Search Results for author: Liu Yang

Found 84 papers, 23 papers with code

Annotating Interruption in Dyadic Human Interaction

no code implementations LREC 2022 Liu Yang, Catherine Achard, Catherine Pelachaud

Integrating the existing interruption and turn switch classification methods, we propose a new annotation schema to annotate different types of interruptions through timeliness, switch accomplishment and speech content level.

SeqDialN: Sequential Visual Dialog Network in Joint Visual-Linguistic Representation Space

1 code implementation ACL (dialdoc) 2021 Liu Yang, Fanqi Meng, Xiao Liu, Ming-Kuang Daniel Wu, Vicent Ying, James Xu

In this work, we formulate a visual dialog as an information flow in which each piece of information is encoded with the joint visual-linguistic representation of a single dialog round.

Visual Dialog

MetaDefa: Meta-learning based on Domain Enhancement and Feature Alignment for Single Domain Generalization

no code implementations27 Nov 2023 Can Sun, Hao Zheng, Zhigang Hu, Liu Yang, Meiguang Zheng, Bo Xu

The single domain generalization(SDG) based on meta-learning has emerged as an effective technique for solving the domain-shift problem.

Dual-stream contrastive predictive network with joint handcrafted feature view for SAR ship classification

no code implementations26 Nov 2023 Xianting Feng, Hao Zheng, Zhigang Hu, Liu Yang, Meiguang Zheng

Most existing synthetic aperture radar (SAR) ship classification technologies heavily rely on correctly labeled data, ignoring the discriminative features of unlabeled SAR ship images.

Double Reverse Regularization Network Based on Self-Knowledge Distillation for SAR Object Classification

no code implementations26 Nov 2023 Bo Xu, Hao Zheng, Zhigang Hu, Liu Yang, Meiguang Zheng

In current synthetic aperture radar (SAR) object classification, one of the major challenges is the severe overfitting issue due to the limited dataset (few-shot) and noisy data.

Looped Transformers are Better at Learning Learning Algorithms

no code implementations21 Nov 2023 Liu Yang, Kangwook Lee, Robert Nowak, Dimitris Papailiopoulos

Transformers have demonstrated effectiveness in \emph{in-context solving} data-fitting problems from various (latent) models, as reported by Garg et al.

SkyMath: Technical Report

1 code implementation25 Oct 2023 Liu Yang, Haihua Yang, Wenjun Cheng, Lei Lin, Chenxia Li, Yifu Chen, Lunan Liu, Jianfei Pan, Tianwen Wei, Biye Li, Liang Zhao, Lijie Wang, Bo Zhu, Guoliang Li, Xuejie Wu, Xilin Luo, Rui Hu

Large language models (LLMs) have shown great potential to solve varieties of natural language processing (NLP) tasks, including mathematical reasoning.

GSM8K Language Modelling +2

Fine-Tune Language Models as Multi-Modal Differential Equation Solvers

1 code implementation9 Aug 2023 Liu Yang, Siting Liu, Stanley J. Osher

Also, we introduce a novel approach to train a language-model-like architecture, or directly fine-tune existing language models, for in-context operator learning.

Efficient Neural Network Language Modelling +1

In-Context Operator Learning with Data Prompts for Differential Equation Problems

2 code implementations17 Apr 2023 Liu Yang, Siting Liu, Tingwei Meng, Stanley J. Osher

This paper introduces a new neural-network-based approach, namely In-Context Operator Networks (ICON), to simultaneously learn operators from the prompted data and apply it to new questions during the inference stage, without any weight update.

Operator learning

Both Efficiency and Effectiveness! A Large Scale Pre-ranking Framework in Search System

no code implementations5 Apr 2023 Qihang Zhao, Rui-Jie Zhu, Liu Yang, He Yongming, Bo Zhou, Luo Cheng

In the realm of search systems, multi-stage cascade architecture is a prevalent method, typically consisting of sequential modules such as matching, pre-ranking, and ranking.

feature selection

A Survey on Vertical Federated Learning: From a Layered Perspective

no code implementations4 Apr 2023 Liu Yang, Di Chai, Junxue Zhang, Yilun Jin, Leye Wang, Hao liu, Han Tian, Qian Xu, Kai Chen

From the hardware layer to the vertical federated system layer, researchers contribute to various aspects of VFL.

