5 citations · 17 across the 23 of their papers we have counts for
12 papers · 1 filter
Unknown Aware AI-Generated Content Attribution
Ellie Thieu, Jifan Zhang, Haoyue Bai
The rapid advancement of photorealistic generative models has made it increasingly important to attribute the origin of synthetic content, moving beyond binary real or fake detecti…
Causal Representation Learning from Network Data
Jifan Zhang, Michelle M. Li, Elena Zheleva
Causal disentanglement from soft interventions is identifiable under the assumptions of linear interventional faithfulness and availability of both observational and interventional…
Deep Active Learning in the Open World
Tian Xie, Jifan Zhang, Haoyue Bai +1
Machine learning models deployed in open-world scenarios often encounter unfamiliar conditions and perform poorly in unanticipated situations. As AI systems advance and find applic…
AHA: Human-Assisted Out-of-Distribution Generalization and Detection
Haoyue Bai, Jifan Zhang, Robert Nowak
Modern machine learning models deployed often encounter distribution shifts in real-world applications, manifesting as covariate or semantic out-of-distribution (OOD) shifts. These…
Humor in AI: Massive Scale Crowd-Sourced Preferences and Benchmarks for Cartoon Captioning
Jifan Zhang, Lalit Jain, Yang Guo +9
We present a novel multimodal preference dataset for creative tasks, consisting of over 250 million human ratings on more than 2.2 million captions, collected through crowdsourcing…
Improved Algorithm for Deep Active Learning under Imbalance via Optimal Separation
Shyam Nuggehalli, Jifan Zhang, Lalit Jain +1
Class imbalance severely impacts machine learning performance on minority classes in real-world applications. While various solutions exist, active learning offers a fundamental fi…
LabelBench: A Comprehensive Framework for Benchmarking Adaptive Label-Efficient Learning
Jifan Zhang, Yifang Chen, Gregory Canal +8
Labeled data are critical to modern machine learning applications, but obtaining labels can be expensive. To mitigate this cost, machine learning methods, such as transfer learning…
Algorithm Selection for Deep Active Learning with Imbalanced Datasets
Jifan Zhang, Shuai Shao, Saurabh Verma +1
Label efficiency has become an increasingly important objective in deep learning applications. Active learning aims to reduce the number of labeled examples needed to train deep ne…
PathProx: A Proximal Gradient Algorithm for Weight Decay Regularized Deep Neural Networks
Liu Yang, Jifan Zhang, Joseph Shenouda +3
Weight decay is one of the most widely used forms of regularization in deep learning, and has been shown to improve generalization and robustness. The optimization objective drivin…
GALAXY: Graph-based Active Learning at the Extreme
Jifan Zhang, Julian Katz-Samuels, Robert Nowak
Active learning is a label-efficient approach to train highly effective models while interactively selecting only small subsets of unlabelled data for labelling and training. In "o…
Improved Algorithms for Agnostic Pool-based Active Classification
Julian Katz-Samuels, Jifan Zhang, Lalit Jain +1
We consider active learning for binary classification in the agnostic pool-based setting. The vast majority of works in active learning in the agnostic setting are inspired by the…
Learning to Actively Learn: A Robust Approach
Jifan Zhang, Lalit Jain, Kevin Jamieson
This work proposes a procedure for designing algorithms for specific adaptive data collection tasks like active learning and pure-exploration multi-armed bandits. Unlike the design…