Zobrazeno 1 - 10
of 60 808
pro vyhledávání: '"A, Vélez"'
Autor:
Bovenzi, Inko, Carmel, Adi, Hu, Michael, Hurwitz, Rebecca M., McBride, Fiona, Benac, Leo, Ayala, José Roberto Tello, Doshi-Velez, Finale
In aims to uncover insights into medical decision-making embedded within observational data from clinical settings, we present a novel application of Inverse Reinforcement Learning (IRL) that identifies suboptimal clinician actions based on the actio
Externí odkaz:
http://arxiv.org/abs/2411.05237
We consider the problem of estimating the transition dynamics $T^*$ from near-optimal expert trajectories in the context of offline model-based reinforcement learning. We develop a novel constraint-based method, Inverse Transition Learning, that trea
Externí odkaz:
http://arxiv.org/abs/2411.05174
Autor:
Velez, Amilcar
This paper studies the properties of debiased machine learning (DML) estimators under a novel asymptotic framework, offering insights for improving the performance of these estimators in applications. DML is an estimation method suited to economic mo
Externí odkaz:
http://arxiv.org/abs/2411.01864
When explaining black-box machine learning models, it's often important for explanations to have certain desirable properties. Most existing methods `encourage' desirable properties in their construction of explanations. In this work, we demonstrate
Externí odkaz:
http://arxiv.org/abs/2410.23880
Publikováno v:
IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 1, pp. 190-199, Jan. 2022
A major challenges of deep learning (DL) is the necessity to collect huge amounts of training data. Often, the lack of a sufficiently large dataset discourages the use of DL in certain applications. Typically, acquiring the required amounts of data c
Externí odkaz:
http://arxiv.org/abs/2410.22748
Vehicles are sophisticated machines equipped with sensors that provide real-time data for onboard driving assistance systems. Due to the wide variety of traffic, road, and weather conditions, continuous system enhancements are essential. Connectivity
Externí odkaz:
http://arxiv.org/abs/2410.21934
In this work, we introduce a generalized framework for multiscale state-space modeling that incorporates nested nonlinear dynamics, with a specific focus on Bayesian learning under switching regimes. Our framework captures the complex interactions be
Externí odkaz:
http://arxiv.org/abs/2410.19074
Autor:
Alfaro, R., Alvarez, C., Arteaga-Velázquez, J. C., Rojas, D. Avila, Solares, H. A. Ayala, Babu, R., Belmont-Moreno, E., Caballero-Mora, K. S., Capistrán, T., Carramiñana, A., Casanova, S., Cotti, U., Cotzomi, J., de León, S. Coutiño, De la Fuente, E., Depaoli, D., Di Lalla, N., Hernandez, R. Diaz, Dingus, B. L., DuVernois, M. A., Durocher, M., Díaz-Vélez, J. C., Engel, K., Espinoza, C., Fan, K. L., Fang, K., Fraija, N., Fraija, S., García-González, J. A., Garfias, F., Muñoz, A. Gonzalez, González, M. M., Goodman, J. A., Groetsch, S., Harding, J. P., Herzog, I., Hinton, J., Huang, D., Hueyotl-Zahuantitla, F., Hüntemeyer, P., Iriarte, A., Joshi, V., Kaufmann, S., Kieda, D., de León, C., Lee, J., Vargas, H. León, Linnemann, J. T., Longinotti, A. L., Luis-Raya, G., Malone, K., Martinez, O., Martínez-Castro, J., Matthews, J. A., Miranda-Romagnoli, P., Morales-Soto, J. A., Moreno, E., Mostafá, M., Nayerhoda, A., Nellen, L., Newbold, M., Nisa, M. U., Noriega-Papaqui, R., Olivera-Nieto, L., Omodei, N., Osorio, M., Araujo, Y. Pérez, Pérez-Pérez, E. G., Rho, C. D., Rosa-González, D., Ruiz-Velasco, E., Salazar, H., Salazar-Gallegos, D., Sandoval, A., Schneider, M., Serna-Franco, J., Smith, A. J., Son, Y., Springer, R. W., Tibolla, O., Tollefson, K., Torres, I., Torres-Escobedo, R., Turner, R., Ureña-Mena, F., Varela, E., Villaseñor, L., Wang, X., Watson, I. J., Willox, E., Yun-Cárcamo, S., Zhou, H.
Publikováno v:
Nature.634(2024)557-560
Microquasars are laboratories for the study of jets of relativistic particles produced by accretion onto a spinning black hole. Microquasars are near enough to allow detailed imaging of spatial features across the multiwavelength spectrum. The recent
Externí odkaz:
http://arxiv.org/abs/2410.16117
Autor:
Shi, Claudia, Beltran-Velez, Nicolas, Nazaret, Achille, Zheng, Carolina, Garriga-Alonso, Adrià, Jesson, Andrew, Makar, Maggie, Blei, David M.
Large language models (LLMs) demonstrate surprising capabilities, but we do not understand how they are implemented. One hypothesis suggests that these capabilities are primarily executed by small subnetworks within the LLM, known as circuits. But ho
Externí odkaz:
http://arxiv.org/abs/2410.13032
Within batch reinforcement learning, safe policy improvement (SPI) seeks to ensure that the learnt policy performs at least as well as the behavior policy that generated the dataset. The core challenge in SPI is seeking improvements while balancing r
Externí odkaz:
http://arxiv.org/abs/2410.09361