yusuke1997/dblp-id-title huggingface dataset:
Towards Physically Reliable Molecular Representation Learning
Why Rules are Complex: Real-Valued Probabilistic Logic Programs are not Fully Expressive
Learning Belief Representations for Imitation Learning in POMDPs
Root Cause Analysis of Failures from Partial Causal Structures
An Empirical Evaluation of Possible Variations of Lazy Propagation
Relevant Explanations: Allowing Disjunctive Assignments
Principle of relevant information for graph sparsification
Towards Scalable Bayesian Transformers: Investigating stochastic subset selection for NLP
Monte-Carlo optimizations for resource allocation problems in stochastic network systems
Causal Identification under Markov Equivalence
Conditional counterfactual causal effect for individual attribution
Sequential Information Elicitation in Multi-Agent Systems
LocalNewton: Reducing communication rounds for distributed learning
In- or out-of-distribution detection via dual divergence estimation
Multi-group Uncertainty Quantification for Long-form Text Generation
Representing Context-Sensitive Knowledge in a Network Formalism: A Preliminary Report
Advances in Boosting
Time Series Analysis using a Kernel based Multi-Modal Uncertainty Decomposition Framework
Improving Optimization-Based Approximate Inference by Clamping Variables
Noisy L0-sparse subspace clustering on dimensionality reduced data
Calibrated Regression Against An Adversary Without Regret
Learning with Scope, with Application to Information Extraction and Classification
Prediction Markets, Mechanism Design, and Cooperative Game Theory
Efficient Feature Group Sequencing for Anytime Linear Prediction
Software Tools for Uncertain Reasoning: An Introduction
Causal Eligibility Traces for Confounding Robust Off-Policy Evaluation
A Non-Parametric Bayesian Method for Inferring Hidden Causes
A Tractable Inference Algorithm for Diagnosing Multiple Diseases
The Inconsistent Use of Measures of Certainty in Artificial Intelligence Research
Graphical Models for Game Theory
Efficiently Escaping Saddle Points for Policy Optimization
Sparse Multi-Prototype Classification
Reframed GES with a neural conditional dependence measure
MCMC for Doubly-intractable Distributions
Collaborative Ensemble Learning: Combining Collaborative and Content-Based Information Filtering via Hierarchical Bayes
Visualization of Collaborative Data
Sequential Thresholds: Context Sensitive Default Extensions
Deciding Morality of Graphs is NP-complete
Fast Planning in Stochastic Games
Causal Discovery from a Mixture of Experimental and Observational Data
Robust Graphical Modeling with t-Distributions
Unsupervised Spectral Learning
A Two-Round Variant of EM for Gaussian Mixtures
Models for Truthful Online Double Auctions
Mutual Information Based Knowledge Transfer Under State-Action Dimension Mismatch
Lifelong bandit optimization: no prior and no regret
Exploiting Inferential Structure in Neural Processes
Belief Optimization for Binary Networks: A Stable Alternative to Loopy Belief Propagation
On the role of model uncertainties in Bayesian optimisation
USIM-DAL: Uncertainty-aware Statistical Image Modeling-based Dense Active Learning for Super-resolution
Aligned Diffusion Schrödinger Bridges
A Tractable Approach to Finding Closest Truncated-commute-time Neighbors in Large Graphs
ELBO, regularized maximum likelihood, and their common one-sample approximation for training stochastic neural networks
COS-DPO: Conditioned One-Shot Multi-Objective Fine-Tuning Framework
ContextFlow++: Generalist-Specialist Flow-based Generative Models with Mixed-variable Context Encoding
Neuro-symbolic entropy regularization
The Causal Information Bottleneck and Optimal Causal Variable Abstractions
