Statistical Learning
GNNEvaluator: Evaluating GNN Performance On Unseen Graphs Without Labels
DiscGraph set captures wide-range and diverse graph data distribution discrepancies through a discrepancy measurement function, which exploits the outputs of GNNs related to latent node embeddings and node class predictions.
Mitigating Source Bias for Fairer Weak Supervision
Theoretically, we show that it is possible for our approach to simultaneously improve both accuracy and fairness--in contrast to standard fairness approaches that suffer from tradeoffs. Empirically, we show that our technique improves accuracy on weak supervision baselines by as much as 32% while reducing demographic parity gap by 82.5%.
2D-OOB: Attributing Data Contribution Through Joint Valuation Framework
Data valuation has emerged as a powerful framework for quantifying each datum's
Bayesian Model Selection Approach to Boundary Detection with Non-Local Priors
Fei Jiang, Guosheng Yin, Francesca Dominici
Neural Information Processing Systems http://nips.cc/
Streaming Kernel PCA with $\tilde{O}(\sqrt{n})$ Random Features
Md Enayat Ullah, Poorya Mianjy, Teodor Vanislavov Marinov, Raman Arora
Neural Information Processing Systems http://nips.cc/
Multi-objects Generation with Amortized Structural Regularization
Taufik Xu, Chongxuan LI, Jun Zhu, Bo Zhang
Neural Information Processing Systems http://nips.cc/
Natasha 2: Faster Non-Convex Optimization Than SGD
In diverse world of deep learning research has given rise to numerous architectures for neural networks(convolutionalones,longshorttermmemoryones,etc). However,tothisdate,theunderlying training algorithms for neural networks are still stochastic gradient descent (SGD) and its heuristic variants.
