Search Results for author: Mohan Kankanhalli

Found 74 papers, 42 papers with code

Identifying Worry in Twitter: Beyond Emotion Analysis

no code implementations EMNLP (NLP+CSS) 2020 Reyha Verma, Christian von der Weth, Jithin Vachery, Mohan Kankanhalli

Identifying the worries of individuals and societies plays a crucial role in providing social support and enhancing policy decision-making.

Decision Making Emotion Recognition

Finetuning Text-to-Image Diffusion Models for Fairness

no code implementations11 Nov 2023 Xudong Shen, Chao Du, Tianyu Pang, Min Lin, Yongkang Wong, Mohan Kankanhalli

The rapid adoption of text-to-image diffusion models in society underscores an urgent need to address their biases.


UNK-VQA: A Dataset and A Probe into Multi-modal Large Models' Abstention Ability

1 code implementation17 Oct 2023 Yanyang Guo, Fangkai Jiao, Zhiqi Shen, Liqiang Nie, Mohan Kankanhalli

Teaching Visual Question Answering (VQA) models to refrain from answering unanswerable questions is necessary for building a trustworthy AI system.

Question Answering Visual Question Answering

PELA: Learning Parameter-Efficient Models with Low-Rank Approximation

1 code implementation16 Oct 2023 Yangyang Guo, Guangzhi Wang, Mohan Kankanhalli

This allows for direct and efficient utilization of the low-rank model for downstream fine-tuning tasks.

Prior-Free Continual Learning with Unlabeled Data in the Wild

1 code implementation16 Oct 2023 Tao Zhuo, Zhiyong Cheng, Hehe Fan, Mohan Kankanhalli

Existing CL methods usually reduce forgetting with task priors, \ie using task identity or a subset of previously seen samples for model training.

Continual Learning Image Classification

AutoLoRa: A Parameter-Free Automated Robust Fine-Tuning Framework

no code implementations3 Oct 2023 Xilie Xu, Jingfeng Zhang, Mohan Kankanhalli

To mitigate this issue, we propose a low-rank (LoRa) branch that disentangles RFT into two distinct components: optimizing natural objectives via the LoRa branch and adversarial objectives via the FE.

Adversarial Robustness Scheduling

Distill to Delete: Unlearning in Graph Networks with Knowledge Distillation

no code implementations28 Sep 2023 Yash Sinha, Murari Mandal, Mohan Kankanhalli

Our work takes a novel approach to address these challenges in graph unlearning through knowledge distillation, as it distills to delete in GNN (D2DGN).

Knowledge Distillation

Semantic-Guided Feature Distillation for Multimodal Recommendation

1 code implementation6 Aug 2023 Fan Liu, Huilin Chen, Zhiyong Cheng, Liqiang Nie, Mohan Kankanhalli

The teacher model first extracts rich modality features from the generic modality feature by considering both the semantic information of items and the complementary information of multiple modalities.

Representation Learning

DPMix: Mixture of Depth and Point Cloud Video Experts for 4D Action Segmentation

no code implementations31 Jul 2023 Yue Zhang, Hehe Fan, Yi Yang, Mohan Kankanhalli

The proposed method, named Mixture of Depth and Point cloud video experts (DPMix), achieved the first place in the 4D Action Segmentation Track of the HOI4D Challenge 2023.

Action Segmentation Human-Object Interaction Detection +2

Sample Less, Learn More: Efficient Action Recognition via Frame Feature Restoration

1 code implementation27 Jul 2023 Harry Cheng, Yangyang Guo, Liqiang Nie, Zhiyong Cheng, Mohan Kankanhalli

Training an effective video action recognition model poses significant computational challenges, particularly under limited resource budgets.

Action Recognition Temporal Action Localization

Keyword-Aware Relative Spatio-Temporal Graph Networks for Video Question Answering

no code implementations25 Jul 2023 Yi Cheng, Hehe Fan, Dongyun Lin, Ying Sun, Mohan Kankanhalli, Joo-Hwee Lim

The main challenge in video question answering (VideoQA) is to capture and understand the complex spatial and temporal relations between objects based on given questions.

graph construction Question Answering +1

Towards Generalizable Deepfake Detection by Primary Region Regularization

no code implementations24 Jul 2023 Harry Cheng, Yangyang Guo, Tianyi Wang, Liqiang Nie, Mohan Kankanhalli

The existing deepfake detection methods have reached a bottleneck in generalizing to unseen forgeries and manipulation approaches.

