yusuke1997/dblp-id-title huggingface dataset:
Convergence Analysis of Linear Coupling with Inexact Proximal Operator
Most: multi-source domain adaptation via optimal transport for student-teacher learning
Learning Why Things Change: The Difference-Based Causality Learner
Critical Influence of Overparameterization on Sharpness-aware Minimization
Induction and uncertainty management techniques applied to veterinary medical diagnosis
On Smoothing and Inference for Topic Models
Model-Free Reinforcement Learning with Skew-Symmetric Bilinear Utilities
Nonparametric Fisher Geometry with Application to Density Estimation
Fisher-Bures Adversary Graph Convolutional Networks
Lifted Relax, Compensate and then Recover: From Approximate to Exact Lifted Probabilistic Inference
Variational zero-inflated Gaussian processes with sparse kernels
Statistical Translation, Heat Kernels and Expected Distances
Belief Revision with Uncertain Inputs in the Possibilistic Setting
Bandits with Side Observations: Bounded vs. Logarithmic Regret
Robust identifiability in linear structural equation models of causal inference
Taming the Noise in Reinforcement Learning via Soft Updates
Evaluating high-order predictive distributions in deep learning
Relational Causal Discovery with Latent Confounders
Human-in-the-Loop Mixup
A Fast Algorithm for Matrix Eigen-decompositionn
Deep Mixture of Experts via Shallow Embedding
Case-Factor Diagrams for Structured Probabilistic Modeling
Proceedings of the UAI 2014 Workshop Causal Inference: Learning and Prediction co-located with 30th Conference on Uncertainty in Artificial Intelligence (UAI 2014), Quebec City, Canada, July 27, 2014
Differentially Private Variational Inference for Non-conjugate Models
An Experimental Comparison of Several Clustering and Initialization Methods
On the Geometry of Bayesian Graphical Models with Hidden Variables
Differentiable Pareto-Smoothed Weighting for High-Dimensional Heterogeneous Treatment Effect Estimation
Incentive Decision Processes
Causal effect estimation from observational and interventional data through matrix weighted linear estimators
Stein Variational Evolution Strategies
Symmetric Collaborative Filtering Using the Noisy Sensor Model
Approximate Separability for Weak Interaction in Dynamic Systems
Learning in Markov games: Can we exploit a general-sum opponent?
Systematized event-aware learning for multi-object tracking
MIDAS: An Influence Diagram for Management of Mildew in Winter Wheat
The Probability of a Possibility: Adding Uncertainty to Default Rules
Robust Learning Equilibrium
Graphical readings of possibilistic logic bases
Causal inference with treatment measurement error: a nonparametric instrumental variable approach
Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasets
Personalized Peer Truth Serum for Eliciting Multi-Attribute Personal Data
A polynomial algorithm for computing the optimal repair strategy in a system with independent component failures
Reasoning With Conditional Ceteris Paribus Preference Statements
Learning Topological Representations with Bidirectional Graph Attention Network for Solving Job Shop Scheduling Problem
Generalized Bayesian quadrature with spectral kernels
Interpretation and Generalization of Score Matching
Statistical Decisions Using Likelihood Information Without Prior Probabilities
Sensitivity Analysis for Probability Assessments in Bayesian Networks
A backwards view for assessment
A Unified Data Representation Learning for Non-parametric Two-sample Testing
Bayesian spillover graphs for dynamic networks
Reasoning about Expectation
Towards Conditional Independence Test for Relational Data
An unsupervised video game playstyle metric via state discretization
Estimation and clustering with infinite rankings
Fractional Moments on Bandit Problems
Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments
Expectation-Propogation for the Generative Aspect Model
