Zobrazeno 1 - 10
of 100
pro vyhledávání: '"Mądry, Aleksander"'
Autor:
Chan, Jun Shern, Chowdhury, Neil, Jaffe, Oliver, Aung, James, Sherburn, Dane, Mays, Evan, Starace, Giulio, Liu, Kevin, Maksin, Leon, Patwardhan, Tejal, Weng, Lilian, Mądry, Aleksander
We introduce MLE-bench, a benchmark for measuring how well AI agents perform at machine learning engineering. To this end, we curate 75 ML engineering-related competitions from Kaggle, creating a diverse set of challenging tasks that test real-world
Externí odkaz:
http://arxiv.org/abs/2410.07095
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 misinterpretation, or fabricated? To help answer these questions, we i
Externí odkaz:
http://arxiv.org/abs/2409.00729
Autor:
Jain, Saachi, Hamidieh, Kimia, Georgiev, Kristian, Ilyas, Andrew, Ghassemi, Marzyeh, Madry, Aleksander
Machine learning models can fail on subgroups that are underrepresented during training. While techniques such as dataset balancing can improve performance on underperforming groups, they require access to training group annotations and can end up re
Externí odkaz:
http://arxiv.org/abs/2406.16846
Most modern recommendation algorithms are data-driven: they generate personalized recommendations by observing users' past behaviors. A common assumption in recommendation is that how a user interacts with a piece of content (e.g., whether they choos
Externí odkaz:
http://arxiv.org/abs/2405.05596
How does the internal computation of a machine learning model transform inputs into predictions? In this paper, we introduce a task called component modeling that aims to address this question. The goal of component modeling is to decompose an ML mod
Externí odkaz:
http://arxiv.org/abs/2404.11534
Pre-training is a widely used approach to develop models that are robust to distribution shifts. However, in practice, its effectiveness varies: fine-tuning a pre-trained model improves robustness significantly in some cases but not at all in others
Externí odkaz:
http://arxiv.org/abs/2403.00194
When selecting data for training large-scale models, standard practice is to filter for examples that match human notions of data quality. Such filtering yields qualitatively clean datapoints that intuitively should improve model behavior. However, i
Externí odkaz:
http://arxiv.org/abs/2401.12926
Many human-facing algorithms -- including those that power recommender systems or hiring decision tools -- are trained on data provided by their users. The developers of these algorithms commonly adopt the assumption that the data generating process
Externí odkaz:
http://arxiv.org/abs/2312.17666
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-that is, identifying specific training examples which caused an image to
Externí odkaz:
http://arxiv.org/abs/2312.06205
Autor:
Khaddaj, Alaa, Leclerc, Guillaume, Makelov, Aleksandar, Georgiev, Kristian, Salman, Hadi, Ilyas, Andrew, Madry, Aleksander
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 such attacks typically involves viewing these inserted examples as outl
Externí odkaz:
http://arxiv.org/abs/2307.10163