Search Results for author: Bo Tang

Found 49 papers, 14 papers with code

PyEPO: A PyTorch-based End-to-End Predict-then-Optimize Library for Linear and Integer Programming

1 code implementation28 Jun 2022 Bo Tang, Elias B. Khalil

PyEPO provides a simple interface for the definition of new optimization problems, the implementation of state-of-the-art predict-then-optimize training algorithms, the use of custom neural network architectures, and the comparison of end-to-end approaches with the two-stage approach.

UHGEval: Benchmarking the Hallucination of Chinese Large Language Models via Unconstrained Generation

1 code implementation26 Nov 2023 Xun Liang, Shichao Song, Simin Niu, Zhiyu Li, Feiyu Xiong, Bo Tang, Zhaohui Wy, Dawei He, Peng Cheng, Zhonghao Wang, Haiying Deng

These techniques encompass the use of directed hallucination induction and strategies that deliberately alter authentic text to produce hallucinations.

Benchmarking Hallucination +2

Grimoire is All You Need for Enhancing Large Language Models

1 code implementation7 Jan 2024 Ding Chen, Shichao Song, Qingchen Yu, Zhiyu Li, Wenjin Wang, Feiyu Xiong, Bo Tang

In this paper, we propose a method SLEICL that involves learning from examples using strong language models and then summarizing and transferring these learned skills to weak language models for inference and application.

In-Context Learning

Controlled Text Generation for Large Language Model with Dynamic Attribute Graphs

1 code implementation17 Feb 2024 Xun Liang, Hanyu Wang, Shichao Song, Mengting Hu, Xunzhi Wang, Zhiyu Li, Feiyu Xiong, Bo Tang

In this study, we introduce a pluggable CTG framework for Large Language Models (LLMs) named Dynamic Attribute Graphs-based controlled text generation (DATG).

Attribute Language Modelling +2

Face2Exp: Combating Data Biases for Facial Expression Recognition

1 code implementation CVPR 2022 Dan Zeng, Zhiyuan Lin, Xiao Yan, YuTing Liu, Fei Wang, Bo Tang

To combat the mismatch between FR and FER data, Meta-Face2Exp uses a circuit feedback mechanism, which improves the base network with the feedback from the adaptation network.

Face Recognition Facial Expression Recognition +1

Multi-domain Recommendation with Embedding Disentangling and Domain Alignment

1 code implementation10 Aug 2023 Wentao Ning, Xiao Yan, Weiwen Liu, Reynold Cheng, Rui Zhang, Bo Tang

We propose a new MDR method named EDDA with two key components, i. e., embedding disentangling recommender and domain alignment, to tackle the two challenges respectively.

Transfer Learning

Safe Offline Reinforcement Learning with Real-Time Budget Constraints

1 code implementation1 Jun 2023 Qian Lin, Bo Tang, Zifan Wu, Chao Yu, Shangqin Mao, Qianlong Xie, Xingxing Wang, Dong Wang

Aiming at promoting the safe real-world deployment of Reinforcement Learning (RL), research on safe RL has made significant progress in recent years.

reinforcement-learning Reinforcement Learning (RL)

CaVE: A Cone-Aligned Approach for Fast Predict-then-optimize with Binary Linear Programs

1 code implementation12 Dec 2023 Bo Tang, Elias B. Khalil

The end-to-end predict-then-optimize framework, also known as decision-focused learning, has gained popularity for its ability to integrate optimization into the training procedure of machine learning models that predict the unknown cost (objective function) coefficients of optimization problems from contextual instance information.

Off-Policy Primal-Dual Safe Reinforcement Learning

1 code implementation26 Jan 2024 Zifan Wu, Bo Tang, Qian Lin, Chao Yu, Shangqin Mao, Qianlong Xie, Xingxing Wang, Dong Wang

Results on benchmark tasks show that our method not only achieves an asymptotic performance comparable to state-of-the-art on-policy methods while using much fewer samples, but also significantly reduces constraint violation during training.

reinforcement-learning Safe Reinforcement Learning

Debiasing Recommendation with Personal Popularity

1 code implementation12 Feb 2024 Wentao Ning, Reynold Cheng, Xiao Yan, Ben Kao, Nan Huo, Nur AI Hasan Haldar, Bo Tang

Many methods have been proposed to reduce GP bias but they fail to notice the fundamental problem of GP, i. e., it considers popularity from a \textit{global} perspective of \textit{all users} and uses a single set of popular items, and thus cannot capture the interests of individual users.

counterfactual Counterfactual Inference

Overcoming Long-term Catastrophic Forgetting through Adversarial Neural Pruning and Synaptic Consolidation

1 code implementation19 Dec 2019 Jian Peng, Bo Tang, Hao Jiang, Zhuo Li, Yinjie Lei, Tao Lin, Haifeng Li

It is due to two facts: first, as the model learns more tasks, the intersection of the low-error parameter subspace satisfying for these tasks becomes smaller or even does not exist; second, when the model learns a new task, the cumulative error keeps increasing as the model tries to protect the parameter configuration of previous tasks from interference.

