Search Results for author: QIngwei Lin

Found 19 papers, 8 papers with code

WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct

1 code implementation18 Aug 2023 Haipeng Luo, Qingfeng Sun, Can Xu, Pu Zhao, JianGuang Lou, Chongyang Tao, Xiubo Geng, QIngwei Lin, Shifeng Chen, Dongmei Zhang

Through extensive experiments on two mathematical reasoning benchmarks, namely GSM8k and MATH, we reveal the extraordinary capabilities of our model.

GSM8K Mathematical Reasoning

A Survey of Time Series Anomaly Detection Methods in the AIOps Domain

no code implementations1 Aug 2023 Zhenyu Zhong, Qiliang Fan, Jiacheng Zhang, Minghua Ma, Shenglin Zhang, Yongqian Sun, QIngwei Lin, Yuzhi Zhang, Dan Pei

Internet-based services have seen remarkable success, generating vast amounts of monitored key performance indicators (KPIs) as univariate or multivariate time series.

Anomaly Detection Time Series +1

Robust Positive-Unlabeled Learning via Noise Negative Sample Self-correction

1 code implementation1 Aug 2023 Zhangchi Zhu, Lu Wang, Pu Zhao, Chao Du, Wei zhang, Hang Dong, Bo Qiao, QIngwei Lin, Saravan Rajmohan, Dongmei Zhang

To mitigate the impact of label uncertainty and improve the robustness of learning with positive and unlabeled data, we propose a new robust PU learning method with a training strategy motivated by the nature of human learning: easy cases should be learned first.

ImDiffusion: Imputed Diffusion Models for Multivariate Time Series Anomaly Detection

1 code implementation3 Jul 2023 Yuhang Chen, Chaoyun Zhang, Minghua Ma, Yudong Liu, Ruomeng Ding, Bowen Li, Shilin He, Saravan Rajmohan, QIngwei Lin, Dongmei Zhang

To the best of our knowledge, ImDiffusion represents a pioneering approach that combines imputation-based techniques with time series anomaly detection, while introducing the novel use of diffusion models to the field.

Anomaly Detection Imputation +2

WizardCoder: Empowering Code Large Language Models with Evol-Instruct

1 code implementation14 Jun 2023 Ziyang Luo, Can Xu, Pu Zhao, Qingfeng Sun, Xiubo Geng, Wenxiang Hu, Chongyang Tao, Jing Ma, QIngwei Lin, Daxin Jiang

Moreover, our model even outperforms the largest closed LLMs, Anthropic's Claude and Google's Bard, on HumanEval and HumanEval+.

Code Generation

Introspective Tips: Large Language Model for In-Context Decision Making

no code implementations19 May 2023 Liting Chen, Lu Wang, Hang Dong, Yali Du, Jie Yan, Fangkai Yang, Shuang Li, Pu Zhao, Si Qin, Saravan Rajmohan, QIngwei Lin, Dongmei Zhang

The emergence of large language models (LLMs) has substantially influenced natural language processing, demonstrating exceptional results across various tasks.

Decision Making Language Modelling +2

Empower Large Language Model to Perform Better on Industrial Domain-Specific Question Answering

1 code implementation19 May 2023 Zezhong Wang, Fangkai Yang, Pu Zhao, Lu Wang, Jue Zhang, Mohit Garg, QIngwei Lin, Dongmei Zhang

Large Language Model (LLM) has gained popularity and achieved remarkable results in open-domain tasks, but its performance in real industrial domain-specific scenarios is average since there is no specific knowledge in it.

Language Modelling Large Language Model +2

Augmented Large Language Models with Parametric Knowledge Guiding

no code implementations8 May 2023 Ziyang Luo, Can Xu, Pu Zhao, Xiubo Geng, Chongyang Tao, Jing Ma, QIngwei Lin, Daxin Jiang

We demonstrate that our PKG framework can enhance the performance of "black-box" LLMs on a range of domain knowledge-intensive tasks that require factual (+7. 9%), tabular (+11. 9%), medical (+3. 0%), and multimodal (+8. 1%) knowledge.

Conservative State Value Estimation for Offline Reinforcement Learning

1 code implementation14 Feb 2023 Liting Chen, Jie Yan, Zhengdao Shao, Lu Wang, QIngwei Lin, Dongmei Zhang

In this paper, we propose Conservative State Value Estimation (CSVE), a new approach that learns conservative V-function via directly imposing penalty on OOD states.

D4RL reinforcement-learning +1

Learning Cooperative Oversubscription for Cloud by Chance-Constrained Multi-Agent Reinforcement Learning

no code implementations21 Nov 2022 Junjie Sheng, Lu Wang, Fangkai Yang, Bo Qiao, Hang Dong, Xiangfeng Wang, Bo Jin, Jun Wang, Si Qin, Saravan Rajmohan, QIngwei Lin, Dongmei Zhang

To address these two limitations, this paper formulates the oversubscription for cloud as a chance-constrained optimization problem and propose an effective Chance Constrained Multi-Agent Reinforcement Learning (C2MARL) method to solve this problem.

Multi-agent Reinforcement Learning reinforcement-learning +1

Distributed Evolution Strategies for Black-box Stochastic Optimization

no code implementations9 Apr 2022 Xiaoyu He, Zibin Zheng, Chuan Chen, Yuren Zhou, Chuan Luo, QIngwei Lin

This work concerns the evolutionary approaches to distributed stochastic black-box optimization, in which each worker can individually solve an approximation of the problem with nature-inspired algorithms.

Evolutionary Algorithms

A Surrogate Objective Framework for Prediction+Programming with Soft Constraints

no code implementations NeurIPS 2021 Kai Yan, Jie Yan, Chuan Luo, Liting Chen, QIngwei Lin, Dongmei Zhang

Prediction+optimization is a common real-world paradigm where we have to predict problem parameters before solving the optimization problem.

Portfolio Optimization

A Surrogate Objective Framework for Prediction+Optimization with Soft Constraints

1 code implementation22 Nov 2021 Kai Yan, Jie Yan, Chuan Luo, Liting Chen, QIngwei Lin, Dongmei Zhang

Prediction+optimization is a common real-world paradigm where we have to predict problem parameters before solving the optimization problem.

Portfolio Optimization

Automatic Loss Function Search for Predict-Then-Optimize Problems with Strong Ranking Property

no code implementations ICLR 2022 Boshi Wang, Jialin Yi, Hang Dong, Bo Qiao, Chuan Luo, QIngwei Lin

Combinatorial optimization problems with parameters to be predicted from side information are commonly seen in a variety of problems during the paradigm shift from reactive decision making to proactive decision making.

Combinatorial Optimization Decision Making

Improving the Performance of Stochastic Local Search for Maximum Vertex Weight Clique Problem Using Programming by Optimization

no code implementations27 Feb 2020 Yi Chu, Chuan Luo, Holger H. Hoos, QIngwei Lin, Haihang You

The maximum vertex weight clique problem (MVWCP) is an important generalization of the maximum clique problem (MCP) that has a wide range of real-world applications.

Label Mapping Neural Networks with Response Consolidation for Class Incremental Learning

no code implementations20 May 2019 Xu Zhang, Yang Yao, Baile Xu, Lekun Mao, Furao Shen, Jian Zhao, QIngwei Lin

In this paper, it is the first time to discuss the difficulty without support of old classes in class incremental learning, which is called as softmax suppression problem.

class-incremental learning Class Incremental Learning +2

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