yusuke1997/dblp-id-title huggingface dataset:
The curious case of adversarially robust models: More data can help, double descend, or hurt generalization
RCAP: Robust, Class-Aware, Probabilistic Dynamic Dataset Pruning
NP-DRAW: A Non-Parametric Structured Latent Variable Model for Image Generation
Markov Logic in Infinite Domains
Implementing Evidential Reasoning in Expert Systems
Counterfactuals and Policy Analysis in Structural Models
Parallel Gaussian Process Regression with Low-Rank Covariance Matrix Approximations
Bayesian inference for vertex-series-parallel partial orders
Calculation of Entailed Rank Constraints in Partially Non-Linear and Cyclic Models
Identifiability of sparse causal effects using instrumental variables
BEARS Make Neuro-Symbolic Models Aware of their Reasoning Shortcuts
Efficient Learning of Minimax Risk Classifiers in High Dimensions
About Updating
Identifying Independencies in Causal Graphs with Feedback
Hybrid Probabilistic Programs: Algorithms and Complexity
Representing, Combining and Using Uncertain Estimates
On the logic of causal models
Bregman divergence as general framework to estimate unnormalized statistical models
Probabilsitic semantics and defaults
Fast Interactive Search under a Scale-Free Comparison Oracle
Anchored Causal Inference in the Presence of Measurement Error
A Polynomial-time Nash Equilibrium Algorithm for Repeated Stochastic Games
Budget Constraints in Prediction Markets
T-Norm Based Reasoning in Situation Assessment Applications
Policy Iteration for Factored MDPs
Cognitive Processing of Causal Knowledge
Graph Contrastive Learning under Heterophily via Graph Filters
Condition number bounds for causal inference
Approximately Bayes-optimal pseudo-label selection
Calculating Uncertainty Intervals from Conditional Convex Sets of Probabilities
Computationally-optimal rea-resource strategies for independent, uninterruptible methods
Counting Background Knowledge Consistent Markov Equivalent Directed Acyclic Graphs
Factorization of Discrete Probability Distributions
MDPs with Unawareness in Robotics
The Efficacy of the POMDP-RTI Approach for Early Reading Intervention
A constraint satisfaction framework for decision under uncertainty
Bayesian Biosurveillance of Disease Outbreaks
PAC-Reasoning in Relational Domains
Sample Average Approximation for Black-Box Variational Inference
Conjoint Modeling of Temporal Dependencies in Event Streams
Locally Conditioned Belief Propagation
Rollout Sampling Policy Iteration for Decentralized POMDPs
Complexity Analysis and Variational Inference for Interpretation-based Probabilistic Description Logic
Variational Approximations between Mean Field Theory and the Junction Tree Algorithm
Sampling-free Uncertainty Estimation in Gated Recurrent Units with Applications to Normative Modeling in Neuroimaging
Propensity Score Matching for Causal Inference with Relational Data
Video In Sentences Out
Two-stage holistic and contrastive explanation of image classification
A new approach to updating beliefs
Contrastive prototype learning with augmented embeddings for few-shot learning
Beyond Dirichlet-based Models: When Bayesian Neural Networks Meet Evidential Deep Learning
Vector-space Analysis of Belief-state Approximation for POMDPs
Faster Convergence of Stochastic Gradient Langevin Dynamics for Non-Log-Concave Sampling
Conditional Utility, Utility Independence, and Utility Networks
Probabilistic Multi-Dimensional Classification
Noisy-OR Models with Latent Confounding
Inferring latent structures via information inequalities
On the Complexity of Nash Equilibrium Reoptimization
A fuzzy relation-based extension of Reggia's relational model for diagnosis handling uncertain and incomplete information
Modeling Discrete Interventional Data using Directed Cyclic Graphical Models
A Variational Approach for Approximating Bayesian Networks by Edge Deletion
Syntax-based Default Reasoning as Probabilistic Model-based Diagnosis
Revisiting the Berkeley Admissions data: Statistical Tests for Causal Hypotheses
Large Deviation Methods for Approximate Probabilistic Inference
Latent Topic Analysis for Predicting Group Purchasing Behavior on the Social Web
