yusuke1997/dblp-id-title huggingface dataset:
Random Clique Covers for Graphs with Local Density and Global Sparsity
Minimum Message Length Clustering Using Gibbs Sampling
A summary of a new normative theory of probabilistic logic
Optimal Transport Alignment of User Preferences from Ratings and Texts
DistriBlock: Identifying adversarial audio samples by leveraging characteristics of the output distribution
An Improved Convergence Analysis of Stochastic Variance-Reduced Policy Gradient
UAI '93: Proceedings of the Ninth Annual Conference on Uncertainty in Artificial Intelligence, The Catholic University of America, Providence, Washington, DC, USA, July 9-11, 1993
Semi-supervised Sequential Generative Models
Constrained Approximate Maximum Entropy Learning of Markov Random Fields
Finite-sample guarantees for Nash Q-learning with linear function approximation
The Anchors Hierarchy: Using the Triangle Inequality to Survive High Dimensional Data
Decayed MCMC Filtering
i 2 VAE: Interest Information Augmentation with Variational Regularizers for Cross-Domain Sequential Recommendation
Stochastic Bandit Models for Delayed Conversions
Censored Exploration and the Dark Pool Problem
Hinge-loss Markov Random Fields: Convex Inference for Structured Prediction
Recovering Articulated Object Models from 3D Range Data
Tensor-train density estimation
Convergence rates for localized actor-critic in networked Markov potential games
Nonparametric Bayesian Multi-Facet Clustering for Longitudinal Data
Dynamic Maintenance of Kernel Density Estimation Data Structure: From Practice to Theory
UAI '99: Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, Stockholm, Sweden, July 30 - August 1, 1999
FeDCM: Federated Learning of Deep Causal Generative Models
Probability and Asset Updating using Bayesian Networks for Combinatorial Prediction Markets
Feature-to-Feature Regression for a Two-Step Conditional Independence Test
Bayesian Inference in Treewidth-Bounded Graphical Models Without Indegree Constraints
Trade-Based Asset Models for Combinatorial Prediction Markets
Quantum Kernelized Bandits
Regularized online DR-submodular optimization
Coordinating Users of Shared Facilities via Data-driven Predictive Assistants and Game Theory
Variable importance matching for causal inference
Theory-Based Inductive Learning: An Integration of Symbolic and Quantitative Methods
Patch-Prompt Aligned Bayesian Prompt Tuning for Vision-Language Models
Compact Mathematical Programs For DEC-MDPs With Structured Agent Interactions
Structuring Bodies of Evidence
Planar Cycle Covering Graphs
Generating New Beliefs from Old
Causal Inference amid Missingness-Specific Independences and Mechanism Shifts
Bayesian Control for Concentrating Mixed Nuclear Waste
One-Shot Inference in Markov Random Fields
Monotonicity in Bayesian Networks
Heuristic Search Value Iteration for POMDPs
A constrained Bayesian approach to out-of-distribution prediction
An Algorithm for Finding Minimum d-Separating Sets in Belief Networks
Exploiting compositionality to explore a large space of model structures
Automated Analytic Asymptotic Evaluation of the Marginal Likelihood for Latent Models
The Relativity of Causal Knowledge
An Algebraic Semantics for Possibilistic Logic
Utility-Based Abstraction and Categorization
Learning sparse representations of preferences within Choquet expected utility theory
Evidence as opinions of experts
Towards Characterizing Markov Equivalence Classes for Directed Acyclic Graphs with Latent Variables
A Quantum Information Theoretic Approach to Tractable Probabilistic Models
A Method for Integrating Utility Analysis
Bounds on the Bethe Free Energy for Gaussian Networks
Off-policy Predictive Control with Causal Sensitivity Analysis
A Tool that Uses Human Factors to Simplify Model Building and Facilitate More Accurate Strategic Decisions
