Search Results for author: Mark Coates

Found 41 papers, 16 papers with code

Multi-resolution Time-Series Transformer for Long-term Forecasting

2 code implementations7 Nov 2023 Yitian Zhang, Liheng Ma, Soumyasundar Pal, Yingxue Zhang, Mark Coates

Recent architectures learn complex temporal patterns by segmenting a time-series into patches and using the patches as tokens.

Time Series Time Series Forecasting

Interacting Diffusion Processes for Event Sequence Forecasting

no code implementations26 Oct 2023 Mai Zeng, Florence Regol, Mark Coates

Our model is composed of two diffusion processes, one for the time intervals and one for the event types.

Denoising Point Processes

Jointly-Learned Exit and Inference for a Dynamic Neural Network : JEI-DNN

no code implementations13 Oct 2023 Florence Regol, Joud Chataoui, Mark Coates

Training an EDNN architecture is challenging as it consists of two intertwined components: the gating mechanism (GM) that controls early-exiting decisions and the intermediate inference modules (IMs) that perform inference from intermediate representations.

Substituting Data Annotation with Balanced Updates and Collective Loss in Multi-label Text Classification

no code implementations24 Sep 2023 Muberra Ozmen, Joseph Cotnareanu, Mark Coates

Multi-label text classification (MLTC) is the task of assigning multiple labels to a given text, and has a wide range of application domains.

Language Modelling Multi Label Text Classification +3

Graph Inductive Biases in Transformers without Message Passing

1 code implementation27 May 2023 Liheng Ma, Chen Lin, Derek Lim, Adriana Romero-Soriano, Puneet K. Dokania, Mark Coates, Philip Torr, Ser-Nam Lim

Graph inductive biases are crucial for Graph Transformers, and previous works incorporate them using message-passing modules and/or positional encodings.

Graph Classification Graph Regression +2

Structure Aware Incremental Learning with Personalized Imitation Weights for Recommender Systems

no code implementations2 May 2023 Yuening Wang, Yingxue Zhang, Antonios Valkanas, Ruiming Tang, Chen Ma, Jianye Hao, Mark Coates

In contrast, for users who have static preferences, model performance can benefit greatly from preserving as much of the user's long-term preferences as possible.

Incremental Learning Knowledge Distillation +1

Diffusing Gaussian Mixtures for Generating Categorical Data

1 code implementation8 Mar 2023 Florence Regol, Mark Coates

Learning a categorical distribution comes with its own set of challenges.

Spectral Augmentations for Graph Contrastive Learning

no code implementations6 Feb 2023 Amur Ghose, Yingxue Zhang, Jianye Hao, Mark Coates

Contrastive learning has emerged as a premier method for learning representations with or without supervision.

Contrastive Learning Graph Embedding +1

Intent-aware Multi-source Contrastive Alignment for Tag-enhanced Recommendation

no code implementations11 Nov 2022 Haolun Wu, Yingxue Zhang, Chen Ma, Wei Guo, Ruiming Tang, Xue Liu, Mark Coates

To offer accurate and diverse recommendation services, recent methods use auxiliary information to foster the learning process of user and item representations.

Decision Making Recommendation Systems +2

DyG2Vec: Efficient Representation Learning for Dynamic Graphs

2 code implementations30 Oct 2022 Mohammad Ali Alomrani, Mahdi Biparva, Yingxue Zhang, Mark Coates

Temporal graph neural networks have shown promising results in learning inductive representations by automatically extracting temporal patterns.

Dynamic Link Prediction Dynamic Node Classification +2

Evaluation of Categorical Generative Models -- Bridging the Gap Between Real and Synthetic Data

no code implementations28 Oct 2022 Florence Regol, Anja Kroon, Mark Coates

We validate our evaluation procedure with synthetic experiments on both synthetic generative models and current state-of-the-art categorical generative models.

Contrastive Learning for Time Series on Dynamic Graphs

no code implementations21 Sep 2022 Yitian Zhang, Florence Regol, Antonios Valkanas, Mark Coates

We propose a framework called GraphTNC for unsupervised learning of joint representations of the graph and the time-series.

Activity Recognition Anomaly Detection +3

Adapting Triplet Importance of Implicit Feedback for Personalized Recommendation

1 code implementation2 Aug 2022 Haolun Wu, Chen Ma, Yingxue Zhang, Xue Liu, Ruiming Tang, Mark Coates

In order to effectively utilize such information, most research adopts the pairwise ranking method on constructed training triplets (user, positive item, negative item) and aims to distinguish between positive items and negative items for each user.

