yusuke1997/dblp-id-title huggingface dataset:
Iterative Join-Graph Propagation
Finite-sample Analysis of Greedy-GQ with Linear Function Approximation under Markovian Noise
Efficient Multi-Class Selective Sampling on Graphs
A Smart-Dumb/Dumb-Smart Algorithm for Efficient Split-Merge MCMC
Minimax regret based elicitation of generalized additive utilities
Cross-domain adaptive transfer reinforcement learning based on state-action correspondence
Soft-Robust Actor-Critic Policy-Gradient
Boosting as a Product of Experts
Efficient Algorithm for Estimation of Qualitative Expected Utility in Possibilistic Case-based Reasoning
Learning Structural Changes of Gaussian Graphical Models in Controlled Experiments
FLASH: Automating federated learning using CASH
What Uncertainty Judgments Can Tell About the Underlying Subjective Probabilities
The Optimal Reward Baseline for Gradient-Based Reinforcement Learning
Interpretable Policies for Dynamic Product Recommendations
Quasi-Newton Hamiltonian Monte Carlo
Statistically Efficient Greedy Equivalence Search
A Generalized Bayesian Approach to Distribution-on-Distribution Regression
Learning Factor Graphs in Polynomial Time & Sample Complexity
Normative Engineering Risk Management Systems
Sampling-Free Variational Inference of Bayesian Neural Networks by Variance Backpropagation
Markov Determinantal Point Processes
A Dual Approach to Scalable Verification of Deep Networks
Stochastic Embeddings : A Probabilistic and Geometric Analysis of Out-of-Distribution Behavior
The Application of Dempster Shafer Theory to a Logic-Based Visual Recognition System
Enumerating Optimal Cost-Constrained Adjustment Sets
How unfair is private learning?
Nested Markov Properties for Acyclic Directed Mixed Graphs
Robust statistical comparison of random variables with locally varying scale of measurement
Dynamic Network Updating Techniques for Diagnostic Reasoning
Corruption-Robust Variance-aware Algorithms for Generalized Linear Bandits under Heavy-tailed Rewards
Maximum Entropy and the Glasses You are Looking Through
Optimization Framework for Semi-supervised Attributed Graph Coarsening
Decomposition of Probabilities of Causation with Two Mediators
The Long-Run Behavior of Continuous Time Bayesian Networks
Convex Structure Learning for Bayesian Networks: Polynomial Feature Selection and Approximate Ordering
Active Semi-Supervised Learning using Submodular Functions
Scaling Probabilistic Circuits via Data Partitioning
A Simple Insight into Iterative Belief Propagation's Success
Message-Passing Algorithms for Quadratic Programming Formulations of MAP Estimation
On Continuous Monitoring of Risk Violations under Unknown Shift
Learning from Label Proportions and Covariate-shifted Instances
Fast inference and transfer of compositional task structures for few-shot task generalization
Unified PAC-Bayesian Study of Pessimism for Offline Policy Learning with Regularized Importance Sampling
Static and Dynamic Values of Computation in MCTS
Error Estimation in Approximate Bayesian Belief Network Inference
Adjustment Criteria in Causal Diagrams: An Algorithmic Perspective
Comparative Analysis of Probabilistic Models for Activity Recognition with an Instrumented Walker
Bayesian Networks on Income Tax Audit Selection - A Case Study of Brazilian Tax Administration
Non-stationary Domain Generalization: Theory and Algorithm
Loosely consistent emphatic temporal-difference learning
Probabilistically robust conformal prediction
Triply Stochastic Gradients on Multiple Kernel Learning
Qualitativce probabilistic networks for planning under uncertainty
Phase Transition of Tractability in Constraint Satisfaction and Bayesian Network Inference
A Maximum Likelihood Approach For Selecting Sets of Alternatives
A causal bayesian model for the diagnosis of appendicitis
Robust learning Bayesian networks for prior belief
The Mondrian Kernel
A principled analysis of merging operations in possibilistic logic
Efficient Rollout Strategies for Bayesian Optimization
Disentangling mixtures of unknown causal interventions
