yusuke1997/dblp-id-title huggingface dataset:
A Decision-Analytic Model for Using Scientific Data
Logarithmic-Time Updates and Queries in Probabilistic Networks
Semi-supervised Learning with Density Based Distances
Relaxed Multivariate Bernoulli Distribution and Its Applications to Deep Generative Models
Max-Product Belief Propagation for Linear Programming: Applications to Combinatorial Optimization
Model-Augmented Conditional Mutual Information Estimation for Feature Selection
Advantages and a Limitation of Using LEG Nets in a Real Time Problem
A Monte-Carlo Algorithm for Dempster-Shafer Belief
Causal Discovery with General Non-Linear Relationships using Non-Linear ICA
Counterexample-guided Planning
Map Learning with Indistinguishable Locations
Discovery of extended summary graphs in time series
Methods for Computing State Similarity in Markov Decision Processes
SPUDD: Stochastic Planning using Decision Diagrams
Self-supervised representations for multi-view reinforcement learning
Federated Rényi Fair Inference in Federated Heterogeneous System
Dealing with uncertainty on the initial state of a Petri net
A generalized mean field algorithm for variational inference in exponential families
Voronoi density estimator for high-dimensional data: Computation, compactification and convergence
Collapsible IDA: Collapsing Parental Sets for Locally Estimating Possible Causal Effects
Fraud/Uncollectible Debt Detection Using a Bayesian Network Based Learning System: A Rare Binary Outcome with Mixed Data Structures
Object Conditioning for Causal Inference
Improving the Accuracy and Efficiency of MAP Inference for Markov Logic
Constraint-based Causal Discovery from a Collection of Conditioning Sets
Interpolating Conditional Density Trees
A Tractable POMDP for Dynamic Sequencing with Applications to Personalized Internet Content Provision
Reasoning about the Value of Decision-Model Refinement: Methods and Application
Amortized Inference for Gaussian Process Hyperparameters of Structured Kernels
Relieving the elicitation burden of Bayesian Belief Networks
A Clustering Approach to Solving Large Stochastic Matching Problems
Bootstrap Your Conversions: Thompson Sampling for Partially Observable Delayed Rewards
Semi-supervised learning of partial differential operators and dynamical flows
Predicting the behavior of interacting humans by fusing data from multiple sources
Probabilistic vs. Fuzzy Reasoning
Tighter Generalization Bounds for Iterative Differentially Private Learning Algorithms
Targeted Learning for Variable Importance
Causal Discovery with Linear Non-Gaussian Models under Measurement Error: Structural Identifiability Results
Tightening LP Relaxations for MAP using Message Passing
Improving sign-random-projection via count sketch
Efficient Inference of Gaussian-Process-Modulated Renewal Processes with Application to Medical Event Data
Advances in Exact Bayesian Structure Discovery in Bayesian Networks
Quadratic metric elicitation for fairness and beyond
Efficiently Deciding Algebraic Equivalence of Bow-Free Acyclic Path Diagrams
Qualitative Models for Decision Under Uncertainty without the Commensurability Assumption
Stochastic Portfolio Theory: A Machine Learning Approach
Non-parametric Domain Approximation for Scalable Gibbs Sampling in MLNs
Incremental Probabilistic Inference
Learning the Dimensionality of Hidden Variables
Accurate and Scalable Stochastic Gaussian Process Regression via Learnable Coreset-based Variational Inference
Unsupervised constrained community detection via self-expressive graph neural network
Anytime Decision Making with Imprecise Probabilities
Distributed Anytime MAP Inference
Reformulating Inference Problems Through Selective Conditioning
Multi-View Learning over Structured and Non-Identical Outputs
Constrained Automated Mechanism Design for Infinite Games of Incomplete Information
Sublinear time algorithms for greedy selection in high dimensions
Intelligent Robots in an Uncertain World
Recursively-Constrained Partially Observable Markov Decision Processes
Qualitative Measures of Ambiguity
The Bregman Variational Dual-Tree Framework
Sum-Product Networks: A New Deep Architecture
On the Complexity of Solving Markov Decision Problems
Symbolic Probabilistic Inference with Evidence Potential
Learning to Predict from Crowdsourced Data
Nonuniform Dynamic Discretization in Hybrid Networks
