From the 1 of 10 linked papers with an AI index.
10 papers
From Compensation Design to Budget-Feasible Mechanisms: A Constant Approximation for Subadditive Valuations
Ioannis Anagnostides, Kshipra Bhawalkar, Christopher Liaw +3
Budget-feasible mechanism design is a classic framework introduced by Singer, but there is still a wide gap between existing upper and lower bounds. In this paper, we significantly…
Compensation Design
Ioannis Anagnostides, Kshipra Bhawalkar, Christopher Liaw +5
The paper defines the problem of compensation design, proposing simple cost‑oblivious payment rules that guarantee the existence of pure Nash equilibria with a price of anarchy clo…
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…
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…
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…