Principal component analysis in the stochastic differential privacy model
Geometric Implications of the Naive Bayes Assumption
Argumentative inference in uncertain and inconsistent knowledge bases
Joint Stochastic Approximation and Its Application to Learning Discrete Latent Variable Models
Probabilistic Logic Programming under Inheritance with Overriding
Tightening Fractional Covering Upper Bounds on the Partition Function for High-Order Region Graphs
Global Conditioning for Probabilistic Inference in Belief Networks
Possibility as similarity; the semantics of fuzzy logic
Entropic Inequality Constraints from e-separation Relations in Directed Acyclic Graphs with Hidden Variables
Revisiting the Equivalence of Bayesian Neural Networks and Gaussian Processes: On the Importance of Learning Activations
Mitigating Transformer Overconfidence via Lipschitz Regularization
Crowdsourcing Control: Moving Beyond Multiple Choice
Neural markov logic networks
The Information Bottleneck EM Algorithm
Fast Stochastic Quadrature for Approximate Maximum-Likelihood Estimation
Conditional Chow-Liu Tree Structures for Modeling Discrete-Valued Vector Time Series
Matrix Tile Analysis
Bayesian Networks Applied to Therapy Monitoring
Similarity Measures on Preference Structures, Part II: Utility Functions
Conditional abstraction trees for sample-efficient reinforcement learning
Multi-objective Influence Diagrams
Optimistic Regret Bounds for Online Learning in Adversarial Markov Decision Processes
Variational inference with continuously-indexed normalizing flows
Adaptive Monotone Shrinkage for Regression
Epistemological relevance and statistical knowledge
Integrating Probabilistic Rules into Neural Networks: A Stochastic EM Learning Algorithm
Decentralized Planning for Non-dedicated Agent Teams with Submodular Rewards in Uncertain Environments
On Densification for Minwise Hashing
Hilbert Space Embeddings of Predictive State Representations
A Finite Population Likelihood Ratio Test of the Sharp Null Hypothesis for Compliers
The Structure of Signals: Causal Interdependence Models for Games of Incomplete Information
Copula-based deep survival models for dependent censoring
AutoGP: Exploring the Capabilities and Limitations of Gaussian Process Models
The complexity of nonconvex-strongly-concave minimax optimization
Bayesian network learning with cutting planes
Speeding up the binary Gaussian process classification
The myth of modularity in rule-based systems for reasoning with uncertainty
Improving Compressed Counting
Inference Networks and the Evaluation of Evidence: Alternative Analyses
Approximate Bayesian Computation with Path Signatures
Learning and Solving Many-Player Games through a Cluster-Based Representation
Markov Logic Networks for Knowledge Base Completion: A Theoretical Analysis Under the MCAR Assumption
Multitasking: Optimal Planning for Bandit Superprocesses
CP 2 : Leveraging Geometry for Conformal Prediction via Canonicalization
Generalized Fast Approximate Energy Minimization via Graph Cuts: a-Expansion b-Shrink Moves
The Application of Algorithmic Probability to Problems in Artificial Intelligence
Towards a general-purpose belief maintenance system
Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence, UAI 2015, July 12-16, 2015, Amsterdam, The Netherlands
Learning the Structure of Dynamic Probabilistic Networks
Towards painless policy optimization for constrained MDPs
Online Bayesian Multiple Kernel Bipartite Ranking
Learning Intrinsic Rewards as a Bi-Level Optimization Problem
Real-Time Resource Allocation for Tracking Systems
Learning Bayesian Network Parameters with Prior Knowledge about Context-Specific Qualitative Influences
Causal Models for Growing Networks
Loopy Belief Propagation for Approximate Inference: An Empirical Study