Learning from Low Rank Tensor Data: A Random Tensor Theory Perspective
Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks
Relational Bayesian Networks
Dynamic Network Models for Forecasting
Exploiting Locality in Searching the Web
Approximation algorithm for submodular maximization under submodular cover
A general non-probabilistic theory of inductive reasoning
A Revision-Based Approach to Resolving Conflicting Information
Bounded Rationality in Las Vegas: Probabilistic Finite Automata Play Multi-Armed Bandits
Scaling Up Decentralized MDPs Through Heuristic Search
Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, UAI 2020, virtual online, August 3-6, 2020
Robust distillation for worst-class performance: on the interplay between teacher and student objectives
Learning about Individuals from Group Statistics
A variational approximation for analyzing the dynamics of panel data
Gaussian Processes for Big Data
Snorkel: Beyond Hand-labeled Data
Identifying untrustworthy predictions in neural networks by geometric gradient analysis
Learning Accurate and Interpretable Decision Trees
Learning a neural Pareto manifold extractor with constraints
Feature learning and random features in standard finite-width convolutional neural networks: An empirical study
Three Approaches to Probability Model Selection
Variational refinement for importance sampling using the forward Kullback-Leibler divergence
Defending SVMs against poisoning attacks: the hardness and DBSCAN approach
Quantifier Elimination for Statistical Problems
Causal discovery with heterogeneous observational data
Encoding Markov logic networks in Possibilistic Logic
Letting Uncertainty Guide Your Multimodal Machine Translation
Power Mean Estimation in Stochastic Monte-Carlo Tree Search
From Deterministic ODEs to Dynamic Structural Causal Models
Approximate Causal Abstractions
Regstar: efficient strategy synthesis for adversarial patrolling games
Robust Independence Testing for Constraint-Based Learning of Causal Structure
Active Model Selection
Studies in Lower Bounding Probabilities of Evidence using the Markov Inequality
Inference Policies
Can Evidence be Combined in the Dempster-Shafer Theory?
Identifying Optimal Sequential Decisions
Distribution-free uncertainty quantification for classification under label shift
MAIES: A Tool for DNA Mixture Analysis
Partial-Label Learning with Conformal Candidate Cleaning
Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence, UAI 2016, June 25-29, 2016, New York City, NY, USA
A Knowledge Acquisition Tool for Bayesian-Network Troubleshooters
On Triangulating Dynamic Graphical Models
Multi-layer random features and the approximation power of neural networks
Minimum Encoding Approaches for Predictive Modeling
PoRB-Nets: Poisson Process Radial Basis Function Networks
A Constraint Satisfaction Approach to the Robust Spanning Tree Problem with Interval Data
Probabilistic Acceptance
Learning with Missing Features
The Minimum Information Principle for Discriminative Learning
A Weighted Mini-Bucket Bound for Solving Influence Diagram
Bias-aware Boolean Matrix Factorization Using Disentangled Representation Learning
Optimal Factory Scheduling using Stochastic Dominance A*
A Bayesian Network Scoring Metic that Is Based on Globally Uniform Parameter Priors
Estimating Accuracy from Unlabeled Data
Neural Optimal Transport with Lagrangian Costs
Fair Contextual Multi-Armed Bandits: Theory and Experiments
Robust Quickest Change Detection for Unnormalized Models
Skewness Ranking Optimization for Personalized Recommendation
A Hybrid Anytime Algorithm for the Construction of Causal Models From Sparse Data
Geometric rates of convergence for kernel-based sampling algorithms
Coupling Adaptive Batch Sizes with Learning Rates
Planning under Continuous Time and Resource Uncertainty: A Challenge for AI
IPF for Discrete Chain Factor Graphs
Efficiently Inducing Features of Conditional Random Fields
On the Computability of AIXI
Accelerated Stochastic Block Coordinate Gradient Descent for Sparsity Constrained Nonconvex Optimization
Cutting Through Privacy: A Hyperplane-Based Data Reconstruction Attack in Federated Learning
