yusuke1997/dblp-id-title huggingface dataset:
Gaussian process nowcasting: application to COVID-19 mortality reporting
Multiple Source Adaptation and the Rényi Divergence
Generalised Probabilistic Modelling and Improved Uncertainty Estimation in Comparative LLM-as-a-judge
Evaluating computational models of explanation using human judgments
Optimizing Memory-Bounded Controllers for Decentralized POMDPs
Stochastic Complexity of Bayesian Networks
A Computational Scheme for Reasoning in Dynamic Probabilistic Networks
Clustering a union of linear subspaces via matrix factorization and innovation search
Any Time Probabilistic Reasoning for Sensor Validation
A Complete Anytime Algorithm for Treewidth
ST-MAML : A stochastic-task based method for task-heterogeneous meta-learning
Revisiting differentially private linear regression: optimal and adaptive prediction & estimation in unbounded domain
Incentivizing honest performative predictions with proper scoring rules
MFA: Multi-layer Feature-aware Attack for Object Detection
The Decision-Theoretic Interactive Video Advisor
Propagating uncertainty in bayesian networks by probabilistic logic sampling
Faster Perfect Sampling of Bayesian Network Structures
On the Relation between Kappa Calculus and Probabilistic Reasoning
Probabilistic Conceptual Network: A Belief Representation Scheme for Utility-Based Categorization
Quantifying Nonlocal Informativeness in High-Dimensional, Loopy Gaussian Graphical Models
Well-Defined Function-Space Variational Inference in Bayesian Neural Networks via Regularized KL-Divergence
Reasoning at the Right Time Granularity
A Non-Numeric Approach to Multi-Criteria/Multi-Expert Aggregation Based on Approximate Reasoning
Factored Filtering of Continuous-Time Systems
A Characterization of Markov Equivalence Classes for Directed Acyclic Graphs with Latent Variables
Mixture Approximations to Bayesian Networks
Corporate Evidential Decision Making in Performance Prediction Domains
An Introduction to Algorithms for Inference in Belief Nets
Belief functions and default reasoning
Best-First AND/OR Search for Most Probable Explanations
Learning and Inference in Tractable Probabilistic Knowledge Bases
Staying in shape: learning invariant shape representations using contrastive learning
HUGS: Combining Exact Inference and Gibbs Sampling in junction Trees
Using Submodular Optimization to Approximate Minimum-Size Abductive Path Explanations for Tree-Based Models
Unsupervised Discovery of El Nino Using Causal Feature Learning on Microlevel Climate Data
Importance Sampling over Sets: A New Probabilistic Inference Scheme
Global explanations with decision rules: a co-learning approach
Efficient learning of sparse and decomposable PDEs using random projection
A Practical Method for Solving Contextual Bandit Problems Using Decision Trees
Practical model-based diagnosis with qualitative possibilistic uncertainty
Argumentation as a General Framework for Uncertain Reasoning
Ignorance and the Expressiveness of Single- and Set-Valued Probability Models of Belief
Belief Updating by Enumerating High-Probability Independence-based Assignments
Matroid Bandits: Fast Combinatorial Optimization with Learning
Off-policy TD( l) with a true online equivalence
Polynomial-time algorithm for learning optimal tree-augmented dynamic Bayesian networks
Probabilities of Causation for Continuous and Vector Variables
Identifying causal effects in maximally oriented partially directed acyclic graphs
Distributed Parallel Inference on Large Factor Graphs
Uncertainty Handling in Expert Systems: Uniform vs. Task-Specific Formalisms
Comparative uncertainty, belief functions and accepted beliefs
Causal inference and causal explanation with background knowledge
ResIST: Layer-wise decomposition of ResNets for distributed training
Valuation-based systems for discrete optimisation
Computer Adaptive Testing Using the Same-Decision Probability
Trusted re-weighting for label distribution learning
Efficient Clustering with Limited Distance Information
A Nonmyopic Approach to Cost-Constrained Bayesian Optimization