Federated Learning Privacy Preserving

GP-NAS-ensemble: a model for NAS Performance Prediction

no code implementations23 Jan 2023 Kunlong Chen, Liu Yang, Yitian Chen, Kunjin Chen, Yidan Xu, Lujun Li

It is of great significance to estimate the performance of a given model architecture without training in the application of Neural Architecture Search (NAS) as it may take a lot of time to evaluate the performance of an architecture.

Ensemble Learning Neural Architecture Search

Reliable and Interpretable Personalized Federated Learning

no code implementations CVPR 2023 Zixuan Qin, Liu Yang, Qilong Wang, Yahong Han, QinGhua Hu

When there are large differences in data distribution among clients, it is crucial for federated learning to design a reliable client selection strategy and an interpretable client communication framework to better utilize group knowledge.

Personalized Federated Learning

PathProx: A Proximal Gradient Algorithm for Weight Decay Regularized Deep Neural Networks

no code implementations6 Oct 2022 Liu Yang, Jifan Zhang, Joseph Shenouda, Dimitris Papailiopoulos, Kangwook Lee, Robert D. Nowak

Weight decay is one of the most widely used forms of regularization in deep learning, and has been shown to improve generalization and robustness.

Generative Adversarial Learning for Intelligent Trust Management in 6G Wireless Networks

no code implementations2 Aug 2022 Liu Yang, Yun Li, Simon X. Yang, Yinzhi Lu, Tan Guo, Keping Yu

Next, the integration of AI and trust management is developed to optimize the intelligence and security.


An Evolutionary Game based Secure Clustering Protocol with Fuzzy Trust Evaluation and Outlier Detection for Wireless Sensor Networks

no code implementations21 Jul 2022 Liu Yang, Yinzhi Lu, Simon X. Yang, Yuanchang Zhong, Tan Guo, Zhifang Liang

To acquire secured data delivery and address the conflict between security and energy, in this paper we present an evolutionary game based secure clustering protocol with fuzzy trust evaluation and outlier detection for WSNs.

Clustering Outlier Detection

A Secure Clustering Protocol with Fuzzy Trust Evaluation and Outlier Detection for Industrial Wireless Sensor Networks

no code implementations20 Jul 2022 Liu Yang, Yinzhi Lu, Simon X. Yang, Tan Guo, Zhifang Liang

And then a density based outlier detection mechanism is introduced to acquire an adaptive trust threshold used to isolate the malicious nodes from being cluster heads.

Clustering Outlier Detection

An Intelligent Trust Cloud Management Method for Secure Clustering in 5G enabled Internet of Medical Things

no code implementations19 Jul 2022 Liu Yang, Keping Yu, Simon X. Yang, Chinmay Chakraborty, Yinzhi Lu, Tan Guo

To assure secure and reliable communication in 5G edge computing and D2D enabled IoMT systems, this paper presents an intelligent trust cloud management method.

Edge-computing Management

An Intelligent Deterministic Scheduling Method for Ultra-Low Latency Communication in Edge Enabled Industrial Internet of Things

no code implementations17 Jul 2022 Yinzhi Lu, Liu Yang, Simon X. Yang, Qiaozhi Hua, Arun Kumar Sangaiah, Tan Guo, Keping Yu

Then a non-collision theory based deterministic scheduling (NDS) method is proposed to achieve ultra-low latency communication for the time-sensitive flows.


Secure Forward Aggregation for Vertical Federated Neural Networks

no code implementations28 Jun 2022 Shuowei Cai, Di Chai, Liu Yang, Junxue Zhang, Yilun Jin, Leye Wang, Kun Guo, Kai Chen

In this paper, we focus on SplitNN, a well-known neural network framework in VFL, and identify a trade-off between data security and model performance in SplitNN.

Federated Learning Privacy Preserving

Rare Gems: Finding Lottery Tickets at Initialization

1 code implementation24 Feb 2022 Kartik Sreenivasan, Jy-yong Sohn, Liu Yang, Matthew Grinde, Alliot Nagle, Hongyi Wang, Eric Xing, Kangwook Lee, Dimitris Papailiopoulos

Frankle & Carbin conjecture that we can avoid this by training "lottery tickets", i. e., special sparse subnetworks found at initialization, that can be trained to high accuracy.

Practical and Secure Federated Recommendation with Personalized Masks

no code implementations18 Aug 2021 Liu Yang, Junxue Zhang, Di Chai, Leye Wang, Kun Guo, Kai Chen, Qiang Yang

In this paper, we proposed federated masked matrix factorization (FedMMF) to protect the data privacy in federated recommender systems without sacrificing efficiency and effectiveness.