Extending Factor Graphs so as to Unify Directed and Undirected Graphical Models
Out-of-distribution Robust Optimization
1 Billion Pages = 1 Million Dollars? Mining the Web to Play "Who Wants to be a Millionaire?"
Apprenticeship Learning using Inverse Reinforcement Learning and Gradient Methods
The Recovery of Causal Poly-Trees from Statistical Data
Robust principal component analysis for generalized multi-view models
A Bayesian Approach to Tackling Hard Computational Problems
Expectation consistency for calibration of neural networks
Hierarchical Indian buffet neural networks for Bayesian continual learning
Partial likelihood Thompson sampling
R&D Analyst: An Interactive Approach to Normative Decision System Model Construction
Convex Coding
Comparing EM with GD in Mixture Models of Two Components
Predictive Whittle networks for time series
MetaCOG: A Heirarchical Probabilistic Model for Learning Meta-Cognitive Visual Representations
Exact Count of Boundary Pieces of ReLU Classifiers: Towards the Proper Complexity Measure for Classification
A Tighter Analysis of Randomised Policy Iteration
Deconvolution of mixing time series on a graph
Determinantal Clustering Processes - A Nonparametric Bayesian Approach to Kernel Based Semi-Supervised Clustering
Marginal Weighted Maximum Log-likelihood for Efficient Learning of Perturb-and-Map models
Cyclic test time augmentation with entropy weight method
BEEM : Bucket Elimination with External Memory
Detecting Change-Points in Time Series by Maximum Mean Discrepancy of Ordinal Pattern Distributions
Nonparametric Clustering with Distance Dependent Hierarchies
Budgeted Semi-supervised Support Vector Machine
A Case Study in Knowledge Discovery and Elicitation in an Intelligent Tutoring Application
Efficient Probabilistic Inference with Partial Ranking Queries
Alternating Projections for Learning with Expectation Constraints
Generalization and Learnability in Multiple Instance Regression
A Unified Approach to Imprecision and Sensitivity of Beliefs in Expert Systems
Bethe and Related Pairwise Entropy Approximations
Credal Networks under Maximum Entropy
Uncertainty and Incompleteness: Breaking the Symmetry of Defeasible Reasoning
On the Privacy Risks of Spiking Neural Networks: A Membership Inference Analysis
Overcoming Language Priors for Visual Question Answering via Loss Rebalancing Label and Global Context
A Comparison of Lauritzen-Spiegelhalter, Hugin, and Shenoy-Shafer Architectures for Computing Marginals of Probability Distributions
Studying the Effect of GNN Spatial Convolutions On The Embedding Space's Geometry
A Differential Approach to Inference in Bayesian Networks
Query DAGs: A practical paradigm for implementing belief-network inference
Fed-LAMB: Layer-wise and Dimension-wise Locally Adaptive Federated Learning
Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence, UAI 2018, Monterey, California, USA, August 6-10, 2018
Invariant Gaussian Process Latent Variable Models and Application in Causal Discovery
Time-Critical Dynamic Decision Making
Optimistic Thompson Sampling-based algorithms for episodic reinforcement learning
Logarithmic Time Parallel Bayesian Inference
Cross-covariance modelling via DAGs with hidden variables
Graph-based semi-supervised learning through the lens of safety
Belief as summarization and meta-support
AUTM flow: atomic unrestricted time machine for monotonic normalizing flows
Dantzig Selector with an Approximately Optimal Denoising Matrix and its Application in Sparse Reinforcement Learning
A Temporal Bayesian Network for Diagnosis and Prediction
A Spectral Algorithm for Learning Class-Based n-gram Models of Natural Language
Online Constrained Model-based Reinforcement Learning
Bayesian Learning in Undirected Graphical Models: Approximate MCMC Algorithms
Supply Restoration in Power Distribution Systems: A Case Study in Integrating Model-Based Diagnosis and Repair Planning
Regularized Maximum Likelihood for Intrinsic Dimension Estimation