Value Function Approximation in Noisy Environments Using Locally Smoothed Regularized Approximate Linear Programs
Modeling Uncertain Temporal Evolutions in Model-based Diagnosis
Size-constrained k-submodular maximization in near-linear time
Auxiliary Gibbs Sampling for Inference in Piecewise-Constant Conditional Intensity Models
On the Convergence of Continual Learning with Adaptive Methods
On Maximum a Posteriori Estimation of Hidden Markov Processes
Addressing fairness in classification with a model-agnostic multi-objective algorithm
DAVID: influence diagram processing system for the macintosh
Investigating a Generalization of Probabilistic Material Implication and Bayesian Conditionals
Error Bounds for Physics-Informed Neural Networks in Fokker-Planck PDEs
Almost-everywhere Algorithmic Stability and Generalization Error
Two Facets of SDE Under an Information-Theoretic Lens: Generalization of SGD via Training Trajectories and via Terminal States
Knowledge integration for Conditional Probability Assessments
PAC-Bayesian Contrastive Unsupervised Representation Learning
PAC-Bayesian Policy Evaluation for Reinforcement Learning
Strategy Selection in Influence Diagrams using Imprecise Probabilities
Elliptical Slice Sampling with Expectation Propagation
Finding Optimal Bayesian Network Structures with Constraints Learned from Data
Neural Likelihoods via Cumulative Distribution Functions
Memory Mechanism for Unsupervised Anomaly Detection
A Construction of Bayesian Networks from Databases Based on an MDL Principle
Estimation Rates for Sparse Linear Cyclic Causal Models
Counting Markov Equivalence Classes by Number of Immoralities
Policy Improvement for POMDPs Using Normalized Importance Sampling
Block-Value Symmetries in Probabilistic Graphical Models
SGD with low-dimensional gradients with applications to private and distributed learning
Continuously indexed Potts models on unoriented graphs
Multiplicative Factorization of Noisy-Max
Stackmix: a complementary mix algorithm
Any-Space Probabilistic Inference
Order-of-Magnitude Influence Diagrams
Counterfactual Reasoning in Linear Structural Equation Models
BPR: Bayesian Personalized Ranking from Implicit Feedback
Literal or Pedagogic Human? Analyzing Human Model Misspecification in Objective Learning
Separation Properties of Sets of Probability Measures
Merging uncertain knowledge bases in a possibilistic logic framework
UAI '92: Proceedings of the Eighth Annual Conference on Uncertainty in Artificial Intelligence, Stanford University, Stanford, CA, USA, July 17-19, 1992
Interactive Learning from Unlabeled Instructions
Conformal Prediction without Nonconformity Scores
Fine-Grained matching with multi-perspective similarity modeling for cross-modal retrieval
Structure and Parameter Learning for Causal Independence and Causal Interaction Models
Problem formulation as the reduction of a decision model
Probability Distillation: A Caveat and Alternatives
Holographic Feature Representations of Deep Networks
Recognition Networks for Approximate Inference in BN20 Networks
Inference in Hybrid Bayesian Networks Using Mixtures of Gaussians
Response Aware Model-Based Collaborative Filtering
Improving the Scalability of Optimal Bayesian Network Learning with External-Memory Frontier Breadth-First Branch and Bound Search
Graphical-model Based Multiple Testing under Dependence, with Applications to Genome-wide Association Studies
Probabilistic reasoning about ship images
On Identifying Total Effects in the Presence of Latent Variables and Selection bias
Deterministic MDPs with Adversarial Rewards and Bandit Feedback
Training Neural Nets to Aggregate Crowdsourced Responses
A Case Study in Complexity Estimation: Towards Parallel Branch-and-Bound over Graphical Models
Reachability Under Uncertainty
Quantization of Large Language Models with an Overdetermined Basis
Analysis in HUGIN of data conflict
Semi-supervised learning, causality, and the conditional cluster assumption
