yusuke1997/dblp-id-title huggingface dataset:
No-regret Exploration in Contextual Reinforcement Learning
The Relationship Between AND/OR Search and Variable Elimination
Computational Advantages of Relevance Reasoning in Bayesian Belief Networks
Generating structure of latent variable models for nested data
Computing upper and lower bounds on likelihoods in intractable networks
Sequential Model-Based Ensemble Optimization
A Qualitative Markov Assumption and Its Implications for Belief Change
Causal Discovery of Linear Cyclic Models from Multiple Experimental Data Sets with Overlapping Variables
A mutually exciting latent space Hawkes process model for continuous-time networks
Boltzmann Machine Learning with the Latent Maximum Entropy Principle
When do Numbers Really Matter?
Learning Parameters by Prediction Markets and Kelly Rule for Graphical Models
Causal networks: semantics and expressiveness
Domain Generalization via Multidomain Discriminant Analysis
SPOOK: A system for probabilistic object-oriented knowledge representation
Implicit Training of Inference Network Models for Structured Prediction
Propositional and Relational Bayesian Networks Associated with Imprecise and Qualitat
Probabilistic Graph Circuits: Deep Generative Models for Tractable Probabilistic Inference over Graphs
Node Splitting: A Scheme for Generating Upper Bounds in Bayesian Networks
Joint Discovery of Haplotype Blocks and Complex Trait Associations from SNP Sequences
A Probabilistic Model for Sensor Validation
On the Testable Implications of Causal Models with Hidden Variables
Bayesian Models to Assess Risk of Corruption of Federal Management Units
Graphical Models and Exponential Families
MixupE: Understanding and improving Mixup from directional derivative perspective
A Bayesian nonparametric conditional two-sample test with an application to Local Causal Discovery
A Function Approximation Approach to Estimation of Policy Gradient for POMDP with Structured Policies
Deep kernels with probabilistic embeddings for small-data learning
Markov equivalence of max-linear Bayesian networks
Solving structured hierarchical games using differential backward induction
Why Out-of-Distribution detection experiments are not reliable - subtle experimental details muddle the OOD detector rankings
Uncertain Congestion Games with Assorted Human Agent Populations
Propagation using Chain Event Graphs
Exploring Localization in Bayesian Networks for Large Expert Systems
Truth as Utility: A Conceptual Synthesis
Mixed-Membership Stochastic Block Models for Weighted Networks
Lighter-Communication Distributed Machine Learning via Sufficient Factor Broadcasting
Aggregating Across Multiple Levels of Granularity to Meet Customer and Organizational Query Requirements
The DLR Hierarchy of Approximate Inference
High Level Path Planning with Uncertainty
Fedvarp: Tackling the variance due to partial client participation in federated learning
Bayesian optimization and attribute adjustment
Fast Belief Update Using Order-of-Magnitude Probabilities
Towards a Theoretical Understanding of Negative Transfer in Collective Matrix Factorization
A Synthesis of Logical and Probabilistic Reasoning for Program Understanding and Debugging
Rank/Norm Regularization with Closed-Form Solutions: Application to Subspace Clustering
UAI 2008, Proceedings of the 24th Conference in Uncertainty in Artificial Intelligence, Helsinki, Finland, July 9-12, 2008
Temporal-Difference Networks for Dynamical Systems with Continuous Observations and Actions
Bayesian Belief Network Inference Using Simulation
Combinatorial categorized bandits with expert rankings
Classification of Sets using Restricted Boltzmann Machines
Structuring Causal Tree Models with Continuous Variables
Polynomial Constraints in Causal Bayesian Networks
Efficient greedy coordinate descent via variable partitioning
Batch-Mode Active Learning via Error Bound Minimization
