Search Results for author: Fangkai Yang

Found 23 papers, 4 papers with code

Introducing GenCeption for Multimodal LLM Benchmarking: You May Bypass Annotations

1 code implementation22 Feb 2024 Lele Cao, Valentin Buchner, Zineb Senane, Fangkai Yang

We propose GenCeption, a novel and annotation-free MLLM evaluation framework that merely requires unimodal data to assess inter-modality semantic coherence and inversely reflects the models' inclination to hallucinate.


COIN: Chance-Constrained Imitation Learning for Uncertainty-aware Adaptive Resource Oversubscription Policy

no code implementations13 Jan 2024 Lu Wang, Mayukh Das, Fangkai Yang, Chao Duo, Bo Qiao, Hang Dong, Si Qin, Chetan Bansal, QIngwei Lin, Saravan Rajmohan, Dongmei Zhang, Qi Zhang

We address the challenge of learning safe and robust decision policies in presence of uncertainty in context of the real scientific problem of adaptive resource oversubscription to enhance resource efficiency while ensuring safety against resource congestion risk.

Imitation Learning Management

TaskWeaver: A Code-First Agent Framework

1 code implementation29 Nov 2023 Bo Qiao, Liqun Li, Xu Zhang, Shilin He, Yu Kang, Chaoyun Zhang, Fangkai Yang, Hang Dong, Jue Zhang, Lu Wang, Minghua Ma, Pu Zhao, Si Qin, Xiaoting Qin, Chao Du, Yong Xu, QIngwei Lin, Saravan Rajmohan, Dongmei Zhang

TaskWeaver provides support for rich data structures, flexible plugin usage, and dynamic plugin selection, and leverages LLM coding capabilities for complex logic.

Natural Language Understanding

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

1 code implementation19 May 2023 Fangkai Yang, Pu Zhao, Zezhong Wang, Lu Wang, Jue Zhang, Mohit Garg, QIngwei Lin, Saravan Rajmohan, 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 due to its lack of specific domain knowledge.

Language Modelling Large Language Model +2

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

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

TDM: Trustworthy Decision-Making via Interpretability Enhancement

no code implementations13 Aug 2021 Daoming Lyu, Fangkai Yang, Hugh Kwon, Wen Dong, Levent Yilmaz, Bo Liu

Human-robot interactive decision-making is increasingly becoming ubiquitous, and trust is an influential factor in determining the reliance on autonomy.

Decision Making

A Human-Centered Data-Driven Planner-Actor-Critic Architecture via Logic Programming

no code implementations18 Sep 2019 Daoming Lyu, Fangkai Yang, Bo Liu, Steven Gustafson

Recent successes of Reinforcement Learning (RL) allow an agent to learn policies that surpass human experts but suffers from being time-hungry and data-hungry.

General Knowledge Reinforcement Learning (RL)

Introduction to the 35th International Conference on Logic Programming Special Issue

no code implementations10 Aug 2019 Esra Erdem, Andrea Formisano, German Vidal, Fangkai Yang

We are proud to introduce this special issue of Theory and Practice of Logic Programming (TPLP), dedicated to the regular papers accepted for the 35th International Conference on Logic Programming (ICLP).

A Joint Planning and Learning Framework for Human-Aided Decision-Making

no code implementations17 Jun 2019 Daoming Lyu, Fangkai Yang, Bo Liu, Steven Gustafson

Conventional reinforcement learning (RL) allows an agent to learn policies via environmental rewards only, with a long and slow learning curve, especially at the beginning stage.

Decision Making General Knowledge +1

Interpretable Automated Machine Learning in Maana(TM) Knowledge Platform

no code implementations6 May 2019 Alexander Elkholy, Fangkai Yang, Steven Gustafson

Machine learning is becoming an essential part of developing solutions for many industrial applications, but the lack of interpretability hinders wide industry adoption to rapidly build, test, deploy and validate machine learning models, in the sense that the insight of developing machine learning solutions are not structurally encoded, justified and transferred.

BIG-bench Machine Learning Meta-Learning

Integrating Task-Motion Planning with Reinforcement Learning for Robust Decision Making in Mobile Robots

no code implementations21 Nov 2018 Yuqian Jiang, Fangkai Yang, Shiqi Zhang, Peter Stone

In the outer loop, the plan is executed, and the robot learns from the execution experience via model-free RL, to further improve its task-motion plans.

Decision Making Motion Planning +2

SDRL: Interpretable and Data-efficient Deep Reinforcement Learning Leveraging Symbolic Planning

no code implementations31 Oct 2018 Daoming Lyu, Fangkai Yang, Bo Liu, Steven Gustafson

The three components cross-fertilize each other and eventually converge to an optimal symbolic plan along with the learned subtasks, bringing together the advantages of long-term planning capability with symbolic knowledge and end-to-end reinforcement learning directly from a high-dimensional sensory input.

Decision Making reinforcement-learning +2

Learning Socially Appropriate Robot Approaching Behavior Toward Groups using Deep Reinforcement Learning

1 code implementation16 Oct 2018 Yuan Gao, Fangkai Yang, Martin Frisk, Daniel Hernandez, Christopher Peters, Ginevra Castellano

Deep reinforcement learning has recently been widely applied in robotics to study tasks such as locomotion and grasping, but its application to social human-robot interaction (HRI) remains a challenge.

reinforcement-learning Reinforcement Learning (RL)

On the Semantics of Gringo

no code implementations20 Dec 2013 Amelia Harrison, Vladimir Lifschitz, Fangkai Yang

Input languages of answer set solvers are based on the mathematically simple concept of a stable model.

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