Image Classification

A Local Density-Based Approach for Local Outlier Detection

no code implementations28 Jun 2016 Bo Tang, Haibo He

A Relative Density-based Outlier Score (RDOS) is introduced to measure the local outlierness of objects, in which the density distribution at the location of an object is estimated with a local KDE method based on extended nearest neighbors of the object.

Density Estimation Object +1

Kernel-based Generative Learning in Distortion Feature Space

no code implementations21 Jun 2016 Bo Tang, Paul M. Baggenstoss, Haibo He

The recognition diversity indicates that a hybrid combination of the proposed generative classifier and the discriminative classifier could further improve the classification performance.

General Classification

FSMJ: Feature Selection with Maximum Jensen-Shannon Divergence for Text Categorization

no code implementations20 Jun 2016 Bo Tang, Haibo He

In this paper, we present a new wrapper feature selection approach based on Jensen-Shannon (JS) divergence, termed feature selection with maximum JS-divergence (FSMJ), for text categorization.

feature selection Text Categorization

EEF: Exponentially Embedded Families with Class-Specific Features for Classification

no code implementations11 May 2016 Bo Tang, Steven Kay, Haibo He, Paul M. Baggenstoss

In this letter, we present a novel exponentially embedded families (EEF) based classification method, in which the probability density function (PDF) on raw data is estimated from the PDF on features.

Classification feature selection +2

Toward Optimal Feature Selection in Naive Bayes for Text Categorization

no code implementations9 Feb 2016 Bo Tang, Steven Kay, Haibo He

Based on the JMH-divergence, we develop two efficient feature selection methods, termed maximum discrimination ($MD$) and $MD-\chi^2$ methods, for text categorization.

feature selection General Classification +2

Find a Reasonable Ending for Stories: Does Logic Relation Help the Story Cloze Test?

no code implementations13 Dec 2018 Mingyue Shang, Zhenxin Fu, Hongzhi Yin, Bo Tang, Dongyan Zhao, Rui Yan

In this paper, we incorporate the logic information with the help of the Natural Language Inference (NLI) task to the Story Cloze Test (SCT).

Cloze Test Natural Language Inference +2

On the Recursive Teaching Dimension of VC Classes

no code implementations NeurIPS 2016 Xi Chen, Yu Cheng, Bo Tang

This is the first upper bound for $RTD(C)$ that depends only on $VCD(C)$, independent of the size of the concept class $|C|$ and its~domain size $n$.

Stragglers Are Not Disaster: A Hybrid Federated Learning Algorithm with Delayed Gradients

no code implementations12 Feb 2021 Xingyu Li, Zhe Qu, Bo Tang, Zhuo Lu

Federated learning (FL) is a new machine learning framework which trains a joint model across a large amount of decentralized computing devices.

Federated Learning

Constrained Radar Waveform Design for Range Profiling

no code implementations18 Mar 2021 Bo Tang, Jun Liu, Hai Wang, Yihua Hu

Range profiling refers to the measurement of target response along the radar slant range.

Radar waveform design

Interpretable performance analysis towards offline reinforcement learning: A dataset perspective

no code implementations12 May 2021 Chenyang Xi, Bo Tang, Jiajun Shen, Xinfu Liu, Feiyu Xiong, Xueying Li

We make it open-source for fair and comprehensive competitions between offline RL algorithms with complete datasets and checkpoints being provided.

Offline RL Q-Learning +2

Learning by Active Forgetting for Neural Networks

no code implementations21 Nov 2021 Jian Peng, Xian Sun, Min Deng, Chao Tao, Bo Tang, Wenbo Li, Guohua Wu, QingZhu, Yu Liu, Tao Lin, Haifeng Li

This paper presents a learning model by active forgetting mechanism with artificial neural networks.