Bilevel Optimization

Bag Graph: Multiple Instance Learning using Bayesian Graph Neural Networks

1 code implementation22 Feb 2022 Soumyasundar Pal, Antonios Valkanas, Florence Regol, Mark Coates

Since a meaningful graph representing dependencies between bags is rarely available, we propose to use a Bayesian GNN framework that can generate a likely graph structure for scenarios where there is uncertainty in the graph or when no graph is available.

Multiple Instance Learning Weakly-supervised Learning

Spectral image clustering on dual-energy CT scans using functional regression mixtures

1 code implementation31 Jan 2022 Segolene Brivet, Faicel Chamroukhi, Mark Coates, Reza Forghani, Peter Savadjiev

In this paper, we develop novel functional data analysis (FDA) techniques and adapt them to the analysis of DECT decay curves.

Clustering Image Clustering +1

Generalizable Cross-Graph Embedding for GNN-based Congestion Prediction

no code implementations10 Nov 2021 Amur Ghose, Vincent Zhang, Yingxue Zhang, Dong Li, Wulong Liu, Mark Coates

To address this limitation, we propose a framework that can directly learn embeddings for the given netlist to enhance the quality of our node features.

Graph Embedding

RNN with Particle Flow for Probabilistic Spatio-temporal Forecasting

1 code implementation10 Jun 2021 Soumyasundar Pal, Liheng Ma, Yingxue Zhang, Mark Coates

Spatio-temporal forecasting has numerous applications in analyzing wireless, traffic, and financial networks.

Bayesian Inference Spatio-Temporal Forecasting +2

TIE: A Framework for Embedding-based Incremental Temporal Knowledge Graph Completion

1 code implementation17 Apr 2021 Jiapeng Wu, Yishi Xu, Yingxue Zhang, Chen Ma, Mark Coates, Jackie Chi Kit Cheung

The model has to adapt to changes in the TKG for efficient training and inference while preserving its performance on historical knowledge.

Decision Making Information Retrieval +4

Probabilistic Metric Learning with Adaptive Margin for Top-K Recommendation

no code implementations13 Jan 2021 Chen Ma, Liheng Ma, Yingxue Zhang, Ruiming Tang, Xue Liu, Mark Coates

Personalized recommender systems are playing an increasingly important role as more content and services become available and users struggle to identify what might interest them.

Metric Learning Recommendation Systems

Knowledge-Enhanced Top-K Recommendation in Poincaré Ball

no code implementations13 Jan 2021 Chen Ma, Liheng Ma, Yingxue Zhang, Haolun Wu, Xue Liu, Mark Coates

To effectively make use of the knowledge graph, we propose a recommendation model in the hyperbolic space, which facilitates the learning of the hierarchical structure of knowledge graphs.

Knowledge Graphs Recommendation Systems

On Batch-size Selection for Stochastic Training for Graph Neural Networks

no code implementations1 Jan 2021 Yaochen Hu, Amit Levi, Ishaan Kumar, Yingxue Zhang, Mark Coates

In recent years deep learning has become an important framework for supervised learning.

GraphSeam: Supervised Graph Learning Framework for Semantic UV Mapping

no code implementations27 Nov 2020 Fatemeh Teimury, Bruno Roy, Juan Sebastián Casallas, David MacDonald, Mark Coates

In this work, we use the power of supervised GNNs for the first time to propose a fully automated UV mapping framework that enables users to replicate their desired seam styles while reducing distortion and seam length.

Graph Learning Graphics

GraphSAIL: Graph Structure Aware Incremental Learning for Recommender Systems

1 code implementation25 Aug 2020 Yishi Xu, Yingxue Zhang, Wei Guo, Huifeng Guo, Ruiming Tang, Mark Coates

We develop a Graph Structure Aware Incremental Learning framework, GraphSAIL, to address the commonly experienced catastrophic forgetting problem that occurs when training a model in an incremental fashion.

Incremental Learning Recommendation Systems

A Framework for Recommending Accurate and Diverse ItemsUsing Bayesian Graph Convolutional Neural Networks

1 code implementation Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining 2020 Jianing Sun, Wei Guo, Dengcheng Zhang, Yingxue Zhang, Florence Regol, Yaochen Hu, Huifeng Guo, Ruiming Tang, Han Yuan, Xiuqiang He, Mark Coates

Because of the multitude of relationships existing in recommender systems, Graph Neural Networks (GNNs) based approaches have been proposed to better characterize the various relationships between a user and items while modeling a user's preferences.