Exploiting Structure in Cooperative Bayesian Games
Latent Composite Likelihood Learning for the Structured Canonical Correlation Model
Identification In Missing Data Models Represented By Directed Acyclic Graphs
Online Semi-Supervised Learning on Quantized Graphs
Recognizing Activities and Spatial Context Using Wearable Sensors
Introducing Variable Importance Tradeoffs into CP-Nets
Directed reduction algorithms and decomposable graphs
Learning Game Representations from Data Using Rationality Constraints
Planning, scheduling and uncertainty in the sequence of future events
UAI '97: Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence, Brown University, Providence, Rhode Island, USA, August 1-3, 1997
BanditQ: Fair Bandits with Guaranteed Rewards
Learning to Stabilize Unknown LTI Systems on a Single Trajectory under Stochastic Noise
Some Practical Issues in Constructing Belief Networks
Qualitative Possibilistic Mixed-Observable MDPs
Planning in POMDPs Using Multiplicity Automata
Modeling financial uncertainty with multivariate temporal entropy-based curriculums
Learning Continuous Time Bayesian Networks
(Nearly) Optimal Differentially Private Stochastic Multi-Arm Bandits
A Mirror Descent Perspective of Smoothed Sign Descent
Strategically efficient exploration in competitive multi-agent reinforcement learning
Increasing effect sizes of pairwise conditional independence tests between random vectors
Active Learning for Developing Personalized Treatment
Case-based off-policy evaluation using prototype learning
Exact and Approximate Inference in Associative Hierarchical Networks using Graph Cuts
Modeling Social Networks with Node Attributes using the Multiplicative Attribute Graph Model
Bayesian Optimization with Unknown Constraints
Formal Model of Uncertainty for Possibilistic Rules
Masking the Unknown: Leveraging Masked Samples for Enhanced Data Augmentation
On the Inductive Biases of Demographic Parity-based Fair Learning Algorithms
Importance Sampling via Variational Optimization
Greedy Policy Search: A Simple Baseline for Learnable Test-Time Augmentation
Belief Propagation by Message Passing in Junction Trees: Computing Each Message Faster Using GPU Parallelization
Efficient Algorithms for Logistic Contextual Slate Bandits with Bandit Feedback
Variable Noise and Dimensionality Reduction for Sparse Gaussian processes
A Unified Particle-Optimization Framework for Scalable Bayesian Sampling
Experiments with interval-valued uncertainty
Possibilistic Conditioning and Propagation
Learning to Rank for Active Learning via Multi-Task Bilevel Optimization
EntProp: High Entropy Propagation for Improving Accuracy and Robustness
Asymptotically Optimal Linear Best Feasible Arm Identification with Fixed Budget
Truly Proximal Policy Optimization
Supervised Restricted Boltzmann Machines
Convex-constrained Sparse Additive Modeling and Its Extensions
MSP-SR: Multi-Stage Probabilistic Generative Super Resolution with Scarce High-Resolution Data
Heteroskedastic Geospatial Tracking with Distributed Camera Networks
On the Optimality of Tree-reweighted Max-product Message-passing
Transforming Personal Artifacts into Probabilistic Narratives
Reducing Exploration of Dying Arms in Mortal Bandits
Implementation and Comparison of Solution Methods for Decision Processes with Non-Markovian Rewards
Nested Dictionary Learning for Hierarchical Organization of Imagery and Text
Memory-Effcient Symbolic Online Planning for Factored MDPs
A Probabilistic Framework for Multi-Label Learning with Unseen Labels
Near-optimal Thompson sampling-based algorithms for differentially private stochastic bandits
FlightPatchNet: Multi-Scale Patch Network with Differential Coding for Short-Term Flight Trajectory Prediction
Optimal control of partially observable Markov decision processes with finite linear temporal logic constraints
Decision Making Using Probabilistic Inference Methods