Mitigating statistical bias within differentially private synthetic data
Combating the instability of mutual information-based losses via regularization
On Stable Multi-Agent Behavior in Face of Uncertainty
Hierarchical Bayesian Survival Analysis and Projective Covariate Selection in Cardiovascular Event Risk Prediction
On the Relationship Between Probabilistic Circuits and Determinantal Point Processes
On Loopy Belief Propagation - Local Stability Analysis for Non-Vanishing Fields
Unsupervised Attributed Dynamic Network Embedding with Stability Guarantees
Off-policy learning based on weighted importance sampling with linear computational complexity
Loglinear models for first-order probabilistic reasoning
Probabilistic inverse reinforcement learning in unknown environments
Position-Aware ListMLE: A Sequential Learning Process for Ranking
Guaranteed Prediction Sets for Functional Surrogate Models
Lower Bounds on the Size of Markov Equivalence Classes
Incremental Map Generation by Low Cost Robots Based on Possibility/Necessity Grids
Learning Causal Abstractions of Linear Structural Causal Models
Intelligent Affect: Rational Decision Making for Socially Aligned Agents
Formal verification of neural networks for safety-critical tasks in deep reinforcement learning
Projected Subgradient Methods for Learning Sparse Gaussians
Psychophysical Detection Testing with Bayesian Active Learning
Explicit pairwise factorized graph neural network for semi-supervised node classification
Parameter adjustment in Bayes networks. The generalized noisy OR-gate
Methods for representing bias in Bayesian networks
Dynamic Blocking and Collapsing for Gibbs Sampling
Elicitation of Probabilities for Belief Networks: Combining Qualitative and Quantitative Information
Contaminated Multivariate Time-Series Anomaly Detection with Spatio-Temporal Graph Conditional Diffusion Models
Learning to Cooperate via Policy Search
The structure of bayes networks for visual recognition
On Information Regularization
Computing reference classes
Factorized Multi-Modal Topic Model
Problem-Focused Incremental Elicitation of Multi-Attribute Utility Models
Correlated Non-Parametric Latent Feature Models
UAI '03, Proceedings of the 19th Conference in Uncertainty in Artificial Intelligence, Acapulco, Mexico, August 7-10 2003
A Slice Sampler for Restricted Hierarchical Beta Process with Applications to Shared Subspace Learning
Topological parameters for time-space tradeoff
Optimal Resource Allocation with Semi-Bandit Feedback
Learning to select computations
Efficient Approximations for the Marginal Likelihood of Incomplete Data Given a Bayesian Network
Iterative Splits of Quadratic Bounds for Scalable Binary Tensor Factorization
How Occam's Razor Provides a Neat Definition of Direct Causation
Proceedings of the 2013 UAI Application Workshops: Big Data meet Complex Models and Models for Spatial, Temporal and Network Data, Bellvue, Washington, USA
Unsupervised Active Learning in Large Domains
Set-valued prediction in hierarchical classification with constrained representation complexity
Identifying Causal Changes Between Linear Structural Equation Models
Robust modal regression with direct gradient approximation of modal regression risk
Order-Optimal Global Convergence for Actor-Critic with General Policy and Neural Critic Parametrization
The Bayesian Structural EM Algorithm
Hierarchical probabilistic model for blind source separation via Legendre transformation
Optimal amortized regret in every interval
Qualitative Decision Theory with Sugeno Integrals
α-Former: Local-Feature-Aware (L-FA) Transformer
CLP(BN): Constraint Logic Programming for Probabilistic Knowledge
"I Can Name that Bayesian Network in Two Matrixes!"
