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Found 36 papers, 1 papers with code
Causal Unit Selection using Tractable Arithmetic Circuits
The unit selection problem aims to find objects, called units, that optimize a causal objective function which describes the objects' behavior in a causal context (e. g., selecting customers who are about to churn but would most likely change their mind if encouraged).
Identifying Causal Effects Under Functional Dependencies
We study the identification of causal effects, motivated by two improvements to identifiability which can be attained if one knows that some variables in a causal graph are functionally determined by their parents (without needing to know the specific functions).
Tractable Bounding of Counterfactual Queries by Knowledge Compilation
We discuss the problem of bounding partially identifiable queries, such as counterfactuals, in Pearlian structural causal models.
Logic for Explainable AI
We discuss in this tutorial a comprehensive, semantical and computational theory of explainability along these dimensions which is based on some recent developments in symbolic logic.
A New Class of Explanations for Classifiers with Non-Binary Features
We show that these explanations can be significantly improved in the presence of non-binary features, leading to a new class of explanations that relay more information about decisions and the underlying classifiers.
An Algorithm and Complexity Results for Causal Unit Selection
The unit selection problem aims to identify objects, called units, that are most likely to exhibit a desired mode of behavior when subjected to stimuli (e. g., customers who are about to churn but would change their mind if encouraged).
On the Complexity of Counterfactual Reasoning
We show that counterfactual reasoning is no harder than associational or interventional reasoning on fully specified SCMs in the context of two computational frameworks.
On the Computation of Necessary and Sufficient Explanations
In this paper, we refer to the prime implicates of a complete reason as necessary reasons for the decision.
Causal Inference Using Tractable Circuits
One can compile a non-parametric causal graph into an arithmetic circuit that supports inference in time linear in the circuit size.
Tractable Boolean and Arithmetic Circuits
Tractable Boolean and arithmetic circuits have been studied extensively in AI for over two decades now.
On Quantifying Literals in Boolean Logic and Its Applications to Explainable AI
This leads to a refinement of quantified Boolean logic with literal quantification as its primitive.
On Symbolically Encoding the Behavior of Random Forests
Recent work has shown that the input-output behavior of some machine learning systems can be captured symbolically using Boolean expressions or tractable Boolean circuits, which facilitates reasoning about the behavior of these systems.
A New Perspective on Learning Context-Specific Independence
We propose to first learn a functional and parameterized representation of a conditional probability table (CPT), such as a neural network.
Three Modern Roles for Logic in AI
We consider three modern roles for logic in artificial intelligence, which are based on the theory of tractable Boolean circuits: (1) logic as a basis for computation, (2) logic for learning from a combination of data and knowledge, and (3) logic for reasoning about the behavior of machine learning systems.
On Tractable Representations of Binary Neural Networks
We consider the compilation of a binary neural network's decision function into tractable representations such as Ordered Binary Decision Diagrams (OBDDs) and Sentential Decision Diagrams (SDDs).
On The Reasons Behind Decisions
We present a theory for unveiling the reasons behind the decisions made by Boolean classifiers and study some of its theoretical and practical implications.
An Advance on Variable Elimination with Applications to Tensor-Based Computation
We present new results on the classical algorithm of variable elimination, which underlies many algorithms including for probabilistic inference.
On the Relative Expressiveness of Bayesian and Neural Networks
A neural network computes a function.
A Symbolic Approach to Explaining Bayesian Network Classifiers
We propose an approach for explaining Bayesian network classifiers, which is based on compiling such classifiers into decision functions that have a tractable and symbolic form.
Tractability in Structured Probability Spaces
Recently, the Probabilistic Sentential Decision Diagram (PSDD) has been proposed as a framework for systematically inducing and learning distributions over structured objects, including combinatorial objects such as permutations and rankings, paths and matchings on a graph, etc.
On Compiling DNNFs without Determinism
On the theoretical side, we show that the new method could generate exponentially smaller DNNFs than deterministic ones, even by adding a single auxiliary variable.
On Relaxing Determinism in Arithmetic Circuits
The past decade has seen a significant interest in learning tractable probabilistic representations.
Human-Level Intelligence or Animal-Like Abilities?
The vision systems of the eagle and the snake outperform everything that we can make in the laboratory, but snakes and eagles cannot build an eyeglass or a telescope or a microscope.
Learning Bayesian networks with ancestral constraints
Our approach is based on a recently proposed framework for optimal structure learning based on non-decomposable scores, which is general enough to accommodate ancestral constraints.
Tractable Operations for Arithmetic Circuits of Probabilistic Models
We consider tractable representations of probability distributions and the polytime operations they support.
Tractable Learning for Complex Probability Queries
We propose a tractable learner that guarantees efficient inference for a broader class of queries.
Dual Decomposition from the Perspective of Relax, Compensate and then Recover
Relax, Compensate and then Recover (RCR) is a paradigm for approximate inference in probabilistic graphical models that has previously provided theoretical and practical insights on iterative belief propagation and some of its generalizations.
Decomposing Parameter Estimation Problems
We propose a technique for decomposing the parameter learning problem in Bayesian networks into independent learning problems.
Query DAGs: A Practical Paradigm for Implementing Belief Network Inference
We describe a new paradigm for implementing inference in belief networks, which relies on compiling a belief network into an arithmetic expression called a Query DAG (Q-DAG).
When do Numbers Really Matter?
Common wisdom has it that small distinctions in the probabilities quantifying a Bayesian network do not matter much for the resultsof probabilistic queries.
On the Role of Canonicity in Bottom-up Knowledge Compilation
We consider the problem of bottom-up compilation of knowledge bases, which is usually predicated on the existence of a polytime function for combining compilations using Boolean operators (usually called an Apply function).
Skolemization for Weighted First-Order Model Counting
First-order model counting emerged recently as a novel reasoning task, at the core of efficient algorithms for probabilistic logics.
EDML for Learning Parameters in Directed and Undirected Graphical Models
Second, it facilitates the design of EDML algorithms for new graphical models, leading to a new algorithm for learning parameters in Markov networks.
On the Complexity and Approximation of Binary Evidence in Lifted Inference
Recent theoretical results show, for example, that conditioning on evidence which corresponds to binary relations is #P-hard, suggesting that no lifting is to be expected in the worst case.
Proceedings of the Eighteenth Conference on Uncertainty in Artificial Intelligence (2002)
This is the Proceedings of the Eighteenth Conference on Uncertainty in Artificial Intelligence, which was held in Alberta, Canada, August 1-4 2002
Approximating MAP by Compensating for Structural Relaxations
We identify a second approach to compensation that is based on a more refined idealized case, resulting in a new approximation with distinct properties.
