AAAI-2024-Papers Application Reasoning under Uncertainty 🆔 Title Repo Paper Video s-ID: Causal Effect Identification in a Sub-population ➖ On Estimating the Gradient of the Expected Information Gain in Bayesian Experimental Design ➖ Backward Responsibility in Transition Systems Using General Power Indices ➖ The Expected Loss of Preconditioned Langevin Dynamics Reveals the Hessian Rank ➖ Pandora’s Problem with Deadlines ➖ Minibatch Stochastic Three Points Method for Unconstrained Smooth Minimization ➖ Identification of Causal Structure with Latent Variables Based on Higher Order Cumulants ➖ Direct Amortized Likelihood Ratio Estimation ➖ Probabilistic Offline Policy Ranking with Approximate Bayesian Computation ➖ Generalized Bradley-Terry Models for Score Estimation from Paired Comparisons ➖ Identifiability of Direct Effects from Summary Causal Graphs ➖ Model Counting and Sampling via Semiring Extensions ➖ Identification for Tree-Shaped Structural Causal Models in Polynomial Time ➖ Learning GAI-Decomposable Utility Models for Multiattribute Decision Making ➖ Uncertainty Quantification in Heterogeneous Treatment Effect Estimation with Gaussian-Process-Based Partially Linear Model ➖ Learning Diffusions under Uncertainty ➖ Robustly Improving Bandit Algorithms with Confounded and Selection Biased Offline Data: A Causal Approach ➖ Effectiveness of Constant Stepsize in Markovian LSA and Statistical Inference ➖ Piecewise Linear Transformation – Propagating Aleatoric Uncertainty in Neural Networks ➖ Probabilities of Causation with Nonbinary Treatment and Effect ➖ Unit Selection with Nonbinary Treatment and Effect ➖ Solving Satisfiability Modulo Counting for Symbolic and Statistical AI Integration with Provable Guarantees ➖ TNPAR: Topological Neural Poisson Auto-Regressive Model for Learning Granger Causal Structure from Event Sequences ➖ Colour Passing Revisited: Lifted Model Construction with Commutative Factors ➖ Root Cause Explanation of Outliers under Noisy Mechanisms ➖ Identification of Causal Structure in the Presence of Missing Data with Additive Noise Model ➖ Causal Discovery from Poisson Branching Structural Causal Model Using High-Order Cumulant with Path Analysis ➖ A Fixed-Parameter Tractable Algorithm for Counting Markov Equivalence Classes with the Same Skeleton ➖ Learning Bayesian Network Classifiers to Minimize the Class Variable Parameters ➖ Bayesian Inference with Complex Knowledge Graph Evidence ➖ Exact, Fast and Expressive Poisson Point Processes via Squared Neural Families ➖ Inference and Learning in Dynamic Decision Networks Using Knowledge Compilation ➖ Linear-Time Algorithms for Front-Door Adjustment in Causal Graphs ➖ Neural Causal Abstractions ➖ Federated Contextual Cascading Bandits with Asynchronous Communication and Heterogeneous Users ➖ Causal-Driven Skill Prerequisite Structure Discovery ➖ Deep Copula-Based Survival Analysis for Dependent Censoring with Identifiability Guarantees ➖
