13 citations · 14 across the 7 of their papers we have counts for
7 papers
AI Research Preference Models
Thomas Simon Foster, Bassel Al Omari, Tingchen Fu +30
AI research agents (AIRA) can now carry machine learning experiments from proposal through implementation and evaluation. Yet progress on frontier tasks is throttled by the cost of…
Training AI Co-Scientists Using Rubric Rewards
Shashwat Goel, Rishi Hazra, Dulhan Jayalath +8
AI co-scientists are emerging as a tool to assist human researchers in achieving their research goals. A crucial feature of these AI co-scientists is the ability to generate a rese…
What Does It Take to Be a Good AI Research Agent? Studying the Role of Ideation Diversity
Alexis Audran-Reiss, Jordi Armengol-Estapé, Karen Hambardzumyan +17
AI research agents offer the promise to accelerate scientific progress by automating the design, implementation, and training of machine learning models. However, the field is stil…
SAM 3: Segment Anything with Concepts
Nicolas Carion, Laura Gustafson, Yuan-Ting Hu +35
We present Segment Anything Model (SAM) 3, a unified model that detects, segments, and tracks objects in images and videos based on concept prompts, which we define as either short…
AI Research Agents for Machine Learning: Search, Exploration, and Generalization in MLE-bench
Edan Toledo, Karen Hambardzumyan, Martin Josifoski +22
AI research agents are demonstrating great potential to accelerate scientific progress by automating the design, implementation, and training of machine learning models. We focus o…
MiniMax Learning of Interpretable Factored Stochastic Policies from Conjoint Data, with Uncertainty Quantification
Connor T. Jerzak, Priyanshi Chandra, Rishi Hazra
We study offline policy optimization over exponentially large factorial action spaces from randomized preference data, showing how conjoint experiments can estimate interpretable s…
Active Learning: Actively reducing redundancies in Active Learning methods for Sequence Tagging and Machine Translation
Rishi Hazra, Parag Dutta, Shubham Gupta +2
While deep learning is a powerful tool for natural language processing (NLP) problems, successful solutions to these problems rely heavily on large amounts of annotated samples. Ho…