18 citations · 63 across the 24 of their papers we have counts for
24 papers
SKOLR: Structured Koopman Operator Linear RNN for Time-Series Forecasting
Yitian Zhang, Liheng Ma, Antonios Valkanas +2
Koopman operator theory provides a framework for nonlinear dynamical system analysis and time-series forecasting by mapping dynamics to a space of real-valued measurement functions…
MODL: Multilearner Online Deep Learning
Antonios Valkanas, Boris N. Oreshkin, Mark Coates
Online deep learning tackles the challenge of learning from data streams by balancing two competing goals: fast learning and deep learning. However, existing research primarily emp…
CKGConv: General Graph Convolution with Continuous Kernels
Liheng Ma, Soumyasundar Pal, Yitian Zhang +3
The existing definitions of graph convolution, either from spatial or spectral perspectives, are inflexible and not unified. Defining a general convolution operator in the graph do…
Personalized Negative Reservoir for Incremental Learning in Recommender Systems
Antonios Valkanas, Yuening Wang, Yingxue Zhang +1
Recommender systems have become an integral part of online platforms. Every day the volume of training data is expanding and the number of user interactions is constantly increasin…
Population Monte Carlo with Normalizing Flow
Soumyasundar Pal, Antonios Valkanas, Mark Coates
Adaptive importance sampling (AIS) methods provide a useful alternative to Markov Chain Monte Carlo (MCMC) algorithms for performing inference of intractable distributions. Populat…
Multi-resolution Time-Series Transformer for Long-term Forecasting
Yitian Zhang, Liheng Ma, Soumyasundar Pal +2
The performance of transformers for time-series forecasting has improved significantly. Recent architectures learn complex temporal patterns by segmenting a time-series into patche…
Interacting Diffusion Processes for Event Sequence Forecasting
Mai Zeng, Florence Regol, Mark Coates
Neural Temporal Point Processes (TPPs) have emerged as the primary framework for predicting sequences of events that occur at irregular time intervals, but their sequential nature…
Jointly-Learned Exit and Inference for a Dynamic Neural Network : JEI-DNN
Florence Regol, Joud Chataoui, Mark Coates
Large pretrained models, coupled with fine-tuning, are slowly becoming established as the dominant architecture in machine learning. Even though these models offer impressive perfo…
Substituting Data Annotation with Balanced Updates and Collective Loss in Multi-label Text Classification
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. Most existing approaches require an en…
Graph Inductive Biases in Transformers without Message Passing
Liheng Ma, Chen Lin, Derek Lim +5
Transformers for graph data are increasingly widely studied and successful in numerous learning tasks. Graph inductive biases are crucial for Graph Transformers, and previous works…
Structure Aware Incremental Learning with Personalized Imitation Weights for Recommender Systems
Yuening Wang, Yingxue Zhang, Antonios Valkanas +4
Recommender systems now consume large-scale data and play a significant role in improving user experience. Graph Neural Networks (GNNs) have emerged as one of the most effective re…
Diffusing Gaussian Mixtures for Generating Categorical Data
Florence Regol, Mark Coates
Learning a categorical distribution comes with its own set of challenges. A successful approach taken by state-of-the-art works is to cast the problem in a continuous domain to tak…
Neighbor Auto-Grouping Graph Neural Networks for Handover Parameter Configuration in Cellular Network
Mehrtash Mehrabi, Walid Masoudimansour, Yingxue Zhang +5
The mobile communication enabled by cellular networks is the one of the main foundations of our modern society. Optimizing the performance of cellular networks and providing massiv…
Spectral Augmentations for Graph Contrastive Learning
Amur Ghose, Yingxue Zhang, Jianye Hao +1
Contrastive learning has emerged as a premier method for learning representations with or without supervision. Recent studies have shown its utility in graph representation learnin…
Bidirectional Learning for Offline Model-based Biological Sequence Design
Can Chen, Yingxue Zhang, Xue Liu +1
Offline model-based optimization aims to maximize a black-box objective function with a static dataset of designs and their scores. In this paper, we focus on biological sequence d…
Intent-aware Multi-source Contrastive Alignment for Tag-enhanced Recommendation
Haolun Wu, Yingxue Zhang, Chen Ma +4
To offer accurate and diverse recommendation services, recent methods use auxiliary information to foster the learning process of user and item representations. Many SOTA methods f…
Evaluation of Categorical Generative Models -- Bridging the Gap Between Real and Synthetic Data
Florence Regol, Anja Kroon, Mark Coates
The machine learning community has mainly relied on real data to benchmark algorithms as it provides compelling evidence of model applicability. Evaluation on synthetic datasets ca…
DyG2Vec: Efficient Representation Learning for Dynamic Graphs
Mohammad Ali Alomrani, Mahdi Biparva, Yingxue Zhang +1
Temporal graph neural networks have shown promising results in learning inductive representations by automatically extracting temporal patterns. However, previous works often rely…
Contrastive Learning for Time Series on Dynamic Graphs
Yitian Zhang, Florence Regol, Antonios Valkanas +1
There have been several recent efforts towards developing representations for multivariate time-series in an unsupervised learning framework. Such representations can prove benefic…
Bidirectional Learning for Offline Infinite-width Model-based Optimization
Can Chen, Yingxue Zhang, Jie Fu +2
In offline model-based optimization, we strive to maximize a black-box objective function by only leveraging a static dataset of designs and their scores. This problem setting aris…
Node Copying: A Random Graph Model for Effective Graph Sampling
Florence Regol, Soumyasundar Pal, Jianing Sun +3
There has been an increased interest in applying machine learning techniques on relational structured-data based on an observed graph. Often, this graph is not fully representative…
Adapting Triplet Importance of Implicit Feedback for Personalized Recommendation
Haolun Wu, Chen Ma, Yingxue Zhang +3
Implicit feedback is frequently used for developing personalized recommendation services due to its ubiquity and accessibility in real-world systems. In order to effectively utiliz…
Bag Graph: Multiple Instance Learning using Bayesian Graph Neural Networks
Soumyasundar Pal, Antonios Valkanas, Florence Regol +1
Multiple Instance Learning (MIL) is a weakly supervised learning problem where the aim is to assign labels to sets or bags of instances, as opposed to traditional supervised learni…
Motion Inbetweening via Deep -Interpolator
Boris N. Oreshkin, Antonios Valkanas, Félix G. Harvey +3
We show that the task of synthesizing human motion conditioned on a set of key frames can be solved more accurately and effectively if a deep learning based interpolator operates i…