yusuke1997/dblp-id-title huggingface dataset:
Batch norm with entropic regularization turns deterministic autoencoders into generative models
Ensembles of Kernel Predictors
Regret Minimizing Equilibria and Mechanisms for Games with Strict Type Uncertainty
Three Arguments for Extending the Framework of Probability
Large-Margin Determinantal Point Processes
Generating Synthetic Datasets by Interpolating along Generalized Geodesics
Discovering Cyclic Causal Models with Latent Variables: A General SAT-Based Procedure
Conditional Independence and Markov Properties in Possibility Theory
Effective sketching methods for value function approximation
A Factorized Variational Technique for Phase Unwrapping in Markov Random Field
Adjustment Identification Distance: A gadjid for Causal Structure Learning
Compatibility of Quantitative and Qualitative Representations of Belief
Proceedings of the 13th UAI Bayesian Modeling Applications Workshop (BMAW 2016) co-located with the 32nd Conference on Uncertainty in Artificial Intelligence (UAI 2016), New York City, NY, USA, June 25, 2016
Common Voting Rules as Maximum Likelihood Estimators
Probabilistic evaluation of sequential plans from causal models with hidden variables
The Infinite Latent Events Model
AND/OR Search for Marginal MAP
A direct method for estimating a causal ordering in a linear non-Gaussian acyclic model
Variational Learning in Mixed-State Dynamic Graphical Models
Experiments with Random Projection
Support and Plausibility Degrees in Generalized Functional Models
Flexible Decomposition Algorithms for Weakly Coupled Markov Decision Problems
Decision-Theoretic Troubleshooting: A Framework for Repair and Experiment
Approximate Kernel Density Estimation under Metric-based Local Differential Privacy
Bounding the optimal value function in compositional reinforcement learning
Optimal Stochastic Strongly Convex Optimization with a Logarithmic Number of Projections
Individual fairness in feature-based pricing for monopoly markets
Asymmetric Separation for Local Independence Graphs
An Online Learning-based Framework for Tracking
Monte-Carlo Planning: Theoretically Fast Convergence Meets Practical Efficiency
Learning with Confident Examples: Rank Pruning for Robust Classification with Noisy Labels
MDPs with Unawareness
A decentralized policy gradient approach to multi-task reinforcement learning
Two Sides of Miscalibration: Identifying Over and Under-Confidence Prediction for Network Calibration
A new constructive criterion for Markov equivalence of MAGs
Optimal Zero-shot Regret Minimization for Selective Classification with Out-of-Distribution Detection
Multi-objective Bayesian optimization over high-dimensional search spaces
Causal Independence for Knowledge Acquisition and Inference
Fast Graph Construction Using Auction Algorithm
Co-training for Policy Learning
Variational Algorithms for Marginal MAP
Regret Analysis of Bandit Problems with Causal Background Knowledge
Modeling Documents with Deep Boltzmann Machines
Base Models for Parabolic Partial Differential Equations
Toward Universal Laws of Outlier Propagation
Bidirectional Attention as a Mixture of Continuous Word Experts
Causal information splitting: Engineering proxy features for robustness to distribution shifts
Relative Entropy, Probabilistic Inference, and AI
Hybrid Deep Discriminative/Generative Models for Semi-Supervised Learning
Lifted reasoning meets weighted model integration
Knowledge representation combining quaternion path integration and depth-wise atrous circular convolution
How Private Are Commonly-Used Voting Rules?