Theory Refinement on Bayesian Networks
Venn-Abers Predictors
An Efficient Quantile Spatial Scan Statistic for Finding Unusual Regions in Continuous Spatial Data with Covariates
End-User Construction of Influence Diagrams for Bayesian Statistics
Reputation Systems: An Axiomatic Approach
Inferring Parameters and Structure of Latent Variable Models by Variational Bayes
Uncertainty in Artificial Intelligence, 15-19 July 2024, Universitat Pompeu Fabra, Barcelona, Spain
Adaptive Human-Robot Collaboration using Type-Based IRL
Bayesian Filtering with Online Gaussian Process Latent Variable Models
Identification of Conditional Interventional Distributions
Integrated Nested Laplace Approximation for Bayesian Nonparametric Phylodynamics
Multi-task and meta-learning with sparse linear bandits
Generating Explanations for Evidential Reasoning
The sum-and-lattice-points method based on an evidential-reasoning system applied to the real-time vehicle guidance problem
MOB-ESP and other Improvements in Probability Estimation
Domain Adaptation with Cauchy-Schwarz Divergence
Latent Structured Ranking
Measuring the Hardness of Stochastic Sampling on Bayesian Networks with Deterministic Causalities: the k-Test
Monotonicity regularization: Improved penalties and novel applications to disentangled representation learning and robust classification
An Object-oriented Spatial and Temporal Bayesian Network for Managing Willows in an American Heritage River Catchment
Near-Optimal Target Learning With Stochastic Binary Signals
ASTRA: Understanding the practical impact of robustness for probabilistic programs
Causal discovery under a confounder blanket
Partially adaptive regularized multiple regression analysis for estimating linear causal effects
Minimum cross entropy reasoning in recursive causal networks
Object-Oriented Bayesian Networks
Dynamic Programming for Structured Continuous Markov Decision Problems
A Scalable Method for Solving High-Dimensional Continuous POMDPs Using Local Approximation
Optimal decomposition of belief networks
Identifying the Relevant Nodes Without Learning the Model
SubMix: Learning to Mix Graph Sampling Heuristics
Computing Belief Commitments Using Tensor Products
From imprecise probability assessments to conditional probabilities with quasi additive classes of conditioning events
Empirical Analysis of Predictive Algorithms for Collaborative Filtering
Multi-label Image Classification with A Probabilistic Label Enhancement Model
Blind Construction of Optimal Nonlinear Recursive Predictors for Discrete Sequences
Offline Bayesian Aleatoric and Epistemic Uncertainty Quantification and Posterior Value Optimisation in Finite-State MDPs
Identifying confounders using additive noise models
Faster lifting for two-variable logic using cell graphs
Likelihood Computations Using Value Abstraction
MOHITO: Multi-Agent Reinforcement Learning using Hypergraphs for Task-Open Systems
Gradient Methods for Stackelberg Games
Approximate Learning in Complex Dynamic Bayesian Networks
Learning Probabilistic Relational Dynamics for Multiple Tasks
Towards tractable optimism in model-based reinforcement learning
Uncertainty in Artificial Intelligence, Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence, UAI 2022, 1-5 August 2022, Eindhoven, The Netherlands
Calibrated and Conformal Propensity Scores for Causal Effect Estimation
Bounded Approximate Symbolic Dynamic Programming for Hybrid MDPs
Perturbation type categorization for multiple adversarial perturbation robustness
An Inequality Paradigm for Probabilistic Knowledge: The Logic of Conditional Probability Intervals
Combinatorial semi-bandit in the non-stationary environment
Expectation Propagation for Continuous Time Bayesian Networks
Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, UAI 2021, Virtual Event, 27-30 July 2021
Do Bayesian variational autoencoders know what they don't know?