Expectation Propagation for approximate Bayesian inference
Bayesian Vote Manipulation: Optimal Strategies and Impact on Welfare
An explore-then-commit algorithm for submodular maximization under full-bandit feedback
Solving POMDPs by Searching in Policy Space
Dynamic Mechanism Design for Markets with Strategic Resources
Temporal abstractions-augmented temporally contrastive learning: An alternative to the Laplacian in RL
Approximations for Decision Making in the Dempster-Shafer Theory of Evidence
Model Criticism of Bayesian Networks with Latent Variables
UCP-Networks: A Directed Graphical Representation of Conditional Utilities
Group Sparse Priors for Covariance Estimation
Exploration Analysis in Finite-Horizon Turn-based Stochastic Games
Bayesian Poker
Planning under Uncertainty with Weighted State Scenarios
Disciplined Convex Stochastic Programming: A New Framework for Stochastic Optimization
Conformal Prediction Sets for Deep Generative Models via Reduction to Conformal Regression
Feature Selection via Block-Regularized Regression
On characterizing Inclusion of Bayesian Networks
Efficient Multitask Feature and Relationship Learning
Resolving label uncertainty with implicit posterior models
Process, structure and modularity in reasoning with uncertainty
Simulation-Based Game Theoretic Analysis of Keyword Auctions with Low-Dimensional Bidding Strategies
Marginal Causal Consistency in Constraint-based Causal Learning
Stein Variational Policy Gradient
GNN2GNN: Graph neural networks to generate neural networks
Scaling integer arithmetic in probabilistic programs
Stochastic Generative Flow Networks
Belief-Kinematics Jeffrey-s Rules in the Theory of Evidence
Random probabilistic circuits
Learning Generative Models of Similarity Matrices
FlexAE: flexibly learning latent priors for wasserstein auto-encoders
Localised Natural Causal Learning Algorithms for Weak Consistency Conditions
Extended Lifted Inference with Joint Formulas
Joint Nonparametric Precision Matrix Estimation with Confounding
Matrix games with bandit feedback
On Considering Uncertainty and Alternatives in Low-Level Vision
A Comparison of Axiomatic Approaches to Qualitative Decision Making Using Possibility Theory
Conditional Bayesian Quadrature
Stochastic model for sunk cost bias
Bounding the expected run-time of nonconvex optimization with early stopping
Modeling Events with Cascades of Poisson Processes
One-Class Support Measure Machines for Group Anomaly Detection
Learning Hidden Markov Models with Geometrical Constraints
Improving Predictive Accuracy Using Smart-Data rather than Big-Data: A Case Study of Soccer Teams' Evolving Performance
A Univariate Bound of Area Under ROC
CUE: An Uncertainty Interpretation Framework for Text Classifiers Built on Pre-Trained Language Models
Evaluation of Uncertain Inference Models III: The Role of Tuning
A Bayesian Multiresolution Independence Test for Continuous Variables
Learning mixed graphical models from data with p larger than n
Join Graph Decomposition Bounds for Influence Diagrams
Cascading Bandits for Large-Scale Recommendation Problems
Designing Informative Securities
Nested Junction Trees
Limitations of Skeptical Default Reasoning
Quantum Speedups for Bayesian Network Structure Learning
Proceedings of the UAI 2015 Workshop on Advances in Causal Inference co-located with the 31st Conference on Uncertainty in Artificial Intelligence (UAI 2015), Amsterdam, The Netherlands, July 16, 2015
Towards robust episodic meta-learning
Non-Monotonic Negation in Probabilistic Deductive Databases
Refinement and coarsening of Bayesian networks
Decision Flexibility
Neighborhood Regularized l^1-Graph
Concept Forgetting via Label Annealing
Bayesian Optimization with Binary Auxiliary Information
Stein Variational Adaptive Importance Sampling
A Belief-Function Based Decision Support System
