1 citations · 1 across the 4 of their papers we have counts for
7 papers
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…
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…
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…
A New Lower Bound for the Random Offerer Mechanism in Bilateral Trade using AI-Guided Evolutionary Search
Yang Cai, Vineet Gupta, Zun Li +1
The celebrated Myerson--Satterthwaite theorem shows that in bilateral trade, no mechanism can be simultaneously fully efficient, Bayesian incentive compatible (BIC), and budget bal…
A Computationally Efficient Sparsified Online Newton Method
Fnu Devvrit, Sai Surya Duvvuri, Rohan Anil +3
Second-order methods hold significant promise for enhancing the convergence of deep neural network training; however, their large memory and computational demands have limited thei…