Federated Learning Recommendation Systems

The Threat of Offensive AI to Organizations

no code implementations30 Jun 2021 Yisroel Mirsky, Ambra Demontis, Jaidip Kotak, Ram Shankar, Deng Gelei, Liu Yang, Xiangyu Zhang, Wenke Lee, Yuval Elovici, Battista Biggio

Although offensive AI has been discussed in the past, there is a need to analyze and understand the threat in the context of organizations.

Learning Functional Priors and Posteriors from Data and Physics

no code implementations8 Jun 2021 Xuhui Meng, Liu Yang, Zhiping Mao, Jose del Aguila Ferrandis, George Em Karniadakis

In summary, the proposed method is capable of learning flexible functional priors, and can be extended to big data problems using stochastic HMC or normalizing flows since the latent space is generally characterized as low dimensional.

Meta-Learning regression

Passage Retrieval for Outside-Knowledge Visual Question Answering

1 code implementation9 May 2021 Chen Qu, Hamed Zamani, Liu Yang, W. Bruce Croft, Erik Learned-Miller

We first conduct sparse retrieval with BM25 and study expanding the question with object names and image captions.

Image Captioning Passage Retrieval +3

Natural Language Understanding with Privacy-Preserving BERT

no code implementations15 Apr 2021 Chen Qu, Weize Kong, Liu Yang, Mingyang Zhang, Michael Bendersky, Marc Najork

We investigate the privacy and utility implications of applying dx-privacy, a variant of Local Differential Privacy, to BERT fine-tuning in NLU applications.

Language Modelling Natural Language Understanding +1

Measure-conditional Discriminator with Stationary Optimum for GANs and Statistical Distance Surrogates

no code implementations17 Jan 2021 Liu Yang, Tingwei Meng, George Em Karniadakis

We propose a simple but effective modification of the discriminators, namely measure-conditional discriminators, as a plug-and-play module for different GANs.

Transfer Learning

Flow-based Generative Models for Learning Manifold to Manifold Mappings

1 code implementation18 Dec 2020 Xingjian Zhen, Rudrasis Chakraborty, Liu Yang, Vikas Singh

Partly due to this gap, there are also no modality transfer/translation models for manifold-valued data whereas numerous such methods based on generative models are available for natural images.

Learning to Expand: Reinforced Pseudo-relevance Feedback Selection for Information-seeking Conversations

no code implementations25 Nov 2020 Haojie Pan, Cen Chen, Chengyu Wang, Minghui Qiu, Liu Yang, Feng Ji, Jun Huang

More specifically, we propose a reinforced selector to extract useful PRF terms to enhance response candidates and a BERT-based response ranker to rank the PRF-enhanced responses.

FedEval: A Holistic Evaluation Framework for Federated Learning

1 code implementation19 Nov 2020 Di Chai, Leye Wang, Liu Yang, Junxue Zhang, Kai Chen, Qiang Yang

In this paper, we propose a holistic evaluation framework for FL called FedEval, and present a benchmarking study on seven state-of-the-art FL algorithms.

Benchmarking Federated Learning +1

Long Range Arena: A Benchmark for Efficient Transformers

5 code implementations8 Nov 2020 Yi Tay, Mostafa Dehghani, Samira Abnar, Yikang Shen, Dara Bahri, Philip Pham, Jinfeng Rao, Liu Yang, Sebastian Ruder, Donald Metzler

In the recent months, a wide spectrum of efficient, fast Transformers have been proposed to tackle this problem, more often than not claiming superior or comparable model quality to vanilla Transformer models.

Benchmarking Long-range modeling

Solving Inverse Stochastic Problems from Discrete Particle Observations Using the Fokker-Planck Equation and Physics-informed Neural Networks

no code implementations24 Aug 2020 Xiaoli Chen, Liu Yang, Jinqiao Duan, George Em. Karniadakis

The Fokker-Planck (FP) equation governing the evolution of the probability density function (PDF) is applicable to many disciplines but it requires specification of the coefficients for each case, which can be functions of space-time and not just constants, hence requiring the development of a data-driven modeling approach.

Generative Ensemble Regression: Learning Particle Dynamics from Observations of Ensembles with Physics-Informed Deep Generative Models

no code implementations5 Aug 2020 Liu Yang, Constantinos Daskalakis, George Em. Karniadakis

Particle coordinates at a single time instant, possibly noisy or truncated, are recorded in each snapshot but are unpaired across the snapshots.


