Zobrazeno 1 - 10
of 26
pro vyhledávání: '"Lew, Alexander"'
Autor:
Becker, McCoy R., Lew, Alexander K., Wang, Xiaoyan, Ghavami, Matin, Huot, Mathieu, Rinard, Martin C., Mansinghka, Vikash K.
Publikováno v:
PLDI 2024
Compared to the wide array of advanced Monte Carlo methods supported by modern probabilistic programming languages (PPLs), PPL support for variational inference (VI) is less developed: users are typically limited to a predefined selection of variatio
Externí odkaz:
http://arxiv.org/abs/2406.15742
Autor:
Huot, Mathieu, Ghavami, Matin, Lew, Alexander K., Schaechtle, Ulrich, Freer, Cameron E., Shelby, Zane, Rinard, Martin C., Saad, Feras A., Mansinghka, Vikash K.
This article presents GenSQL, a probabilistic programming system for querying probabilistic generative models of database tables. By augmenting SQL with only a few key primitives for querying probabilistic models, GenSQL enables complex Bayesian infe
Externí odkaz:
http://arxiv.org/abs/2406.15652
Autor:
Wong, Lionel, Grand, Gabriel, Lew, Alexander K., Goodman, Noah D., Mansinghka, Vikash K., Andreas, Jacob, Tenenbaum, Joshua B.
How does language inform our downstream thinking? In particular, how do humans make meaning from language--and how can we leverage a theory of linguistic meaning to build machines that think in more human-like ways? In this paper, we propose rational
Externí odkaz:
http://arxiv.org/abs/2306.12672
Autor:
Arya, Gaurav, Seyer, Ruben, Schäfer, Frank, Chandra, Kartik, Lew, Alexander K., Huot, Mathieu, Mansinghka, Vikash K., Ragan-Kelley, Jonathan, Rackauckas, Christopher, Schauer, Moritz
We develop an algorithm for automatic differentiation of Metropolis-Hastings samplers, allowing us to differentiate through probabilistic inference, even if the model has discrete components within it. Our approach fuses recent advances in stochastic
Externí odkaz:
http://arxiv.org/abs/2306.07961
Even after fine-tuning and reinforcement learning, large language models (LLMs) can be difficult, if not impossible, to control reliably with prompts alone. We propose a new inference-time approach to enforcing syntactic and semantic constraints on t
Externí odkaz:
http://arxiv.org/abs/2306.03081
We introduce a new setting, the category of $\omega$PAP spaces, for reasoning denotationally about expressive differentiable and probabilistic programming languages. Our semantics is general enough to assign meanings to most practical probabilistic a
Externí odkaz:
http://arxiv.org/abs/2302.10636
Deploying interactive systems in-the-wild requires adaptability to situations not encountered in lab environments. Our work details our experience about the impact of architecture choice on behavior reusability and reactivity while deploying a public
Externí odkaz:
http://arxiv.org/abs/2302.00191
Publikováno v:
POPL 2023
Optimizing the expected values of probabilistic processes is a central problem in computer science and its applications, arising in fields ranging from artificial intelligence to operations research to statistical computing. Unfortunately, automatic
Externí odkaz:
http://arxiv.org/abs/2212.06386
Publikováno v:
UAI 2022
A key design constraint when implementing Monte Carlo and variational inference algorithms is that it must be possible to cheaply and exactly evaluate the marginal densities of proposal distributions and variational families. This takes many interest
Externí odkaz:
http://arxiv.org/abs/2203.02836
Automatic differentiation (AD) aims to compute derivatives of user-defined functions, but in Turing-complete languages, this simple specification does not fully capture AD's behavior: AD sometimes disagrees with the true derivative of a differentiabl
Externí odkaz:
http://arxiv.org/abs/2111.15456