Probabilistic Index Maps for Modeling Natural Signals
Scalable Joint Modeling of Longitudinal and Point Process Data for Disease Trajectory Prediction and Improving Management of Chronic Kidney Disease
Graph Cuts is a Max-Product Algorithm
Proceedings of the Thirty-Third Conference on Uncertainty in Artificial Intelligence, UAI 2017, Sydney, Australia, August 11-15, 2017
Structure-aware robustness certificates for graph classification
Arguing for Decisions: A Qualitative Model of Decision Making
On Fast Convergence of Proximal Algorithms for SQRT-Lasso Optimization: Don't Worry About its Nonsmooth Loss Function
Multi-Target Optimisation via Bayesian Optimisation and Linear Programming
Hierarchical Affinity Propagation
Bayesian inference for radar imagery based surveillance
A Variational Approximation for Bayesian Networks with Discrete and Continuous Latent Variables
Active Model Estimation in Markov Decision Processes
CCMI : Classifier based Conditional Mutual Information Estimation
Variational Inference of Penalized Regression with Submodular Functions
Induction, of and by Probability
Exclusivity Graph Approach to Instrumental Inequalities
Region-Based Incremental Pruning for POMDPs
Experimentally comparing uncertain inference systems to probability
Context-dependent feature analysis with random forests
Efficient Bayesian Nonparametric Modelling of Structured Point Processes
Use of Dempster-Shafer Conflict Metric to Detect Interpretation Inconsistency
Risk Agoras: Dialectical Argumentation for Scientific Reasoning
The past does matter: correlation of subsequent states in trajectory predictions of Gaussian Process models
Efficient enumeration of instantiations in Bayesian networks
Improved Uncertainty Quantification in Physics-Informed Neural Networks Using Error Bounds and Solution Bundles
Complexity of Mechanism Design
Multi-Task Feature Learning Via Efficient l2, 1-Norm Minimization
Imitation Learning with a Value-Based Prior
Approximating Higher-Order Distances Using Random Projections
Learning Arithmetic Circuits
Multi-Domain Collaborative Filtering
Semiparametric causal sufficient dimension reduction of multidimensional treatments
Shoring up the foundations: fusing model embeddings and weak supervision
Periodic Kernel Approximation by Index Set Fourier Series Features
Analysis of Semi-Supervised Learning with the Yarowsky Algorithm
Multi-view graph contrastive learning for solving vehicle routing problems
Markov Network Structure Learning via Ensemble-of-Forests Models
Offline Changepoint Detection With Gaussian Processes
MAP Estimation, Message Passing, and Perfect Graphs
A Branch-and-Bound Algorithm for MDL Learning Bayesian Networks
Making life better one large system at a time: Challenges for UAI research
A New Characterization of Probabilities in Bayesian Networks
Improved Variational Inference in Discrete VAEs using Error Correcting Codes
Active Information Acquisition for Linear Optimization
Complete Dictionary Learning via ℓ p -norm Maximization
Online Factorization and Partition of Complex Networks by Random Walk
Unsupervised Multi-view Nonlinear Graph Embedding
Complete Characterization for Adjustment in Summary Causal Graphs of Time Series
Implementing Probabilistic Reasoning
Structure Learning of Linear Gaussian Structural Equation Models with Weak Edges
CATE Estimation With Potential Outcome Imputation From Local Regression
Expressing Relational and Temporal Knowledge in Visual Probabilistic Networks
Stein Random Feature Regression
Latent Kullback Leibler Control for Continuous-State Systems using Probabilistic Graphical Models
Predicting Conditional Quantiles via Reduction to Classification
Causal Reasoning in Graphical Time Series Models
On the Convergence of Bound Optimization Algorithms
Reversible Jump MCMC Simulated Annealing for Neural Networks
Evaluating Bayesian deep learning for radio galaxy classification
