Search Results for author: Chao Qian

Found 47 papers, 11 papers with code

FlowPrecision: Advancing FPGA-Based Real-Time Fluid Flow Estimation with Linear Quantization

no code implementations4 Mar 2024 Tianheng Ling, Julian Hoever, Chao Qian, Gregor Schiele

In industrial and environmental monitoring, achieving real-time and precise fluid flow measurement remains a critical challenge.


Escaping Local Optima in Global Placement

no code implementations28 Feb 2024 Ke Xue, Xi Lin, Yunqi Shi, Shixiong Kai, Siyuan Xu, Chao Qian

Placement is crucial in the physical design, as it greatly affects power, performance, and area metrics.

Reinforced In-Context Black-Box Optimization

1 code implementation27 Feb 2024 Lei Song, Chenxiao Gao, Ke Xue, Chenyang Wu, Dong Li, Jianye Hao, Zongzhang Zhang, Chao Qian

In this paper, we propose RIBBO, a method to reinforce-learn a BBO algorithm from offline data in an end-to-end fashion.

In-Context Learning Meta-Learning

A First Step Towards Runtime Analysis of Evolutionary Neural Architecture Search

no code implementations22 Jan 2024 Zeqiong Lv, Chao Qian, Yanan sun

Evolutionary neural architecture search (ENAS) employs evolutionary algorithms to find high-performing neural architectures automatically, and has achieved great success.

Binary Classification Evolutionary Algorithms +1

Quality-Diversity Algorithms Can Provably Be Helpful for Optimization

no code implementations19 Jan 2024 Chao Qian, Ke Xue, Ren-Jian Wang

In this paper, we try to shed some light on the optimization ability of QD algorithms via rigorous running time analysis.

Evolutionary Algorithms

Stochastic Bayesian Optimization with Unknown Continuous Context Distribution via Kernel Density Estimation

1 code implementation16 Dec 2023 Xiaobin Huang, Lei Song, Ke Xue, Chao Qian

Considering that the estimated PDF may have high estimation error when the true distribution is complicated, we further propose the second algorithm that optimizes the distributionally robust objective.

Bayesian Optimization Density Estimation

On-Device Soft Sensors: Real-Time Fluid Flow Estimation from Level Sensor Data

no code implementations25 Nov 2023 Tianheng Ling, Chao Qian, Gregor Schiele

Soft sensors are crucial in bridging autonomous systems' physical and digital realms, enhancing sensor fusion and perception.

Sensor Fusion

Migrant Resettlement by Evolutionary Multi-objective Optimization

no code implementations13 Oct 2023 Dan-Xuan Liu, Yu-Ran Gu, Chao Qian, Xin Mu, Ke Tang

In this paper, we propose a new framework MR-EMO based on Evolutionary Multi-objective Optimization, which reformulates Migrant Resettlement as a bi-objective optimization problem that maximizes the expected number of employed migrants and minimizes the number of dispatched migrants simultaneously, and employs a Multi-Objective Evolutionary Algorithm (MOEA) to solve the bi-objective problem.

Towards Running Time Analysis of Interactive Multi-objective Evolutionary Algorithms

no code implementations12 Oct 2023 Tianhao Lu, Chao Bian, Chao Qian

Meanwhile, we present a variant of OneMinMax, and prove that R-NSGA-II can be exponentially slower than NSGA-II.

Decision Making Evolutionary Algorithms

Diversity from Human Feedback

no code implementations10 Oct 2023 Ren-Jian Wang, Ke Xue, Yutong Wang, Peng Yang, Haobo Fu, Qiang Fu, Chao Qian

DivHF learns a behavior descriptor consistent with human preference by querying human feedback.

Combinatorial Optimization Ensemble Learning

Enhancing Energy-efficiency by Solving the Throughput Bottleneck of LSTM Cells for Embedded FPGAs

no code implementations4 Oct 2023 Chao Qian, Tianheng Ling, Gregor Schiele

To process sensor data in the Internet of Things(IoTs), embedded deep learning for 1-dimensional data is an important technique.

Neural Network Driven, Interactive Design for Nonlinear Optical Molecules Based on Group Contribution Method

no code implementations15 Sep 2023 Jinming Fan, Chao Qian, Shaodong Zhou

A Lewis-mode group contribution method (LGC) -- multi-stage Bayesian neural network (msBNN) -- evolutionary algorithm (EA) framework is reported for rational design of D-Pi-A type organic small-molecule nonlinear optical materials is presented.

Submodular Maximization under the Intersection of Matroid and Knapsack Constraints

no code implementations18 Jul 2023 Yu-Ran Gu, Chao Bian, Chao Qian

Submodular maximization arises in many applications, and has attracted a lot of research attentions from various areas such as artificial intelligence, finance and operations research.

Movie Recommendation

Robust multi-agent coordination via evolutionary generation of auxiliary adversarial attackers

1 code implementation10 May 2023 Lei Yuan, Zi-Qian Zhang, Ke Xue, Hao Yin, Feng Chen, Cong Guan, Li-He Li, Chao Qian, Yang Yu

Concretely, to avoid the ego-system overfitting to a specific attacker, we maintain a set of attackers, which is optimized to guarantee the attackers high attacking quality and behavior diversity.