An optimization and generalization analysis for max-pooling networks
A sufficiently fast algorithm for finding close to optimal junction trees
Approximating the Bethe Partition Function
From Relational Databases to Belief Networks
Bayesian Structure Learning for Markov Random Fields with a Spike and Slab Prior
Bandits with costly reward observations
Lifted Message Passing as Reparametrization of Graphical Models
Budget Allocation Exploiting Label Correlation between Instances
A Defect in Dempster-Shafer Theory
Identifying Dynamic Sequential Plans
Risk-aware curriculum generation for heavy-tailed task distributions
Local Discovery by Partitioning: Polynomial-Time Causal Discovery Around Exposure-Outcome Pairs
Quantifying the Strategyproofness of Mechanisms via Metrics on Payoff Distributions
Characterizing Tightness of LP Relaxations by Forbidding Signed Minors
On early extinction and the effect of travelling in the SIR model
Reasoning about Bayesian Network Classifiers
Active Search and Bandits on Graphs using Sigma-Optimality
Identifying reasoning patterns in games
A Generalization of the Noisy-Or Model
CoSPA: An improved masked language model with copy mechanism for Chinese spelling correction
A Delayed Column Generation Strategy for Exact k-Bounded MAP Inference in Markov Logic Networks
Counting Belief Propagation
Learning explainable templated graphical models
Bayesian Network Learning with Discrete Case-Control Data
Adaptivity Complexity for Causal Graph Discovery
Integrating Planning and Execution in Stochastic Domains
Preference Elicitation For General Random Utility Models
Creative Agents: Empowering Agents with Imagination for Creative Tasks
Management of Uncertainty
Hashing-Based Approximate Probabilistic Inference in Hybrid Domains
Layerwidth: Analysis of a New Metric for Directed Acyclic Graphs
Evidential Reasoning with Conditional Belief Functions
A New Algorithm for Maximum Likelihood Estimation in Gaussian Graphical Models for Marginal Independence
Hypothesis Management in Situation-Specific Network Construction
The Topological Fusion of Bayes Nets
CORL: A Continuous-state Offset-dynamics Reinforcement Learner
Discriminative ordering through ensemble consensus
Causal Bounds and Instruments
Differentiable Probabilistic Models of Scientific Imaging with the Fourier Slice Theorem
Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data Augmentation
Confidence Inference in Bayesian Networks
TCE: A Test-Based Approach to Measuring Calibration Error
Representation Policy Iteration
Scoring and Searching over Bayesian Networks with Causal and Associative Priors
Variational message passing neural network for Maximum-A-Posteriori (MAP) inference
Incentives in Group Decision-Making With Uncertainty and Subjective Beliefs
Scalable Algorithms for Learning High-Dimensional Linear Mixed Models
Q* Approximation Schemes for Batch Reinforcement Learning: A Theoretical Comparison
Convergent message passing algorithms - a unifying view
Influence Diagrams for the Optimization of a Vehicle Speed Profile
Possibilistic decreasing persistence
Efficient Bayesian Inference for a Gaussian Process Density Model
MAP Estimation of Semi-Metric MRFs via Hierarchical Graph Cuts
Mini-Bucket Heuristics for Improved Search
Marginalization in Composed Probabilistic Models
Discriminative Switching Linear Dynamical Systems applied to Physiological Condition Monitoring
Approximate Inference Algorithms for Hybrid Bayesian Networks with Discrete Constraints
The Consistency Hypothesis in Uncertainty Quantification for Large Language Models
Dynamic Trees: A Structured Variational Method Giving Efficient Propagation Rules
Information theoretic approach to detect collusion in multi-agent games
Multivariate Information Bottleneck
Probabilistic causal reasoning
A Characterization of Markov Equivalence Classes of Relational Causal Models under Path Semantics
Marginalizing Out Future Passengers in Group Elevator Control
Hybrid Bayesian Networks with Linear Deterministic Variables
Revisiting the general identifiability problem
