3 citations · 3 across the 4 of their papers we have counts for
6 papers
Stellar Colosseum: A Many-Agent Harness for Long-Horizon Research in Mathematics and Theoretical Computer Science
Honghao Lin, David P. Woodruff, Yuan Deng +3
Language models can produce plausible short proofs, but may still be unreliable on long-horizon research problems, where progress depends on a sequence of uncertain and interdepend…
Linear and Neural Dueling Bandits with Delayed Feedback
Xiangyi Wang, Pingchen Lu, Jie Mao +4
Contextual dueling bandits form a cornerstone of preference-based decision-making, with critical applications in recommender systems and large language model alignment. However, st…
Accelerating Scientific Research with Gemini: Case Studies and Common Techniques
David P. Woodruff, Vincent Cohen-Addad, Lalit Jain +33
Recent advances in large language models (LLMs) have opened new avenues for accelerating scientific research. While models are increasingly capable of assisting with routine tasks,…
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…
Approximately Efficient Bilateral Trade with Samples
Yuan Deng, Jieming Mao, Balasubramanian Sivan +2
We study the social efficiency of bilateral trade between a seller and a buyer. In the classical Bayesian setting, the celebrated Myerson-Satterthwaite impossibility theorem states…
Retraining with Predicted Hard Labels Provably Increases Model Accuracy
Rudrajit Das, Inderjit S. Dhillon, Alessandro Epasto +5
The performance of a model trained with noisy labels is often improved by simply \textit{retraining} the model with its \textit{own predicted hard labels} (i.e., 1/0 labels). Yet,…