DeepFake Detection Face Swapping

Mining Conditional Part Semantics with Occluded Extrapolation for Human-Object Interaction Detection

no code implementations19 Jul 2023 Guangzhi Wang, Yangyang Guo, Mohan Kankanhalli

Human-Object Interaction Detection is a crucial aspect of human-centric scene understanding, with important applications in various domains.

Human-Object Interaction Detection Scene Understanding

A Study on Differentiable Logic and LLMs for EPIC-KITCHENS-100 Unsupervised Domain Adaptation Challenge for Action Recognition 2023

no code implementations13 Jul 2023 Yi Cheng, Ziwei Xu, Fen Fang, Dongyun Lin, Hehe Fan, Yongkang Wong, Ying Sun, Mohan Kankanhalli

Our research focuses on the innovative application of a differentiable logic loss in the training to leverage the co-occurrence relations between verb and noun, as well as the pre-trained Large Language Models (LLMs) to generate the logic rules for the adaptation to unseen action labels.

Action Recognition Unsupervised Domain Adaptation

Continual Learning with Strong Experience Replay

no code implementations23 May 2023 Tao Zhuo, Zhiyong Cheng, Zan Gao, Mohan Kankanhalli

Experience Replay (ER) is a simple and effective rehearsal-based strategy, which optimizes the model with current training data and a subset of old samples stored in a memory buffer.

Continual Learning Image Classification

What Makes for Good Visual Tokenizers for Large Language Models?

1 code implementation20 May 2023 Guangzhi Wang, Yixiao Ge, Xiaohan Ding, Mohan Kankanhalli, Ying Shan

In our benchmark, which is curated to evaluate MLLMs visual semantic understanding and fine-grained perception capabilities, we discussed different visual tokenizers pre-trained with dominant methods (i. e., DeiT, CLIP, MAE, DINO), and observe that: i) Fully/weakly supervised models capture more semantics than self-supervised models, but the gap is narrowed by scaling up the pre-training dataset.

Image Captioning Object Counting +2

DSFNet: Dual Space Fusion Network for Occlusion-Robust 3D Dense Face Alignment

1 code implementation CVPR 2023 Heyuan Li, Bo wang, Yu Cheng, Mohan Kankanhalli, Robby T. Tan

Thanks to the proposed fusion module, our method is robust not only to occlusion and large pitch and roll view angles, which is the benefit of our image space approach, but also to noise and large yaw angles, which is the benefit of our model space method.

 Ranked #1 on 3D Face Reconstruction on AFLW2000-3D (Mean NME metric)

3D Face Reconstruction Face Alignment +1

Enhancing Adversarial Contrastive Learning via Adversarial Invariant Regularization

1 code implementation30 Apr 2023 Xilie Xu, Jingfeng Zhang, Feng Liu, Masashi Sugiyama, Mohan Kankanhalli

To improve transferability, the existing work introduced the standard invariant regularization (SIR) to impose style-independence property to SCL, which can exempt the impact of nuisance style factors in the standard representation.

Contrastive Learning

Efficient Adversarial Contrastive Learning via Robustness-Aware Coreset Selection

1 code implementation8 Feb 2023 Xilie Xu, Jingfeng Zhang, Feng Liu, Masashi Sugiyama, Mohan Kankanhalli

Adversarial contrastive learning (ACL) does not require expensive data annotations but outputs a robust representation that withstands adversarial attacks and also generalizes to a wide range of downstream tasks.

Contrastive Learning

Learning to Agree on Vision Attention for Visual Commonsense Reasoning

no code implementations4 Feb 2023 Zhenyang Li, Yangyang Guo, Kejie Wang, Fan Liu, Liqiang Nie, Mohan Kankanhalli

Visual Commonsense Reasoning (VCR) remains a significant yet challenging research problem in the realm of visual reasoning.

Visual Commonsense Reasoning

Text to Point Cloud Localization with Relation-Enhanced Transformer

no code implementations13 Jan 2023 Guangzhi Wang, Hehe Fan, Mohan Kankanhalli

To overcome these two challenges, we propose a unified Relation-Enhanced Transformer (RET) to improve representation discriminability for both point cloud and natural language queries.