Greedy modality selection via approximate submodular maximization
Toward learning human-aligned cross-domain robust models by countering misaligned features
Predicting Latent Variables with Knowledge and Data: A Case Study in Trauma Care
Possibilistic logic bases and possibilistic graphs
A Calculus for Causal Relevance
Knowledge Acquisition Techniques for Intelligent Decision Systems: Integrating Axotl and Aquinas in DDUCKS
Unsupervised Learning of Latent Physical Properties Using Perception-Prediction Networks
Convex Relaxations of Bregman Divergence Clustering
Spectral Methods for Ranking with Scarce Data
Adaptive Conditional Quantile Neural Processes
Smoothing Multivariate Performance Measures
Multistate analysis with infinite mixtures of Markov chains
Annealed MAP
Negative Tree Reweighted Belief Propagation
Pearl's Calculus of Intervention Is Complete
Laplace's Method Approximations for Probabilistic Inference in Belief Networks with Continuous Variables
Polynomial Semantics of Tractable Probabilistic Circuits
Modus Ponens Generating Function in the Class of \Lambda-Valuations of Plausibility
Planning and Acting under Uncertainty: A New Model for Spoken Dialogue System
Conditional Independence in Possibility Theory
Bayesian Random Fields: The Bethe-Laplace Approximation
Unconstrained Influence Diagrams
Assessing the value of a candidate: Comparing belief function and possibility theories
Most Relevant Explanation: Properties, Algorithms, and Evaluations
Budget Allocation using Weakly Coupled, Constrained Markov Decision Processes
Symbolic Probabilistic Inference in Large BN20 Networks
Constraint Propagation with Imprecise Conditional Probabilities
Statistically robust neural network classification
GALGO: A Genetic ALGOrithm Decision Support Tool for Complex Uncertain Systems Modeled with Bayesian Belief Networks
Evaluating influence diagrams with decision circuits
A Standard Approach for Optimizing Belief Network Inference Using Query DAGs
Provable Inductive Robust PCA via Iterative Hard Thresholding
Second order probabilities for uncertain and conflicting evidence
MutualNeRF: Improve the Performance of NeRF under Limited Samples with Mutual Information Theory
Coordinates: Probabilistic Forecasting of Presence and Availability
Copula for Instance-wise Feature Selection and Rank
Dynamic Teaching in Sequential Decision Making Environments
Per-decision Multi-step Temporal Difference Learning with Control Variates
Learning Gaussian Networks
Causally Abstracted Multi-armed Bandits
Prediction, Expectation, and Surprise: Methods, Designs, and Study of a Deployed Traffic Forecasting Service
Estimating transfer entropy under long ranged dependencies
Defeasible Reasoning and Uncertainty: Comments
Hybrid Generative/Discriminative Learning for Automatic Image Annotation
Efficient and transferable adversarial examples from bayesian neural networks
Response Time Improves Gaussian Process Models for Perception and Preferences
Markov Chains on Orbits of Permutation Groups
Irrelevance and Independence Relations in quasi-Bayesian Networks
Myopic Value of Information in Influence Diagrams
Weighted Model Counting With Function Symbols
Partial identification of dose responses with hidden confounders
Similarity networks for the construction of multiple-faults belief networks
A State-Transition DBN for Management of Willows in an American Heritage River Catchment
Exceptional Subclasses in Qualitative Probability
Characterising Interventions in Causal Games
Be Greedy: How Chromatic Number meets Regret Minimization in Graph Bandits
Variational Training for Large-Scale Noisy-OR Bayesian Networks
Multi-output Gaussian Processes for uncertainty-aware recommender systems
Marginalizing in Undirected Graph and Hypergraph Models
Testification of Condorcet Winners in dueling bandits
Towards Provably Efficient Learning of Imperfect Information Extensive-Form Games with Linear Function Approximation
Optimal Coordinated Planning Amongst Self-Interested Agents with Private State
Gaussian Process Structural Equation Models with Latent Variables
Efficient Monte Carlo Tree Search via On-the-Fly State-Conditioned Action Abstraction
Minimal Assumption Distribution Propagation in Belief Networks
A Bayesian Perspective on Confidence
State Sequence Analysis in Hidden Markov Models
Dynamical Kinds and their Discovery
Using Dempster-Shafer theory in knowledge representation
Computing Nonvacuous Generalization Bounds for Deep (Stochastic) Neural Networks with Many More Parameters than Training Data
Learning Continuous-Time Social Network Dynamics
Experimentation under Treatment Dependent Network Interference
Divide and Orthogonalize: Efficient Continual Learning with Local Model Space Projection
Learning Bayesian Networks with Local Structure
Learning invariant weights in neural networks
Estimating the Value of Computation in Flexible Information Refinement
Understanding the Bethe Approximation: When and How can it go Wrong?
