3 papers
cs.LG2025
Efficiently Verifiable Proofs of Data Attribution
Ari Karchmer, Martin Pawelczyk, Seth Neel
Data attribution methods aim to answer useful counterfactual questions like "what would a ML model's prediction be if it were trained on a different dataset?" However, estimation o…
cs.CL2025
Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities
Gheorghe Comanici, Eric Bieber, Mike Schaekermann +3431
In this report, we introduce the Gemini 2.X model family: Gemini 2.5 Pro and Gemini 2.5 Flash, as well as our earlier Gemini 2.0 Flash and Flash-Lite models. Gemini 2.5 Pro is our…
cs.LG2024
Attribute-to-Delete: Machine Unlearning via Datamodel Matching
Kristian Georgiev, Roy Rinberg, Sung Min Park +4
Machine unlearning -- efficiently removing the effect of a small "forget set" of training data on a pre-trained machine learning model -- has recently attracted significant researc…