1 citations · 1 across the 3 of their papers we have counts for
8 papers
One Model, Two Markets: Bid-Aware Generative Recommendation
Yanchen Jiang, Zhe Feng, Christopher P. Mah +2
Generative Recommender Systems using semantic ids, such as TIGER (Rajput et al., 2023), have emerged as a widely adopted competitive paradigm in sequential recommendation. However,…
Scaling Inference-Time Computation via Opponent Simulation: Enabling Online Strategic Adaptation in Repeated Negotiation
Xiangyu Liu, Di Wang, Zhe Feng +1
While large language models (LLMs) have emerged as powerful decision-makers across a wide range of single-agent and stationary environments, fewer efforts have been devoted to sett…
Inference-time Unlearning Using Conformal Prediction
Somnath Basu Roy Chowdhury, Rahul Kidambi, Avinava Dubey +4
Machine unlearning is the process of efficiently removing specific information from a trained machine learning model without retraining from scratch. Existing unlearning methods, w…
Incentive-Aligned Multi-Source LLM Summaries
Yanchen Jiang, Zhe Feng, Aranyak Mehta
Large language models (LLMs) are increasingly used in modern search and answer systems to synthesize multiple, sometimes conflicting, texts into a single response, yet current pipe…
Position Auctions in AI-Generated Content
Santiago Balseiro, Kshipra Bhawalkar, Yuan Deng +7
We consider an extension to the classic position auctions in which sponsored creatives can be added within AI generated content rather than shown in predefined slots. New challenge…
Equilibria and Learning in Modular Marketplaces
Kshipra Bhawalkar, Jeff Dean, Christopher Liaw +2
We envision a marketplace where diverse entities offer specialized "modules" through APIs, allowing users to compose the outputs of these modules for complex tasks within a given b…