Spectral Estimation of Conditional Random Graph Models for Large-Scale Network Data
Kernel Conditional Moment Test via Maximum Moment Restriction
Model-Based Probabilistic Situation Inference in Hierarchical Hypothesis Spaces
Bayesian federated estimation of causal effects from observational data
χSPN: Characteristic Interventional Sum-Product Networks for Causal Inference in Hybrid Domains
Higher Order Probabilities
Efficient debiased evidence estimation by multilevel Monte Carlo sampling
Bethe-ADMM for Tree Decomposition based Parallel MAP Inference
Fast Policy Learning through Imitation and Reinforcement
Intervention target estimation in the presence of latent variables
Learning Joint Nonlinear Effects from Single-variable Interventions in the Presence of Hidden Confounders
Combining pseudo-point and state space approximations for sum-separable Gaussian Processes
Beyond Structural Causal Models: Causal Constraints Models
Iterative Channel Estimation for Discrete Denoising under Channel Uncertainty
Neural tangent kernel at initialization: linear width suffices
Statistical Mechanics of Semi-Supervised Clustering in Sparse Graphs (Abstract)
Label Distribution Learning using the Squared Neural Family on the Probability Simplex
Learning Diagnostic Policies from Examples by Systematic Search
Robust learning of tractable probabilistic models
Estimation of Personalized Effects Associated With Causal Pathways
A Hybrid Algorithm to Compute Marginal and Joint Beliefs in Bayesian Networks and Its Complexity
Choosing Among Interpretations of Probability
On Convergence of Federated Averaging Langevin Dynamics
A Dynamic Programming Model for Determining Bidding Strategies in Sequential Auctions: Quasi-linear Utility and Budget Constraints
Learning with Non-Convex Truncated Losses by SGD
On the Choice of Regions for Generalized Belief Propagation
TX-Ray: Quantifying and Explaining Model-Knowledge Transfer in (Un-)Supervised NLP
Regret-based Reward Elicitation for Markov Decision Processes
Learning Max-Margin Tree Predictors
Convergence Behavior of an Adversarial Weak Supervision Method
Inference with Causal Independence in the CPSC Network
Critical Remarks on Single Link Search in Learning Belief Networks
Efficient resource allocation with fairness constraints in restless multi-armed bandits
Large-scale Submodular Greedy Exemplar Selection with Structured Similarity Matrices
Complexity of the Exact Solution to the Test Sequencing Problem
SPvR: Structured Pruning via Ranking
Efficient Induction of Finite State Automata
Learning the Structure of Probabilistic Sentential Decision Diagrams
Speeding Up Planning in Markov Decision Processes via Automatically Constructed Abstraction
Identifiability of Causal Graphs using Functional Models
Learning Graphical Models of Images, Videos and Their Spatial Transformations
Exponential Families for Conditional Random Fields
Representing Heuristic Knowledge in D-S Theory
Hierarchical Evidence Accumulation in the Pseiki System and Experiments in Model-Driven Mobile Robot Navigation
PROVIDE: a probabilistic framework for unsupervised video decomposition
Generalized Qualitative Probability: Savage revisited
Closed-Form Learning of Markov Networks from Dependency Networks
On random kernels of residual architectures
Some Experiments with Real-time Decision Algorithms
Statistical Significance of Feature Importance Rankings
Generalized Expected Utility as a Universal Decision Rule - A Step Forward
Fast and scalable score-based kernel calibration tests
On the definition and computation of causal treewidth
High-confidence error estimates for learned value functions
Discounting and Combination Operations in Evidential Reasoning
Sensitivity Analysis for Threshold Decision Making with Dynamic Networks
Being Bayesian about Network Structure
Slice Sampling for General Completely Random Measures
Overdispersed Black-Box Variational Inference
Empirical Evaluation of Approximation Algorithms for Probabilistic Decoding
Learning to Rank With Bregman Divergences and Monotone Retargeting
On-line Prediction with Kernels and the Complexity Approximation Principle
Structured Priors for Structure Learning
Exact Inference of Hidden Structure from Sample Data in noisy-OR Networks
An Experimental Comparison of Numerical and Qualitative Probabilistic Reasoning
Ordinal causal discovery
Entailment in Probability of Thresholded Generalizations
How well does your sampler really work?