Mechanism Design with Execution Uncertainty
Graph-Coupled HMMs for Modeling the Spread of Infection
Collaborative Multi-output Gaussian Processes
Characterization and Greedy Learning of Interventional Markov Equivalence Classes of Directed Acyclic Graphs (Abstract)
A robustness test for estimating total effects with covariate adjustment
Dependent Randomized Rounding for Budget Constrained Experimental Design
Mixup Regularization: A Probabilistic Perspective
Low-precision arithmetic for fast Gaussian processes
Modal logics of higher-order probability
Dimension Correction for Hierarchical Latent Class Models
Updating with belief functions, ordinal conditional functions and possibility measures
The Complexity of Approximately Solving Influence Diagrams
Fast predictive uncertainty for classification with Bayesian deep networks
Efficient Parallel Estimation for Markov Random Fields
BELIEF - Bayesian Sign Entropy Regularization for LIME Framework
Proceedings of the Fifth UAI Bayesian Modeling Applications Workshop (UAI-AW 2007), Vancouver, British Columbia, Canada, July 19, 2007
Learning Periodic Human Behaviour Models from Sparse Data for Crowdsourcing Aid Delivery in Developing Countries
A Bayesian Matrix Factorization Model for Relational Data
Fast Non-convex Matrix Sensing with Optimal Sample Complexity
Jeffrey's rule of conditioning generalized to belief functions
How to Fix a Broken Confidence Estimator: Evaluating Post-hoc Methods for Selective Classification with Deep Neural Networks
Deliberation and its Role in the Formation of Intentions
An Approximate Nonmyopic Computation for Value of Information
Assessing the Impact of Context Inference Error and Partial Observability on RL Methods for Just-In-Time Adaptive Interventions
Personalized federated domain adaptation for item-to-item recommendation
Learning Hidden Markov Models for Regression using Path Aggregation
A Graph-Based Inference Method for Conditional Independence
Reforming Generative Autoencoders via Goodness-of-Fit Hypothesis Testing
q-Paths: Generalizing the geometric annealing path using power means
Defeasible Decisions: What the Proposal Is and Isn't
Stable Independance and Complexity of Representation
Approximation Algorithms for the Loop Cutset Problem
A Bayesian approach for bandit online optimization with switching cost
Alternative Markov and Causal Properties for Acyclic Directed Mixed Graphs
Propagation of 2-Monotone Lower Probabilities on an Undirected Graph
Reasoning, Metareasoning, and Mathematical Truth: Studies of Theorem Proving under Limited Resources
Normalized Online Learning
Steps Towards Programs that Manage Uncertainty
Propagating uncertainty in rule based cognitive modelling
Predicting the likely behaviours of continuous nonlinear systems in equilibrium
Localized Partial Evaluation of Belief Networks
Modelling Information Incorporation in Markets, with Application to Detecting and Explaining Events
Stability of SGD: Tightness analysis and improved bounds
Modeling Multiple Annotator Expertise in the Semi-Supervised Learning Scenario
Scalable and Flexible Clustering of Grouped Data via Parallel and Distributed Sampling in Versatile Hierarchical Dirichlet Processes
Missing Data as a Causal and Probabilistic Problem
Lower Bound on Howard Policy Iteration for Deterministic Markov Decision Processes
Reflected Schrödinger Bridge for Constrained Generative Modeling
Partially Specified Belief Functions
Structured Prediction: From Gaussian Perturbations to Linear-Time Principled Algorithms
Finite-Time Analysis of Kernelised Contextual Bandits
Knowledge Structures and Evidential Reasoning in Decision Analysis
Some extensions of probabilistic logic
Decision Tree Induction Systems: A Bayesian Analysis
Distributed Planning in Hierarchical Factored MDPs
Lipschitz Parametrization of Probabilistic Graphical Models
Sensitivity analysis in decision circuits
Generalized Belief Propagation on Tree Robust Structured Region Graphs
Nonparametric Bayesian Logic
Differentially private multi-party data release for linear regression
Stochastic Discriminative EM
Variance-dependent best arm identification
Causal Inference by Surrogate Experiments: z-Identifiability
A near-optimal high-probability swap-Regret upper bound for multi-agent bandits in unknown general-sum games
A Bayesian Approach to Learning Bayesian Networks with Local Structure
