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1. Probabilistic Disassembly

   https://doi.org/10.1109/ICSE.2019.00121  

   Miller, Kenneth ; Kwon, Yonghwi ; Sun, Yi ; Zhang, Zhuo ; Zhang, Xiangyu ; Lin, Zhiqiang ( May 2019 , Proceedings of the 41st International Conference on Software Engineering)

   Disassembling stripped binaries is a prominent challenge for binary analysis, due to the interleaving of code segments and data, and the difficulties of resolving control transfer targets of indirect calls and jumps. As a result, most existing disassemblers have both false positives (FP) and false negatives (FN). We observe that uncertainty is inevitable in disassembly due to the information loss during compilation and code generation. Therefore, we propose to model such uncertainty using probabilities and propose a novel disassembly technique, which computes a probability for each address in the code space, indicating its likelihood of being a true positive instruction. The probability is computed from a set of features that are reachable to an address, including control flow and data flow features. Our experiments with more than two thousands binaries show that our technique does not have any FN and has only 3.7% FP. In comparison, a state-of-the-art superset disassembly technique has 85% FP. A rewriter built on our disassembly can generate binaries that are only half of the size of those by superset disassembly and run 3% faster. While many widely-used disassemblers such as IDA and BAP suffer from missing function entries, our experiment also shows that even without any function entry information, our disassembler can still achieve 0 FN and 6.8% FP. 

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2. PROBABILISTIC CAUSALITY

   https://doi.org/10.1111/j.1468-0114.1980.tb00004.x  

   Salmon, Wesley C. ( January 1980 , Pacific Philosophical Quarterly)
3. Probabilistic Lexicase Selection

   https://doi.org/10.1145/3583131.3590375  

   Ding, Li ; Pantridge, Edward ; Spector, Lee ( July 2023 , Genetic and Evolutionary Computation Conference)
4. Probabilistic Dependency Graphs

   Richardson, O. ; Halpern, J. Y. ( January 2021 , Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21))

   null (Ed.)

   We introduce Probabilistic Dependency Graphs (PDGs), a new class of directed graphical models. PDGs can capture inconsistent beliefs in a natural way and are more modular than Bayesian Networks (BNs), in that they make it easier to incorporate new information and restructure the representation. We show by example how PDGs are an especially natural modeling tool. We provide three semantics for PDGs, each of which can be derived from a scoring function (on joint distributions over the variables in the network) that can be viewed as representing a distribution's incompatibility with the PDG. For the PDG corresponding to a BN, this function is uniquely minimized by the distribution the BN represents, showing that PDG semantics extend BN semantics. We show further that factor graphs and their exponential families can also be faithfully represented as PDGs, while there are significant barriers to modeling a PDG with a factor graph. 

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5. Probabilistic Sufficient Explanations

   Wang, E. ; Khosravi, P. ; Van den Broeck, G. ( January 2021 , Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI))

   null (Ed.)