Chi-square Tests Driven Method for Learning the Structure of Factored MDPs
Model-Based Bayesian Reinforcement Learning in Large Structured Domains
Causal Learning for Partially Observed Stochastic Dynamical Systems
A Unified framework for Order-of-magnitude Confidence Relations
Ranking Under Uncertainty
Modified Retrace for Off-Policy Temporal Difference Learning
Learning Robust XGBoost Ensembles for Regression Tasks
Structural Controllability and Observability in Influence Diagrams
Evaluating Influence Diagrams using LIMIDs
Kernel-based Conditional Independence Test and Application in Causal Discovery
Information design for multiple independent and self-interested defenders: Work less, pay off more
Dynamic Stochastic Orienteering Problems for Risk-Aware Applications
Learning in Multi-Player Stochastic Games
Convolutional Factor Graphs as Probabilistic Models
Learning by Transduction
Selling Data at an Auction under Privacy Constraints
Staged Mixture Modelling and Boosting
Can we Defend Against the Unknown? An Empirical Study About Threshold Selection for Neural Network Monitoring
Discriminative Probabilistic Models for Relational Data
Learning Conventions in Multiagent Stochastic Domains using Likelihood Estimates
Conditionally optimistic exploration for cooperative deep multi-agent reinforcement learning
IDA with Background Knowledge
Inductive synthesis of finite-state controllers for POMDPs
Heavy-tailed linear bandit with Huber regression
Physics guided neural networks for spatio-temporal super-resolution of turbulent flows
A decoder suffices for query-adaptive variational inference
Utilize Old Coordinates: Faster Doubly Stochastic Gradients for Kernel Methods
A Probabilistic Neuro-symbolic Layer for Algebraic Constraint Satisfaction
Discovering Cyclic Causal Models by Independent Components Analysis
The Complexity of Plan Existence and Evaluation in Probabilistic Domains
Bayesian inference approach for entropy regularized reinforcement learning with stochastic dynamics
Simulation Approaches to General Probabilistic Inference on Belief Networks
A Model-Based Approach to Rounding in Spectral Clustering
Future gradient descent for adapting the temporal shifting data distribution in online recommendation systems
Playing games against nature: optimal policies for renewable resource allocation
The neural moving average model for scalable variational inference of state space models
Learning Selectively Conditioned Forest Structures with Applications to DBNs and Classification
Recovering from Selection Bias using Marginal Structure in Discrete Models
Self-distribution distillation: efficient uncertainty estimation
Which Spatial Partition Trees are Adaptive to Intrinsic Dimension?
Robust Collective Classification against Structural Attacks
Learning robust representation for reinforcement learning with distractions by reward sequence prediction
Adaptive inference on general graphical models
A Fast Proximal Point Method for Computing Exact Wasserstein Distance
Sampling First Order Logical Particles
A Kernel Test for Three-Variable Interactions with Random Processes
A model for non-monotonic reasoning using Dempster's rule
Adaptivity and Optimality: A Universal Algorithm for Online Convex Optimization
Online Parameter-Free Learning of Multiple Low Variance Tasks
How to Exploit Structure while Solving Weighted Model Integration Problems
Perfect Tree-like Markovian Distributions
A semantic approach to non-monotonic entailments
Parity calibration
Residual Reweighted Conformal Prediction for Graph Neural Networks
Toward Learning Graphical and Causal Process Models
Lazy Propagation in Junction Trees
Robust reinforcement learning under minimax regret for green security
On the Relation between Policy Improvement and Off-Policy Minimum-Variance Policy Evaluation
Hybrid Variational/Gibbs Collapsed Inference in Topic Models
Sequential Learning under Probabilistic Constraints
Modeling extremes with d-max-decreasing neural networks
Learning the Structure of Causal Models with Relational and Temporal Dependence
Approximate Planning for Factored POMDPs using Belief State Simplification