Reviewing continual learning from the perspective of human-level intelligence

no code implementations23 Nov 2021 Yifan Chang, Wenbo Li, Jian Peng, Bo Tang, Yu Kang, Yinjie Lei, Yuanmiao Gui, Qing Zhu, Yu Liu, Haifeng Li

Different from previous reviews that mainly focus on the catastrophic forgetting phenomenon in CL, this paper surveys CL from a more macroscopic perspective based on the Stability Versus Plasticity mechanism.

Continual Learning

Connectivity-constrained interactive annotations for panoptic segmentation

no code implementations25 Sep 2019 Ruobing Shen, Bo Tang, Ismail Ben Ayed, Andrea Lodi, Thomas Guthier

Large-scale ground truth data sets are of crucial importance for deep learning based segmentation models, but annotating per-pixel masks is prohibitively time consuming.

Panoptic Segmentation Segmentation +1

Overcome Anterograde Forgetting with Cycled Memory Networks

no code implementations4 Dec 2021 Jian Peng, Dingqi Ye, Bo Tang, Yinjie Lei, Yu Liu, Haifeng Li

This work proposes a general framework named Cycled Memory Networks (CMN) to address the anterograde forgetting in neural networks for lifelong learning.

Transfer Learning

FedLGA: Towards System-Heterogeneity of Federated Learning via Local Gradient Approximation

no code implementations22 Dec 2021 Xingyu Li, Zhe Qu, Bo Tang, Zhuo Lu

Federated Learning (FL) is a decentralized machine learning architecture, which leverages a large number of remote devices to learn a joint model with distributed training data.

Federated Learning

LoMar: A Local Defense Against Poisoning Attack on Federated Learning

no code implementations8 Jan 2022 Xingyu Li, Zhe Qu, Shangqing Zhao, Bo Tang, Zhuo Lu, Yao Liu

Federated learning (FL) provides a high efficient decentralized machine learning framework, where the training data remains distributed at remote clients in a network.

Density Estimation Edge-computing +2

A Probabilistic Model-Based Robust Waveform Design for MIMO Radar Detection

no code implementations9 Apr 2022 Xuyang Wang, Bo Tang, Ming Zhang

This paper addresses robust waveform design for multiple-input-multiple-output (MIMO) radar detection.

Generalized Federated Learning via Sharpness Aware Minimization

no code implementations6 Jun 2022 Zhe Qu, Xingyu Li, Rui Duan, Yao Liu, Bo Tang, Zhuo Lu

Therefore, in this paper, we revisit the solutions to the distribution shift problem in FL with a focus on local learning generality.

Federated Learning Privacy Preserving

BCRLSP: An Offline Reinforcement Learning Framework for Sequential Targeted Promotion

no code implementations16 Jul 2022 Fanglin Chen, Xiao Liu, Bo Tang, Feiyu Xiong, Serim Hwang, Guomian Zhuang

During deployment, we combine the offline RL model with the LP model to generate a robust policy under the budget constraints.

Offline RL reinforcement-learning +1

Waveform Design for Mutual Interference Mitigation in Automotive Radar

no code implementations8 Aug 2022 Arindam Bose, Bo Tang, Wenjie Huang, Mojtaba Soltanalian, Jian Li

The mutual interference between similar radar systems can result in reduced radar sensitivity and increased false alarm rates.

AI Testing Framework for Next-G O-RAN Networks: Requirements, Design, and Research Opportunities

no code implementations8 Nov 2022 Bo Tang, Vijay K. Shah, Vuk Marojevic, Jeffrey H. Reed

This article presents a general automated, distributed and AI-enabled testing framework to test AI models deployed in O-RAN in terms of their decision-making performance, vulnerability and security.

Decision Making

DGI: Easy and Efficient Inference for GNNs

no code implementations28 Nov 2022 Peiqi Yin, Xiao Yan, Jinjing Zhou, Qiang Fu, Zhenkun Cai, James Cheng, Bo Tang, Minjie Wang

In this paper, we develop Deep Graph Inference (DGI) -- a system for easy and efficient GNN model inference, which automatically translates the training code of a GNN model for layer-wise execution.

Connectivity-constrained Interactive Panoptic Segmentation

no code implementations13 Dec 2022 Ruobing Shen, Bo Tang, Andrea Lodi, Ismail Ben Ayed, Thomas Guthier

We address interactive panoptic annotation, where one segment all object and stuff regions in an image.