FALCON: Adaptive Cross-Domain APT Attack Investigation with Federated Causal Learning
Representing Aggregate Belief through the Competitive Equilibrium of a Securities Market
Stochastic Layer-Wise Precision in Deep Neural Networks
Action Networks: A Framework for Reasoning about Actions and Change under Uncertainty
Binary Join Trees
RL, but don't do anything I wouldn't do
Distributed adversarial training to robustify deep neural networks at scale
Causal Discovery for time series from multiple datasets with latent contexts
The Temporal Logic of Causal Structures
Proceedings of the Ninth UAI Bayesian Modeling Applications Workshop, Catalina Island, United States, August 18, 2012
Fast Nonparametric Conditional Density Estimation
On Testability and Goodness of Fit Tests in Missing Data Models
Evidence Absorption and Propagation through Evidence Reversals
Sensor Placement for Spatial Gaussian Processes with Integral Observations
Beyond Sin-Squared Error: Linear Time Entrywise Uncertainty Quantification for Streaming PCA
A Reinforcement Learning Approach to Weaning of Mechanical Ventilation in Intensive Care Units
Feature Selection and Dualities in Maximum Entropy Discrimination
Testing Generalizability in Causal Inference
On the distribution of penultimate activations of classification networks
Evaluating Anytime Algorithms for Learning Optimal Bayesian Networks
Learning STRIPS Operators from Noisy and Incomplete Observations
Speedy Model Selection (SMS) for Copula Models
Training generative neural networks via Maximum Mean Discrepancy optimization
Contrastive learning for supervised graph matching
Meta-learning without data via Wasserstein distributionally-robust model fusion
Generalised Wishart Processes
Stable Spectral Learning Based on Schur Decomposition
Universal Convexification via Risk-Aversion
On the Detection of Concept Changes in Time-Varying Data Stream by Testing Exchangeability
Information theoretic meta learning with Gaussian processes
Incorporating Metadata into Dynamic Topic Analysis
The AI&M Procedure for Learning from Incomplete Data
Adversarial Sets for Regularising Neural Link Predictors
Reasoning about Uncertainty in Metric Spaces
On Discarding, Caching, and Recalling Samples in Active Learning
Identification of Personalized Effects Associated With Causal Pathways
Loopy Belief Propogation and Gibbs Measures
Models of Consensus for Multiple Agent Systems
Equilibrium Computation in Multidimensional Congestion Games: CSP and Learning Dynamics Approaches
Revisiting Reweighted Wake-Sleep for Models with Stochastic Control Flow
Kidney Exchange with Inhomogeneous Edge Existence Uncertainty
Automated Construction of Sparse Bayesian Networks from Unstructured Probabilistic Models and Domain Information
ℓ ∞ -Bounds of the MLE in the BTL Model under General Comparison Graphs
Building Conformal Prediction Intervals with Approximate Message Passing
Greedy relaxations of the sparsest permutation algorithm
LoSAM: Local Search in Additive Noise Models with Mixed Mechanisms and General Noise for Global Causal Discovery
Probabilistic selection of inducing points in sparse Gaussian processes
When Extragradient Meets PAGE: Bridging Two Giants to Boost Variational Inequalities
Interdependent Defense Games: Modeling Interdependent Security under Deliberate Attacks
Hierarchical Solution of Markov Decision Processes using Macro-actions
Temperature Optimization for Bayesian Deep Learning
Counterfactual inference of second Opinions
Bounding the Uncertainty of Graphical Games: The Complexity of Simple Requirements, Pareto and Strong Nash Equilibria
Solving Influence Diagrams using HUGIN, Shafer-Shenoy and Lazy Propagation
Inference Algorithms for Similarity Networks
Aggregating Learned Probabilistic Beliefs
Large-Flip Importance Sampling
Dynamic visualization for L1 fusion convex clustering in near-linear time
DBN-Based Combinatorial Resampling for Articulated Object Tracking