Randomized Exploration for Non-Stationary Stochastic Linear Bandits
Understanding Pathologies of Deep Heteroskedastic Regression
A framework of fuzzy evidential reasoning
Active Learning with Distributional Estimates
Energy-based Predictive Representations for Partially Observed Reinforcement Learning
Approximately Optimal Monitoring of Plan Preconditions
Differentially Private No-regret Exploration in Adversarial Markov Decision Processes
Research on video adversarial attack with long living cycle
ARCO1: An Application of Belief Networks to the Oil Market
Semi-Supervised Learning: the Case When Unlabeled Data is Equally Useful
On the Complexity of Policy Iteration
Privacy-Preserving Neural Processes for Probabilistic User Modeling
On Hyper-Parameter Estimation In Empirical Bayes: A Revisit of The MacKay Algorithm
Weighted model counting with conditional weights for Bayesian networks
Optimal junction Trees
RE-SORT: Removing Spurious Correlation in Multilevel Interaction for CTR Prediction
Accelerating Voting by Quantum Computation
Learning Mixtures of Submodular Shells with Application to Document Summarization
Probability Estimation in Face of Irrelevant Information
A Spectral Algorithm for Latent Junction Trees
Learning Equivalence Classes of Bayesian Network Structures
Learning soft interventions in complex equilibrium systems
Proceedings of the UAI 2016 Workshop on Causation: Foundation to Application co-located with the 32nd Conference on Uncertainty in Artificial Intelligence (UAI 2016), Jersey City, USA, June 29, 2016
Online Generalized Magician's Problem with Multiple Workers
Towards Solving the Multiple Extension Problem: Combining Defaults and Probability
Additive Belief-Network Models
Maximum likelihood fitting of acyclic directed mixed graphs to binary data
ViBid: Linear Vision Transformer with Bidirectional Normalization
Invariant representation learning for treatment effect estimation
A Unified Setting for Inference and Decision: An Argumentation-based Approach
Complexity of Solving Decision Trees with Skew-Symmetric Bilinear Utility
Correlated weights in infinite limits of deep convolutional neural networks
Epsilon-Safe Planning
Learning to Map Sentences to Logical Form: Structured Classification with Probabilistic Categorial Grammars
Dynamical Systems Trees
Unsupervised Joint Alignment and Clustering using Bayesian Nonparametrics
Toward a Characterization of Uncertainty Measure for the Dempster-Shafer Theory
RES: A Relative Method for Evidential Reasoning
Bayesian network learning by compiling to weighted MAX-SAT
Multiclass classification for Hawkes processes
Adversarial Cost-Sensitive Classification
Compressed Inference for Probabilistic Sequential Models
Greed is good: correspondence recovery for unlabeled linear regression
Sensitivities: An Alternative to Conditional Probabilities for Bayesian Belief Networks
Score and Information for Recursive Exponential Models with Incomplete Data
Using New Data to Refine a Bayesian Network
Learning Bayesian Networks from Incomplete Data with Stochastic Search Algorithms
Effcient Transition Probability Computation for Continuous-Time Branching Processes via Compressed Sensing
Degrees of Freedom in Deep Neural Networks
Amortized Bayesian Optimization over Discrete Spaces
A Graph Theoretic Approach for Preference Learning with Feature Information
Metrics for Markov Decision Processes with Infinite State Spaces
ELF: Federated Langevin Algorithms with Primal, Dual and Bidirectional Compression
Latent Representation Entropy Density for Distribution Shift Detection
Sinkhorn AutoEncoders
Political Dimensionality Estimation Using a Probabilistic Graphical Model
Non-Parametric Path Analysis in Structural Causal Models
Efficiently learning the graph for semi-supervised learning
On Bayesian Network Approximation by Edge Deletion
Forecasting Sleep Apnea with Dynamic Network Models
On heuristics for finding loop cutsets in multiply-connected belief networks
Test for non-negligible adverse shifts
Asymptotic optimality for active learning processes
Bayesian Hierarchical Mixtures of Experts
Learning good interventions in causal graphs via covering
Nonparametric Bayesian inference of item-level features in classifier combination
Practical Linear Value-approximation Techniques for First-order MDPs
Refining reasoning in qualitative probabilistic networks