Multidimensional counting grids: Inferring word order from disordered bags of words
A Probabilistic Approach for Detection and Analysis of Cognitive Flow
Revisiting Non-Progressive Influence Models: Scalable Influence Maximization in Social Networks
Conditional Average Treatment Effect Estimation Under Hidden Confounders
Epsilon-BMC: A Bayesian Ensemble Approach to Epsilon-Greedy Exploration in Model-Free Reinforcement Learning
A heuristic for statistical seriation
Expectation Propogation for Approximate Inference in Dynamic Bayesian Networks
Approximate Bayesian Inference via Bitstring Representations
A factorization criterion for acyclic directed mixed graphs
Ideal reformulation of belief networks
Learning Deep Hidden Nonlinear Dynamics from Aggregate Data
Attribution of predictive uncertainties in classification models
Asymptotic Model Selection for Naive Bayesian Networks
Learning large Bayesian networks with expert constraints
DyGMAE: A Novel Dynamic Graph Masked Autoencoder for Link Prediction
Approximation Algorithms for Observer Aware MDPs
Measurement Error and Causal Discovery
Modal Logics for Qualitative Possibility and Beliefs
Ending-based Strategies for Part-of-Speech Tagging
Neural Architecture Search Finds Robust Models by Knowledge Distillation
ReZero is all you need: fast convergence at large depth
Learning Bayesian Networks from Incomplete Databases
A Logic for Reasoning about Upper Probabilities
Compact Value-Function Representations for Qualitative Preferences
Inverse Reinforcement Learning via Deep Gaussian Process
BISCUIT: Causal Representation Learning from Binary Interactions
Incorporating Expressive Graphical Models in VariationalApproximations: Chain-graphs and Hidden Variables
Variational- and metric-based deep latent space for out-of-distribution detection
Bounded Rationality in Wagering Mechanisms
High-Level Information Fusion with Bayesian Semantics
A Transformational Characterization of Markov Equivalence for Directed Acyclic Graphs with Latent Variables
Bayesian Interactive Decision Support for Multi-Attribute Problems with Even Swaps
Online Heavy-tailed Change-point detection
Learning to Acquire Information
On probability distributions over possible worlds
The Computational Complexity of Sensitivity Analysis and Parameter Tuning
Data poisoning attacks on off-policy policy evaluation methods
Learning Causal Response Representations through Direct Effect Analysis
Proceedings of the Eleventh UAI Bayesian Modeling Applications Workshop co-located with the 30th Conference on Uncertainty in Artificial Intelligence, BMA@UAI 2014, Quebec City, Quebec, Canada, July 27, 2014
Low Frequency Adversarial Perturbation
Normalizing Flows for Conformal Regression
Combining predictions from linear models when training and test inputs differ
Decision-Theoretic Planning with Concurrent Temporally Extended Actions
Generative Archimedean copulas
Averaging Weights Leads to Wider Optima and Better Generalization
Classifier Learning with Supervised Marginal Likelihood
Contingency-aware influence maximization: A reinforcement learning approach
Block Neural Autoregressive Flow
The Indian Chefs Process
Constrained differentially private federated learning for low-bandwidth devices
Min-d-Occur: Ensuring Future Occurrences in Streaming Sets
Causal Discovery with Hidden Confounders using the Algorithmic Markov Condition
Guess-and-Verify Heuristics for Reducing Uncertainties in Expert Classification Systems
Variational Inference for Gaussian Processes with Panel Count Data
Provably Efficient Adversarial Imitation Learning with Unknown Transitions
Combinatorial Bandits for Incentivizing Agents with Dynamic Preferences
Measure Selection: Notions of Rationality and Representation Independence
Instance-Wise Monotonic Calibration by Constrained Transformation
Non-Convex Rank Minimization via an Empirical Bayesian Approach
Evaulation of uncertain inference models I: PROSPECTOR
Conformal Risk Control for Ordinal Classification
Impact of Learning Strategies on the Quality of Bayesian Networks: An Empirical Evaluation