Can Evolutionary Clustering Have Theoretical Guarantees?

no code implementations4 Dec 2022 Chao Qian

We prove that for discrete $k$-median clustering under individual fairness, the approximation performance of the GSEMO can be theoretically guaranteed with respect to both the objective function and the fairness constraint.

Clustering Evolutionary Algorithms +1

Multi-agent Dynamic Algorithm Configuration

1 code implementation13 Oct 2022 Ke Xue, Jiacheng Xu, Lei Yuan, Miqing Li, Chao Qian, Zongzhang Zhang, Yang Yu

MA-DAC formulates the dynamic configuration of a complex algorithm with multiple types of hyperparameters as a contextual multi-agent Markov decision process and solves it by a cooperative multi-agent RL (MARL) algorithm.

Multi-Armed Bandits Reinforcement Learning (RL)

Analyzing the Expected Hitting Time of Evolutionary Computation-based Neural Architecture Search Algorithms

no code implementations11 Oct 2022 Zeqiong Lv, Chao Qian, Gary G. Yen, Yanan sun

Evolutionary computation-based neural architecture search (ENAS) is a popular technique for automating architecture design of deep neural networks.

Neural Architecture Search

Monte Carlo Tree Search based Variable Selection for High Dimensional Bayesian Optimization

1 code implementation4 Oct 2022 Lei Song, Ke Xue, Xiaobin Huang, Chao Qian

Bayesian optimization (BO) is a class of popular methods for expensive black-box optimization, and has been widely applied to many scenarios.

Bayesian Optimization Variable Selection

Heterogeneous Multi-agent Zero-Shot Coordination by Coevolution

no code implementations9 Aug 2022 Ke Xue, Yutong Wang, Cong Guan, Lei Yuan, Haobo Fu, Qiang Fu, Chao Qian, Yang Yu

Generating agents that can achieve zero-shot coordination (ZSC) with unseen partners is a new challenge in cooperative multi-agent reinforcement learning (MARL).

Multi-agent Reinforcement Learning

Robust Subset Selection by Greedy and Evolutionary Pareto Optimization

no code implementations3 May 2022 Chao Bian, Yawen Zhou, Chao Qian

We first show that the greedy algorithm can obtain an approximation ratio of $1-e^{-\beta\gamma}$, where $\beta$ and $\gamma$ are the correlation and submodularity ratios of the objective functions, respectively; and then propose EPORSS, an evolutionary Pareto optimization algorithm that can utilize more time to find better subsets.

Neural Network Pruning by Cooperative Coevolution

no code implementations12 Apr 2022 Haopu Shang, Jia-Liang Wu, Wenjing Hong, Chao Qian

Neural network pruning is a popular model compression method which can significantly reduce the computing cost with negligible loss of accuracy.

Evolutionary Algorithms Model Compression +1

Running Time Analysis of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) using Binary or Stochastic Tournament Selection

no code implementations22 Mar 2022 Chao Bian, Chao Qian

Evolutionary algorithms (EAs) have been widely used to solve multi-objective optimization problems, and have become the most popular tool.

Evolutionary Algorithms

An Adaptive and Scalable ANN-based Model-Order-Reduction Method for Large-Scale TO Designs

no code implementations20 Mar 2022 Ren Kai Tan, Chao Qian, Dan Xu, Wenjing Ye

Most models are trained to work with the design problem similar to that used for data generation and require retraining if the design problem changes.

Cantilever Beam

Effective and Imperceptible Adversarial Textual Attack via Multi-objectivization

1 code implementation2 Nov 2021 Shengcai Liu, Ning Lu, Wenjing Hong, Chao Qian, Ke Tang

The field of adversarial textual attack has significantly grown over the last few years, where the commonly considered objective is to craft adversarial examples (AEs) that can successfully fool the target model.

Semantic Similarity Semantic Textual Similarity

Result Diversification by Multi-objective Evolutionary Algorithms with Theoretical Guarantees

1 code implementation18 Oct 2021 Chao Qian, Dan-Xuan Liu, Zhi-Hua Zhou

Experiments on the applications of web-based search, multi-label feature selection and document summarization show the superior performance of the GSEMO over the state-of-the-art algorithms (i. e., the greedy algorithm and local search) under both static and dynamic environments.

Document Summarization Evolutionary Algorithms +2

An adaptive artificial neural network-based generative design method for layout designs

no code implementations29 Jan 2021 Chao Qian, Renkai Tan, Wenjing Ye

In recent years, machine learning methods such as artificial neural networks have been used increasingly to speed up the design process.

BIG-bench Machine Learning Generative Adversarial Network +1

Accelerating gradient-based topology optimization design with dual-model neural networks

no code implementations14 Sep 2020 Chao Qian, Wenjing Ye

In this work, neural networks are used as efficient surrogate models for forward and sensitivity calculations in order to greatly accelerate the design process of topology optimization.

