17 citations · 28 across the 13 of their papers we have counts for
6 papers · 1 filter
Layer-Parallel Inference Reduces Encrypted Nonlinear Depth in Transformers
Ligong Han, Kai Xu, Hao Wang +3
Fully homomorphic encryption (FHE) enables computation on encrypted data, but practical encrypted Transformer inference is bottlenecked by the sequential composition of many nonlin…
Enhancing Counterfactual Classification via Self-Training
Ruijiang Gao, Max Biggs, Wei Sun +1
Unlike traditional supervised learning, in many settings only partial feedback is available. We may only observe outcomes for the chosen actions, but not the counterfactual outcome…
Loss Functions for Discrete Contextual Pricing with Observational Data
Max Biggs, Ruijiang Gao, Wei Sun
We study a pricing setting where each customer is offered a contextualized price based on customer and/or product features. Often only historical sales data are available, so we ob…
Cost-Accuracy Aware Adaptive Labeling for Active Learning
Ruijiang Gao, Maytal Saar-tsechansky
Conventional active learning algorithms assume a single labeler that produces noiseless label at a given, fixed cost, and aim to achieve the best generalization performance for giv…
Robust Conditional GAN from Uncertainty-Aware Pairwise Comparisons
Ligong Han, Ruijiang Gao, Mun Kim +3
Conditional generative adversarial networks have shown exceptional generation performance over the past few years. However, they require large numbers of annotations. To address th…
Unsupervised Domain Adaptation via Calibrating Uncertainties
Ligong Han, Yang Zou, Ruijiang Gao +2
Unsupervised domain adaptation (UDA) aims at inferring class labels for unlabeled target domain given a related labeled source dataset. Intuitively, a model trained on source domai…