar 2021 Ziyi Chen, Yi Zhou, Rongrong Chen

Under Markovian sampling and linear function approximation, we proved that the finite-time sample complexity of both algorithms for achieving an $\epsilon$-accurate solution is in the order of $\mathcal{O}(\epsilon^{-1}\ln \epsilon^{-1})$, matching the near-optimal sample complexity of centralized TD(0) and TDC.

Proximal Gradient Descent-Ascent: Variable Convergence under KŁ Geometry

no code implementations ICLR 2021 Ziyi Chen, Yi Zhou, Tengyu Xu, Yingbin Liang

By leveraging this Lyapunov function and the K{\L} geometry that parameterizes the local geometries of general nonconvex functions, we formally establish the variable convergence of proximal-GDA to a critical point $x^*$, i. e., $x_t\to x^*, y_t\to y^*(x^*)$.

Neural Networked Assisted Tree Search for the Personnel Rostering Problem

no code implementations24 Oct 2020 Ziyi Chen, Patrick De Causmaecker, Yajie Dou

The personnel rostering problem is the problem of finding an optimal way to assign employees to shifts, subject to a set of hard constraints which all valid solutions must follow, and a set of soft constraints which define the relative quality of valid solutions.

valid

FedCluster: Boosting the Convergence of Federated Learning via Cluster-Cycling

no code implementations22 Sep 2020 Cheng Chen, Ziyi Chen, Yi Zhou, Bhavya Kailkhura

We develop FedCluster--a novel federated learning framework with improved optimization efficiency, and investigate its theoretical convergence properties.

Federated Learning

Momentum with Variance Reduction for Nonconvex Composition Optimization

no code implementations15 May 2020 Ziyi Chen, Yi Zhou

This paper complements the existing literature by developing various momentum schemes with SPIDER-based variance reduction for non-convex composition optimization.

An LSTM Recurrent Network for Step Counting

no code implementations10 Feb 2018 Ziyi Chen

Smartphones with sensors such as accelerometer and gyroscope can be used as pedometers and navigators.

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