On the Sample Complexity of Learning Bayesian Networks
Neural Active Learning Meets the Partial Monitoring Framework
Fixed-Budget Best-Arm Identification with Heterogeneous Reward Variances
The Complexity of Decentralized Control of Markov Decision Processes
Enhanced Equilibria-Solving via Private Information Pre-Branch Structure in Adversarial Team Games
Monolingual Probabilistic Programming Using Generalized Coroutines
Maximizing the Spread of Cascades Using Network Design
Hierarchical infinite relational model
Model-Based Influence Diagrams for Machine Vision
An MDP-based Recommender System
Investigating vulnerabilities of deep neural policies
The Automatic Training of Rule Bases that Use Numerical Uncertainty Representations
Computing Optimal Security Strategies for Interdependent Assets
A Probabilistic Network of Predicates
Exploiting Evidence in Probabilistic Inference
GPS-ABC: Gaussian Process Surrogate Approximate Bayesian Computation
Sparse Structure Exploration and Re-optimization for Vision Transformer
Practical Multi-fidelity Bayesian Optimization for Hyperparameter Tuning
Extend Transferable Belief Models with Probabilistic Priors
Empirical Mechanism Design: Designing Mechanisms from Data
Utility-based control for computer vision
Bayesian Networks from the Point of View of Chain Graphs
Quantum Annealing for Variational Bayes Inference
Hierarchical evidence and belief functions
An Extended Cencov-Campbell Characterization of Conditional Information Geometry
Residual Belief Propagation: Informed Scheduling for Asynchronous Message Passing
A Distance-Based Branch and Bound Feature Selection Algorithm
Communication Efficient Coresets for Empirical Loss Minimization
Modeling uncertain and vague knowledge in possibility and evidence theories
Source Separation and Higher-Order Causal Analysis of MEG and EEG
Efficient Selection of Disambiguating Actions for Stereo Vision
Interval Influence Diagrams
Causal additive models with unobserved variables
Efficient Estimation of the Value of Information in Monte Carlo Models
On the Number of Experiments Sufficient and in the Worst Case Necessary to Identify All Causal Relations Among N Variables
Sample Boosting Algorithm (SamBA) - An interpretable greedy ensemble classifier based on local expertise for fat data
Direct and Indirect Effects of Sequential Treatments
Qualtitative propagation and scenario-based scheme for exploiting probabilistic reasoning
A Probabilistic Analysis of Marker-Passing Techniques for Plan-Recognition
Approximation Complexity of Maximum A Posteriori Inference in Sum-Product Networks
Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalization
A possibilistic handling of partially ordered information
Message Passing for Soft Constraint Dual Decomposition
A Lightweight Inference Method for Image Classification
Unsupervised program synthesis for images by sampling without replacement
Evidence with Uncertain Likelihoods
EiGLasso: Scalable Estimation of Cartesian Product of Sparse Inverse Covariance Matrices
A trajectory is worth three sentences: multimodal transformer for offline reinforcement learning
Axiomatizing Causal Reasoning
A new parameter Learning Method for Bayesian Networks with Qualitative Influences
On the Testability of Causal Models With Latent and Instrumental Variables
Parametric Dependability Analysis through Probabilistic Horn Abduction
Cold-start Recommendation by Personalized Embedding Region Elicitation
PALM: Probabilistic area loss Minimization for Protein Sequence Alignment
Towards better certified segmentation via diffusion models
Solving Hybrid Influence Diagrams with Deterministic Variables
A free lunch from the noise: Provable and practical exploration for representation learning
Fast Calculation of Feature Contributions in Boosting Trees
Uncertain Inferences and Uncertain Conclusions
Thompson sampling for Markov games with piecewise stationary opponent policies
Learning Bayesian Networks: A Unification for Discrete and Gaussian Domains
Mixture-of-Parents Maximum Entropy Markov Models
Matrix Coherence and the Nystrom Method