Propagation of belief functions: a distributed approach
On Identifiability of Conditional Causal Effects
Non-monotonicity in probabilistic reasoning
Learning Multi-interest Embedding with Dynamic Graph Cluster for Sequention Recommendation
Verifying Individual Fairness in Machine Learning Models
Just Trial Once: Ongoing Causal Validation of Machine Learning Models
Subseasonal climate prediction in the western US using Bayesian spatial models
Support Recovery in Sparse PCA with General Missing Data
Unstructuring User Preferences: Efficient Non-Parametric Utility Revelation
Proximal Interacting Particle Langevin Algorithms
Provably efficient representation selection in Low-rank Markov Decision Processes: from online to offline RL
PLSO: A generative framework for decomposing nonstationary time-series into piecewise stationary oscillatory components
Lifted Marginal MAP Inference
Conference on Uncertainty in Artificial Intelligence, Rio Othon Palace, Rio de Janeiro, Brazil, 21-25 July 2025
an entropy-driven system for construction of probabilistic expert systems from databases
Computing Optimal Bayesian Decisions for Rank Aggregation via MCMC Sampling
Multiple Instance Learning by Discriminative Training of Markov Networks
Decentralized Data Fusion and Active Sensing with Mobile Sensors for Modeling and Predicting Spatiotemporal Traffic Phenomena
Detecting critical treatment effect bias in small subgroups
Discrete Sampling using Semigradient-based Product Mixtures
Low-rank Matrix Bandits with Heavy-tailed Rewards
Network Engineering for Complex Belief Networks
Sequential Update of Bayesian Network Structure
Stochastic Optimal Control in Continuous Space-Time Multi-Agent Systems
Lower Bounds for Exact Model Counting and Applications in Probabilistic Databases
Solving Multistage Influence Diagrams using Branch-and-Bound Search
Value Function Approximation in Zero-Sum Markov Games
SPPM: Sparse Privacy Preserving Mappings
Abstracting Probabilistic Actions
Finite-Time Analysis of Three-Timescale Constrained Actor-Critic and Constrained Natural Actor-Critic Algorithms
Causal Mechanism-based Model Constructions
Causal Discovery from Changes
Do we become wiser with time? On causal equivalence with tiered background knowledge
Multi-winner approval voting goes epistemic
Individual Planning in Open and Typed Agent Systems
Sketching curvature for efficient out-of-distribution detection for deep neural networks
Treatment Choice in Heterogeneous Populations Using Experiments without Covariate Data
A Logic Programming Framework for Possibilistic Argumentation with Vague Knowledge
Inference and sampling of point processes from diffusion excursions
Perception, Attention, and Resources: A Decision-Theoretic Approach to Graphics Rendering
Computational Investigation of Low-Discrepancy Sequences in Simulation Algorithms for Bayesian Networks
Meta Reinforcement Learning with Latent Variable Gaussian Processes
Collapsing Sequence-Level Data-Policy Coverage via Poisoning Attack in Offline Reinforcement Learning
Active approximately metric-fair learning
Minimax sample complexity for turn-based stochastic game
Bounding Search Space Size via (Hyper)tree Decompositions
Modeling failure priors and persistence in model-based diagnosis
The Phylogenetic Indian Buffet Process: A Non-Exchangeable Nonparametric Prior for Latent Features
Probabilistic Similarity Logic
Structured Proportional Jump Processes
UAI '02, Proceedings of the 18th Conference in Uncertainty in Artificial Intelligence, University of Alberta, Edmonton, Alberta, Canada, August 1-4, 2002
Small Sample Inference for Generalization Error in Classification Using the CUD Bound
Active multi-fidelity Bayesian online changepoint detection
Birds of an odd feather: guaranteed out-of-distribution (OOD) novel category detection
Neural ensemble search via Bayesian sampling
Hypothesis Management Framework: a flexible design pattern for belief networks in decision support systems
VADIS: Investigating Inter-View Representation Biases for Multi-View Partial Multi-Label Learning