Generalization error bounds for deep unfolding RNNs
Comparing Bayesian Network Classifiers
Instrumentality Tests Revisited
A Bayesian Variant of Shafer's Commonalities For Modelling Unforeseen Events
Uniform-PAC Guarantees for Model-Based RL with Bounded Eluder Dimension
Subsampled Stochastic Variance-Reduced Gradient Langevin Dynamics
When Ignorance is Bliss
Transforming Prioritized Defaults and Specificity into Parallel Defaults
Pruning bayesian networks for efficient computation
What Counterfactuals Can Be Tested
Evaluating Las Vegas Algorithms: Pitfalls and Remedies
Algorithms for Learning Decomposable Models and Chordal Graphs
Distortion estimates for approximate Bayesian inference
Decision Theory and Autonomous Systems
Conformal Prediction for Federated Graph Neural Networks with Missing Neighbor Information
Mitigating Overconfidence in Out-of-Distribution Detection by Capturing Extreme Activations
Identifying Learning Trajectories in an Educational Video Game
Distributionally Robust Optimization as a Scalable Framework to Characterize Extreme Value Distributions
Fast Value Iteration for Goal-Directed Markov Decision Processes
Confidence in causal discovery with linear causal models
Over the Top-1: Uncertainty-Aware Cross-Modal Retrieval with CLIP
Maximum Likelihood Bounded Tree-Width Markov Networks
Robust Combination of Local Controllers
Algebraic Statistics in Model Selection
Graph classification Gaussian processes via spectral features
Hamiltonian ABC
MAP Estimation, Linear Programming and Belief Propagation with Convex Free Energies
High-dimensional Bayesian optimization with sparse axis-aligned subspaces
Solving MAP Exactly using Systematic Search
Cascading Linear Submodular Bandits: Accounting for Position Bias and Diversity in Online Learning to Rank
Discovering the Hidden Structure of Complex Dynamic Systems
Bayesian streaming sparse Tucker decomposition
Inference with Possibilistic Evidence
Partial Order MCMC for Structure Discovery in Bayesian Networks
A Decision-based View of Causality
Plausibility Measures: A User's Guide
A Risk Calculator for the Pulmonary Arterial Hypertension Based on a Bayesian Network
Deep Sigma Point Processes
Scalable Matrix-valued Kernel Learning for High-dimensional Nonlinear Multivariate Regression and Granger Causality
Exploiting First-Order Regression in Inductive Policy Selection
Approximating the Partition Function by Deleting and then Correcting for Model Edges
Differentiable user models
Inequality Constraints in Causal Models with Hidden Variables
Linearizing contextual bandits with latent state dynamics
Computing probability intervals under independency constraints
Action redundancy in reinforcement learning
Learning to learn generative programs with Memoised Wake-Sleep
Causes and Explanations in the Structural-Model Approach : Tractable Cases
AUC Maximization in Imbalanced Lifelong Learning
Faster Stochastic Variational Inference using Proximal-Gradient Methods with General Divergence Functions
Massively parallel reweighted wake-sleep
Query Expansion in Information Retrieval Systems using a Bayesian Network-Based Thesaurus
A Probabilistic Approach to Hierarchical Model-based Diagnosis
A Homogenization Approach for Gradient-Dominated Stochastic Optimization
Belief Maintenance in Bayesian Networks
Reconstructing Pompeian Households
Adapting Text Embeddings for Causal Inference
Compositional uncertainty in deep Gaussian processes
Multiple Wasserstein Gradient Descent Algorithm for Multi-Objective Distributional Optimization
A Hierarchical Graphical Model for Record Linkage
Almost Optimal Intervention Sets for Causal Discovery
Metrics for Probabilistic Geometries
Real-Time Inference with Large-Scale Temporal Bayes Nets
Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, UAI 2019, Tel Aviv, Israel, July 22-25, 2019
CT-NOR: Representing and Reasoning About Events in Continuous Time
Hokusai - Sketching Streams in Real Time
New Limits for Knowledge Compilation and Applications to Exact Model Counting
Two-Way Latent Grouping Model for User Preference Prediction