Deciding Consistency of Databases Containing Defeasible and Strict Information
A Correlated Worker Model for Grouped, Imbalanced and Multitask Data
Optimal Submanifold Structure in Log-linear Models
MDPose: real-time multi-person pose estimation via mixture density model
Evidential reasoning in a computer vision system
On-the-fly adaptation of patrolling strategies in changing environments
Completing Knowledge by Competing Hierarchies
Using Tree-Decomposable Structures to Approximate Belief Networks
Fixing the Bethe approximation: How structural modifications in a graph improve belief propagation
Sparse Gaussian Processes for Bayesian Optimization
Search for Choquet-optimal paths under uncertainty
Probability-Raising Causality for Uncertain Parametric Markov Decision Processes with PAC Guarantees
Boosting in the presence of label noise
Collective Diffusion Over Networks: Models and Inference
Sequential Bayesian Optimisation for Spatial-Temporal Monitoring
Probabilistic Reasoning about Actions in Nonmonotonic Causal Theories
An Anytime Algorithm for Decision Making under Uncertainty
Adapting Prediction Sets to Distribution Shifts Without Labels
Local calibration: metrics and recalibration
Semi-supervised novelty detection using ensembles with regularized disagreement
Approximating MAP using Local Search
Equivalence and synthesis of causal models
Learning and certification under instance-targeted poisoning
The transferable belief model and other interpretations of Dempster-Shafer's model
Linearity Properties of Bayes Nets with Binary Variables
A Bayesian Probability Calculus for Density Matrices
Probabilistic Reasoning in Predictive Expert Systems
Automatic Tuning of Interactive Perception Applications
On the Theory and Practice of Privacy-Preserving Bayesian Data Analysis
How Likely Are Two Voting Rules Different?
Axioms for probability and belief-function proagation
Implementing Resolute Choice Under Uncertainty
How to use dropout correctly on residual networks with batch normalization
Defining Explanation in Probabilistic Systems
Cautious Propagation in Bayesian Networks
Bandits with partially observable confounded data
A Probabilistic Calculus of Actions
Constraint-based Causal Discovery for Non-Linear Structural Causal Models with Cycles and Latent Confounders
Knowledge Intensive Learning of Cutset Networks
Natural language adversarial defense through synonym encoding
HELM: Highly Efficient Learning of Mixed copula networks
Algorithms for Irrelevance-Based Partial MAPs
Keep-Alive Caching for the Hawkes process
Electing the Most Probable Without Eliminating the Irrational: Voting Over Intransitive Domains
Doubly non-central beta matrix factorization for DNA methylation data
Gradient-based optimization for multi-resource spatial coverage problems
Forward Backward Greedy Algorithms for Multi-Task Learning with Faster Rates
Local Message Passing on Frustrated Systems
A Cost-Effective Framework for Preference Elicitation and Aggregation
Amortized Nesterov's Momentum: A Robust Momentum and Its Application to Deep Learning
On Finding Minimal w-cutset
Dirichlet Proportions Model for Hierarchically Coherent Probabilistic Forecasting
Credibility discounting in the theory of approximate reasoning
A Hybrid Approach to Reasoning Under Uncertainty
An improved variational approximate posterior for the deep Wishart process
Periodical Moving Average Accelerates Gradient Accumulation for Post-Training
Faster algorithms for Markov equivalence
Learning in Multi-Level Stochastic Games with Delayed Information
Walking on Two Legs: Learning Image Segmentation with Noisy Labels
Automated Planning in Repeated Adversarial Games
Pure Exploration in Asynchronous Federated Bandits
Correlated Tag Learning in Topic Model
Differential Privacy in Cooperative Multiagent Planning
Differentially Private Top-k Selection via Stability on Unknown Domain
Bayesian Online Prediction of Change Points
Pix2Code: Learning to Compose Neural Visual Concepts as Programs
Reasoning about Probabilities in Dynamic Systems using Goal Regression
Averaging of Decomposable Graphs by Dynamic Programming and Sampling