Robust k-means++
Solving Factored MDPs with Continuous and Discrete Variables
Markov Chain Monte Carlo using Tree-Based Priors on Model Structure
On implementing usual values
Active Imitation Learning via Reduction to I.I.D. Active Learning
Expectation programming: Adapting probabilistic programming systems to estimate expectations efficiently
Causal Inference by Minimizing the Dual Norm of Bias: Kernel Matching & Weighting Estimators for Causal Effects
Bias-corrected peaks-over-threshold estimation of the CVaR
Correlated Quantization for Faster Nonconvex Distributed Optimization
Monte Carlo inference via greedy importance sampling
Decoupling Homophily and Reciprocity with Latent Space Network Models
An Efficient Implementation of Belief Function Propagation
Efficient inference for dynamic topic modeling with large vocabularies
Metric Learning from Limited Pairwise Preference Comparisons
Plan Recognition in Stories and in Life
Randomized Value Functions via Multiplicative Normalizing Flows
Performative Reinforcement Learning in Gradually Shifting Environments
Robust reconstruction of causal graphical models based on conditional 2-point and 3-point information
The Variational Homoencoder: Learning to learn high capacity generative models from few examples
Interval-Based Decisions for Reasoning Systems
Embedding Senses via Dictionary Bootstrapping
Abstraction in Belief Networks: The Role of Intermediate States in Diagnostic Reasoning
AdaCat: Adaptive categorical discretization for autoregressive models
On MAP Inference by MWSS on Perfect Graphs
Constraint Processing in Lifted Probabilistic Inference
Fast Reliability Estimation for Neural Networks with Adversarial Attack-Driven Importance Sampling
Probabilistic Conditional Preference Networks
Irregular-Time Bayesian Networks
Iterated risk measures for risk-sensitive Markov decision processes with discounted cost
General Identifiability with Arbitrary Surrogate Experiments
MESA: Maximum Entropy by Simulated Annealing
Non-parametric inference of relational dependence
Regret guarantees for model-based reinforcement learning with long-term average constraints
Reading Dependencies from Polytree-Like Bayesian Networks
Provably Adaptive Average Reward Reinforcement Learning for Metric Spaces
Using POMDPs to Forecast Kindergarten Students' Reading Comprehension
C-MI-GAN : Estimation of Conditional Mutual Information using MinMax formulation
Tracking with ranked signals
Is There a Role for Qualitative Risk Assessment?
A Mixed Graphical Model for Rhythmic Parsing
Nonparametric Divergence Estimation with Applications to Machine Learning on Distributions
Robustness Analysis of Bayesian Networks with Local Convex Sets of Distributions
Probabilistic surrogate networks for simulators with unbounded randomness
Residual bootstrap exploration for stochastic linear bandit
Convergent Message-Passing Algorithms for Inference over General Graphs with Convex Free Energies
A Heuristic Bayesian Approach to Knowledge Acquisition: Application to the Analysis of Tissue-Type Plasminogen Activator
When is particle filtering efficient for planning in partially observed linear dynamical systems?
Learning Bayesian Network Structure from Correlation-Immune Data
Enhancing Uncertainty Quantification in Large Language Models through Semantic Graph Density
Graph-Grammar Assistance for Automated Generation of Influence Diagrams
Constructing Belief Networks to Evaluate Plans
Planning with Partially Observable Markov Decision Processes: Advances in Exact Solution Method
Risk-limiting financial audits via weighted sampling without replacement
Content Sharing Design for Social Welfare in Networked Disclosure Game
Graphical Models for Bandit Problems
A Bayesian Approach to Constraint Based Causal Inference
Real Time Estimation of Bayesian Networks
Possibilistic Constraint Satisfaction Problems or "How to Handle Soft Constraints?"