Minimax Optimal Nonsmooth Nonparametric Regression via Fractional Laplacian Eigenmaps
aHUGIN: A System Creating Adaptive Causal Probabilistic Networks
VQ-Flows: Vector quantized local normalizing flows
Logical Inference Algorithms and Matrix Representations for Probabilistic Conditional Independence
Nonlinear Causal Discovery for Grouped Data
Approximate Evidential Reasoning Using Local Conditioning and Conditional Belief Functions
To smooth a cloud or to pin it down: Expressiveness guarantees and insights on score matching in denoising diffusion models
Revisiting Kernel Attention with Correlated Gaussian Process Representation
Robust Optimization with Diffusion Models for Green Security
Combining Spatial and Telemetric Features for Learning Animal Movement Models
Induction of Selective Bayesian Classifiers
Equilibrium aggregation: encoding sets via optimization
Towards unsupervised open world semantic segmentation
Warped Mixtures for Nonparametric Cluster Shapes
Pulcinella: A General Tool for Propagating Uncertainty in Valuation Networks
Sequential Inference for Latent Force Models
Belief Updating and Learning in Semi-Qualitative Probabilistic Networks
Learning Inclusion-Optimal Chordal Graphs
Dependency Networks for Collaborative Filtering and Data Visualization
Improved Memory-Bounded Dynamic Programming for Decentralized POMDPs
Exact Inference in Networks with Discrete Children of Continuous Parents
Conditional Plausibility Measures and Bayesian Networks
Ordinal Boltzmann Machines for Collaborative Filtering
Hierarchical Mixtures-of-Experts for Exponential Family Regression Models with Generalized Linear Mean Functions: A Survey of Approximation and Consistency Results
Robust Probabilistic Inference in Distributed Systems
Measuring data leakage in machine-learning models with Fisher information
Fair Optimal Stopping Policy for Matching with Mediator
Constraint-based Causal Discovery: Conflict Resolution with Answer Set Programming
Causal Discovery for Linear Non-Gaussian Models with Disjoint Cycles
Inferring Causal Direction from Relational Data
Inference of a rumor's source in the independent cascade model
Probability Judgment in Artificial Intelligence
Contextual policy transfer in reinforcement learning domains via deep mixtures-of-experts
Bayesian quantile and expectile optimisation
Quantile-Regret Minimisation in Infinitely Many-Armed Bandits
Robust textual embedding against word-level adversarial attacks
Orthogonal Gromov-Wasserstein discrepancy with efficient lower bound
Trumpets: Injective flows for inference and inverse problems
Optimally-Weighted Herding is Bayesian Quadrature
Iterated INLA for State and Parameter Estimation in Nonlinear Dynamical Systems
Exploring Exploration in Bayesian Optimization
Logit-based ensemble distribution distillation for robust autoregressive sequence uncertainties
Causal discovery of linear acyclic models with arbitrary distributions
Federated learning of models pre-trained on different features with consensus graphs
Adaptive Time-Stepping Schedules for Diffusion Models
Incremental Pruning: A Simple, Fast, Exact Method for Partially Observable Markov Decision Processes
Markov Random Walk Representations with Continuous Distributions
A Bayesian Framework for Community Detection Integrating Content and Link
Contrasting Temporal Bayesian Network Models for Analyzing HIV Mutations
Stable Independence in Perfect Maps
Sensitivity analysis for finite Markov chains in discrete time
Using First-Order Probability Logic for the Construction of Bayesian Networks
An Efficient Minibatch Acceptance Test for Metropolis-Hastings
Composing Efficient, Robust Tests for Policy Selection
Sequential core-set Monte Carlo
Discretized Approximations for POMDP with Average Cost
On the effectiveness of adversarial training against common corruptions
Inference in Polytrees with Sets of Probabilities
A Geometric Traversal Algorithm for Reward-Uncertain MDPs
Safely Interruptible Agents
A policy gradient approach for optimization of smooth risk measures
A Language for Planning with Statistics