Self-Guided Evolution Strategies with Historical Estimated Gradients

1 code implementation IJCAI 2020 Fei-Yu Liu, Zi-Niu Li, Chao Qian

Evolution Strategies (ES) are a class of black-box optimization algorithms and have been widely applied to solve problems, e. g., in reinforcement learning (RL), where the true gradient is unavailable.

Reinforcement Learning (RL)

Multi-objective Evolutionary Algorithms are Still Good: Maximizing Monotone Approximately Submodular Minus Modular Functions

no code implementations12 Oct 2019 Chao Qian

To complement this line of research, this paper studies the problem class of maximizing monotone approximately submodular minus modular functions (i. e., $f=g-c$) with a size constraint, where $g$ is a non-negative monotone approximately submodular function and $c$ is a non-negative modular function, resulting in the objective function $f$ being non-monotone non-submodular.

Evolutionary Algorithms Experimental Design

Bayesian Optimization using Pseudo-Points

no code implementations12 Oct 2019 Chao Qian, Hang Xiong, Ke Xue

Bayesian optimization (BO) is a popular approach for expensive black-box optimization, with applications including parameter tuning, experimental design, robotics.

Bayesian Optimization Experimental Design

On the Robustness of Median Sampling in Noisy Evolutionary Optimization

no code implementations28 Jul 2019 Chao Bian, Chao Qian, Yang Yu, Ke Tang

Sampling is a popular strategy, which evaluates the objective a couple of times, and employs the mean of these evaluation results as an estimate of the objective value.

Evolutionary Algorithms

Running Time Analysis of the (1+1)-EA for Robust Linear Optimization

no code implementations17 Jun 2019 Chao Bian, Chao Qian, Ke Tang, Yang Yu

Evolutionary algorithms (EAs) have found many successful real-world applications, where the optimization problems are often subject to a wide range of uncertainties.

Evolutionary Algorithms

Maximizing Monotone DR-submodular Continuous Functions by Derivative-free Optimization

no code implementations16 Oct 2018 Yibo Zhang, Chao Qian, Ke Tang

Under a convex polytope constraint, we prove that LDGM can achieve a $(1-e^{-\beta}-\epsilon)$-approximation guarantee after $O(1/\epsilon)$ iterations, which is the same as the best previous gradient-based algorithm.

Analysis of Noisy Evolutionary Optimization When Sampling Fails

no code implementations11 Oct 2018 Chao Qian, Chao Bian, Yang Yu, Ke Tang, Xin Yao

In noisy evolutionary optimization, sampling is a common strategy to deal with noise.

ZOOpt: Toolbox for Derivative-Free Optimization

3 code implementations31 Dec 2017 Yu-Ren Liu, Yi-Qi Hu, Hong Qian, Chao Qian, Yang Yu

Recent advances in derivative-free optimization allow efficient approximation of the global-optimal solutions of sophisticated functions, such as functions with many local optima, non-differentiable and non-continuous functions.

BIG-bench Machine Learning Distributed Optimization

Subset Selection under Noise

no code implementations NeurIPS 2017 Chao Qian, Jing-Cheng Shi, Yang Yu, Ke Tang, Zhi-Hua Zhou

The problem of selecting the best $k$-element subset from a universe is involved in many applications.

Maximizing Submodular or Monotone Approximately Submodular Functions by Multi-objective Evolutionary Algorithms

no code implementations20 Nov 2017 Chao Qian, Yang Yu, Ke Tang, Xin Yao, Zhi-Hua Zhou

To provide a general theoretical explanation of the behavior of EAs, it is desirable to study their performance on general classes of combinatorial optimization problems.

Combinatorial Optimization Evolutionary Algorithms

Running Time Analysis of the (1+1)-EA for OneMax and LeadingOnes under Bit-wise Noise

no code implementations2 Nov 2017 Chao Qian, Chao Bian, Wu Jiang, Ke Tang

We analyze the running time of the (1+1)-EA solving OneMax and LeadingOnes under bit-wise noise for the first time, and derive the ranges of the noise level for polynomial and super-polynomial running time bounds.

Evolutionary Algorithms

A Lower Bound Analysis of Population-based Evolutionary Algorithms for Pseudo-Boolean Functions

no code implementations10 Jun 2016 Chao Qian, Yang Yu, Zhi-Hua Zhou

Our results imply that the increase of population size, while usually desired in practice, bears the risk of increasing the lower bound of the running time and thus should be carefully considered.

Evolutionary Algorithms

Subset Selection by Pareto Optimization

no code implementations NeurIPS 2015 Chao Qian, Yang Yu, Zhi-Hua Zhou

Selecting the optimal subset from a large set of variables is a fundamental problem in various learning tasks such as feature selection, sparse regression, dictionary learning, etc.

Dictionary Learning feature selection +1

Analyzing Evolutionary Optimization in Noisy Environments

no code implementations20 Nov 2013 Chao Qian, Yang Yu, Zhi-Hua Zhou

On a representative problem where the noise has a strong negative effect, we examine two commonly employed mechanisms in EAs dealing with noise, the re-evaluation and the threshold selection strategies.

Evolutionary Algorithms

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