Fast Heterogeneous Federated Learning with Hybrid Client Selection
Representing Time in Causal Probabilistic Networks
Vacant holes for unsupervised detection of the outliers in compact latent representation
On Supervised Selection of Bayesian Networks
Probabilistic Structured Predictors
On the Detection of Conflicts in Diagnostic Bayesian Networks Using Abstraction
Mixed variable Bayesian optimization with frequency modulated kernels
Active Learning Framework for Incomplete Networks
UAI '96: Proceedings of the Twelfth Annual Conference on Uncertainty in Artificial Intelligence, Reed College, Portland, Oregon, USA, August 1-4, 1996
MSCGrapher: Learning Multi-Scale Dynamic Correlations for Multivariate Time Series Forecasting
Approximate Inference in Structured Instances with Noisy Categorical Observations
KNET: integrating hypermedia and normative bayesian modeling
Expectation Maximization and Complex Duration Distributions for Continuous Time Bayesian Networks
Parameterizing the Distance Distribution of Undirected Networks
Variational Sparse Coding
Fast Newton methods for the group fused lasso
Evidential Reasoning in a Categorial Perspective
Value-Directed Belief State Approximation for POMDPs
Consensus ranking under the exponential model
Unsupervised Feature Selection towards Pattern Discrimination Power
Belief Revision in Probability Theory
From Influence Diagrams to junction Trees
Path Planning under Time-Dependent Uncertainty
Causality in Bayesian Belief Networks
Provably Efficient Third-Person Imitation from Offline Observation
Stable Gradient Descent
Diagnostics for conditional density models and Bayesian inference algorithms
An Improved Admissible Heuristic for Learning Optimal Bayesian Networks
Efficient Stepwise Selection in Decomposable Models
Convergence Analysis of Gradient-Based Learning in Continuous Games
Confidence Factors, Empiricism and the Dempster-Shafer Theory of Evidence
Incremental Model-based Learners With Formal Learning-Time Guarantees
Models vs inductive inference for dealing with probabilistic knowledge
Kernel-Based Just-In-Time Learning for Passing Expectation Propagation Messages
Augmenting and Tuning Knowledge Graph Embeddings
A Note on the Measure of Discord
A hierarchical approach to designing approximate reasoning-based controllers for dynamic physical systems
OCEAN: Online Task Inference for Compositional Tasks with Context Adaptation
On the Value of Correlation
Structured Reachability Analysis for Markov Decision Processes
Optimal Limited Contingency Planning
Implementation of Continuous Bayesian Networks Using Sums of Weighted Gaussians
Exploiting the Rule Structure for Decision Making within the Independent Choice Logic
Progressive Abstraction Refinement for Sparse Sampling
Coherent Knowledge Processing at Maximum Entropy by SPIRIT
A Definition and Graphical Representation for Causality
Importance Weighted Consensus Monte Carlo for Distributed Bayesian Inference
Coresets for Estimating Means and Mean Square Error with Limited Greedy Samples
Exploring Multiple Dimensions of Parallelism in Junction Tree Message Passing
Near-Optimal Interdiction of Factored MDPs
Bayesian Model Averaging Using the k-best Bayesian Network Structures
Proceedings of the Twelfth UAI Bayesian Modeling Applications Workshop (BMAW 2015) co-located with the 31st Conference on Uncertainty in Artificial Intelligence (UAI 2015), Amsterdam, The Netherlands, July 16, 2015
A Logic for Reasoning about Evidence
Counterfactual Probabilities: Computational Methods, Bounds and Applications
Finite-horizon equilibria for neuro-symbolic concurrent stochastic games
Approximate implication with d-separation
Robust Gaussian process regression with the trimmed marginal likelihood
Greedy Block Coordinate Descent for Large Scale Gaussian Process Regression
Self-Confirming Price Prediction Strategies for Simultaneous One-Shot Auctions
From Fields to Trees
Uncertainty in Artificial Intelligence, UAI 2023, July 31 - 4 August 2023, Pittsburgh, PA, USA
