Zobrazeno 1 - 10
of 30 235
pro vyhledávání: '"Bartlett, P"'
Autor:
Wang, Hanyin, Xu, Qiping, Liu, Bolun, Hussein, Guleid, Korsapati, Hariprasad, Labban, Mohamad El, Iheasirim, Kingsley, Hassan, Mohamed, Anil, Gokhan, Bartlett, Brian, Sun, Jimeng
Process-supervised reward models (PRMs), which verify large language model (LLM) outputs step-by-step, have achieved significant success in mathematical and coding problems. However, their application to other domains remains largely unexplored. In t
Externí odkaz:
http://arxiv.org/abs/2412.12583
The creation of the ICH E9 (R1) estimands framework has led to more precise specification of the treatment effects of interest in the design and statistical analysis of clinical trials. However, it is unclear how the new framework relates to causal i
Externí odkaz:
http://arxiv.org/abs/2412.12380
Recent advances have revealed that the rate of convergence of the expected test error in deep supervised learning decays as a function of the intrinsic dimension and not the dimension $d$ of the input space. Existing literature defines this intrinsic
Externí odkaz:
http://arxiv.org/abs/2412.09779
We present a collection of numerical bootstrap computations for 3d CFTs with a U(1) global symmetry. We test the accuracy of our method and fix conventions through a computation of bounds on the OPE coefficients for low-lying operators in the free fe
Externí odkaz:
http://arxiv.org/abs/2412.01608
Autor:
Zhang, Jiaxin, Dashti, S. Ghazaleh, Carlin, John B., Lee, Katherine J., Bartlett, Jonathan W., Moreno-Betancur, Margarita
When using multiple imputation (MI) for missing data, maintaining compatibility between the imputation model and substantive analysis is important for avoiding bias. For example, some causal inference methods incorporate an outcome model with exposur
Externí odkaz:
http://arxiv.org/abs/2411.13829
Contextuality is a key characteristic that separates quantum from classical phenomena and an important tool in understanding the potential advantage of quantum computation. However, when assessing the quantum resources available for quantum informati
Externí odkaz:
http://arxiv.org/abs/2411.09919
Federated Learning (FL) has emerged as a groundbreaking paradigm in collaborative machine learning, emphasizing decentralized model training to address data privacy concerns. While significant progress has been made in optimizing federated learning,
Externí odkaz:
http://arxiv.org/abs/2410.20659
Autor:
Sun, Hanshi, Haider, Momin, Zhang, Ruiqi, Yang, Huitao, Qiu, Jiahao, Yin, Ming, Wang, Mengdi, Bartlett, Peter, Zanette, Andrea
The safe and effective deployment of Large Language Models (LLMs) involves a critical step called alignment, which ensures that the model's responses are in accordance with human preferences. Prevalent alignment techniques, such as DPO, PPO and their
Externí odkaz:
http://arxiv.org/abs/2410.20290
Autor:
Sui, Ce, Bartlett, Deaglan J., Pandey, Shivam, Desmond, Harry, Ferreira, Pedro G., Wandelt, Benjamin D.
Current and future large scale structure surveys aim to constrain the neutrino mass and the equation of state of dark energy. We aim to construct accurate and interpretable symbolic approximations to the linear and nonlinear matter power spectra as a
Externí odkaz:
http://arxiv.org/abs/2410.14623
We identify regimes where post-selection can be used scalably in quantum error correction (QEC) to improve performance. We use statistical mechanical models to analytically quantify the performance and thresholds of post-selected QEC, with a focus on
Externí odkaz:
http://arxiv.org/abs/2410.07598