15 citations · 61 across the 13 of their papers we have counts for
13 papers
AutoHint: Automatic Prompt Optimization with Hint Generation
Hong Sun, Xue Li, Yinchuan Xu +5
This paper presents AutoHint, a novel framework for automatic prompt engineering and optimization for Large Language Models (LLM). While LLMs have demonstrated remarkable ability i…
DeepTagger: Knowledge Enhanced Named Entity Recognition for Web-Based Ads Queries
Simiao Zuo, Pengfei Tang, Xinyu Hu +3
Named entity recognition (NER) is a crucial task for online advertisement. State-of-the-art solutions leverage pre-trained language models for this task. However, three major chall…
Efficient Long Sequence Modeling via State Space Augmented Transformer
Simiao Zuo, Xiaodong Liu, Jian Jiao +4
Transformer models have achieved superior performance in various natural language processing tasks. However, the quadratic computational cost of the attention mechanism limits its…
TEDL: A Two-stage Evidential Deep Learning Method for Classification Uncertainty Quantification
Xue Li, Wei Shen, Denis Charles
In this paper, we propose TEDL, a two-stage learning approach to quantify uncertainty for deep learning models in classification tasks, inspired by our findings in experimenting wi…
Masked LARk: Masked Learning, Aggregation and Reporting worKflow
Joseph J. Pfeiffer, Denis Charles, Davis Gilton +3
Today, many web advertising data flows involve passive cross-site tracking of users. Enabling such a mechanism through the usage of third party tracking cookies (3PC) exposes sensi…
Causal Inference in the Presence of Interference in Sponsored Search Advertising
Razieh Nabi, Joel Pfeiffer, Murat Ali Bayir +2
In classical causal inference, inferring cause-effect relations from data relies on the assumption that units are independent and identically distributed. This assumption is violat…
Causal Transfer Random Forest: Combining Logged Data and Randomized Experiments for Robust Prediction
Shuxi Zeng, Murat Ali Bayir, Joesph J. Pfeiffer +2
It is often critical for prediction models to be robust to distributional shifts between training and testing data. From a causal perspective, the challenge is to distinguish the s…
Self-Supervised Contextual Bandits in Computer Vision
Aniket Anand Deshmukh, Abhimanu Kumar, Levi Boyles +3
Contextual bandits are a common problem faced by machine learning practitioners in domains as diverse as hypothesis testing to product recommendations. There have been a lot of app…
Data Transformation Insights in Self-supervision with Clustering Tasks
Abhimanu Kumar, Aniket Anand Deshmukh, Urun Dogan +2
Self-supervision is key to extending use of deep learning for label scarce domains. For most of self-supervised approaches data transformations play an important role. However, up…
A Unified Batch Online Learning Framework for Click Prediction
Rishabh Iyer, Nimit Acharya, Tanuja Bompada +2
We present a unified framework for Batch Online Learning (OL) for Click Prediction in Search Advertisement. Machine Learning models once deployed, show non-trivial accuracy and cal…
Modeling and Simultaneously Removing Bias via Adversarial Neural Networks
John Moore, Joel Pfeiffer, Kai Wei +5
In real world systems, the predictions of deployed Machine Learned models affect the training data available to build subsequent models. This introduces a bias in the training data…
ICE: Enabling Non-Experts to Build Models Interactively for Large-Scale Lopsided Problems
Patrice Simard, David Chickering, Aparna Lakshmiratan +7
Quick interaction between a human teacher and a learning machine presents numerous benefits and challenges when working with web-scale data. The human teacher guides the machine to…
Counterfactual Reasoning and Learning Systems
Léon Bottou, Jonas Peters, Joaquin Quiñonero-Candela +6
This work shows how to leverage causal inference to understand the behavior of complex learning systems interacting with their environment and predict the consequences of changes t…