Is Probability Theory Sufficient for Dealing with Uncertainty in AI: A Negative View
Nesting Probabilistic Programs
Random Algorithms for the Loop Cutset Problem
An experimental procedure for evaluating user-centered methods for rapid Bayesian network construction
A New Class of upper Bounds on the Log Partition Function
On the Construction of the Inclusion Boundary Neighbourhood for Markov Equivalence Classes of Bayesian Network Structures
Measurement Dependence Inducing Latent Causal Models
Products, Abstractions and Inclusions of Causal Spaces
A new algorithm for finding MAP assignments to belief networks
Cumulative distribution networks and the derivative-sum-product algorithm
Quantification of Credal Uncertainty in Machine Learning: A Critical Analysis and Empirical Comparison
Approximate Inference and Constrained Optimization
RAPID: A Reachable Anytime Planner for Imprecisely-sensed Domains
Incremental computation of the value of perfect information in stepwise-decomposable influence diagrams
Verbal Expressions for Probability Updates: How Much More Probable is "Much More Probable"?
The Dynamic of Belief in the Transferable Belief Model and Specialization-Generalization Matrices
BaRT: A Bayesian Reasoning Tool for Knowledge Based Systems
Restless and uncertain: Robust policies for restless bandits via deep multi-agent reinforcement learning
Towards Precision of Probabilistic Bounds Propagation
On the Identifiability and Estimation of Functional Causal Models in the Presence of Outcome-Dependent Selection
Library-style Ontologies to Support Varying Model Views
Existence and Finiteness Conditions for Risk-Sensitive Planning: Results and Conjectures
A Conditional Random Field for Discriminatively-trained Finite-state String Edit Distance
Bayesian Supervised Dictionary learning‎
Maximum Entropy for Collaborative Filtering
Clustered Sparse Bayesian Learning
Consensus Rules
Learning from Point Sets with Observational Bias
Observations from field trials with several elicitation techniques in an ecological domain
A Hybrid Approach to Reasoning with Partially Elicited Preference Models
Active Uncertainty Calibration in Bayesian ODE Solvers
User-Centered Methods for Rapid Creation and Validation of Bayesian Belief Networks
A Characterization of the Dirichlet Distribution with Application to Learning Bayesian Networks
Finding Optimal Bayesian Networks
Sparse-Matrix Belief Propagation
Tuning Algorithmic and Architectural Hyperparameters in Graph-Based Semi-Supervised Learning with Provable Guarantees
An Uncertainty Framework for Classification
YGGDRASIL-A statistical package for learning Split Models
Sparse Nested Markov models with Log-linear Parameters
Does Momentum Help in Stochastic Optimization? A Sample Complexity Analysis
EERO: Early Exit with Reject Option for Efficient Classification with limited budget
Jana: Jointly amortized neural approximation of complex Bayesian models
A Global Markov Property for Solutions of Stochastic Difference Equations and the corresponding Full Time Graphs
Election Control by Manipulating Issue Significance
Posterior sampling-based online learning for the stochastic shortest path model
The Survival Filter: Joint Survival Analysis with a Latent Time Series
pRSL: Interpretable multi-label stacking by learning probabilistic rules
An Object-Oriented Dynamic Bayesian Decision Network Model for Grasslands Adaptive Management
CIGMO: Categorical invariant representations in a deep generative framework
Hierarchical Maximum Margin Learning for Multi-Class Classification
Analysis of Thompson Sampling for Stochastic Sleeping Bandits
L2 Regularization for Learning Kernels
Testing Identifiability of Causal Effects
Monotone Closure of Relaxed Constraints in Submodular Optimization: Connections Between Minimization and Maximization
What is the Right Notion of Distance between Predict-then-Optimize Tasks?