Panoptic Segmentation Segmentation

Constant-Modulus Waveform Design for Dual-Function Radar-Communication Systems in the Presence of Clutter

no code implementations28 Feb 2023 Wenjun Wu, Bo Tang, Xuyang Wang

We investigate the constant-modulus (CM) waveform design for dual-function radar communication systems in the presence of clutter. To minimize the interference power and enhance the target acquisition performance, we use the signal-to-interference-plus-noise-ratio as the design metric. In addition, to ensure the quality of the service for each communication user, we enforce a constraint on the synthesis error of every communication signals. An iterative algorithm, which is based on cyclic optimization, Dinkinbach's transform, and alternating direction of method of multipliers, is proposed to tackle the encountered non-convex optimization problem. Simulations illustrate that the CM waveforms synthesized by the proposed algorithm allow to suppress the clutter efficiently and control the synthesis error of communication signals to a low level.

Co-Design for Spectral Coexistence between RIS-aided MIMO Radar and MIMO Communication Systems

no code implementations4 Apr 2023 Da Li, Bo Tang, Xuyang Wang, Wenjun Wu, Lei Xue

Reconfigurable intelligent surface (RIS) refers to a signal reflection surface containing a large number of low-cost passive reflecting elements.

Multi-Spectrally Constrained Low-PAPR Waveform Optimization for MIMO Radar Space-Time Adaptive Processing

no code implementations5 Apr 2023 Da Li, Bo Tang, Lei Xue

This paper focuses on the joint design of transmit waveforms and receive filters for airborne multiple-input-multiple-output (MIMO) radar systems in spectrally crowded environments.

How Simulation Helps Autonomous Driving:A Survey of Sim2real, Digital Twins, and Parallel Intelligence

no code implementations2 May 2023 Xuemin Hu, Shen Li, Tingyu Huang, Bo Tang, Rouxing Huai, Long Chen

In general, a large scale of testing in simulation environment is conducted and then the learned driving knowledge is transferred to the real world, so how to adapt driving knowledge learned in simulation to reality becomes a critical issue.

Autonomous Driving

G-Mix: A Generalized Mixup Learning Framework Towards Flat Minima

no code implementations7 Aug 2023 Xingyu Li, Bo Tang

Deep neural networks (DNNs) have demonstrated promising results in various complex tasks.

AdaER: An Adaptive Experience Replay Approach for Continual Lifelong Learning

no code implementations7 Aug 2023 Xingyu Li, Bo Tang, Haifeng Li

Continual lifelong learning is an machine learning framework inspired by human learning, where learners are trained to continuously acquire new knowledge in a sequential manner.

Class Incremental Learning Incremental Learning

HiBid: A Cross-Channel Constrained Bidding System with Budget Allocation by Hierarchical Offline Deep Reinforcement Learning

no code implementations29 Dec 2023 Hao Wang, Bo Tang, Chi Harold Liu, Shangqin Mao, Jiahong Zhou, Zipeng Dai, Yaqi Sun, Qianlong Xie, Xingxing Wang, Dong Wang

Online display advertising platforms service numerous advertisers by providing real-time bidding (RTB) for the scale of billions of ad requests every day.

Data Augmentation

RL-MPCA: A Reinforcement Learning Based Multi-Phase Computation Allocation Approach for Recommender Systems

no code implementations27 Dec 2023 Jiahong Zhou, Shunhui Mao, Guoliang Yang, Bo Tang, Qianlong Xie, Lebin Lin, Xingxing Wang, Dong Wang

The existing studies focus on dynamically allocating CRs in queue truncation scenarios (i. e., allocating the size of candidates), and formulate the CR allocation problem as an optimization problem with constraints.

Model Selection Recommendation Systems +1

NewsBench: Systematic Evaluation of LLMs for Writing Proficiency and Safety Adherence in Chinese Journalistic Editorial Applications

no code implementations29 Feb 2024 Miao Li, Ming-Bin Chen, Bo Tang, Shengbin Hou, Pengyu Wang, Haiying Deng, Zhiyu Li, Feiyu Xiong, Keming Mao, Peng Cheng, Yi Luo

This study presents NewsBench, a novel benchmark framework developed to evaluate the capability of Large Language Models (LLMs) in Chinese Journalistic Writing Proficiency (JWP) and their Safety Adherence (SA), addressing the gap between journalistic ethics and the risks associated with AI utilization.

Ethics

Long and Short-Term Constraints Driven Safe Reinforcement Learning for Autonomous Driving

no code implementations27 Mar 2024 Xuemin Hu, Pan Chen, Yijun Wen, Bo Tang, Long Chen

Reinforcement learning (RL) has been widely used in decision-making tasks, but it cannot guarantee the agent's safety in the training process due to the requirements of interaction with the environment, which seriously limits its industrial applications such as autonomous driving.

Autonomous Driving Decision Making +2

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