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Explore efficient algorithms for maintaining shortest paths in dynamic graphs undergoing deletions, with a focus on near-optimal deterministic data structures and adaptive adversary scenarios.
Explore efficient algorithms for accessing answers to unions of conjunctive queries, focusing on ideal time guarantees and fine-grained complexity in query evaluation.
Explore fine-grained complexity theory, its applications in logic, and its role in query evaluation, offering insights into computational efficiency and algorithmic analysis.
Explore fine-grained complexity logic and query evaluation in relational algorithms, focusing on advanced techniques for optimizing database operations and query processing.
Explore fine-grained complexity, logic, and query evaluation in relational algorithms, focusing on advanced concepts and their applications in database systems.
Efficient marginal MAP solver for probabilistic circuits using iterative transformations and pruning, enabling exact solutions without search for decision-making problems.
Explores efficient direct access techniques for signed conjunctive queries, extending tractability results to queries with negative atoms using circuit-based approaches.
Explore compressed yet lossless representations of relational data, their efficient processing, and applications in algorithms, probabilistic databases, and in-database machine learning.
Explore efficient approximation algorithms for counting and sampling satisfying assignments in structured DNNF circuits, leveraging recent tree automata results.
Explore efficient enumeration of satisfying assignments in circuits, with applications to logic queries and database theory. Learn about maintaining enumeration structures on dynamic data.
Explore advanced probabilistic inference in hybrid domains using Weighted Model Integration. Learn about solvers, tractability analysis, and applications in Bayesian deep learning.
Explores an algorithm for counting Skolem functions in quantified Boolean formulas, addressing challenges in synthesis problems and providing PAC guarantees for approximate counting.
Exploring subtractive mixtures in probabilistic circuits: increased expressiveness, efficient learning, and inference for complex distributions. Theoretical and empirical advantages over traditional additive mixtures.
Exploring tractable circuits' applications in cryptography and continuous generative models, showcasing advancements in neuro-symbolic approaches and probabilistic inference for AI reasoning.
Explore extended algebraic decision diagrams (XADDs) for representing piecewise functions, enabling efficient computations and novel solutions in probabilistic inference, optimization, and decision-making.
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