Bias aware probabilistic Boolean matrix factorization
Linear Algebra Approach to Separable Bayesian Networks
Possible World Partition Sequences: A Unifying Framework for Uncertain Reasoning
A Fusion Algorithm for Solving Bayesian Decision Problems
Conditionally independent data generation
Amortized variance reduction for doubly stochastic objective
A Decision Calculus for Belief Functions in Valuation-based Systems
Enhancing QPNs for Trade-off Resolution
UAI 2009, Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, Montreal, QC, Canada, June 18-21, 2009
Complex Markov Logic Networks: Expressivity and Liftability
End-to-end Training of Deep Probabilistic CCA on Paired Biomedical Observations
Fuzzy Geometric Relations to Represent Hierarchical Spatial Information
Adaptive Reward Design for Reinforcement Learning
Battle of Bandits
Conditioning on Disjunctive Knowledge: Simpson's Paradox in Default Logic
Dynamic Jointrees
Robust Bayesian recourse
Linear Contour Learning: A Method for Supervised Dimension Reduction
MMEL: A Joint Learning Framework for Multi-Mention Entity Linking
Plausible reasoning from spatial observations
Baysian numerical integration with neural networks
Sparse-posterior Gaussian Processes for general likelihoods
Identifying Finite Mixtures of Nonparametric Product Distributions and Causal Inference of Confounders
Metaprobability and Dempster-Shafer in Evidential Reasoning
Gaussian Process Networks
CounteRGAN: Generating counterfactuals for real-time recourse and interpretability using residual GANs
An Evaluation of Two Alternatives to Minimax
Cooperative Meta-Learning with Gradient Augmentation
An Integrated, Conditional Model of Information Extraction and Coreference with Appli
Some Properties of Plausible Reasoning
An Interpretable and Sample Efficient Deep Kernel for Gaussian Process
Guiding Time-Varying Generative Models with Natural Gradients on Exponential Family Manifold
Incremental compilation of Bayesian networks
Mutation-driven follow the regularized leader for last-iterate convergence in zero-sum games
A one-sample decentralized proximal algorithm for non-convex stochastic composite optimization
Cutset Sampling with Likelihood Weighting
Learning To Invert: Simple Adaptive Attacks for Gradient Inversion in Federated Learning
Generalization Bounds for Transfer Learning under Model Shift
Anytime State-Based Solution Methods for Decision Processes with non-Markovian Rewards
Sampling and Inference for Beta Neutral-to-the-Left Models of Sparse Networks
Integer programming-based error-correcting output code design for robust classification
Belief change with noisy sensing in the situation calculus
SpinSVAR: Estimating Structural Vector Autoregression Assuming Sparse Input
Compiling Dyanamic Fault Trees into Dynamic Bayesian Nets for Reliability Analysis: the RADYBAN Tool
On the effects of quantisation on model uncertainty in Bayesian neural networks
A competitive analysis of online failure-aware assignment
Pairwise Supervised Hashing with Bernoulli Variational Auto-Encoder and Self-Control Gradient Estimator
Max-margin learning with the Bayes factor
Convergent Propagation Algorithms via Oriented Trees
Graphical Representations of Consensus Belief
Learning Bayesian Nets that Perform Well
Optimal Statistical Hypothesis Testing for Social Choice
Identifying Causal Effects with Computer Algebra
Score-based vs Constraint-based Causal Learning in the Presence of Confounders
Gradient descent in matrix factorization: Understanding large initialization
Synthesis of Strategies in Influence Diagrams
Balanced Mini-batch Sampling for SGD Using Determinantal Point Processes
Scalable nonparametric Bayesian learning for dynamic velocity fields
Stochastic continuous normalizing flows: training SDEs as ODEs
Pairwise Cluster Comparison for Learning Latent Variable Models
Automated Dependence Plots