SeqDialN: Sequential Visual Dialog Networks in Joint Visual-Linguistic Representation Space

1 code implementation2 Aug 2020 Liu Yang

IP based SeqDialN is our baseline with a simple 2-layer LSTM design that achieves decent performance.

Visual Dialog

DS-Sync: Addressing Network Bottlenecks with Divide-and-Shuffle Synchronization for Distributed DNN Training

no code implementations7 Jul 2020 Weiyan Wang, Cengguang Zhang, Liu Yang, Kai Chen, Kun Tan

However, due to the global synchronization nature, its performance can be significantly influenced by network bottlenecks caused by either static topology heterogeneity or dynamic bandwidth contentions.

BIG-bench Machine Learning

Match$^2$: A Matching over Matching Model for Similar Question Identification

no code implementations21 Jun 2020 Zizhen Wang, Yixing Fan, Jiafeng Guo, Liu Yang, Ruqing Zhang, Yanyan Lan, Xue-Qi Cheng, Hui Jiang, Xiaozhao Wang

However, it has long been a challenge to properly measure the similarity between two questions due to the inherent variation of natural language, i. e., there could be different ways to ask a same question or different questions sharing similar expressions.

Community Question Answering

Open-Retrieval Conversational Question Answering

1 code implementation22 May 2020 Chen Qu, Liu Yang, Cen Chen, Minghui Qiu, W. Bruce Croft, Mohit Iyyer

We build an end-to-end system for ORConvQA, featuring a retriever, a reranker, and a reader that are all based on Transformers.

Conversational Question Answering Conversational Search +2

Beyond 512 Tokens: Siamese Multi-depth Transformer-based Hierarchical Encoder for Long-Form Document Matching

1 code implementation26 Apr 2020 Liu Yang, Mingyang Zhang, Cheng Li, Michael Bendersky, Marc Najork

In order to better capture sentence level semantic relations within a document, we pre-train the model with a novel masked sentence block language modeling task in addition to the masked word language modeling task used by BERT.

Clustering Information Retrieval +8

B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Data

no code implementations13 Mar 2020 Liu Yang, Xuhui Meng, George Em. Karniadakis

In this Bayesian framework, the Bayesian neural network (BNN) combined with a PINN for PDEs serves as the prior while the Hamiltonian Monte Carlo (HMC) or the variational inference (VI) could serve as an estimator of the posterior.

Variational Inference

Reinforcement Learning for Active Flow Control in Experiments

1 code implementation6 Mar 2020 Dixia Fan, Liu Yang, Michael S. Triantafyllou, George Em. Karniadakis

We demonstrate experimentally the feasibility of applying reinforcement learning (RL) in flow control problems by automatically discovering active control strategies without any prior knowledge of the flow physics.

Fluid Dynamics Robotics

Sparse Sinkhorn Attention

1 code implementation ICML 2020 Yi Tay, Dara Bahri, Liu Yang, Donald Metzler, Da-Cheng Juan

We propose Sparse Sinkhorn Attention, a new efficient and sparse method for learning to attend.

Document Classification Image Generation +2

APTER: Aggregated Prognosis Through Exponential Reweighting

no code implementations20 Feb 2020 Kristiaan Pelckmans, Liu Yang

This paper considers the task of learning how to make a prognosis of a patient based on his/her micro-array expression levels.

IART: Intent-aware Response Ranking with Transformers in Information-seeking Conversation Systems

1 code implementation3 Feb 2020 Liu Yang, Minghui Qiu, Chen Qu, Cen Chen, Jiafeng Guo, Yongfeng Zhang, W. Bruce Croft, Haiqing Chen

We also perform case studies and analysis of learned user intent and its impact on response ranking in information-seeking conversations to provide interpretation of results.

Representation Learning

A GMM based algorithm to generate point-cloud and its application to neuroimaging

no code implementations5 Nov 2019 Liu Yang, Rudrasis Chakraborty

Experimental validation has been performed to show that the proposed scheme can generate new 3D structures using interpolation techniques, i. e., given two 3D structures represented as point-clouds, we can generate point-clouds in between.

An "augmentation-free" rotation invariant classification scheme on point-cloud and its application to neuroimaging

no code implementations5 Nov 2019 Liu Yang, Rudrasis Chakraborty

Though in the medical imaging community, 3D point-cloud processing is not a "go-to" choice, it is a canonical way to preserve rotation invariance.