Natural Language Queries

PointListNet: Deep Learning on 3D Point Lists

no code implementations CVPR 2023 Hehe Fan, Linchao Zhu, Yi Yang, Mohan Kankanhalli

Deep neural networks on regular 1D lists (e. g., natural languages) and irregular 3D sets (e. g., point clouds) have made tremendous achievements.

Deep Regression Unlearning

1 code implementation15 Oct 2022 Ayush K Tarun, Vikram S Chundawat, Murari Mandal, Mohan Kankanhalli

In the last few years, there have been notable developments in machine unlearning to remove the information of certain training data efficiently and effectively from ML models.

Inference Attack Membership Inference Attack +1

Privacy-Preserving Synthetic Data Generation for Recommendation Systems

1 code implementation27 Sep 2022 Fan Liu, Zhiyong Cheng, Huilin Chen, Yinwei Wei, Liqiang Nie, Mohan Kankanhalli

At the item level, a synthetic data generation module is proposed to generate a synthetic item corresponding to the selected item based on the user's preferences.

Privacy Preserving Recommendation Systems +1

Distance Matters in Human-Object Interaction Detection

1 code implementation5 Jul 2022 Guangzhi Wang, Yangyang Guo, Yongkang Wong, Mohan Kankanhalli

2) Insufficient number of distant interactions in benchmark datasets results in under-fitting on these instances.

Human-Object Interaction Detection Scene Understanding

A Unified End-to-End Retriever-Reader Framework for Knowledge-based VQA

1 code implementation30 Jun 2022 Yangyang Guo, Liqiang Nie, Yongkang Wong, Yibing Liu, Zhiyong Cheng, Mohan Kankanhalli

On the other hand, pertaining to the implicit knowledge, the multi-modal implicit knowledge for knowledge-based VQA still remains largely unexplored.

Question Answering Retrieval +1

Effective Abstract Reasoning with Dual-Contrast Network

1 code implementation ICLR 2021 Tao Zhuo, Mohan Kankanhalli

As a step towards improving the abstract reasoning capability of machines, we aim to solve Raven's Progressive Matrices (RPM) with neural networks, since solving RPM puzzles is highly correlated with human intelligence.

PSTNet: Point Spatio-Temporal Convolution on Point Cloud Sequences

1 code implementation ICLR 2021 Hehe Fan, Xin Yu, Yuhang Ding, Yi Yang, Mohan Kankanhalli

Then, a spatial convolution is employed to capture the local structure of points in the 3D space, and a temporal convolution is used to model the dynamics of the spatial regions along the time dimension.

3D Action Recognition Semantic Segmentation

Can Bad Teaching Induce Forgetting? Unlearning in Deep Networks using an Incompetent Teacher

1 code implementation17 May 2022 Vikram S Chundawat, Ayush K Tarun, Murari Mandal, Mohan Kankanhalli

It facilitates the provision for removal of certain set or class of data from an already trained ML model without requiring retraining from scratch.

Disentangled Multimodal Representation Learning for Recommendation

1 code implementation10 Mar 2022 Fan Liu, Huilin Chen, Zhiyong Cheng, AnAn Liu, Liqiang Nie, Mohan Kankanhalli

However, existing methods ignore the fact that different modalities contribute differently towards a user's preference on various factors of an item.

Recommendation Systems Representation Learning

Joint Answering and Explanation for Visual Commonsense Reasoning

1 code implementation25 Feb 2022 Zhenyang Li, Yangyang Guo, Kejie Wang, Yinwei Wei, Liqiang Nie, Mohan Kankanhalli

Given that our framework is model-agnostic, we apply it to the existing popular baselines and validate its effectiveness on the benchmark dataset.

Knowledge Distillation Question Answering +2

On Modality Bias Recognition and Reduction

1 code implementation25 Feb 2022 Yangyang Guo, Liqiang Nie, Harry Cheng, Zhiyong Cheng, Mohan Kankanhalli, Alberto del Bimbo

From the results on four datasets regarding the above three tasks, our method yields remarkable performance improvements compared with the baselines, demonstrating its superiority on reducing the modality bias problem.

Action Recognition Multi-modal Classification +3

Adversarial Attack and Defense for Non-Parametric Two-Sample Tests

1 code implementation7 Feb 2022 Xilie Xu, Jingfeng Zhang, Feng Liu, Masashi Sugiyama, Mohan Kankanhalli

Furthermore, we theoretically find that the adversary can also degrade the lower bound of a TST's test power, which enables us to iteratively minimize the test criterion in order to search for adversarial pairs.