Recommendation Systems

Node Copying for Protection Against Graph Neural Network Topology Attacks

no code implementations9 Jul 2020 Florence Regol, Soumyasundar Pal, Mark Coates

With the increased interest in graph based machine learning techniques, there have been investigations which suggest that these models are also vulnerable to attacks.

Active Learning on Attributed Graphs via Graph Cognizant Logistic Regression and Preemptive Query Generation

no code implementations ICML 2020 Florence Regol, Soumyasundar Pal, Yingxue Zhang, Mark Coates

Node classification in attributed graphs is an important task in multiple practical settings, but it can often be difficult or expensive to obtain labels.

Active Learning Classification +3

Non-Parametric Graph Learning for Bayesian Graph Neural Networks

no code implementations23 Jun 2020 Soumyasundar Pal, Saber Malekmohammadi, Florence Regol, Yingxue Zhang, Yishi Xu, Mark Coates

A Bayesian framework which targets posterior inference of the graph by considering it as a random quantity can be beneficial.

Graph Learning Link Prediction +1

Multi-Graph Convolution Collaborative Filtering

no code implementations1 Jan 2020 Jianing Sun, Yingxue Zhang, Chen Ma, Mark Coates, Huifeng Guo, Ruiming Tang, Xiuqiang He

In this work, we develop a graph convolution-based recommendation framework, named Multi-Graph Convolution Collaborative Filtering (Multi-GCCF), which explicitly incorporates multiple graphs in the embedding learning process.

Collaborative Filtering

Memory Augmented Graph Neural Networks for Sequential Recommendation

1 code implementation26 Dec 2019 Chen Ma, Liheng Ma, Yingxue Zhang, Jianing Sun, Xue Liu, Mark Coates

In addition to the modeling of user interests, we employ a bilinear function to capture the co-occurrence patterns of related items.

Sequential Recommendation

Bayesian Graph Convolutional Neural Networks using Node Copying

no code implementations8 Nov 2019 Soumyasundar Pal, Florence Regol, Mark Coates

Graph convolutional neural networks (GCNN) have numerous applications in different graph based learning tasks.

Node Classification

Bayesian Graph Convolutional Neural Networks Using Non-Parametric Graph Learning

no code implementations26 Oct 2019 Soumyasundar Pal, Florence Regol, Mark Coates

Graph convolutional neural networks (GCNN) have been successfully applied to many different graph based learning tasks including node and graph classification, matrix completion, and learning of node embeddings.

Bayesian Inference General Classification +4

Learning Gaussian Graphical Models with Ordered Weighted L1 Regularization

1 code implementation6 Jun 2019 Cody Mazza-Anthony, Bogdan Mazoure, Mark Coates

We propose two novel estimators based on the Ordered Weighted $\ell_1$ (OWL) norm: 1) The Graphical OWL (GOWL) is a penalized likelihood method that applies the OWL norm to the lower triangle components of the precision matrix.

Computational Efficiency

Bayesian graph convolutional neural networks for semi-supervised classification

1 code implementation27 Nov 2018 Yingxue Zhang, Soumyasundar Pal, Mark Coates, Deniz Üstebay

Graph convolutional neural networks (GCNNs) have been used to address node and graph classification and matrix completion.

General Classification Graph Classification +1

Cost Adaptation for Robust Decentralized Swarm Behaviour

1 code implementation21 Sep 2017 Peter Henderson, Matthew Vertescher, David Meger, Mark Coates

To allay this problem, we use a meta-learning process -- cost adaptation -- which generates the optimization objective for D-RHC to solve based on a set of human-generated priors (cost and constraint functions) and an auxiliary heuristic.

Meta-Learning

Semi-parametric Order-based Generalized Multivariate Regression

no code implementations19 Feb 2016 Milad Kharratzadeh, Mark Coates

In this paper, we consider a generalized multivariate regression problem where the responses are monotonic functions of linear transformations of predictors.

regression

Sparse Multivariate Factor Regression

no code implementations25 Feb 2015 Milad Kharratzadeh, Mark Coates

The first matrix linearly transforms the predictors to a set of latent factors, and the second one regresses the responses on these factors.

Dimensionality Reduction regression

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