Data Augmentation General Classification

POIRot: A rotation invariant omni-directional pointnet

no code implementations29 Oct 2019 Liu Yang, Rudrasis Chakraborty, Stella X. Yu

Our proposed model is rotationally invariant and can preserve geometric shape of a 3D point-cloud.

Data Augmentation Point Cloud Segmentation

Potential Flow Generator with $L_2$ Optimal Transport Regularity for Generative Models

no code implementations29 Aug 2019 Liu Yang, George Em. Karniadakis

We propose a potential flow generator with $L_2$ optimal transport regularity, which can be easily integrated into a wide range of generative models including different versions of GANs and flow-based models.


Attentive History Selection for Conversational Question Answering

2 code implementations26 Aug 2019 Chen Qu, Liu Yang, Minghui Qiu, Yongfeng Zhang, Cen Chen, W. Bruce Croft, Mohit Iyyer

First, we propose a positional history answer embedding method to encode conversation history with position information using BERT in a natural way.

Conversational Question Answering Conversational Search +1

BERT with History Answer Embedding for Conversational Question Answering

1 code implementation14 May 2019 Chen Qu, Liu Yang, Minghui Qiu, W. Bruce Croft, Yongfeng Zhang, Mohit Iyyer

One of the major challenges to multi-turn conversational search is to model the conversation history to answer the current question.

Conversational Question Answering Conversational Search +2

A Hybrid Retrieval-Generation Neural Conversation Model

1 code implementation19 Apr 2019 Liu Yang, Junjie Hu, Minghui Qiu, Chen Qu, Jianfeng Gao, W. Bruce Croft, Xiaodong Liu, Yelong Shen, Jingjing Liu

In this paper, we propose a hybrid neural conversation model that combines the merits of both response retrieval and generation methods.

Retrieval Text Generation +1

Contextual Aware Joint Probability Model Towards Question Answering System

no code implementations17 Apr 2019 Liu Yang, Lijing Song

In this paper, we address the question answering challenge with the SQuAD 2. 0 dataset.

Question Answering Test +1

User Intent Prediction in Information-seeking Conversations

1 code implementation11 Jan 2019 Chen Qu, Liu Yang, Bruce Croft, Yongfeng Zhang, Johanne R. Trippas, Minghui Qiu

Due to the limited communication bandwidth in conversational search, it is important for conversational assistants to accurately detect and predict user intent in information-seeking conversations.

Conversational Search Feature Engineering +1

Learning to Selectively Transfer: Reinforced Transfer Learning for Deep Text Matching

no code implementations30 Dec 2018 Chen Qu, Feng Ji, Minghui Qiu, Liu Yang, Zhiyu Min, Haiqing Chen, Jun Huang, W. Bruce Croft

Specifically, the data selector "acts" on the source domain data to find a subset for optimization of the TL model, and the performance of the TL model can provide "rewards" in turn to update the selector.

Information Retrieval Natural Language Inference +5

Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations

no code implementations5 Nov 2018 Liu Yang, Dongkun Zhang, George Em. Karniadakis

We developed a new class of physics-informed generative adversarial networks (PI-GANs) to solve in a unified manner forward, inverse and mixed stochastic problems based on a limited number of scattered measurements.

Gaussian Processes

Response Ranking with Deep Matching Networks and External Knowledge in Information-seeking Conversation Systems

1 code implementation1 May 2018 Liu Yang, Minghui Qiu, Chen Qu, Jiafeng Guo, Yongfeng Zhang, W. Bruce Croft, Jun Huang, Haiqing Chen

Our models and research findings provide new insights on how to utilize external knowledge with deep neural models for response selection and have implications for the design of the next generation of information-seeking conversation systems.

Knowledge Distillation Retrieval +1

Analyzing and Characterizing User Intent in Information-seeking Conversations

no code implementations23 Apr 2018 Chen Qu, Liu Yang, W. Bruce Croft, Johanne R. Trippas, Yongfeng Zhang, Minghui Qiu

Understanding and characterizing how people interact in information-seeking conversations is crucial in developing conversational search systems.

Conversational Search Question Answering

aNMM: Ranking Short Answer Texts with Attention-Based Neural Matching Model

1 code implementation5 Jan 2018 Liu Yang, Qingyao Ai, Jiafeng Guo, W. Bruce Croft

As an alternative to question answering methods based on feature engineering, deep learning approaches such as convolutional neural networks (CNNs) and Long Short-Term Memory Models (LSTMs) have recently been proposed for semantic matching of questions and answers.

