Mark Coates

Bin Wang

Yingxue Zhang

Jianye Hao

2024-09-19

ArXiv (prépublication)

CKGConv: General Graph Convolution with Continuous Kernels

Liheng Ma

Soumyasundar Pal

Yitian Zhang

Jiaming Zhou

Yingxue Zhang

The existing definitions of graph convolution, either from spatial or spectral perspectives, are inflexible and not unified. Defining a gene… (voir plus)ral convolution operator in the graph domain is challenging due to the lack of canonical coordinates, the presence of irregular structures, and the properties of graph symmetries. In this work, we propose a novel and general graph convolution framework by parameterizing the kernels as continuous functions of pseudo-coordinates derived via graph positional encoding. We name this Continuous Kernel Graph Convolution (CKGConv). Theoretically, we demonstrate that CKGConv is flexible and expressive. CKGConv encompasses many existing graph convolutions, and exhibits a stronger expressiveness, as powerful as graph transformers in terms of distinguishing non-isomorphic graphs. Empirically, we show that CKGConv-based Networks outperform existing graph convolutional networks and perform comparably to the best graph transformers across a variety of graph datasets. The code and models are publicly available at https://github.com/networkslab/CKGConv.

2024-07-08

Proceedings of the 41st International Conference on Machine Learning (publié)

Interacting Diffusion Processes for Event Sequence Forecasting

Mai Zeng

Florence Regol

Neural Temporal Point Processes (TPPs) have emerged as the primary framework for predicting sequences of events that occur at irregular time… (voir plus) intervals, but their sequential nature can hamper performance for long-horizon forecasts. To address this, we introduce a novel approach that incorporates a diffusion generative model. The model facilitates sequence-to-sequence prediction, allowing multi-step predictions based on historical event sequences. In contrast to previous approaches, our model directly learns the joint probability distribution of types and inter-arrival times for multiple events. The model is composed of two diffusion processes, one for the time intervals and one for the event types. These processes interact through their respective denoising functions, which can take as input intermediate representations from both processes, allowing the model to learn complex interactions. We demonstrate that our proposal outperforms state-of-the-art baselines for long-horizon forecasting of TPPs.

2024-07-08

Proceedings of the 41st International Conference on Machine Learning (publié)

MODL: Multilearner Online Deep Learning

Antonios Valkanas

Boris Oreshkin

Online deep learning solves the problem of learning from streams of data, reconciling two opposing objectives: learn fast and learn deep. Ex… (voir plus)isting work focuses almost exclusively on exploring pure deep learning solutions, which are much better suited to handle the"deep"than the"fast"part of the online learning equation. In our work, we propose a different paradigm, based on a hybrid multilearner approach. First, we develop a fast online logistic regression learner. This learner does not rely on backpropagation. Instead, it uses closed form recursive updates of model parameters, handling the fast learning part of the online learning problem. We then analyze the existing online deep learning theory and show that the widespread ODL approach, currently operating at complexity

2024-05-28

ArXiv (prépublication)

CKGConv: General Graph Convolution with Continuous Kernels

Liheng Ma

Soumyasundar Pal

Yitian Zhang

Jiaming Zhou

Yingxue Zhang

The existing definitions of graph convolution, either from spatial or spectral perspectives, are inflexible and not unified. Defining a gene… (voir plus)ral convolution operator in the graph domain is challenging due to the lack of canonical coordinates, the presence of irregular structures, and the properties of graph symmetries. In this work, we propose a novel graph convolution framework by parameterizing the kernels as continuous functions of pseudo-coordinates derived via graph positional encoding. We name this Continuous Kernel Graph Convolution (CKGConv). Theoretically, we demonstrate that CKGConv is flexible and expressive. CKGConv encompasses many existing graph convolutions, and exhibits the same expressiveness as graph transformers in terms of distinguishing non-isomorphic graphs. Empirically, we show that CKGConv-based Networks outperform existing graph convolutional networks and perform comparably to the best graph transformers across a variety of graph datasets.

