Zobrazeno 1 - 10
of 1 960
pro vyhledávání: '"KRAUSE, ANDREAS"'
Reinforcement learning (RL) algorithms aim to balance exploiting the current best strategy with exploring new options that could lead to higher rewards. Most common RL algorithms use undirected exploration, i.e., select random sequences of actions. E
Externí odkaz:
http://arxiv.org/abs/2412.12098
We consider the problem of predicting perturbation effects via causal models. In many applications, it is a priori unknown which mechanisms of a system are modified by an external perturbation, even though the features of the perturbation are availab
Externí odkaz:
http://arxiv.org/abs/2411.14003
Autor:
Bengio, Yoshua, Mindermann, Sören, Privitera, Daniel, Besiroglu, Tamay, Bommasani, Rishi, Casper, Stephen, Choi, Yejin, Goldfarb, Danielle, Heidari, Hoda, Khalatbari, Leila, Longpre, Shayne, Mavroudis, Vasilios, Mazeika, Mantas, Ng, Kwan Yee, Okolo, Chinasa T., Raji, Deborah, Skeadas, Theodora, Tramèr, Florian, Adekanmbi, Bayo, Christiano, Paul, Dalrymple, David, Dietterich, Thomas G., Felten, Edward, Fung, Pascale, Gourinchas, Pierre-Olivier, Jennings, Nick, Krause, Andreas, Liang, Percy, Ludermir, Teresa, Marda, Vidushi, Margetts, Helen, McDermid, John A., Narayanan, Arvind, Nelson, Alondra, Oh, Alice, Ramchurn, Gopal, Russell, Stuart, Schaake, Marietje, Song, Dawn, Soto, Alvaro, Tiedrich, Lee, Varoquaux, Gaël, Yao, Andrew, Zhang, Ya-Qin
This is the interim publication of the first International Scientific Report on the Safety of Advanced AI. The report synthesises the scientific understanding of general-purpose AI -- AI that can perform a wide variety of tasks -- with a focus on und
Externí odkaz:
http://arxiv.org/abs/2412.05282
We propose practical deep Gaussian process models on Riemannian manifolds, similar in spirit to residual neural networks. With manifold-to-manifold hidden layers and an arbitrary last layer, they can model manifold- and scalar-valued functions, as we
Externí odkaz:
http://arxiv.org/abs/2411.00161
Autor:
Okanovic, Patrik, Kirsch, Andreas, Kasper, Jannes, Hoefler, Torsten, Krause, Andreas, Gürel, Nezihe Merve
We introduce MODEL SELECTOR, a framework for label-efficient selection of pretrained classifiers. Given a pool of unlabeled target data, MODEL SELECTOR samples a small subset of highly informative examples for labeling, in order to efficiently identi
Externí odkaz:
http://arxiv.org/abs/2410.13609
Autor:
As, Yarden, Sukhija, Bhavya, Treven, Lenart, Sferrazza, Carmelo, Coros, Stelian, Krause, Andreas
Reinforcement learning (RL) is ubiquitous in the development of modern AI systems. However, state-of-the-art RL agents require extensive, and potentially unsafe, interactions with their environments to learn effectively. These limitations confine RL
Externí odkaz:
http://arxiv.org/abs/2410.09486
Recent efforts in fine-tuning language models often rely on automatic data selection, commonly using Nearest Neighbors retrieval from large datasets. However, we theoretically show that this approach tends to select redundant data, limiting its effec
Externí odkaz:
http://arxiv.org/abs/2410.08020
SHAP values are a popular local feature-attribution method widely used in interpretable and explainable AI. We tackle the problem of efficiently computing these values. We cover both the model-agnostic (black-box) setting, where one only has query ac
Externí odkaz:
http://arxiv.org/abs/2410.06300
Pre-trained generalist policies are rapidly gaining relevance in robot learning due to their promise of fast adaptation to novel, in-domain tasks. This adaptation often relies on collecting new demonstrations for a specific task of interest and apply
Externí odkaz:
http://arxiv.org/abs/2410.05026
Bayesian optimization (BO) is a powerful framework to optimize black-box expensive-to-evaluate functions via sequential interactions. In several important problems (e.g. drug discovery, circuit design, neural architecture search, etc.), though, such
Externí odkaz:
http://arxiv.org/abs/2409.18582