Adversarial Attack Test +1

Learning to Predict Gradients for Semi-Supervised Continual Learning

1 code implementation23 Jan 2022 Yan Luo, Yongkang Wong, Mohan Kankanhalli, Qi Zhao

To explore these issues, we formulate a new semi-supervised continual learning method, which can be generically applied to existing continual learning models.

Continual Learning

Zero-Shot Machine Unlearning

1 code implementation14 Jan 2022 Vikram S Chundawat, Ayush K Tarun, Murari Mandal, Mohan Kankanhalli

In case of machine learning (ML) applications, this necessitates deletion of data not only from storage archives but also from ML models.

Transfer Learning

Fast Yet Effective Machine Unlearning

1 code implementation17 Nov 2021 Ayush K Tarun, Vikram S Chundawat, Murari Mandal, Mohan Kankanhalli

In the impair step, the noise matrix along with a very high learning rate is used to induce sharp unlearning in the model.

Unsupervised Abstract Reasoning for Raven's Problem Matrices

1 code implementation21 Sep 2021 Tao Zhuo, Qiang Huang, Mohan Kankanhalli

Raven's Progressive Matrices (RPM) is highly correlated with human intelligence, and it has been widely used to measure the abstract reasoning ability of humans.

Fair Representation: Guaranteeing Approximate Multiple Group Fairness for Unknown Tasks

1 code implementation1 Sep 2021 Xudong Shen, Yongkang Wong, Mohan Kankanhalli

Motivated by scenarios where data is used for diverse prediction tasks, we study whether fair representation can be used to guarantee fairness for unknown tasks and for multiple fairness notions simultaneously.

Fairness valid

Relation-aware Compositional Zero-shot Learning for Attribute-Object Pair Recognition

1 code implementation10 Aug 2021 Ziwei Xu, Guangzhi Wang, Yongkang Wong, Mohan Kankanhalli

The concept module generates semantically meaningful features for primitive concepts, whereas the visual module extracts visual features for attributes and objects from input images.

Blocking Compositional Zero-Shot Learning

Understanding the Interaction of Adversarial Training with Noisy Labels

no code implementations6 Feb 2021 Jianing Zhu, Jingfeng Zhang, Bo Han, Tongliang Liu, Gang Niu, Hongxia Yang, Mohan Kankanhalli, Masashi Sugiyama

A recent adversarial training (AT) study showed that the number of projected gradient descent (PGD) steps to successfully attack a point (i. e., find an adversarial example in its proximity) is an effective measure of the robustness of this point.

Learning Causal Representation for Training Cross-Domain Pose Estimator via Generative Interventions

no code implementations ICCV 2021 Xiheng Zhang, Yongkang Wong, Xiaofei Wu, Juwei Lu, Mohan Kankanhalli, Xiangdong Li, Weidong Geng

In this work, we take a step towards training robust models for cross-domain pose estimation task, which brings together ideas from causal representation learning and generative adversarial networks.

3D Pose Estimation Domain Generalization +2

Unsupervised Spatial-spectral Network Learning for Hyperspectral Compressive Snapshot Reconstruction

no code implementations18 Dec 2020 Yubao Sun, Ying Yang, Qingshan Liu, Mohan Kankanhalli

Hyperspectral compressive imaging takes advantage of compressive sensing theory to achieve coded aperture snapshot measurement without temporal scanning, and the entire three-dimensional spatial-spectral data is captured by a two-dimensional projection during a single integration period.

Compressive Sensing

Geometry-aware Instance-reweighted Adversarial Training

2 code implementations ICLR 2021 Jingfeng Zhang, Jianing Zhu, Gang Niu, Bo Han, Masashi Sugiyama, Mohan Kankanhalli

The belief was challenged by recent studies where we can maintain the robustness and improve the accuracy.

My Health Sensor, my Classifier: Adapting a Trained Classifier to Unlabeled End-User Data

no code implementations22 Sep 2020 Konstantinos Nikolaidis, Stein Kristiansen, Thomas Plagemann, Vera Goebel, Knut Liestøl, Mohan Kankanhalli, Gunn Marit Traaen, Britt Øverland, Harriet Akre, Lars Aakerøy, Sigurd Steinshamn

In this work, we present an approach for unsupervised domain adaptation (DA) with the constraint, that the labeled source data are not directly available, and instead only access to a classifier trained on the source data is provided.

