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20232026
most citedEfficiency of Non-Truthful Auctions in Auto-bidding with Budget Constraints

1 citations · 2 across the 8 of their papers we have counts for

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9 papers · 1 filter

cs.AI2026

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…

cs.GT2026

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…

cs.GT2026

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…

cs.GT2026

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…

cs.GT2026

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…

stat.ML2026

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…

cs.LG2026

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…

cs.GT2026

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…

cs.GT2026

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…