1 citations · 2 across the 16 of their papers we have counts for
12 papers · 1 filter
Cogentic: Multi-Agent Orchestration for Automated Proof Discovery
Yang Cai, Vineet Gupta, Yanchen Jiang +4
We present Cogentic, a multi-agent harness for automated proof discovery on open research problems. While frontier language models can generate strong mathematical ideas in a singl…
Two-sided Market Design Meets Autobidding
Yang Cai, Christopher Liaw, Aranyak Mehta +2
Autobidding has become a dominant paradigm in online advertising by enabling advertisers to set high-level goals while algorithms handle real-time bid optimization. A prominent exa…
Efficiency of Generalized Proportional First-Price Auctions Under Auto-bidding
Yang Cai, Vineet Gupta, Yanchen Jiang +4
Auto-bidding is now widely adopted in online advertising platforms, allowing advertisers to specify high-level campaign objectives--such as maximizing total value subject to a retu…
Improved Revenue Guarantees for Selling Separately and Bundling
Yang Cai, Vineet Gupta, Yanchen Jiang +4
We study how much revenue a seller can lose by restricting attention to selling separately or grand bundling, in the setting of a single additive buyer with independent item values…
The Power of Recruiting the Smaller Side: Two Additional Traders Suffice in Two-Sided Markets
Yang Cai, Vineet Gupta, Yanchen Jiang +5
We study Bulow-Klemperer-style competition complexity in two-sided double auctions with unit-demand buyers drawn i.i.d. from and unit-supply sellers drawn i.i.d. from…
Prediction with Expert Advice: Anytime Regret with Many Experts Matches the Fixed-Time Constant
Yang Cai, Vineet Gupta, Yanchen Jiang +4
Prediction with expert advice is a fundamental problem in online learning. When the time horizon is known in advance, the minimax cumulative regret over experts is asymptot…
Efficient Online Inverse Optimization with Regret
Yang Cai, Anupam Gupta, Vineet Gupta +7
We give a deterministic algorithm for online inverse linear optimization with regret , uniform in the horizon and time per round. A bound of this order was obtaine…
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
We introduce compensation design, the problem of designing payment rules that incentivize high-quality contributions in decentralized environments. Here, a budget-constrained princ…
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…