2024-05-01

ICML.cc/2024/Conference (poster)

Categorical Generative Model Evaluation via Synthetic Distribution Coarsening

Florence Regol

As we expect to see a rapid integration of generative models in our day to day lives, the development of rigorous methods of evaluation and … (voir plus)analysis for generative models has never been more pressing. Multiple works have highlighted the shortcomings of widely used metrics and exposed how they fail to behave as expected in some settings. So far, the response has been to use a variety of metrics that target different desirable and interpretable properties such as fidelity, diversity, and authenticity, to obtain a clearer picture of a generative model’s capabilities. These methods mainly focus on ordinal data and they all suffer from the same unavoidable issues stemming from estimating quantities of high-dimensional data from a limited number of samples. We propose to take an alternative approach and to return to the synthetic data setting where the ground truth is explicit and known. We focus on nominal categorical data and introduce an evaluation method that can scale to the high-dimensional settings often encountered in practice. Our method involves successively binning the large space to obtain smaller probability spaces and coarser distributions where meaningful statistical estimates can be obtained. This allows us to provide probabilistic guarantees and sample complexities and we illustrate how our method can be applied to distinguish between the capabilities of several state-of-the-art categorical models.

2024-04-18

Proceedings of The 27th International Conference on Artificial Intelligence and Statistics (publié)

proceedings.mlr.press

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

Yitian Zhang

Liheng Ma

Soumyasundar Pal

Yingxue Zhang

2024-04-18

Proceedings of The 27th International Conference on Artificial Intelligence and Statistics (publié)

Personalized Negative Reservoir for Incremental Learning in Recommender Systems

Antonios Valkanas

Yuening Wang

Yingxue Zhang

2024-03-06

ArXiv (prépublication)

Jointly-Learned Exit and Inference for a Dynamic Neural Network

Florence Regol

Joud Chataoui

2024-01-16

ICLR.cc/2024/Conference (poster)

DyG2Vec: Efficient Representation Learning for Dynamic Graphs

Mohammad Alomrani

Mahdi Biparva

Yingxue Zhang

Temporal graph neural networks have shown promising results in learning inductive representations by automatically extracting temporal patte… (voir plus)rns. However, previous works often rely on complex memory modules or inefficient random walk methods to construct temporal representations. To address these limitations, we present an efficient yet effective attention-based encoder that leverages temporal edge encodings and window-based subgraph sampling to generate task-agnostic embeddings. Moreover, we propose a joint-embedding architecture using non-contrastive SSL to learn rich temporal embeddings without labels. Experimental results on 7 benchmark datasets indicate that on average, our model outperforms SoTA baselines on the future link prediction task by 4.23% for the transductive setting and 3.30% for the inductive setting while only requiring 5-10x less training/inference time. Lastly, different aspects of the proposed framework are investigated through experimental analysis and ablation studies. The code is publicly available at https://github.com/huawei-noah/noah-research/tree/master/graph_atlas.

2024-01-08

TMLR (accepté)

Population Monte Carlo With Normalizing Flow

Soumyasundar Pal

Antonios Valkanas

Adaptive importance sampling (AIS) methods provide a useful alternative to Markov Chain Monte Carlo (MCMC) algorithms for performing inferen… (voir plus)ce of intractable distributions. Population Monte Carlo (PMC) algorithms constitute a family of AIS approaches which adapt the proposal distributions iteratively to improve the approximation of the target distribution. Recent work in this area primarily focuses on ameliorating the proposal adaptation procedure for high-dimensional applications. However, most of the AIS algorithms use simple proposal distributions for sampling, which might be inadequate in exploring target distributions with intricate geometries. In this work, we construct expressive proposal distributions in the AIS framework using normalizing flow, an appealing approach for modeling complex distributions. We use an iterative parameter update rule to enhance the approximation of the target distribution. Numerical experiments show that in high-dimensional settings, the proposed algorithm offers significantly improved performance compared to the existing techniques.

2024-01-01

IEEE Signal Processing Letters (publié)

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

Yitian Zhang

Liheng Ma

Soumyasundar Pal

Yingxue Zhang

2023-11-07

ArXiv (prépublication)