Random Reshuffling with Variance Reduction: New Analysis and Better Rates
Quantifying lottery tickets under label noise: accuracy, calibration, and complexity
Offline stochastic shortest path: Learning, evaluation and towards optimality
A Symbolic Approach to Reasoning with Linguistic Quantifiers
Credibility Models
Evacuate or Not? A POMDP Model of the Decision Making of Individuals in Hurricane Evacuation Zones
ARMA Time-Series Modeling with Graphical Models
Causal inference with outcome-dependent missingness and self-censoring
Mechanism Design for Cost Optimal PAC Learning in the Presence of Strategic Noisy Annotators
Robotic Mapping with Polygonal Random Fields
Concurrent Misclassification and Out-of-Distribution Detection for Semantic Segmentation via Energy-Based Normalizing Flow
Phase-shifted adversarial training
AND/OR Multi-Valued Decision Diagrams (AOMDDs) for Weighted Graphical Models
An Ordinal View of Independence with Application to Plausible Reasoning
d-Separation: From Theorems to Algorithms
Dealing with Uncertainty in Fuzzy Inductive Reasoning Methodology
Active Learning of Conditional Mean Embeddings via Bayesian Optimisation
A Framework for Optimizing Paper Matching
Maximal ancestral graph structure learning via exact search
Cost Sensitive Reachability Heuristics for Handling State Uncertainty
Federated stochastic gradient Langevin dynamics
Probabilistic Safety for Bayesian Neural Networks
Maximizing submodular functions under submodular constraints
Time-Conditioned Generative Modeling of Object-Centric Representations for Video Decomposition and Prediction
Pareto navigation gradient descent: a first-order algorithm for optimization in pareto set
Optimal Monte Carlo Estimation of Belief Network Inference
Safe Semi-Supervised Learning of Sum-Product Networks
Preference-based Graphic Models for Collaborative Filtering
Generating the Structure of a Fuzzy Rule under Uncertainty
A SUPER* Algorithm to Optimize Paper Bidding in Peer Review
Correction for Hidden Confounders in the Genetic Analysis of Gene Expression (Abstract)
Towards Robust Relational Causal Discovery
A Sensitivity Analysis of Pathfinder: A Follow-Up Study
Multi-dueling Bandits with Dependent Arms
Identification and Estimation of Conditional Average Partial Causal Effects via Instrumental Variable
Hidden Population Estimation with Indirect Inference and Auxiliary Information
Combination of Upper and Lower Probabilities
Learning Approximately Objective Priors
A Simple Approach for Finding the Globally Optimal Bayesian Network Structure
Understanding and mitigating the limitations of prioritized experience replay
Active Multi-Information Source Bayesian Quadrature
Data dependent randomized smoothing
Boosting AND/OR-based computational protein design: dynamic heuristics and generalizable UFO
Decentralized multi-agent active search for sparse signals
TreeBERT: A tree-based pre-trained model for programming language
Infinite Hidden Relational Models
Computing Low-Entropy Couplings for Large-Support Distributions
Dimension Reduction in Singularly Perturbed Continuous-Time Bayesian Networks
Similarity measure for sparse time course data based on Gaussian processes
A Sequence of Relaxation Constraining Hidden Variable Models
UAI '88: Proceedings of the Fourth Annual Conference on Uncertainty in Artificial Intelligence, Minneapolis, MN, USA, July 10-12, 1988
PAC-Bayesian domain adaptation bounds for multiclass learners
Probabilistic Semantics Guided Discovery of Approximate Functional Dependencies
Limit-sure Reachability for Small Memory Policies in POMDPs is NP-complete
Generating Markov Equivalent Maximal Ancestral Graphs by Single Edge Replacement
More-or-Less CP-Networks
Featurized density ratio estimation
Assessment, Criticism and Improvement of Imprecise Subjective Probabilities for a Medical Expert System
A Multivariate Unimodality Test Harnessing the Dip Statistic of Mahalanobis Distances Over Random Projections
Handling Uncertainty During Plan Recognition in Task-Oriented Consultation Systems
On the Identifiability of the Post-Nonlinear Causal Model
Reasoning about Beliefs and Actions Under Computational Resource Constraints
PathFlow: A normalizing flow generator that finds transition paths
Imaginary Kinematics
Using the structure of d-connecting paths as a qualitative measure of the strength of dependence
