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06/17/2023
OpenGSL: A Comprehensive Benchmark for Graph Structure Learning
Graph Neural Networks (GNNs) have emerged as the de facto standard for r...
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05/03/2023
Pre-train and Search: Efficient Embedding Table Sharding with Pre-trained Neural Cost Models
Sharding a large machine learning model across multiple devices to balan...
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04/21/2023
Interactive System-wise Anomaly Detection
Anomaly detection, where data instances are discovered containing featur...
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02/28/2023
Towards Personalized Preprocessing Pipeline Search
Feature preprocessing, which transforms raw input features into numerica...
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02/18/2023
Fairly Predicting Graft Failure in Liver Transplant for Organ Assigning
Liver transplant is an essential therapy performed for severe liver dise...
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12/23/2022
Bring Your Own View: Graph Neural Networks for Link Prediction with Personalized Subgraph Selection
Graph neural networks (GNNs) have received remarkable success in link pr...
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10/22/2022
SurCo: Learning Linear Surrogates For Combinatorial Nonlinear Optimization Problems
Optimization problems with expensive nonlinear cost functions and combin...
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10/19/2022
RSC: Accelerating Graph Neural Networks Training via Randomized Sparse Computations
The training of graph neural networks (GNNs) is extremely time consuming...
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08/12/2022
AutoShard: Automated Embedding Table Sharding for Recommender Systems
Embedding learning is an important technique in deep recommendation mode...
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05/27/2022
Auto-PINN: Understanding and Optimizing Physics-Informed Neural Architecture
Physics-informed neural networks (PINNs) are revolutionizing science and...
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11/04/2021
Modeling Techniques for Machine Learning Fairness: A Survey
Machine learning models are becoming pervasive in high-stakes applicatio...
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07/06/2021
Dirichlet Energy Constrained Learning for Deep Graph Neural Networks
Graph neural networks (GNNs) integrate deep architectures and topologica...
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06/11/2021
DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learning
Games are abstractions of the real world, where artificial agents learn ...
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05/17/2021
Learning Disentangled Representations for Time Series
Time-series representation learning is a fundamental task for time-serie...
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01/20/2021
Rank the Episodes: A Simple Approach for Exploration in Procedurally-Generated Environments
Exploration under sparse reward is a long-standing challenge of model-fr...
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06/19/2020
AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning
Outlier detection is an important data mining task with numerous practic...
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06/12/2020
Towards Deeper Graph Neural Networks with Differentiable Group Normalization
Graph neural networks (GNNs), which learn the representation of a node b...
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03/12/2020
PyODDS: An End-to-end Outlier Detection System with Automated Machine Learning
Outlier detection is an important task for various data mining applicati...
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12/17/2019
Multi-Channel Graph Convolutional Networks
Graph neural networks (GNN) has been demonstrated to be effective in cla...
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10/10/2019
RLCard: A Toolkit for Reinforcement Learning in Card Games
RLCard is an open-source toolkit for reinforcement learning research in ...
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10/07/2019
PyODDS: An End-to-End Outlier Detection System
PyODDS is an end-to end Python system for outlier detection with databas...
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06/19/2019
Experience Replay Optimization
Experience replay enables reinforcement learning agents to memorize and ...
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11/05/2017