A label efficient two-sample test
General Lower Bounds based on Computer Generated Higher Order Expansions
Explaining Negative Classifications of AI Models in Tumor Diagnosis
Learning and Planning with Timing Information in Markov Decision Processes
A Generative Block-Diagonal Model for Clustering
Proceedings of the Thirtieth Conference on Uncertainty in Artificial Intelligence, UAI 2014, Quebec City, Quebec, Canada, July 23-27, 2014
Meta-learning Control Variates: Variance Reduction with Limited Data
Tradeoffs in Constructing and Evaluating Temporal Influence Diagrams
Correlated Action Effects in Decision Theoretic Regression
Mixture of Normalizing Flows for European Option Pricing
Sylvester Normalizing Flows for Variational Inference
Causal Conclusions that Flip Repeatedly and Their Justification
Learning the Bayesian Network Structure: Dirichlet Prior vs Data
Do-calculus when the True Graph Is Unknown
P3O: Policy-on Policy-off Policy Optimization
Split, count, and share: a differentially private set intersection cardinality estimation protocol
Inference for Belief Networks Using Coupling From the Past
Discrete Probabilistic Inference as Control in Multi-path Environments
Inductive Inference and the Representation of Uncertainty
How Good Are My Predictions? Efficiently Approximating Precision-Recall Curves for Massive Datasets
Composition of Probability Measures on Finite Spaces
Intervening on Network Ties
A Data-Driven State Aggregation Approach for Dynamic Discrete Choice Models
The Lumière Project: Bayesian User Modeling for Inferring the Goals and Needs of Software Users
Generating explanations of decision models based on an augmented representation of uncertainty
Inference for Multiplicative Models
Value Elimination: Bayesian Interence via Backtracking Search
Collaborative Prediction: To Join or To Disjoin Datasets
Causal Reasoning for Events in Continuous Time: A Decision - Theoretic Approach
Updating Probabilities
Constant Step Size Stochastic Gradient Descent for Probabilistic Modeling
An Alternative Proof Method for Possibilistic Logic and its Application to Terminological Logics
Reparameterizing Hybrid Markov Logic Networks to handle Covariate-Shift in Representations
Addressing token uniformity in transformers via singular value transformation
Herding Dynamic Weights for Partially Observed Random Field Models
Intelligent Probabilistic Inference
A scalable Walsh-Hadamard regularizer to overcome the low-degree spectral bias of neural networks
Explanation Trees for Causal Bayesian Networks
Generating Graphoids from Generalised Conditional Probability
Bisimulation Metrics are Optimal Value Functions
Conversation as Action Under Uncertainty
Sequential algorithmic modification with test data reuse
Effects of Treatment on the Treated: Identification and Generalization
Weighted Sets of Probabilities and MinimaxWeighted Expected Regret: New Approaches for Representing Uncertainty and Making Decisions
A Trust-Region Method for Graphical Stein Variational Inference
Variational Relevance Vector Machines
Inferring Strategies from Limited Reconnaissance in Real-time Strategy Games
Intercausal Reasoning with Uninstantiated Ancestor Nodes
A Model for Reasoning with Uncertain Rules in Event Composition Systems
Stochastic Learning for Sparse Discrete Markov Random Fields with Controlled Gradient Approximation Error
Prediction Intervals: Split Normal Mixture from Quality-Driven Deep Ensembles
On Axiomatization of Probabilistic Conditional Independencies
Computing Nash Equilibria of Action-Graph Games
Correcting for selection bias and missing response in regression using privileged information
Multilingual Topic Models for Unaligned Text
Learning and Inference with Expectations
AND/OR Importance Sampling
Fast Learning from Sparse Data
Causal Inference in the Presence of Latent Variables and Selection Bias
Variational Chernoff Bounds for Graphical Models
Pitman-Yor Diffusion Trees
A geometric method for improved uncertainty estimation in real-time
Objection-based Causal Exception Networks
