Search Results for author:
Found 15 papers, 4 papers with code
SAIL: Self-Improving Efficient Online Alignment of Large Language Models
Reinforcement Learning from Human Feedback (RLHF) is a key method for aligning large language models (LLMs) with human preferences.
Sketch-GNN: Scalable Graph Neural Networks with Sublinear Training Complexity
Graph Neural Networks (GNNs) are widely applied to graph learning problems such as node classification.
Spectral Greedy Coresets for Graph Neural Networks
However, owing to the interdependence of graph nodes, coreset selection, which selects subsets of the data examples, has not been successfully applied to speed up GNN training on large graphs, warranting special treatment.
Calibrated Dataset Condensation for Faster Hyperparameter Search
We aim to generate a synthetic validation dataset so that the validation-performance rankings of the models, with different hyperparameters, on the condensed and original datasets are comparable.
WAVES: Benchmarking the Robustness of Image Watermarks
Our evaluation examines two pivotal dimensions: the degree of image quality degradation and the efficacy of watermark detection after attacks.
Transferring Fairness under Distribution Shifts via Fair Consistency Regularization
In many real-world applications, however, such an assumption is often violated as previously trained fair models are often deployed in a different environment, and the fairness of such models has been observed to collapse.
VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization
Our framework avoids the "neighbor explosion" problem of GNNs using quantized representations combined with a low-rank version of the graph convolution matrix.
Ranked #12 on
Node Classification
on Reddit
A Closer Look at Distribution Shifts and Out-of-Distribution Generalization on Graphs
We observe that in most cases, we need both a suitable domain generalization algorithm and a strong GNN backbone model to optimize out-of-distribution test performance.
Understanding Overparameterization in Generative Adversarial Networks
We also empirically study the role of model overparameterization in GANs using several large-scale experiments on CIFAR-10 and Celeb-A datasets.
Understanding Over-parameterization in Generative Adversarial Networks
In this work, we present a comprehensive analysis of the importance of model over-parameterization in GANs both theoretically and empirically.
Robust Optimization as Data Augmentation for Large-scale Graphs
Data augmentation helps neural networks generalize better by enlarging the training set, but it remains an open question how to effectively augment graph data to enhance the performance of GNNs (Graph Neural Networks).
Ranked #1 on
Graph Property Prediction
on ogbg-ppa
GANs with Conditional Independence Graphs: On Subadditivity of Probability Divergences
GANs, however, are designed in a model-free fashion where no additional information about the underlying distribution is available.
Effective Feature Learning with Unsupervised Learning for Improving the Predictive Models in Massive Open Online Courses
A major challenge that has to be addressed when building such models is to design handcrafted features that are effective for the prediction task at hand.
Transfer Learning using Representation Learning in Massive Open Online Courses
It consists of two alternative transfer methods based on representation learning with auto-encoders: a passive approach using transductive principal component analysis and an active approach that uses a correlation alignment loss term.
Selection of Random Walkers that Optimizes the Global Mean First-Passage Time for Search in Complex Networks
We design a method to optimize the global mean first-passage time (GMFPT) of multiple random walkers searching in complex networks for a general target, without specifying the property of the target node.