Detection of Short-Term Temporal Dependencies in Hawkes Processes with Heterogeneous Background Dynamics
Exploiting System Hierarchy to Compute Repair Plans in Probabilistic Model-Based Diagnosis
An Efficient Protocol for Negotiation over Combinatorial Domains with Incomplete Information
Latent Dirichlet Allocation Uncovers Spectral Characteristics of Drought Stressed Plants
Ranking by Dependence - A Fair Criteria
Enabling long-range exploration in minimization of multimodal functions
Efficiently Searching for Frustrated Cycles in MAP Inference
Divergence-Based Motivation for Online EM and Combining Hidden Variable Models
Testing whether linear equations are causal: A free probability theory approach
Continuation Methods for Mixing Heterogenous Sources
Using the Gene Ontology Hierarchy when Predicting Gene Function
Fast Ridge Regression with Randomized Principal Component Analysis and Gradient Descent
Modeling Waveform Shapes with Random E ects Segmental Hidden Markov Models
Piecewise Training for Undirected Models
Advances in Bayesian Network Learning using Integer Programming
Adaptive Stratified Sampling for Precision-Recall Estimation
Learning networks determined by the ratio of prior and data
Adjacency-Faithfulness and Conservative Causal Inference
Generalizing Jeffrey Conditionalization
Efficient Sparse Recovery via Adaptive Non-Convex Regularizers with Oracle Property
How matroids occur in the context of learning Bayesian network structure
Similarity-Driven Cluster Merging Method for Unsupervised Fuzzy Clustering
Products of Hidden Markov Models: It Takes N>1 to Tango
Monitoring a Complez Physical System using a Hybrid Dynamic Bayes Net
A Scheme for Approximating Probabilistic Inference
Probabilistic task modelling for meta-learning
Temporal Action-Graph Games: A New Representation for Dynamic Games
Real-valued All-Dimensions Search: Low-overhead Rapid Searching over Subsets of Attributes
Robustness of Causal Claims
Wasserstein Fair Classification
N-GCN: Multi-scale Graph Convolution for Semi-supervised Node Classification
Consistency Regularization for Domain Generalization with Logit Attribution Matching
Large-scale randomized-coordinate descent methods with non-separable linear constraints
Sparse Stochastic Finite-State Controllers for POMDPs
Learning to Sample in Stochastic Optimization
Query-Answer Causality in Databases: Abductive Diagnosis and View Updates
Modeling Transportation Routines using Hybrid Dynamic Mixed Networks
Probabilistic evaluation of candidate sets for multidisorder diagnosis
MASSIVE: Tractable and Robust Bayesian Learning of Many-Dimensional Instrumental Variable Models
Online Importance Weight Aware Updates
Lifted Representation of Relational Causal Models Revisited: Implications for Reasoning and Structure Learning
Belief and Surprise - A Belief-Function Formulation
Graph reparameterizations for enabling 1000+ Monte Carlo iterations in Bayesian deep neural networks
Monte-Carlo Search for an Equilibrium in Dec-POMDPs
Bayesian structure learning with generative flow networks
Three new sensitivity analysis methods for influence diagrams
My Brain is Full: When More Memory Helps
On Overcoming Miscalibrated Conversational Priors in LLM-based ChatBots
Of Starships and Klingons: Bayesian Logic for the 23rd Century
Truthful Feedback for Sanctioning Reputation Mechanisms
Learning Nonlinear Causal Effect via Kernel Anchor Regression
Causal Modeling
Bandit Algorithms for Tree Search
A Cluster-Cumulant Expansion at the Fixed Points of Belief Propagation
Adaptive Softmax Trees for Many-Class Classification
Structured Arc Reversal and Simulation of Dynamic Probabilistic Networks
Fast MCMC sampling for Markov jump processes and continuous time Bayesian networks
Probabilistic Description Logics
Distribution over Beliefs for Memory Bounded Dec-POMDP Planning
Contextual Weak Independence in Bayesian Networks