Simple and practical algorithms for 𝓁 p -norm low-rank approximation
An Entropy-based Learning Algorithm of Bayesian Conditional Trees
POMDPs under Probabilistic Semantics
Dependent Dirichlet Priors and Optimal Linear Estimators for Belief Net Parameters
A Probablistic Model of Action for Least-Commitment Planning with Information Gathering
FedSPD: A Soft-clustering Approach for Personalized Decentralized Federated Learning
Gibbs Sampling for (Coupled) Infinite Mixture Models in the Stick Breaking Representation
Church: a language for generative models
An Implementation of a Method for Computing the Uncertainty in Inferred Probabilities in Belief Networks
Conjugate Conformal Prediction for Online Binary Classification
Acyclic Linear SEMs Obey the Nested Markov Property
A Bayesian Method for Constructing Bayesian Belief Networks from Databases
Saturated Conditional Independence with Fixed and Undetermined Sets of Incomplete Random Variables
No-Regret Learning of Nash Equilibrium for Black-Box Games via Gaussian Processes
Optimizing Causal Orderings for Generating DAGs from Data
Thompson Sampling is Asymptotically Optimal in General Environments
Integrating Document Clustering and Topic Modeling
Bounding causal effects with leaky instruments
Adaptive Stochastic Dual Coordinate Ascent for Conditional Random Fields
Bennett-type Generalization Bounds: Large-deviation Case and Faster Rate of Convergence
Competitive policy optimization
A Forest Mixture Bound for Block-Free Parallel Inference
An Efficient Triplet-based Algorithm for Evidential Reasoning
Task similarity aware meta learning: theory-inspired improvement on MAML
An empirical comparison of three inference methods
A Formal Solution to the Grain of Truth Problem
A Heuristic Search Algorithm for Solving First-Order MDPs
Probabilistic Flow Circuits: Towards Unified Deep Models for Tractable Probabilistic Inference
Convergence and Asymptotic Normality of Variational Bayesian Approximations for Expon
Multi-Label Bayesian Active Learning with Inter-Label Relationships
Learning Sparse Causal Models is not NP-hard
Fake It Till You Make It: Learning-Compatible Performance Support
Budget Optimization for Sponsored Search: Censored Learning in MDPs
Symbolic Dynamic Programming for Discrete and Continuous State MDPs
Locally Regularized Sparse Graph by Fast Proximal Gradient Descent
Measuring Inconsistency in Probabilistic Knowledge Bases
Ordering-Based Search: A Simple and Effective Algorithm for Learning Bayesian Networks
Bayesian Learning of Loglinear Models for Neural Connectivity
Learning When to Take Advice: A Statistical Test for Achieving A Correlated Equilibrium
Lattice-based Graded Logic: A Multimodal Approach
Using Bayesian Networks to Identify the Causal Effect of Speeding in Individual Vehicle/Pedestrian Collisions
Deterministic policy gradient: Convergence analysis
An Algorithm for Computing Probabilistic Propositions
Accuracy Bounds for Belief Propagation
Conflict and Surprise: Heuristics for Model Revision
Utilities as Random Variables: Density Estimation and Structure Discovery
An Algorithm for the Construction of Bayesian Network Structures from Data
Learning Finite-State Controllers for Partially Observable Environments
Iterative Markov Chain Monte Carlo Computation of Reference Priors and Minimax Risk
Heuristic Ranking in Tightly Coupled Probabilistic Description Logics
End-to-end Conditional Robust Optimization
UAI 2007, Proceedings of the Twenty-Third Conference on Uncertainty in Artificial Intelligence, Vancouver, BC, Canada, July 19-22, 2007
Maximum Margin Bayesian Networks
On reasoning in networks with qualitative uncertainty
Characterizing Data Point Vulnerability as Average-Case Robustness
Using Bayesian Networks to Identify and Prevent Split Purchases in Brazil
UAI '01: Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence, University of Washington, Seattle, Washington, USA, August 2-5, 2001
Exact Reasoning About Uncertainty: On the Design of Expert Systems for Decision Support
Smoothing Proximal Gradient Method for General Structured Sparse Learning