Sleep apnea detection Unsupervised Domain Adaptation

Learning Realistic Patterns from Unrealistic Stimuli: Generalization and Data Anonymization

1 code implementation21 Sep 2020 Konstantinos Nikolaidis, Stein Kristiansen, Thomas Plagemann, Vera Goebel, Knut Liestøl, Mohan Kankanhalli, Gunn Marit Traaen, Britt Øverland, Harriet Akre, Lars Aakerøy, Sigurd Steinshamn

We use sleep monitoring data from both an open and a large closed clinical study and evaluate whether (1) end-users can create and successfully use customized classification models for sleep apnea detection, and (2) the identity of participants in the study is protected.

Sleep apnea detection

Robust Federated Recommendation System

no code implementations15 Jun 2020 Chen Chen, Jingfeng Zhang, Anthony K. H. Tung, Mohan Kankanhalli, Gang Chen

We argue that the key to Byzantine detection is monitoring of gradients of the model parameters of clients.

Recommendation Systems

Hierarchically Fair Federated Learning

no code implementations22 Apr 2020 Jingfeng Zhang, Cheng Li, Antonio Robles-Kelly, Mohan Kankanhalli

When the federated learning is adopted among competitive agents with siloed datasets, agents are self-interested and participate only if they are fairly rewarded.

Fairness Federated Learning +1

Adversarial Learning for Personalized Tag Recommendation

1 code implementation1 Apr 2020 Erik Quintanilla, Yogesh Rawat, Andrey Sakryukin, Mubarak Shah, Mohan Kankanhalli

We demonstrate the effectiveness of the proposed model on two different large-scale and publicly available datasets, YFCC100M and NUS-WIDE.

General Classification Image Classification +2

Evaluating Salient Object Detection in Natural Images with Multiple Objects having Multi-level Saliency

1 code implementation19 Mar 2020 Gökhan Yildirim, Debashis Sen, Mohan Kankanhalli, Sabine Süsstrunk

In this paper, we corroborate based on three subjective experiments on a novel image dataset that objects in natural images are inherently perceived to have varying levels of importance.

object-detection RGB Salient Object Detection +2

Attacks Which Do Not Kill Training Make Adversarial Learning Stronger

1 code implementation ICML 2020 Jingfeng Zhang, Xilie Xu, Bo Han, Gang Niu, Lizhen Cui, Masashi Sugiyama, Mohan Kankanhalli

Adversarial training based on the minimax formulation is necessary for obtaining adversarial robustness of trained models.

Adversarial Robustness

GradMix: Multi-source Transfer across Domains and Tasks

no code implementations9 Feb 2020 Junnan Li, Ziwei Xu, Yongkang Wong, Qi Zhao, Mohan Kankanhalli

Therefore, it is important to develop algorithms that can leverage off-the-shelf labeled dataset to learn useful knowledge for the target task.

Action Recognition Meta-Learning +1

Weakly-Supervised Multi-Person Action Recognition in 360$^{\circ}$ Videos

no code implementations9 Feb 2020 Junnan Li, Jianquan Liu, Yongkang Wong, Shoji Nishimura, Mohan Kankanhalli

To enable research in this direction, we introduce 360Action, the first omnidirectional video dataset for multi-person action recognition.

Action Localization Action Recognition +1

Solving Raven's Progressive Matrices with Neural Networks

1 code implementation5 Feb 2020 Tao Zhuo, Mohan Kankanhalli

Based on the design of the pseudo target, MCPT converts the unsupervised learning problem to a supervised task.


Fast Video Object Segmentation via Mask Transfer Network

no code implementations28 Aug 2019 Tao Zhuo, Zhiyong Cheng, Mohan Kankanhalli

To overcome this limitation, we propose a novel mask transfer network (MTN), which can greatly boost the processing speed of VOS and also achieve a reasonable accuracy.

Semantic Segmentation Video Object Segmentation +1

Explainable Video Action Reasoning via Prior Knowledge and State Transitions

1 code implementation28 Aug 2019 Tao Zhuo, Zhiyong Cheng, Peng Zhang, Yongkang Wong, Mohan Kankanhalli

Finally, by sequentially examining each state transition in the video graph, our method can detect and explain how those actions are executed with prior knowledge, just like the logical manner of thinking by humans.