Extendability of causal graphical models: Algorithms and computational complexity
Structured Convex Optimization under Submodular Constraints
AutoDrop: Training Deep Learning Models with Automatic Learning Rate Drop
Partitioned Linear Programming Approximations for MDPs
Learning with Confidence
Flexible Priors for Exemplar-based Clustering
Approximation by Quantization
Finding Minimal d-separators in Linear Time and Applications
Learning Network of Multivariate Hawkes Processes: A Time Series Approach
Pay or Play
Goal-Based Person Tracking Using a First-Order Probabilistic Model
Collaborative Filtering by Personality Diagnosis: A Hybrid Memory and Model-Based Approach
Pruning Rules for Learning Parsimonious Context Trees
XOR-SGD: provable convex stochastic optimization for decision-making under uncertainty
Possibilistic Assumption based Truth Maintenance System, Validation in a Data Fusion Application
Compositional abstraction error and a category of causal models
Belief Propagation for Structured Decision Making
A Probability Analysis of the Usefulness of Decision Aids
GraphLab: A New Framework For Parallel Machine Learning
Iterative Conditional Fitting for Gaussian Ancestral Graph Models
Bayes? Bluff: Opponent Modelling in Poker
Identifying near-optimal decisions in linear-in-parameter bandit models with continuous decision sets
Using ROBDDs for Inference in Bayesian Networks with Troubleshooting as an Example
Constraining Influence Diagram Structure by Generative Planning: An Application to the Optimization of Oil Spill Response
Guaranteeing Robustness Against Real-World Perturbations In Time Series Classification Using Conformalized Randomized Smoothing
Type-II Errors of Independence Tests Can Lead to Arbitrarily Large Errors in Estimated Causal Effects: An Illustrative Example
A Compact, Hierarchical Q-function Decomposition
Deriving Bounds And Inequality Constraints Using Logical Relations Among Counterfactuals
Using hierarchies to efficiently combine evidence with Dempster's rule of combination
An IRT-based Parameterization for Conditional Probability Tables
Semi-described and semi-supervised learning with Gaussian processes
RDM-DC: Poisoning Resilient Dataset Condensation with Robust Distribution Matching
Probability Update: Conditioning vs. Cross-Entropy
Learning from Sparse Data by Exploiting Monotonicity Constraints
Privacy-Aware Randomized Quantization via Linear Programming
Near-optimal Nonmyopic Value of Information in Graphical Models
Ordering Variables for Weighted Model Integration
Regret Bounds for Decentralized Learning in Cooperative Multi-Agent Dynamical Systems
PEGASUS: A policy search method for large MDPs and POMDPs
Incremental Region Selection for Mini-bucket Elimination Bounds
A Complete Calcultis for Possibilistic Logic Programming with Fuzzy Propositional Variables
Model-based Diagnosis with Qualitative Temporal Uncertainty
Path dependent structural equation models
Reduction of Computational Complexity in Bayesian Networks Through Removal of Weak Dependences
Bayesian Optimal Control of Smoothly Parameterized Systems
Parallel Markov Chain Monte Carlo for Pitman-Yor Mixture Models
Learnable uncertainty under Laplace approximations
Frank-Wolfe Optimization for Symmetric-NMF under Simplicial Constraint
Pre-processing for Triangulation of Probabilistic Networks
Value-Based Abstraction Functions for Abstraction Sampling
Holistic Representations for Memorization and Inference
Inference Using Message Propagation and Topology Transformation in Vector Gaussian Continuous Networks
Sufficient Conditions for Convergence of Loopy Belief Propagation
Probabilistic Explanations for Regression Models
An Empirical Study of w-Cutset Sampling for Bayesian Networks
Safety aware changepoint detection for piecewise i.i.d. bandits
Seeing the Forest Despite the Trees: Large Scale Spatial-Temporal Decision Making
Information/Relevance Influence Diagrams
Enumerating Markov Equivalence Classes of Acyclic Digraph Models
Transferable Meta Learning Across Domains
Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis
Some Properties of joint Probability Distributions
Understanding Sampling Style Adversarial Search Methods
A Generative Bayesian Model for Aggregating Experts' Probabilities
Iterative Decomposition Guided Variable Neighborhood Search for Graphical Model Energy Minimization
Importance Sampling for Fair Policy Selection