Flow-Based Delayed Hawkes Process
Argument Calculus and Networks
Entropy and Belief Networks
Context-dependent similarity
Optimization of Structured Mean Field Objectives
Toward Case-Based Preference Elicitation: Similarity Measures on Preference Structures
Stochastic Segmentation Trees for Multiple Ground Truths
Tree-dependent Component Analysis
Geodesic Slice Sampler for Multimodal Distributions with Strong Curvature
A temporally abstracted Viterbi algorithm
Recursive Monte Carlo and variational inference with auxiliary variables
Fast Kernel Approximations for Latent Force Models and Convolved Multiple-Output Gaussian processes
Differential Analysis of Directed Networks
Survey Propagation Revisited
Robust Model Equivalence using Stochastic Bisimulation for N-Agent Interactive DIDs
Balancing adaptability and non-exploitability in repeated games
Online estimation of similarity matrices with incomplete data
Improved Sampling for Diagnostic Reasoning in Bayesian Networks
Fast Counting in Machine Learning Applications
Empirical bayes approach to truth discovery problems
Firefly Monte Carlo: Exact MCMC with Subsets of Data
Analysis of Nyström method with sequential ridge leverage scores
Learning Algorithms for Multiple Instance Regression
A Unifying Framework for Linearly Solvable Control
CORe: Capitalizing On Rewards in Bandit Exploration
On the Acceptability of Arguments in Preference-based Argumentation
Hybrid Influence Diagrams Using Mixtures of Truncated Exponentials
Toward Experiential Utility Elicitation for Interface Customization
Learning the Causal Structure of Copula Models with Latent Variables
Estimating Well-Performing Bayesian Networks using Bernoulli Mixtures
A Probabilistic Algorithm for Calculating Structure: Borrowing from Simulated Annealing
Classification with abstention but without disparities
An Importance Sampling Algorithm Based on Evidence Pre-propagation
Asymptotic Efficiency of Deterministic Estimators for Discrete Energy-Based Models: Ratio Matching and Pseudolikelihood
Possibility and Necessity Functions over Non-Classical Logics
A Bayesian Network Classifier that Combines a Finite Mixture Model and a NaIve Bayes Model
Machine Learning for Healthcare Data
On the Complexity of Decision Making in Possibilistic Decision Trees
Aggregating Data for Optimal Learning
Closed-form Solutions to a Subclass of Continuous Stochastic Games via Symbolic Dynamic Programming
Lexical Access for Speech Understanding using Minimum Message Length Encoding
Defaults and Infinitesimals Defeasible Inference by Nonarchimedean Entropy-Maximization
Non-monotonic Reasoning and the Reversibility of Belief Change
Recursive Best-First AND/OR Search for Optimization in Graphical Models
Learning Bayesian Networks: The Combination of Knowledge and Statistical Data
Moments of Causal Effects
Federated online clustering of bandits
Coevolutionary Emergent Systems Optimization with Applications to Ultra-High-Dimensional Metasurface Design : OAM Wave Manipulation
The Nature of the Unnormalized Beliefs Encountered in the Transferable Belief Model
Non-Minimal Triangulations for Mixed Stochastic/Deterministic Graphical Models
A Bayesian Method for Causal Modeling and Discovery Under Selection
Satisfaction of Assumptions is a Weak Predictor of Performance
Variational Learning of Gaussian Process Latent Variable Models through Stochastic Gradient Annealed Importance Sampling
Nonconvex stochastic scaled gradient descent and generalized eigenvector problems
SPDF: Sparse Pre-training and Dense Fine-tuning for Large Language Models
The Author-Topic Model for Authors and Documents
Reasoning with Qualitative Probabilities Can Be Tractable
Efficient Optimal Learning for Contextual Bandits
Learning Behaviors with Uncertain Human Feedback
Fast Teammate Adaptation in the Presence of Sudden Policy Change
Scaling Hamiltonian Monte Carlo inference for Bayesian neural networks with symmetric splitting
Tighter Linear Program Relaxations for High Order Graphical Models
Adaptive Threshold Sampling for Pure Exploration in Submodular Bandits