What You See May Not Be What You Get: UCB Bandit Algorithms Robust to ε-Contamination
Handling Uncertain Information: A Review of Numeric and Non-numeric Methods
Asynchronous ε-Greedy Bayesian Optimisation
Efficient Nash Computation in Large Population Games with Bounded Influence
Transformation-based Probabilistic Clustering with Supervision
Comparing Direct and Indirect Temporal-Difference Methods for Estimating the Variance of the Return
Using Arguments for Making Decisions: A Possibilistic Logic Approach
Revisiting Bayesian network learning with small vertex cover
Anytime Planning for Decentralized POMDPs using Expectation Maximization
A Method for Speeding Up Value Iteration in Partially Observable Markov Decision Processes
Learning Bayesian Networks with Restricted Causal Interactions
Monte Carlo Matrix Inversion Policy Evaluation
The Supervised IBP: Neighbourhood Preserving Infinite Latent Feature Models
Bayesian Inference of Log Determinants
Bayesian Inference for Gaussian Mixed Graph Models
A Bayesian Approach toward Active Learning for Collaborative Filtering
Active Sensing as Bayes-Optimal Sequential Decision Making
On Modeling Profiles Instead of Values
Efficient Approximation for Triangulation of Minimum Treewidth
Is the volume of a credal set a good measure for epistemic uncertainty?
An Algorithm for Computing Stochastically Stable Distributions with Applications to Multiagent Learning in Repeated Games
Factored Latent Analysis for far-field Tracking Data
Structured nonlinear variable selection
Convergent and Correct Message Passing Schemes for Optimization Problems over Graphical Models
Numerical representations of acceptance
On the Validity of Covariate Adjustment for Estimating Causal Effects
A Constraint-Propagation Approach to Probabilistic Reasoning
Validation of composite systems by discrepancy propagation
Proportional allocation of indivisible resources under ordinal and uncertain preferences
Comparing Approaches to Uncertain Reasoning: Discussion: System Condemnation Pays Off
GCVR: Reconstruction from Cross-View Enable Sufficient and Robust Graph Contrastive Learning
Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation
Learning from Crowds with Dual-View K-Nearest Neighbor
Regret Minimization Algorithms for the Followers Behaviour Identification in Leadership Games
A Structurally and Temporally Extended Bayesian Belief Network Model: Definitions, Properties, and Modeling Techniques
Learning probabilistic sentential decision diagrams under logic constraints by sampling and averaging
Embarrassingly Parallel MCMC using Deep Invertible Transformations
Know your limits: Uncertainty estimation with ReLU classifiers fails at reliable OOD detection
Exploring compact reinforcement-learning representations with linear regression
Recommendation from Raw Data with Adaptive Compound Poisson Factorization
A causal bandit approach to learning good atomic interventions in presence of unobserved confounders
Communication-Efficient Distributed Primal-Dual Algorithm for Saddle Point Problem
Multi-armed Bandits with Missing Outcomes
Incorporating causal graphical prior knowledge into predictive modeling via simple data augmentation
Epistemic Uncertainty in Conformal Scores: A Unified Approach
Uncertain Reasoning Using Maximum Entropy Inference
Dimension reduction for high-dimensional small counts with KL divergence
Cost-Sharing in Bayesian Knowledge Bases
Probabilistic circuits that know what they don't know
Walking the Values in Bayesian Inverse Reinforcement Learning
Constructing Separators and Adjustment Sets in Ancestral Graphs
Learning Partial Policies to Speedup MDP Tree Search
A Hierarchical Switching Linear Dynamical System Applied to the Detection of Sepsis in Neonatal Condition Monitoring
The Binomial Block Bootstrap Estimator for Evaluating Loss on Dependent Clusters
Resolving Conflicting Arguments under Uncertainties
User Interface Tools for Navigation in Conditional Probability Tables and Elicitation of Probabilities in Bayesian Networks
High Dimensional Discrete Integration over the Hypergrid