Parameter Priors for Directed Acyclic Graphical Models and the Characteriration of Several Probability Distributions
A General Algorithm for Approximate Inference and Its Application to Hybrid Bayes Nets
BeliefPPG: Uncertainty-aware heart rate estimation from PPG signals via belief propagation
Bayesian Optimization over Bounded Domains with the Beta Product Kernel
RDI: An adversarial robustness evaluation metric for deep neural networks based on model statistical features
A Sparse Representation-Based Approach to Linear Regression with Partially Shuffled Labels
Two Procedures for Compiling Influence Diagrams
Bayesian Network Classifiers in a High Dimensional Framework
On Quantified Linguistic Approximation
Description Logics with Fuzzy Concrete Domains
Random Sum-Product Networks: A Simple and Effective Approach to Probabilistic Deep Learning
Computing Best-Response Strategies in Infinite Games of Incomplete Information
Approximate Thompson Sampling via Epistemic Neural Networks
Optimal Threshold Control for Energy Arbitrage with Degradable Battery Storage
Spectrum Identification using a Dynamic Bayesian Network Model of Tandem Mass Spectra
Solving POMDPs by Searching the Space of Finite Policies
Causal screening in dynamical systems
Data augmentation in Bayesian neural networks and the cold posterior effect
Bayesian Rose Trees
Establishing Markov equivalence in cyclic directed graphs
Learning Measurement Models for Unobserved Variables
Population Empirical Bayes
No-regret approximate inference via Bayesian optimisation
UAI '06, Proceedings of the 22nd Conference in Uncertainty in Artificial Intelligence, Cambridge, MA, USA, July 13-16, 2006
Stochastic simulation algorithms for dynamic probabilistic networks
Using Bayesian Attack Detection Models to Drive Cyber Deception
Latent Topic Models for Hypertext
Representing and Reasoning With Probabilistic Knowledge: A Bayesian Approach
Belief Induced by the Partial Knowledge of the Probabilities
Simple Transferability Estimation for Regression Tasks
Multi-Context Models for Reasoning under Partial Knowledge: Generative Process and Inference Grammar
Bayesian exponential family projections for coupled data sources
An Odds Ratio Based Inference Engine
Upgrading Ambiguous Signs in QPNs
Capturing actionable dynamics with structured latent ordinary differential equations
Efficient Inference in Markov Control Problems
Bisimulation-based Approximate Lifted Inference
SENTINEL: taming uncertainty with ensemble based distributional reinforcement learning
Inferring Informational Goals from Free-Text Queries: A Bayesian Approach
The Role of Memory in Stochastic Optimization
Search-Based Methods to Bound Diagnostic Probabilities in Very Large Belief Nets
A Logical Characterization of Constraint-Based Causal Discovery
Multi-source domain adaptation via weighted joint distributions optimal transport
A Structured, Probabilistic Representation of Action
Nilsson's Probabilistic Entailment Extended to Dempster-Shafer Theory
On the inductive bias of neural networks for learning read-once DNFs
Content-based Modeling of Reciprocal Relationships using Hawkes and Gaussian Processes
Learning Riemannian Metrics
Post-hoc loss-calibration for Bayesian neural networks
Functional Wasserstein Bridge Inference for Bayesian Deep Learning
Semantics for Improving Accuracy and Reducing Complexity in Strategic Decision Facilitation Tools
Data sampling affects the complexity of online SGD over dependent data
Subspace Inference for Bayesian Deep Learning
Differentially Private Small Dataset Release Using Random Projections
Pure and Strong Nash Equilibrium Computation in Compactly Representable Aggregate Games
Flat Posterior Does Matter For Bayesian Model Averaging
Lifting in multi-agent systems under uncertainty
Stochastic Logic Programs: Sampling, Inference and Applications
Constructing Situation Specific Belief Networks
Combining Feature and Example Pruning by Uncertainty Minimization