A Polynomial-Time Algorithm for Deciding Equivalence of Directed Cyclic Graphical Models
Toward a Market Model for Bayesian Inference
Learning Fast Optimizers for Contextual Stochastic Integer Programs
PAC-Bayesian Inequalities for Martingales
Optimal expert elicitation to reduce interval uncertainty
Polynomial Value Iteration Algorithms for Detrerminstic MDPs
Computationally Efficient Methods for Invariant Feature Selection with Sparsity
From Qualitative to Quantitative Probabilistic Networks
A System for Ontologically-Grounded Probabilistic Matching
Two-stage Kernel Bayesian Optimization in High Dimensions
One Shot Inverse Reinforcement Learning for Stochastic Linear Bandits
Price Updating in Combinatorial Prediction Markets with Bayesian Networks
Optimal Budget Allocation for Crowdsourcing Labels for Graphs
UAI '94: Proceedings of the Tenth Annual Conference on Uncertainty in Artificial Intelligence, Seattle, Washington, USA, July 29-31, 1994
Inference for probabilistic dependency graphs
Reactive Bayesian Network Computation using Feedback Control: An Empirical Study
The Deterministic Information Bottleneck
Accelerating EM: An Empirical Study
Cycle class consistency with distributional optimal transport and knowledge distillation for unsupervised domain adaptation
On Counterfactual Explanations under Predictive Multiplicity
Semi-Instrumental Variables: A Test for Instrument Admissibility
Exploring High-dimensional Search Space via Voronoi Graph Traversing
Markov Beta Processes for Time Evolving Dictionary Learning
Best arm identification in rare events
Faithful Approximations of Belief Functions
Cooperative Negotiation in Autonomic Systems using Incremental Utility Elicitation
Subset-of-data variational inference for deep Gaussian-processes regression
"Conditional Inter-Causally Independent" Node Distributions, a Property of "Noisy-OR" Models
GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs
A General Approach to Decision Making with Evidential Knowledge
Trading Off Voting Axioms for Privacy
Tuning-Free Coreset Markov Chain Monte Carlo via Hot DoG
Algorithms and Complexity Results for Exact Bayesian Structure Learning
Learning in online MDPs: is there a price for handling the communicating case?
Fast Gaussian Process Posteriors with Product Trees
Efficient Test Selection in Active Diagnosis via Entropy Approximation
Implicit kernel meta-learning using kernel integral forms
Probabilistic logic: some comments and possible use for nonmonotonic reasoning
Full Network Capacity Framework for Sample-Efficient Deep Reinforcement Learning
Adversarial Learning for 3D Matching
Inference for mark-censored temporal point processes
Decision-theoretic planning with communication in open multiagent systems
The Hawkes Edge Partition Model for Continuous-time Event-based Temporal Networks
Some Problems for Convex Bayesians
Decision Making for Symbolic Probability
Off-Policy Policy Gradient with Stationary Distribution Correction
Nyström M-Hilbert-Schmidt independence criterion
f BGD : Learning Embeddings From Positive Unlabeled Data with BGD
Evaluation of Causal Structure Learning Algorithms via Risk Estimation
Annealed Gradient Descent for Deep Learning
Incentivising Diffusion while Preserving Differential Privacy
Bayesian Error-Bars for Belief Net Inference
Discovering Multiple Constraints that are Frequently Approximately Satisfied
On Measurement Bias in Causal Inference
ODD: Overlap-aware Estimation of Model Performance under Distribution Shift
Correlated Learning for Aggregation Systems
Learning high-dimensional DAGs with latent and selection variables (Abstract)
Adaptive Algorithms and Data-Dependent Guarantees for Bandit Convex Optimization
SparsityBoost: A New Scoring Function for Learning Bayesian Network Structure
Multi-modal differentiable unsupervised feature selection
Group-Agent Reinforcement Learning with Heterogeneous Agents
Algebraic Equivalence Class Selection for Linear Structural Equation Models
Quantifying aleatoric and epistemic uncertainty in machine learning: Are conditional entropy and mutual information appropriate measures?