Inference with Seperately Specified Sets of Probabilities in Credal Networks
Sequential Nonparametric Testing with the Law of the Iterated Logarithm
Learning Polytrees
Improved feature importance computation for tree models based on the Banzhaf value
No-Regret Linear Bandits beyond Realizability
An Update Semantics for Defeasible Obligations
Probabilities of Causation: Bounds and Identification
A Measure of Decision Flexibility
Shifted compression framework: generalizations and improvements
Formula-Based Probabilistic Inference
Structure Learning for Cyclic Linear Causal Models
Psychological and Normative Theories of Causal Power and the Probabilities of Causes
Separating Sparse Signals from Correlated Noise in Binary Classification
AND/OR branch-and-bound for computational protein design optimizing K
Deep Gaussian mixture ensembles
Generalized Policy Elimination: an efficient algorithm for Nonparametric Contextual Bandits
Nonparametric exponential family graph embeddings for multiple representation learning
Variational Inference for Gaussian Process Models for Survival Analysis
Multi-View Independent Component Analysis with Shared and Individual Sources
Constructing Lower Probabilities
A Consistent Estimator of the Expected Gradient Outerproduct
Stochastic Graphical Bandits with Heavy-Tailed Rewards
Probabilistic Inference in Influence Diagrams
Image Segmentation in Video Sequences: A Probabilistic Approach
Propagation of Delays in the National Airspace System
Bayesian Network Models for Adaptive Testing
Dependency in DAG models with hidden variables
Offline change detection under contamination
Bayesian Structure Learning for Stationary Time Series
On the Capacitated Facility Location Problem with Scarce Resources
Estimating Causal Effects by Bounding Confounding
Generative Multiple-Instance Learning Models For Quantitative Electromyography
Comparing Expert Systems Built Using Different Uncertain Inference Systems
Fall of Empires: Breaking Byzantine-tolerant SGD by Inner Product Manipulation
On the Number of Samples Needed to Learn the Correct Structure of a Bayesian Network
Template Based Inference in Symmetric Relational Markov Random Fields
Semigraphoids Are Two-Antecedental Approximations of Stochastic Conditional Independence Models
Common Event Tethering to Improve Prediction of Rare Clinical Events
Causal Inference Under Interference And Network Uncertainty
Bayesian Inference in Model-Based Machine Vision
Efficient Gradient Estimation for Motor Control Learning
Solving Asymmetric Decision Problems with Influence Diagrams
Investigating the Impact of Model Width and Density on Generalization in Presence of Label Noise
Optimized auxiliary particle filters: adapting mixture proposals via convex optimization
A Fast Optimization View: Reformulating Single Layer Attention in LLM Based on Tensor and SVM Trick, and Solving It in Matrix Multiplication Time
Graph Self-supervised Learning via Proximity Distribution Minimization
Information theoretic clustering via divergence maximization among clusters
Model Regularization for Stable Sample Rollouts
Statistical Modeling in Continuous Speech Recognition (CSR)
Finite-Memory Near-Optimal Learning for Markov Decision Processes with Long-Run Average Reward
Proceedings of the Sixth UAI Bayesian Modelling Applications Workshop Helsinki, Finland, July 9, 2008
Probabilistic Size-constrained Microclustering
Dyna-Style Planning with Linear Function Approximation and Prioritized Sweeping
Dynamic Bayesian Multinets
Probabilistic spatial transformer networks
Coping with the Limitations of Rational Inference in the Framework of Possibility Theory
Stability of Linear Structural Equation Models of Causal Inference
Label Consistency-based Worker Filtering for Crowdsourcing
A variational approach to stable principal component pursuit
Expert-In-The-Loop Causal Discovery: Iterative Model Refinement Using Expert Knowledge
Scalable Recommendation with Hierarchical Poisson Factorization