Fast Algorithms for Learning with Long N-grams via Suffix Tree Based Matrix Multiplication
Adaptive Foreground and Shadow Detection in Image Sequences
Private Prediction Strikes Back! Private Kernelized Nearest Neighbors with Individual Rényi Filter
An Empirical Comparison of Algorithms for Aggregating Expert Predictions
A comparison of decision alaysis and expert rules for sequential diagnosis
On the Robustness of Most Probable Explanations
On the semi-Markov Equivalence of Causal Models
Statistical mechanical analysis of neural network pruning
Hybrid Bernstein Normalizing Flows for Flexible Multivariate Density Regression with Interpretable Marginals
Molecule Design by Latent Space Energy-Based Modeling and Gradual Distribution Shifting
Exact and approximate hierarchical clustering using A
Partial identification of the maximum mean discrepancy with mismeasured data
Variational Regret Bounds for Reinforcement Learning
Bayesian Active Learning in the Presence of Nuisance Parameters
Probabilistic Collaborative Representation Learning for Personalized Item Recommendation
A method for Planning Given Uncertain and Incomplete Information
Decision-Focused Evaluation of Worst-Case Distribution Shift
Locally Weighted Naive Bayes
Inference-less Density Estimation using Copula Bayesian Networks
Independence and Bayesian Updating Methods
Symbolic Generalization for On-line Planning
Dialectic reasoning with inconsistent information
Efficient Privacy-Preserving Stochastic Nonconvex Optimization
Variational Dual-Tree Framework for Large-Scale Transition Matrix Approximation
Calibrated ensembles can mitigate accuracy tradeoffs under distribution shift
Magic Inference Rules for Probabilistic Deduction under Taxonomic Knowledge
A Bayesian Approach to Learning Causal Networks
Sensitivity Analysis in Bayesian Networks: From Single to Multiple Parameters
Selection of Identifiability Criteria for Total Effects by using Path Diagrams
Qualitative Decision Making Under Possibilistic Uncertainty: Toward more Discriminating Criteria
REGAL: A Regularization based Algorithm for Reinforcement Learning in Weakly Communicating MDPs
Hidden Markov Nonlinear ICA: Unsupervised Learning from Nonstationary Time Series
Display of Information for Time-Critical Decision Making
Strictly Proper Mechanisms with Cooperating Players
Geometric Network Comparisons
Model-Free Robust Reinforcement Learning with Sample Complexity Analysis
Product Trees for Gaussian Process Covariance in Sublinear Time
Non-PSD matrix sketching with applications to regression and optimization
Adaptive Hashing for Model Counting
Fast Proximal Gradient Descent for A Class of Non-convex and Non-smooth Sparse Learning Problems
Renewal Strings for Cleaning Astronomical Databases
Fast Relative-Error Approximation Algorithm for Ridge Regression
Super-Samples from Kernel Herding
A New Model of Plan Recognition
ReVar: Strengthening policy evaluation via reduced variance sampling
Exploiting Uncertain and Temporal Information in Correlation
Path-BN: Towards effective batch normalization in the Path Space for ReLU networks
A Bayesian Nonparametric Model for Spectral Estimation of Metastable Systems
Branch and Bound for Regular Bayesian Network Structure Learing
A Decision Theoretic Approach to Targeted Advertising
Dynamic Bayesian Network Modeling of Vascularization in Engineered Tissues
FDR-SVM: A Federated Distributionally Robust Support Vector Machine via a Mixture of Wasserstein Balls Ambiguity Set
Estimating treatment effects with observed confounders and mediators
Graphical Condition for Identification in recursive SEM
Batch-iFDD for Representation Expansion in Large MDPs
Unbiased gradient estimation for variational auto-encoders using coupled Markov chains
Flexible and Approximate Computation through State-Space Reduction
Stochastic Rank Aggregation
Discovery of Non-gaussian Linear Causal Models using ICA
Reasoning about RoboCup Soccer Narratives
Adversarial Training May Induce Deteriorating Distributions
Characterizing predictable classes of processes
FedAST: Federated Asynchronous Simultaneous Training
On Local Optima in Learning Bayesian Networks
Social Reinforcement Learning to Combat Fake News Spread
The Sensitivity of Counterfactual Fairness to Unmeasured Confounding
Evidential reasoning compared in a network usage prediction testbed: preliminary report
Can a Bayesian Oracle Prevent Harm from an Agent?