Action Analysis

User Diverse Preference Modeling by Multimodal Attentive Metric Learning

1 code implementation21 Aug 2019 Fan Liu, Zhiyong Cheng, Changchang Sun, Yinglong Wang, Liqiang Nie, Mohan Kankanhalli

To tackle this problem, in this paper, we propose a novel Multimodal Attentive Metric Learning (MAML) method to model user diverse preferences for various items.

Metric Learning Recommendation Systems

Quantifying and Alleviating the Language Prior Problem in Visual Question Answering

1 code implementation13 May 2019 Yangyang Guo, Zhiyong Cheng, Liqiang Nie, Yibing Liu, Yinglong Wang, Mohan Kankanhalli

Benefiting from the advancement of computer vision, natural language processing and information retrieval techniques, visual question answering (VQA), which aims to answer questions about an image or a video, has received lots of attentions over the past few years.

Information Retrieval Question Answering +2

$\mathcal{G}$-softmax: Improving Intra-class Compactness and Inter-class Separability of Features

1 code implementation8 Apr 2019 Yan Luo, Yongkang Wong, Mohan Kankanhalli, Qi Zhao

In addition, analysis of the intra-class compactness and inter-class separability demonstrates the advantages of the proposed function over the softmax function, which is consistent with the performance improvement.

General Classification Multi-Label Classification

Towards Robust ResNet: A Small Step but A Giant Leap

no code implementations28 Feb 2019 Jingfeng Zhang, Bo Han, Laura Wynter, Kian Hsiang Low, Mohan Kankanhalli

Our analytical studies reveal that the step factor h in the Euler method is able to control the robustness of ResNet in both its training and generalization.

Learning to Learn from Noisy Labeled Data

1 code implementation CVPR 2019 Junnan Li, Yongkang Wong, Qi Zhao, Mohan Kankanhalli

Despite the success of deep neural networks (DNNs) in image classification tasks, the human-level performance relies on massive training data with high-quality manual annotations, which are expensive and time-consuming to collect.

Ranked #24 on Image Classification on Clothing1M (using extra training data)

Learning with noisy labels Meta-Learning

MMALFM: Explainable Recommendation by Leveraging Reviews and Images

no code implementations12 Nov 2018 Zhiyong Cheng, Xiaojun Chang, Lei Zhu, Rose C. Kanjirathinkal, Mohan Kankanhalli

Then the aspect importance is integrated into a novel aspect-aware latent factor model (ALFM), which learns user's and item's latent factors based on ratings.

Explainable Recommendation

Looking Beyond a Clever Narrative: Visual Context and Attention are Primary Drivers of Affect in Video Advertisements

no code implementations14 Aug 2018 Abhinav Shukla, Harish Katti, Mohan Kankanhalli, Ramanathan Subramanian

Contrary to the popular notion that ad affect hinges on the narrative and the clever use of linguistic and social cues, we find that actively attended objects and the coarse scene structure better encode affective information as compared to individual scene objects or conspicuous background elements.

Attention Transfer from Web Images for Video Recognition

no code implementations3 Aug 2017 Junnan Li, Yongkang Wong, Qi Zhao, Mohan Kankanhalli

However, due to the domain shift problem, the performance of Web images trained deep classifiers tend to degrade when directly deployed to videos.

Action Recognition Temporal Action Localization +1

Multi-Camera Action Dataset for Cross-Camera Action Recognition Benchmarking

no code implementations21 Jul 2016 Wenhui Li, Yongkang Wong, An-An Liu, Yang Li, Yu-Ting Su, Mohan Kankanhalli

To enable the study of this problem, there exist a vast number of action datasets, which are recorded under controlled laboratory settings, real-world surveillance environments, or crawled from the Internet.

Action Recognition Benchmarking +2

Near-Optimal Active Learning of Multi-Output Gaussian Processes

1 code implementation21 Nov 2015 Yehong Zhang, Trong Nghia Hoang, Kian Hsiang Low, Mohan Kankanhalli

This paper addresses the problem of active learning of a multi-output Gaussian process (MOGP) model representing multiple types of coexisting correlated environmental phenomena.

Active Learning Gaussian Processes

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