Generalized Fisher Score for Feature Selection
NRFlow: Towards Noise-Robust Generative Modeling via High-Order Mechanism
Leveraging probabilistic circuits for nonparametric multi-output regression
Local Markov Property for Models Satisfying Composition Axiom
A Practical Riemannian Algorithm for Computing Dominant Generalized Eigenspace
Revisiting Convergence of AdaGrad with Relaxed Assumptions
Exact Structure Discovery in Bayesian Networks with Less Space
UAI 2011, Proceedings of the Twenty-Seventh Conference on Uncertainty in Artificial Intelligence, Barcelona, Spain, July 14-17, 2011
Inference in Probabilistic Answer Set Programs with Imprecise Probabilities via Optimization
Mutual information based Bayesian graph neural network for few-shot learning
Conditional Expectation Propagation
Incremental Preference Elicitation for Decision Making Under Risk with the Rank-Dependent Utility Model
Green Generative Modeling: Recycling Dirty Data using Recurrent Variational Autoencoders
Time-variant variational transfer for value functions
Multi-Process Models - An Application for the Construction of Financial Factor Models
An Empirical Evaluation of a Randomized Algorithm for Probabilistic Inference
If you've trained one you've trained them all: inter-architecture similarity increases with robustness
Probabilistic inference and non-monotonic inference
Nearly Optimal Differentially Private ReLU Regression
Learning Latent Variable Models by Improving Spectral Solutions with Exterior Point Method
Beyond Invisibility: Learning Robust Visible Watermarks for Stronger Copyright Protection
An Order of Magnitude Calculus
Reasoning Under Uncertainty: Some Monte Carlo Results
Causal forecasting: generalization bounds for autoregressive models
On Transformations between Probability and Spolinian Disbelief Functions
Scalable Bayesian Low-Rank Adaptation of Large Language Models via Stochastic Variational Subspace Inference
PDQ-Net: Deep probabilistic dual quaternion network for absolute pose regression on SE(3)
SALSA: A Secure, Adaptive and Label-Agnostic Scalable Algorithm for Machine Unlearning
Offline reinforcement learning under value and density-ratio realizability: The power of gaps
UAI '05, Proceedings of the 21st Conference in Uncertainty in Artificial Intelligence, Edinburgh, Scotland, July 26-29, 2005
Online Learning with Stochastically Partitioning Experts
Treedy: A Heuristic for Counting and Sampling Subsets
Sufficiency, Separability and Temporal Probabilistic Models
A Utility Framework for Bounded-Loss Market Makers
Learning LWF Chain Graphs: A Markov Blanket Discovery Approach
Probabilistic Graphical Models Parameter Learning with Transferred Prior and Constraints
Compact Securities Markets for Pareto Optimal Reallocation of Risk
Sequential Document Representations and Simplicial Curves
Identifying regions of trusted predictions
From Likelihood to Plausibility
An Explanation of and Cure for Minimax Pathology
Tractable computation of expected kernels
Online Forest Density Estimation
Do Vendi Scores Converge with Finite Samples? Truncated Vendi Score for Finite-Sample Convergence Guarantees
Inductive Policy Selection for First-Order MDPs
An Algorithm for Deciding if a Set of Observed Independencies Has a Causal Explanation
The Incomplete Rosetta Stone problem: Identifiability results for Multi-view Nonlinear ICA
Classification of Sparse and Irregularly Sampled Time Series with Mixtures of Expected Gaussian Kernels and Random Features
Efficient solutions for Stochastic Shortest Path Problems with Dead Ends
Refractor Importance Sampling
Generating Bayesian Networks from Probablity Logic Knowledge Bases
A Trajectory-Based Bayesian Approach to Multi-Objective Hyperparameter Optimization with Epoch-Aware Trade-Offs
Inference by Minimizing Size, Divergence, or their Sum
On the distributional properties of adaptive gradients
ALARMS: Alerting and Reasoning Management System for Next Generation Aircraft Hazards
Improvable Gap Balancing for Multi-Task Learning