Exact Inference for Relational Graphical Models with Interpreted Functions: Lifted Probabilistic Inference Modulo Theories
Mixture Representations for Inference and Learning in Boltzmann Machines
Lazy Evaluation of Symmetric Bayesian Decision Problems
Understanding Measures of Uncertainty for Adversarial Example Detection
Inference with Idempotent Valuations
Fair Active Learning in Low-Data Regimes
Semantics for Probabilistic Inference
Discovering causal structures in binary exclusive-or skew acyclic models
Pandering in a (flexible) representative democracy
Representing Bayesian Networks Within Probabilistic Horn Abduction
k-NN Regression on Functional Data with Incomplete Observations
Transfer learning for individual treatment effect estimation
No-regret learning with high-probability in adversarial Markov decision processes
Independence of Causal Influence and Clique Tree Propagation
Bayes-Ball: The Rational Pastime (for Determining Irrelevance and Requisite Information in Belief Networks and Influence Diagrams)
Learning linear non-Gaussian polytree models
MAP Complexity Results and Approximation Methods
Dimension reduction for data with heterogeneous missingness
Envisioning Uncertainty in Geospatial Information
Benign Overfitting in Adversarially Robust Linear Classification
Learning From What You Don't Observe
SASH: Efficient secure aggregation based on SHPRG for federated learning
On the Generation of Alternative Explanations with Implications for Belief Revision
Prediction with Advice of Unknown Number of Experts
Combining Knowledge and Reasoning through Probabilistic Soft Logic for Image Puzzle Solving
A kernel two-sample test with selection bias
99% of Worker-Master Communication in Distributed Optimization Is Not Needed
Inference in Hybrid Networks: Theoretical Limits and Practical Algorithms
On Privacy-Preserving Histograms
An Efficient Message-Passing Algorithm for the M-Best MAP Problem
Simple combinatorial algorithms for combinatorial bandits: corruptions and approximations
Incorporating Side Information in Probabilistic Matrix Factorization with Gaussian Processes
Bayesian Meta-Reasoning: Determining Model Adequacy from within a Small World
Conditional simulation using diffusion Schrödinger bridges
Multi-objects association in perception of dynamical situation
Active Collaborative Filtering
The Thing that we Tried Didn't Work very Well: Deictic Representation in Reinforcement Learning
An Evaluation of an Algorithm for Inductive Learning of Bayesian Belief Networks Using Simulated Data Sets
Model based Bayesian Exploration
A weaker faithfulness assumption based on triple interactions
Bayesian Learning of Kernel Embeddings
Privacy-aware compression for federated data analysis
Causal and interventional Markov boundaries
Generalized Bayesian Posterior Expectation Distillation for Deep Neural Networks
Gene Expression Time Course Clustering with Countably Infinite Hidden Markov Models
Forget-me-not! Contrastive critics for mitigating posterior collapse
An effective negotiating agent framework based on deep offline reinforcement learning
Sufficient Dimensionality Reduction with Irrelevance Statistics
Predictive Linear-Gaussian Models of Stochastic Dynamical Systems
Residual-based error bound for physics-informed neural networks
Learning from Pairwise Marginal Independencies
On the rational scope of probabilistic rule-based inference systems
EDML: A Method for Learning Parameters in Bayesian Networks
GPIRT: A Gaussian Process Model for Item Response Theory
A Criterion for Parameter Identification in Structural Equation Models
The use of conflicts in searching Bayesian networks
Sound Abstraction of Probabilistic Actions in The Constraint Mass Assignment Framework
Topic Models Conditioned on Arbitrary Features with Dirichlet-multinomial Regression
Intercausal Independence and Heterogeneous Factorization
A Flexible Framework for Multi-Objective Bayesian Optimization using Random Scalarizations
Sample-efficient Nonstationary Policy Evaluation for Contextual Bandits
Exploiting Evidence-dependent Sensitivity Bounds
Probabilistic Models for Query Approximation with Large Sparse Binary Data Sets