Bayesian Classification and Feature Selection from Finite Data Sets
Super-Sampling with a Reservoir
Graph Partition Strategies for Generalized Mean Field Inference
Estimation of Effects of Sequential Treatments by Reparameterizing Directed Acyclic Graphs
Combining local search techniques and path following for bimatrix games
BubbleRank: Safe Online Learning to Re-Rank via Implicit Click Feedback
Markov Equivalence Classes for Maximal Ancestral Graphs
Minimizing Expected Losses in Perturbation Models with Multidimensional Parametric Min-cuts
Bayesian Inference in Monte-Carlo Tree Search
Neural probabilistic logic programming in discrete-continuous domains
On the Error of Random Fourier Features
Graphical models for preference and utility
Robust Reconstruction of Causal Graphical Models based on Conditional 2-point and 3-point Information
Time, chance and action
Exploiting Functional Dependence in Bayesian Network Inference
Diversity-enhanced probabilistic ensemble for uncertainty estimation
Algorithms for Approximate Minimization of the Difference Between Submodular Functions, with Applications
Learning by Repetition: Stochastic Multi-armed Bandits under Priming Effect
Transductive and Inductive Outlier Detection with Robust Autoencoders
General Belief Measures
Explaining fast improvement in online imitation learning
Incremental Tradeoff Resolution in Qualitative Probabilistic Networks
Practical privacy-preserving Gaussian process regression via secret sharing
The Factored Frontier Algorithm for Approximate Inference in DBNs
Strong Faithfulness and Uniform Consistency in Causal Inference
On Modal Logics for Qualitative Possibility in a Fuzzy Setting
Generalized Polya Urn for Time-varying Dirichlet Process Mixtures
Distributionally and Adversarially Robust Logistic Regression via Intersecting Wasserstein Balls
Weak to Strong Learning from Aggregate Labels
Causal Discovery from Temporally Aggregated Time Series
Locally Masked Convolution for Autoregressive Models
Causal Discovery with Deductive Reasoning: One Less Problem
The Cost of Troubleshooting Cost Clusters with Inside Information
Nearest neighbor search under uncertainty
Model-Based Hierarchical Clustering
Accounting for Context in Plan Recognition, with Application to Traffic Monitoring
Adaptively Truncating Backpropagation Through Time to Control Gradient Bias
Estimating Mutual Information by Local Gaussian Approximation
ILP-FORMER: Solving Integer Linear Programming with Sequence to Multi-Label Learning
Probabilistic Interpretation for MYCIN's Certainty Factors
Online Bellman Residual Algorithms with Predictive Error Guarantees
Cubic Regularization with Momentum for Nonconvex Optimization
Reasoning with Mass Distributions
A Stratified Simulation Scheme for Inference in Bayesian Belief Networks
Sparse Topical Coding
Sparse Q-learning with Mirror Descent
Probabilistic Models for Anomaly Detection in Remote Sensor Data Streams
separable and transitive graphoids
A combination of cutset conditioning with clique-tree propagation in the Pathfinder system
Countdown Regression: Sharp and Calibrated Survival Predictions
Representation Learning for Clustering: A Statistical Framework
A Study of Scaling Issues in Bayesian Belief Networks for Ship Classification
FROSH: FasteR Online Sketching Hashing
Instance Label Prediction by Dirichlet Process Multiple Instance Learning
Continuous Time Markov Networks
Causal Discovery in the Presence of Measurement Error
Exploiting Qualitative Knowledge in the Learning of Conditional Probabilities of Bayesian Networks
Learning Hierarchical Object Maps of Non-Stationary Environments with Mobile Robots
An Application of Uncertain Reasoning to Requirements Engineering
Mnemonist: Locating Model Parameters that Memorize Training Examples
Belief Propagation: Accurate Marginals or Accurate Partition Function - Where is the Difference?
