29 citations · 78 across the 12 of their papers we have counts for
14 papers
Data-Efficient Scaling-Law Discovery for Recommendation Systems via Coupled Hyperparameter Scaling
John Martabano Landy, Louis C. Tiao, Yao Xuan +10
Large-scale ranking and recommendation models of Web platforms benefit from increased compute much as large language models do, and their scaling behavior is broadly power-law in f…
Agentic Bayesian Optimization through Surrogate-Augmented Autoresearch
Paul Brunzema, Louis Tiao, Nhat Le +3
Bayesian optimization (BO) has become the standard tool for sample-efficient optimization and owes its efficiency to uncertainty-aware search driven by generic statistical priors.…
Location Aware Embedding for Geotargeting in Sponsored Search Advertising
Jelena Gligorijevic, Djordje Gligorijevic, Aravindan Raghuveer +2
Web search has become an inevitable part of everyday life. Improving and monetizing web search has been a focus of major Internet players. Understanding the context of web search q…
Design Once, Deploy at Scale: Template-Driven ML Development for Large Model Ecosystems
Jiang Liu, John Martabano Landy, Yao Xuan +14
Modern computational advertising platforms typically rely on recommendation systems to predict user responses, such as click-through rates, conversion rates, and other optimization…
Learning Concave Bid Shading Strategies in Online Auctions via Measure-valued Proximal Optimization
Iman Nodozi, Djordje Gligorijevic, Abhishek Halder
This work proposes a bid shading strategy for first-price auctions as a measure-valued optimization problem. We consider a standard parametric form for bid shading and formulate th…
An Efficient Deep Distribution Network for Bid Shading in First-Price Auctions
Tian Zhou, Hao He, Shengjun Pan +9
Since 2019, most ad exchanges and sell-side platforms (SSPs), in the online advertising industry, shifted from second to first price auctions. Due to the fundamental difference bet…
Bid Shading by Win-Rate Estimation and Surplus Maximization
Shengjun Pan, Brendan Kitts, Tian Zhou +8
This paper describes a new win-rate based bid shading algorithm (WR) that does not rely on the minimum-bid-to-win feedback from a Sell-Side Platform (SSP). The method uses a modifi…
Bid Shading in The Brave New World of First-Price Auctions
Djordje Gligorijevic, Tian Zhou, Bharatbhushan Shetty +4
Online auctions play a central role in online advertising, and are one of the main reasons for the industry's scalability and growth. With great changes in how auctions are being o…
Time-Aware Prospective Modeling of Users for Online Display Advertising
Djordje Gligorijevic, Jelena Gligorijevic, Aaron Flores
Prospective display advertising poses a great challenge for large advertising platforms as the strongest predictive signals of users are not eligible to be used in the conversion p…
Deep Attention Model for Triage of Emergency Department Patients
Djordje Gligorijevic, Jelena Stojanovic, Wayne Satz +4
Optimization of patient throughput and wait time in emergency departments (ED) is an important task for hospital systems. For that reason, Emergency Severity Index (ESI) system for…
Improving confidence while predicting trends in temporal disease networks
Djordje Gligorijevic, Jelena Stojanovic, Zoran Obradovic
For highly sensitive real-world predictive analytic applications such as healthcare and medicine, having good prediction accuracy alone is often not enough. These kinds of applicat…
Modeling Customer Engagement from Partial Observations
Jelena Stojanovic, Djordje Gligorijevic, Zoran Obradovic
It is of high interest for a company to identify customers expected to bring the largest profit in the upcoming period. Knowing as much as possible about each customer is crucial f…
Deeply Supervised Semantic Model for Click-Through Rate Prediction in Sponsored Search
Jelena Gligorijevic, Djordje Gligorijevic, Ivan Stojkovic +3
In sponsored search it is critical to match ads that are relevant to a query and to accurately predict their likelihood of being clicked. Commercial search engines typically use ma…
Semi-supervised learning for structured regression on partially observed attributed graphs
Jelena Stojanovic, Milos Jovanovic, Djordje Gligorijevic +1
Conditional probabilistic graphical models provide a powerful framework for structured regression in spatio-temporal datasets with complex correlation patterns. However, in real-li…