BLOG: Probabilistic Models with Unknown Objects
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Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
For AI systems, probabilistic programs have been shown to be a promising formalism for world modeling.
“For AI systems, probabilistic programs have been shown to be a promising formalism for world modeling (e.g., Milch et al., 2007;Matheos et al., 2020;Gothoskar et al., 2021;Fremont et al., 2022).”
From Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systems (2024) · cites this paper as Milch et al., 2007
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Percentages, sums and reported differences re-computed from the paper’s own numbers.
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Cites
An introduction to MCMC for machine learning.
Andrieu, C., de Freitas, N., Doucet, A., & Jordan, M. I. (2003) · Machine Learning · doi:10.1023/a:1020281327116 · Cited by 2,451 · see citing works
Flagged first, then by citations
Research works printed but not linked: 5
- Milch, B., Marthi, B., Russell, S., Sontag, D., Ong, D.L., Kolobov, A.: BLOG: Prob- abilistic models with unknown objects. In: Proc. 19th International Joint Conf. on AI. (2005) To appear.
- Milch, B., Marthi, B., Sontag, D., Russell, S., Ong, D.L., Kolobov, A.: Approximate inference for infinite contingent Bayesian networks. In: 10th International Workshop on AI and Statistics. (2005)
- Koller, D., Pfeffer, A.: Probabilistic frame-based systems. In: Proc. 15th National Conf. on AI. (1998) 580-587
- Getoor, L., Friedman, N., Koller, D., Taskar, B.: Learning probabilistic models of relational structure. In: Proc. 18th International Conf. on Machine Learning. (2001) 170-177
- Pasula, H., Marthi, B., Milch, B., Russell, S., Shpitser, I.: Identity uncertainty and citation matching. In: Advances in Neural Information Processing Systems 15. MIT Press, Cambridge, MA (2003)
Cited by
Sorted by citations
Probabilistic machine learning and artificial intelligence
Ghahramani, Z. (2015) · Nature · doi:10.1038/nature14541 · Cited by 1,995 · see citing works
Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systems
Dalrymple, D. "., Skalse, J., Bengio, Y., Russell, S., Tegmark, M., Seshia, S., Omohundro, S., Szegedy, C., Goldhaber, B., Ammann, N., et al. (2024) · arXiv · doi:10.48550/arxiv.2405.06624 · Cited by 12 · see citing works
Self-citation
- Authors
- Milch, B., Marthi, B., Russell, S., Sontag, D., Ong, D. L., & Kolobov, A.
- Venue
- Introduction to Statistical Relational Learning
- Publisher
- The MIT Press
- Published
- 2007-08-31
- Pages
- 373-398
- Cited by
- 23
- References
- 1
- Language
- en
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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