18 citations · 42 across the 10 of their papers we have counts for
11 papers
OpenAI GPT-5 System Card
Aaditya Singh, Adam Fry, Adam Perelman +483
This is the system card published alongside the OpenAI GPT-5 launch, August 2025. GPT-5 is a unified system with a smart and fast model that answers most questions, a deeper reason…
gpt-oss-120b & gpt-oss-20b Model Card
OpenAI, :, Sandhini Agarwal +124
We present gpt-oss-120b and gpt-oss-20b, two open-weight reasoning models that push the frontier of accuracy and inference cost. The models use an efficient mixture-of-expert trans…
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…
ContextCite: Attributing Model Generation to Context
Benjamin Cohen-Wang, Harshay Shah, Kristian Georgiev +1
How do language models use information provided as context when generating a response? Can we infer whether a particular generated statement is actually grounded in the context, a…
Data Debiasing with Datamodels (D3M): Improving Subgroup Robustness via Data Selection
Saachi Jain, Kimia Hamidieh, Kristian Georgiev +3
Machine learning models can fail on subgroups that are underrepresented during training. While techniques such as dataset balancing can improve performance on underperforming group…
The Journey, Not the Destination: How Data Guides Diffusion Models
Kristian Georgiev, Joshua Vendrow, Hadi Salman +2
Diffusion models trained on large datasets can synthesize photo-realistic images of remarkable quality and diversity. However, attributing these images back to the training data-th…
Rethinking Backdoor Attacks
Alaa Khaddaj, Guillaume Leclerc, Aleksandar Makelov +4
In a backdoor attack, an adversary inserts maliciously constructed backdoor examples into a training set to make the resulting model vulnerable to manipulation. Defending against s…
TRAK: Attributing Model Behavior at Scale
Sung Min Park, Kristian Georgiev, Andrew Ilyas +2
The goal of data attribution is to trace model predictions back to training data. Despite a long line of work towards this goal, existing approaches to data attribution tend to for…
Privacy Induces Robustness: Information-Computation Gaps and Sparse Mean Estimation
Kristian Georgiev, Samuel B. Hopkins
We establish a simple connection between robust and differentially-private algorithms: private mechanisms which perform well with very high probability are automatically robust in…
Implicit Bias of Linear Equivariant Networks
Hannah Lawrence, Kristian Georgiev, Andrew Dienes +1
Group equivariant convolutional neural networks (G-CNNs) are generalizations of convolutional neural networks (CNNs) which excel in a wide range of technical applications by explic…
On the Convergence Theory of Debiased Model-Agnostic Meta-Reinforcement Learning
Alireza Fallah, Kristian Georgiev, Aryan Mokhtari +1
We consider Model-Agnostic Meta-Learning (MAML) methods for Reinforcement Learning (RL) problems, where the goal is to find a policy using data from several tasks represented by Ma…