Measuring IIA Violations in Similarity Choices with Bayesian Models
Switching Portfolios
Representing and Solving Asymmetric Bayesian Decision Problems
Discovering Patterns in Biological Sequences by Optimal Segmentation
Hybrid CtrlFormer: Learning Adaptive Search Space Partition for Hybrid Action Control via Transformer-based Monte Carlo Tree Search
MEMR: A Margin Equipped Monotone Retargeting Framework for Ranking
Convexifying the Bethe Free Energy
Unsupervised Learning of Noisy-Or Bayesian Networks
Using Causal Information and Local Measures to Learn Bayesian Networks
Robust Entropy Search for Safe Efficient Bayesian Optimization
Are You Doing What I Think You Are Doing? Criticising Uncertain Agent Models
Incremental Dynamic Construction of Layered Polytree Networks
The Entire Quantile Path of a Risk-Agnostic SVM Classifier
Inference in Multiply Sectioned Bayesian Networks with Extended Shafer-Shenoy and Lazy Propagation
On the Relationship Between Satisfiability and Markov Decision Processes
Perturbed-History Exploration in Stochastic Linear Bandits
Efficient Failure Pattern Identification of Predictive Algorithms
Shortest Path under Uncertainty: Exploration versus Exploitation
Densified Winner Take All (WTA) Hashing for Sparse Datasets
A Theory of Goal-Oriented MDPs with Dead Ends
Solving Cooperative Reliability Games
Modeling Citation Networks Using Latent Random Offsets
Uncertainty-aware pseudo-labeling for quantum calculations
On inference and learning with probabilistic generating circuits
Clique Matrices for Statistical Graph Decomposition and Parameterising Restricted Positive Definite Matrices
Automatic Generation of Four-part Harmony
Policy Iteration for Relational MDPs
Efficient Search-Based Weighted Model Integration
A linear approximation method for probabilistic inference
Valid Bootstraps for Network Embeddings with Applications to Network Visualisation
An axiomatic framework for belief updates
Near-Optimal Regret Bounds for Federated Multi-armed Bandits with Fully Distributed Communication
Imprecise meanings as a cause of uncertainty in medical knowledge-based systems
Diagnosis of Multiple Faults: A Sensitivity Analysis
The Revisiting Problem in Mobile Robot Map Building: A Hierarchical Bayesian Approach
Computational complexity reduction for BN2O networks using similarity of states
Optimization of Inter-Subnet Belief Updating in Multiply Sectioned Bayesian Networks
Asymmetric DQN for partially observable reinforcement learning
Neighbor Similarity and Multimodal Alignment based Product Recommendation Study
Nuclear Norm Regularized Least Squares Optimization on Grassmannian Manifolds
Blackbox optimization of unimodal functions
Improved generalization bounds of group invariant / equivariant deep networks via quotient feature spaces
On Information-Theoretic Measures of Predictive Uncertainty
On provably robust meta-Bayesian optimization
Continuously Tempered Hamiltonian Monte Carlo
UAI '98: Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, University of Wisconsin Business School, Madison, Wisconsin, USA, July 24-26, 1998
Neural Dynamics Discovery via Gaussian Process Recurrent Neural Networks
Enhancing Patient Recruitment Response in Clinical Trials: an Adaptive Learning Framework
Probabilistic AND-OR Attribute Grouping for Zero-Shot Learning
Active learning with label comparisons
Nash Convergence of Gradient Dynamics in General-Sum Games
Correlated Compressive Sensing for Networked Data
Pure Exploration of Multi-Armed Bandits with Heavy-Tailed Payoffs
Causal Transportability of Experiments on Controllable Subsets of Variables: z-Transportability
Context-Specific Independence in Bayesian Networks
Expected Utility Networks
Graphical continuous Lyapunov models
Robust Spatial-Temporal Incident Prediction
Real-Time Robotic Search using Structural Spatial Point Processes
An Efficient Optimal-Equilibrium Algorithm for Two-player Game Trees
Learning Module Networks
Bandits with Knapsacks and Predictions
GeONet: a neural operator for learning the Wasserstein geodesic