On the design of consequential ranking algorithms
Quantum perceptron revisited: Computational-statistical tradeoffs
Quasi-Bayesian nonparametric density estimation via autoregressive predictive updates
Real-Time Scheduling via Reinforcement Learning
A Framework for Comparing Uncertain Inference Systems to Probability
Inference of causal effects when control variables are unknown
Solving Limited-Memory Influence Diagrams Using Branch-and-Bound Search
Robust LogitBoost and Adaptive Base Class (ABC) LogitBoost
Improved learning of Bayesian networks
Factored Particles for Scalable Monitoring
Learnability for the Information Bottleneck
A Logic of Graded Possibility and Certainty Coping with Partial Inconsistency
General-Purpose MCMC Inference over Relational Structures
Universal Graph Contrastive Learning with a Novel Laplacian Perturbation
Balancing utility and scalability in metric differential privacy
Improving Graph Contrastive Learning with Community Structure
A Qualitative Linear Utility Theory for Spohn's Theory of Epistemic Beliefs
Toward Large-Scale Agent Guidance in an Urban Taxi Service
Detecting textual adversarial examples through randomized substitution and vote
Conditional Restricted Boltzmann Machines for Structured Output Prediction
Local explanations via necessity and sufficiency: unifying theory and practice
First-Order Open-Universe POMDPs
Integrating probabilistic, taxonomic and causal knowledge in abductive diagnosis
Improving approximate optimal transport distances using quantization
Low-rank matrix recovery with unknown correspondence
Neural-progressive hedging: Enforcing constraints in reinforcement learning with stochastic programming
Towards Bounding Causal Effects under Markov Equivalence
Truthful Elicitation of Imprecise Forecasts
Probabilistic Models for Agent's Beliefs and Decisions
Learning Belief Networks in Domains with Recursively Embedded Pseudo Independent Submodels
Mixtures of Deterministic-Probabilistic Networks and their AND/OR Search Space
Exploiting Uniform Assignments in First-Order MPE
Confounding Equivalence in Causal Inference
Approximate Kalman Filter Q-Learning for Continuous State-Space MDPs
Evidential Confirmation as Transformed Probability: On the Duality of Priors and Updates
Bayesian Predictive Modelling: Application to Aircraft Short-Term Conflict Alert System
State-Space Abstraction for Anytime Evaluation of Probabilistic Networks
Representation Requirements for Supporting Decision Model Formulation
Transparent Trade-offs between Properties of Explanations
Operator Selection While Planning Under Uncertainty
Differentially private SGDA for minimax problems
Two-phase Attacks in Security Games
Approximate Linear Programming for First-order MDPs
Causal Effect Identification in Heterogeneous Environments from Higher-Order Moments
Deriving A Minimal itI-map of a Belief Network Relative to a Target Ordering of its Nodes
Robustness of model predictions under extension
UAI '00: Proceedings of the 16th Conference in Uncertainty in Artificial Intelligence, Stanford University, Stanford, California, USA, June 30 - July 3, 2000
Cyclic Causal Discovery from Continuous Equilibrium Data
Identifying Conditional Causal Effects
Intuitions about Ordered Beliefs Leading to Probabilistic Models
Bayesian Optimization with Inexact Acquisition: Is Random Grid Search Sufficient?
An Optimal Algorithm for Strongly Convex Min-Min Optimization
One-Bit Compressed Sensing via One-Shot Hard Thresholding
Class balancing GAN with a classifier in the loop
General Markov Model for Solving Patrolling Games
Bayesian Out-Trees
Bounded Rational Decision-Making in Feedforward Neural Networks
Black-box Optimization with Unknown Constraints via Overparameterized Deep Neural Networks
On Separation Criterion and Recovery Algorithm for Chain Graphs
Constrained Bayesian Inference for Low Rank Multitask Learning
Solving multi-model MDPs by coordinate ascent and dynamic programming
Generalized parametric path problems
Memorization Capacity for Additive Fine-Tuning with Small ReLU Networks
Parallel belief revision