35 citations · 105 across the 7 of their papers we have counts for
7 papers
ALF: Advertiser Large Foundation Model for Multi-Modal Advertiser Understanding
Santosh Rajagopalan, Jonathan Vronsky, Songbai Yan +3
We present ALF (Advertiser Large Foundation model), a multi-modal transformer architecture for understanding advertiser behavior and intent across text, image, video, and structure…
ChatHaruhi: Reviving Anime Character in Reality via Large Language Model
Cheng Li, Ziang Leng, Chenxi Yan +11
Role-playing chatbots built on large language models have drawn interest, but better techniques are needed to enable mimicking specific fictional characters. We propose an algorith…
The Label Complexity of Active Learning from Observational Data
Songbai Yan, Kamalika Chaudhuri, Tara Javidi
Counterfactual learning from observational data involves learning a classifier on an entire population based on data that is observed conditioned on a selection policy. This work c…
Exploring Connections Between Active Learning and Model Extraction
Varun Chandrasekaran, Kamalika Chaudhuri, Irene Giacomelli +2
Machine learning is being increasingly used by individuals, research institutions, and corporations. This has resulted in the surge of Machine Learning-as-a-Service (MLaaS) - cloud…
Active Learning with Logged Data
Songbai Yan, Kamalika Chaudhuri, Tara Javidi
We consider active learning with logged data, where labeled examples are drawn conditioned on a predetermined logging policy, and the goal is to learn a classifier on the entire po…
Revisiting Perceptron: Efficient and Label-Optimal Learning of Halfspaces
Songbai Yan, Chicheng Zhang
It has been a long-standing problem to efficiently learn a halfspace using as few labels as possible in the presence of noise. In this work, we propose an efficient Perceptron-base…
Active Learning from Imperfect Labelers
Songbai Yan, Kamalika Chaudhuri, Tara Javidi
We study active learning where the labeler can not only return incorrect labels but also abstain from labeling. We consider different noise and abstention conditions of the labeler…