1 citations · 1 across the 11 of their papers we have counts for
14 papers
ECP-Bench: Benchmarking and Learning Entertainment Content Promotion with Foundation Models
Hyomin Kim, Bowen Chen, Jin Huang +3
Content promotion spans a broad set of skills, from understanding content to forecasting its market reception. However, LLMs' ability to support such promotion decisions remains un…
Visual Prompt Discovery via Semantic Exploration
Jaechang Kim, Yotaro Shimose, Zhao Wang +3
LVLMs encounter significant challenges in image understanding and visual reasoning, leading to critical perception failures. Visual prompts, which incorporate image manipulation co…
WebGen-V Bench: Structured Representation for Enhancing Visual Design in LLM-based Web Generation and Evaluation
Kuang-Da Wang, Zhao Wang, Yotaro Shimose +2
Witnessed by the recent advancements on leveraging LLM for coding and multimodal understanding, we present WebGen-V, a new benchmark and framework for instruction-to-HTML generatio…
Forecasting Clicks in Digital Advertising: Multimodal Inputs and Interpretable Outputs
Briti Gangopadhyay, Zhao Wang, Shingo Takamatsu
Forecasting click volume is a key task in digital advertising, influencing both revenue and campaign strategy. Traditional time series models rely solely on numerical data, often o…
DesignLab: Designing Slides Through Iterative Detection and Correction
Jooyeol Yun, Heng Wang, Yotaro Shimose +2
Designing high-quality presentation slides can be challenging for non-experts due to the complexity involved in navigating various design choices. Numerous automated tools can sugg…
Mirror in the Model: Ad Banner Image Generation via Reflective Multi-LLM and Multi-modal Agents
Zhao Wang, Bowen Chen, Yotaro Shimose +3
Recent generative models such as GPT-4o have shown strong capabilities in producing high-quality images with accurate text rendering. However, commercial design tasks like advertis…
OMS: On-the-fly, Multi-Objective, Self-Reflective Ad Keyword Generation via LLM Agent
Bowen Chen, Zhao Wang, Shingo Takamatsu
Keyword decision in Sponsored Search Advertising is critical to the success of ad campaigns. While LLM-based methods offer automated keyword generation, they face three major limit…
BannerAgency: Advertising Banner Design with Multimodal LLM Agents
Heng Wang, Yotaro Shimose, Shingo Takamatsu
Advertising banners are critical for capturing user attention and enhancing advertising campaign effectiveness. Creating aesthetically pleasing banner designs while conveying the c…
Talk Structurally, Act Hierarchically: A Collaborative Framework for LLM Multi-Agent Systems
Zhao Wang, Sota Moriyama, Wei-Yao Wang +2
Recent advancements in LLM-based multi-agent (LLM-MA) systems have shown promise, yet significant challenges remain in managing communication and refinement when agents collaborate…
Adaptive Budget Optimization for Multichannel Advertising Using Combinatorial Bandits
Briti Gangopadhyay, Zhao Wang, Alberto Silvio Chiappa +1
Effective budget allocation is crucial for optimizing the performance of digital advertising campaigns. However, the development of practical budget allocation algorithms remain li…
Auto-bidding in real-time auctions via Oracle Imitation Learning (OIL)
Alberto Silvio Chiappa, Briti Gangopadhyay, Zhao Wang +1
Online advertising has become one of the most successful business models of the internet era. Impression opportunities are typically allocated through real-time auctions, where adv…
OKG: On-the-Fly Keyword Generation in Sponsored Search Advertising
Zhao Wang, Briti Gangopadhyay, Mengjie Zhao +1
Current keyword decision-making in sponsored search advertising relies on large, static datasets, limiting the ability to automatically set up keywords and adapt to real-time KPI m…
Augmenting Offline RL with Unlabeled Data
Zhao Wang, Briti Gangopadhyay, Jia-Fong Yeh +1
Recent advancements in offline Reinforcement Learning (Offline RL) have led to an increased focus on methods based on conservative policy updates to address the Out-of-Distribution…
Integrating Domain Knowledge for handling Limited Data in Offline RL
Briti Gangopadhyay, Zhao Wang, Jia-Fong Yeh +1
With the ability to learn from static datasets, Offline Reinforcement Learning (RL) emerges as a compelling avenue for real-world applications. However, state-of-the-art offline RL…