SymNet 3.0: Exploiting Long-Range Influences in Learning Generalized Neural Policies for Relational MDPs
Variance reduction in frequency estimators via control variates method
Adaptive Hyper-box Matching for Interpretable Individualized Treatment Effect Estimation
Flexible Prior Elicitation via the Prior Predictive Distribution
Computational aspects of the Mobius transformation
Flexible Approximate Inference via Stratified Normalizing Flows
Robust contrastive learning and nonlinear ICA in the presence of outliers
Possibilistic preference elicitation by minimax regret
Active Learning of Linear Embeddings for Gaussian Processes
InfoDPCCA: Information-Theoretic Dynamic Probabilistic Canonical Correlation Analysis
An Approximate Solution Method for Large Risk-Averse Markov Decision Processes
Flexible Policy Construction by Information Refinement
A Permutation-Based Kernel Conditional Independence Test
Finite-Sample Analysis of Proximal Gradient TD Algorithms
Estimating the Partition Function by Discriminance Sampling
Modifying Bayesian Networks by Probability Constraints
Modelling local and global phenomena with sparse Gaussian processes
Shedding Light on Large Generative Networks: Estimating Epistemic Uncertainty in Diffusion Models
Finite-State Controllers of POMDPs using Parameter Synthesis
Generalizing off-policy learning under sample selection bias
Analysing Sensitivity Data from Probabilistic Networks
Constrained labeling for weakly supervised learning
Finite-time theory for momentum Q-learning
Network Fragments: Representing Knowledge for Constructing Probabilistic Models
Decision Making with Partially Consonant Belief Functions
Convex Relaxation Regression: Black-Box Optimization of Smooth Functions by Learning Their Convex Envelopes
Symbiotic Local Search for Small Decision Tree Policies in MDPs
Binary independent component analysis: a non-stationarity-based approach
Efficient Parametric Projection Pursuit Density Estimation
Probabilistic Embeddings for Frozen Vision-Language Models: Uncertainty Quantification with Gaussian Process Latent Variable Models
A Sound and Complete Algorithm for Learning Causal Models from Relational Data
Symmetric Wasserstein autoencoders
On Minimizing the Impact of Dataset Shifts on Actionable Explanations
Self-Discrepancy Conditional Independence Test
The promises and pitfalls of deep kernel learning
Improving Adversarial Transferability via Decision Boundary Adaptation
Bipolar Possibilistic Representations
Quantum Annealing for Clustering
Feature selection for discovering distributional treatment effect modifiers
Knowledge Intensive Learning of Credal Networks
Bayesian Estimators As Voting Rules
Inverse Covariance Estimation for High-Dimensional Data in Linear Time and Space: Spectral Methods for Riccati and Sparse Models
On testability of the front-door model via Verma constraints
Using Temporal Data for Making Recommendations
Efficient and accurate top-k recovery from choice data
An efficient approach for finding the MPE in belief networks
A Modification to Evidential Probability
Evasion Attacks Against Bayesian Predictive Models
New inference strategies for solving Markov Decision Processes using reversible jump MCMC
New Advances in Inference by Recursive Conditioning
Importance Sampled Stochastic Optimization for Variational Inference
Stratified Analysis of 'Probabilities of Causation'
Virtual Vector Machine for Bayesian Online Classification
Learning Treatment-Response Models from Multivariate Longitudinal Data
Complexity of Inference in Graphical Models
"Ideal Parent" Structure Learning for Continuous Variable Networks
A unifying framework for observer-aware planning and its complexity
Lower Bound Bayesian Networks - An Efficient Inference of Lower Bounds on Probability Distributions in Bayesian Networks
Machine Learning, Clustering and Polymorphy
Sum-product laws and efficient algorithms for imprecise Markov chains
On Convergence and Optimality of Best-Response Learning with Policy Types in Multiagent Systems
Functional causal Bayesian optimization
Improved Dynamic Schedules for Belief Propagation
Leveraging Side Observations in Stochastic Bandits
Adaptive Importance Sampling for Estimation in Structured Domains
Semi-bandit Optimization in the Dispersed Setting
CAN(PLAN)+: Extending the Operational Semantics of the BDI Architecture to deal with Uncertain Information
What is the most likely diagnosis?
Modeling Transitivity in Complex Networks
Valuation Networks and Conditional Independence
SymNet 2.0: Effectively handling Non-Fluents and Actions in Generalized Neural Policies for RDDL Relational MDPs
Inference Complexity in Continuous Time Bayesian Networks
Near-optimal Adaptive Pool-based Active Learning with General Loss
Efficient Inference in Persistent Dynamic Bayesian Networks
Learning to predict: an inductive approach
Hitting times for continuous-time imprecise-Markov chains