Learning relevant contextual variables within Bayesian optimization
The Graphical Identification for Total Effects by using Surrogate Variables
New Techniques for Algorithm Portfolio Design
Risk Sensitive Path Integral Control
Local Structure Discovery in Bayesian Networks
Risk Bounds for Infinitely Divisible Distribution
Loopy Belief Propagation as a Basis for Communication in Sensor Networks
A Probabilistic Logic for Reasoning about Uncertain Temporal Information
Near-Orthogonality Regularization in Kernel Methods
Learning to Draw Samples with Amortized Stein Variational Gradient Descent
Value of Evidence on Influence Diagrams
Learning to learn with Gaussian processes
A Parallel Network for LRCT Segmentation and Uncertainty Mitigation with Fuzzy Sets
Structured Region Graphs: Morphing EP into GBP
Benefits of monotonicity in safe exploration with Gaussian processes
A Possibilistic Model for Qualitative Sequential Decision Problems under Uncertainty in Partially Observable Environments
Semantic Understanding of Professional Soccer Commentaries
Center-Based Relaxed Learning Against Membership Inference Attacks
Target Beliefs for SME-oriented Bayesian Network-based Modeling
Simulation-based Inference for High-dimensional Data using Surjective Sequential Neural Likelihood Estimation
Modifiable Combining Functions
Primal View on Belief Propagation
QuantProb: Generalizing Probabilities along with Predictions for a Pre-trained Classifier
Penalty Logic and its Link with Dempster-Shafer Theory
Learning Distributionally Robust Tractable Probabilistic Models in Continuous Domains
Interpretable differencing of machine learning models
Incremental Learning-to-Learn with Statistical Guarantees
Bayesian Active Distance Metric Learning
Bayesian Discovery of Linear Acyclic Causal Models
Statistical and Causal Robustness for Causal Null Hypothesis Tests
Conditional lndependence in Uncertainty Theories
Efficient Decision-Theoretic Planning: Techniques and Empirical Analysis
On the limitations of Markovian rewards to express multi-objective, risk-sensitive, and modal tasks
A Method for Implementing a Probabilistic Model as a Relational Database
Explanation of Probabilistic Inference
Exploring Parallelism in Learning Belief Networks
CoRE Kernels
Bayes Blocks: An Implementation of the Variational Bayesian Building Blocks Framework
Non-parametric Revenue Optimization for Generalized Second Price auctions.
Market Making with Decreasing Utility for Information
An Upper Bound on the Global Optimum in Parameter Estimation
The Hierarchical Dirichlet Process Hidden Semi-Markov Model
Gaussian Process Topic Models
Merging Knowledge Bases in Possibilistic Logic by Lexicographic Aggregation
Backward Simulation in Bayesian Networks
Piecewise Deterministic Markov Processes for Bayesian Neural Networks
Efficient Inference in Large Discrete Domains
Hydrologic Predictions using Probabilistic Relational Models
Bayes Optimal Feature Selection for Supervised Learning with General Performance Measures
Investigation of Variances in Belief Networks
On First-Order Bounds, Variance and Gap-Dependent Bounds for Adversarial Bandits
Preface and Frontmatter
What Cannot be Learned with Bethe Approximations
A Uniqueness Theorem for Clustering
Integrating case-based and rule based reasoning: the possibilistic connection