Feature Engineering Question Answering

Testing Piecewise Functions

no code implementations23 Jun 2017 Steve Hanneke, Liu Yang

We also identify the optimal dependence on the number of pieces in the query complexity of passive testing in the special case of piecewise constant functions.

Learning with Changing Features

no code implementations29 Apr 2017 Amit Dhurandhar, Steve Hanneke, Liu Yang

In particular, we propose an approach to provably determine the time instant from which the new/changed features start becoming relevant with respect to an output variable in an agnostic (supervised) learning setting.

Change Point Detection

Adaptive and Scalable Android Malware Detection through Online Learning

no code implementations23 Jun 2016 Annamalai Narayanan, Liu Yang, Lihui Chen, Liu Jinliang

In order to perform scalable detection and to adapt to the drift and evolution in malware population, an online passive-aggressive classifier is used.

Android Malware Detection BIG-bench Machine Learning +1

Contextual Weisfeiler-Lehman Graph Kernel For Malware Detection

no code implementations21 Jun 2016 Annamalai Narayanan, Guozhu Meng, Liu Yang, Jinliang Liu, Lihui Chen

To address this, we develop the Contextual Weisfeiler-Lehman kernel (CWLK) which is capable of capturing both these types of information.

Malware Detection

Dynamic matrix factorization with social influence

no code implementations21 Apr 2016 Aleksandr Y. Aravkin, Kush R. Varshney, Liu Yang

Matrix factorization is a key component of collaborative filtering-based recommendation systems because it allows us to complete sparse user-by-item ratings matrices under a low-rank assumption that encodes the belief that similar users give similar ratings and that similar items garner similar ratings.

Collaborative Filtering Recommendation Systems

Statistical Learning under Nonstationary Mixing Processes

no code implementations26 Dec 2015 Steve Hanneke, Liu Yang

Under these conditions, we propose a learning method, and establish that for bounded VC subgraph classes, the cumulative excess risk grows sublinearly in the number of predictions, at a quantified rate.

General Classification

Bounds on the Minimax Rate for Estimating a Prior over a VC Class from Independent Learning Tasks

no code implementations20 May 2015 Liu Yang, Steve Hanneke, Jaime Carbonell

We study the optimal rates of convergence for estimating a prior distribution over a VC class from a sequence of independent data sets respectively labeled by independent target functions sampled from the prior.

Transfer Learning

Learning with a Drifting Target Concept

no code implementations20 May 2015 Steve Hanneke, Varun Kanade, Liu Yang

Some of the results also describe an active learning variant of this setting, and provide bounds on the number of queries for the labels of points in the sequence sufficient to obtain the stated bounds on the error rates.

Active Learning

Minimax Analysis of Active Learning

no code implementations3 Oct 2014 Steve Hanneke, Liu Yang

This work establishes distribution-free upper and lower bounds on the minimax label complexity of active learning with general hypothesis classes, under various noise models.

Active Learning

Buy-in-Bulk Active Learning

no code implementations NeurIPS 2013 Liu Yang, Jaime Carbonell

We additionally study the total cost sufficient for learning, for an abstract notion of the cost of requesting the labels of a given number of examples at once.

Active Learning

Surrogate Losses in Passive and Active Learning

no code implementations16 Jul 2012 Steve Hanneke, Liu Yang

Specifically, it presents an active learning algorithm based on an arbitrary classification-calibrated surrogate loss function, along with an analysis of the number of label requests sufficient for the classifier returned by the algorithm to achieve a given risk under the 0-1 loss.

Active Learning

Active Learning with a Drifting Distribution

no code implementations NeurIPS 2011 Liu Yang

We study the problem of active learning in a stream-based setting, allowing the distribution of the examples to change over time.

Active Learning

Semi-supervised Learning with Weakly-Related Unlabeled Data : Towards Better Text Categorization

no code implementations NeurIPS 2008 Liu Yang, Rong Jin, Rahul Sukthankar

For empirical evaluation, we present a direct comparison with a number of state-of-the-art methods for inductive semi-supervised learning and text categorization; and we show that SSLW results in a significant improvement in categorization accuracy, equipped with a small training set and an unlabeled resource that is weakly related to the test beds."

General Classification Test +1

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