Towards Representation Learning for Weighting Problems in Design-Based Causal Inference
Inference in Probabilistic Logic Programs using Weighted CNF's
Distributedrevision of belief commitment in composite explanations
Optimal Time Bounds for Approximate Clustering
Finite Sample Complexity of Rare Pattern Anomaly Detection
Decision Theoretic Foundations of Graphical Model Selection
The Total Belief Theorem
Convex Point Estimation using Undirected Bayesian Transfer Hierarchies
Integrating Model Construction and Evaluation
Now that I Have a Good Theory of Uncertainty, What Else Do I Need
Hindsight Merging: Diverse Data Generation with Language Models
Sufficient identification conditions and semiparametric estimation under missing not at random mechanisms
Computing Posterior Probabilities of Structural Features in Bayesian Networks
Bayesian Network Parameter Learning using EM with Parameter Sharing
X-MEN: guaranteed XOR-maximum entropy constrained inverse reinforcement learning
Generalized Instrumental Variables
Sample-and-Accumulate Algorithms for Belief Updating in Bayes Networks
Selecting Computations: Theory and Applications
Stochastic Integration via Error-Correcting Codes
Toward General Analysis of Recursive Probability Models
Testing conventional wisdom (of the crowd)
Using Qualitative Relationships for Bounding Probability Distributions
Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence, UAI 2013, Bellevue, WA, USA, August 11-15, 2013
Certification of iterative predictions in Bayesian neural networks
A Multivariate Discretization Method for Learning Bayesian Networks from Mixed Data
Clustering Without (Thinking About) Triangulation
Exact Sampling of Directed Acyclic Graphs from Modular Distributions
Suboptimality Bounds for Stochastic Shortest Path Problems
Laplace approximated Gaussian process state-space models
Combinatonal Optimization by Learning and Simulation of Bayesian Networks
Using Potential Influence Diagrams for Probabilistic Inference and Decision Making
Monte-Carlo Tree Search using Batch Value of Perfect Information
Subspace Clustering with a Twist
variational combinatorial sequential monte carlo methods for bayesian phylogenetic inference
A Linear Belief Function Approach to Portfolio Evaluation
Dynamic construction of belief networks
A Bayesian approach to learning in fault isolation
Probabilistic Disjunctive Logic Programming
Linear Opinion Pooling for Uncertainty Quantification on Graphs
CrysMMNet: Multimodal Representation for Crystal Property Prediction
Selecting Uncertainty Calculi and Granularity: An Experiment in Trading-off Precision and Complexity
IDK Cascades: Fast Deep Learning by Learning not to Overthink
Identifying Homogeneous and Interpretable Groups for Conformal Prediction
UAI '89: Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence, Windsor, Ontario, Canada, August 18-20, 1989
DataSP: A Differential All-to-All Shortest Path Algorithm for Learning Costs and Predicting Paths with Context
An evaluation of structural parameters for probabilistic reasoning: Results on benchmark circuits
Merging Strategies for Sum-Product Networks: From Trees to Graphs
Optimization With Parity Constraints: From Binary Codes to Discrete Integration
Zero Inflation as a Missing Data Problem: a Proxy-based Approach
Superposing many tickets into one: A performance booster for sparse neural network training
Interpretable Almost Matching Exactly With Instrumental Variables
Large-Sample Learning of Bayesian Networks is NP-Hard
Learning Convex Inference of Marginals
Practical Uses of Belief Functions
Sensor Validation Using Dynamic Belief Networks
Improved Vector Pruning in Exact Algorithms for Solving POMDPs
An Alternative Markov Property for Chain Graphs
A Theoretical Study of Y Structures for Causal Discovery
Exploration for Free: How Does Reward Heterogeneity Improve Regret in Cooperative Multi-agent Bandits?
Conditions Under Which Conditional Independence and Scoring Methods Lead to Identical Selection of Bayesian Network Models
On the Convergence of Hierarchical Federated Learning with Partial Worker Participation
An explanation mechanism for bayesian inferencing systems
Using the Dempster-Shafer Scheme in a Mixed-Initiative Expert System Shell
CFW: A Collaborative Filtering System Using Posteriors over Weights of Evidence