UAI 2025 Accepted Papers

The full list of 229 papers accepted at UAI 2025 (Conference on Uncertainty in Artificial Intelligence). Click any title for details, similar papers, and links to the original source. You can also search these papers by meaning, not just keywords.

1. $σ$-Maximal Ancestral Graphsaccepted
2. A Fast Optimization View: Reformulating Single Layer Attention in LLM Based on Tensor and SVM Trick, and Solving It in Matrix Multiplication Timeaccepted
3. A Mirror Descent Perspective of Smoothed Sign Descentaccepted
4. A Multivariate Unimodality Test Harnessing the Dip Statistic of Mahalanobis Distances Over Random Projectionsaccepted
5. A Parallel Network for LRCT Segmentation and Uncertainty Mitigation with Fuzzy Setsaccepted
6. A Probabilistic Neuro-symbolic Layer for Algebraic Constraint Satisfactionaccepted
7. A Quantum Information Theoretic Approach to Tractable Probabilistic Modelsaccepted
8. A Trajectory-Based Bayesian Approach to Multi-Objective Hyperparameter Optimization with Epoch-Aware Trade-Offsaccepted
9. A Trust-Region Method for Graphical Stein Variational Inferenceaccepted
10. A Unified Data Representation Learning for Non-parametric Two-sample Testingaccepted
11. Accurate and Scalable Stochastic Gaussian Process Regression via Learnable Coreset-based Variational Inferenceaccepted
12. Adapting Prediction Sets to Distribution Shifts Without Labelsaccepted
13. Adaptive Human-Robot Collaboration using Type-Based IRLaccepted
14. Adaptive Reward Design for Reinforcement Learningaccepted
15. Adaptive Threshold Sampling for Pure Exploration in Submodular Banditsaccepted
16. Adversarial Training May Induce Deteriorating Distributionsaccepted
17. Aggregating Data for Optimal Learningaccepted
18. An Information-theoretic Perspective of Hierarchical Clustering on Graphsaccepted
19. An Optimal Algorithm for Strongly Convex Min-Min Optimizationaccepted
20. Approximate Bayesian Inference via Bitstring Representationsaccepted
21. Are You Doing Better Than Random Guessing? A Call for Using Negative Controls When Evaluating Causal Discovery Algorithmsaccepted
22. Asymptotically Optimal Linear Best Feasible Arm Identification with Fixed Budgetaccepted
23. Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysisaccepted
24. BELIEF - Bayesian Sign Entropy Regularization for LIME Frameworkaccepted
25. Bayesian Optimization over Bounded Domains with the Beta Product Kernelaccepted
26. Bayesian Optimization with Inexact Acquisition: Is Random Grid Search Sufficient?accepted
27. Best Arm Identification with Possibly Biased Offline Dataaccepted
28. Best Possible Q-Learningaccepted
29. Beyond Invisibility: Learning Robust Visible Watermarks for Stronger Copyright Protectionaccepted
30. Beyond Sin-Squared Error: Linear Time Entrywise Uncertainty Quantification for Streaming PCAaccepted
31. Black-box Optimization with Unknown Constraints via Overparameterized Deep Neural Networksaccepted
32. Budget Allocation Exploiting Label Correlation between Instancesaccepted
33. Building Conformal Prediction Intervals with Approximate Message Passingaccepted
34. CATE Estimation With Potential Outcome Imputation From Local Regressionaccepted
35. COS-DPO: Conditioned One-Shot Multi-Objective Fine-Tuning Frameworkaccepted
36. CP$^2$: Leveraging Geometry for Conformal Prediction via Canonicalizationaccepted
37. Calibrated Regression Against An Adversary Without Regretaccepted
38. Can a Bayesian Oracle Prevent Harm from an Agent?accepted
39. Causal Discovery for Linear Non-Gaussian Models with Disjoint Cyclesaccepted
40. Causal Effect Identification in Heterogeneous Environments from Higher-Order Momentsaccepted
41. Causal Eligibility Traces for Confounding Robust Off-Policy Evaluationaccepted
42. Causal Inference amid Missingness-Specific Independences and Mechanism Shiftsaccepted
43. Causal Models for Growing Networksaccepted
44. Coevolutionary Emergent Systems Optimization with Applications to Ultra-High-Dimensional Metasurface Design : OAM Wave Manipulationaccepted
45. Collaborative Prediction: To Join or To Disjoin Datasetsaccepted
46. Collapsing Sequence-Level Data-Policy Coverage via Poisoning Attack in Offline Reinforcement Learningaccepted
47. Complete Characterization for Adjustment in Summary Causal Graphs of Time Seriesaccepted
48. Computationally Efficient Methods for Invariant Feature Selection with Sparsityaccepted
49. Concept Forgetting via Label Annealingaccepted
50. Conditional Average Treatment Effect Estimation Under Hidden Confoundersaccepted
51. Conformal Prediction Sets for Deep Generative Models via Reduction to Conformal Regressionaccepted
52. Conformal Prediction for Federated Graph Neural Networks with Missing Neighbor Informationaccepted
53. Conformal Prediction without Nonconformity Scoresaccepted
54. Constraint-based Causal Discovery from a Collection of Conditioning Setsaccepted
55. Contaminated Multivariate Time-Series Anomaly Detection with Spatio-Temporal Graph Conditional Diffusion Modelsaccepted
56. Contrast-CAT: Contrasting Activations for Enhanced Interpretability in Transformer-based Text Classifiersaccepted
57. Correlated Quantization for Faster Nonconvex Distributed Optimizationaccepted
58. Corruption-Robust Variance-aware Algorithms for Generalized Linear Bandits under Heavy-tailed Rewardsaccepted
59. Creative Agents: Empowering Agents with Imagination for Creative Tasksaccepted
60. Critical Influence of Overparameterization on Sharpness-aware Minimizationaccepted
61. Cutting Through Privacy: A Hyperplane-Based Data Reconstruction Attack in Federated Learningaccepted
62. DF$^2$: Distribution-Free Decision-Focused Learningaccepted
63. Decomposition of Probabilities of Causation with Two Mediatorsaccepted
64. Dependent Randomized Rounding for Budget Constrained Experimental Designaccepted
65. Discriminative ordering through ensemble consensusaccepted
66. Distributional Reinforcement Learning with Dual Expectile-Quantile Regressionaccepted
67. Distributionally and Adversarially Robust Logistic Regression via Intersecting Wasserstein Ballsaccepted
68. Divide and Orthogonalize: Efficient Continual Learning with Local Model Space Projectionaccepted
69. Do Vendi Scores Converge with Finite Samples? Truncated Vendi Score for Finite-Sample Convergence Guaranteesaccepted
70. DyGMAE: A Novel Dynamic Graph Masked Autoencoder for Link Predictionaccepted
71. Dynamic Maintenance of Kernel Density Estimation Data Structure: From Practice to Theoryaccepted
72. EERO: Early Exit with Reject Option for Efficient Classification with limited budgetaccepted
73. ELBO, regularized maximum likelihood, and their common one-sample approximation for training stochastic neural networksaccepted
74. ELF: Federated Langevin Algorithms with Primal, Dual and Bidirectional Compressionaccepted
75. Efficient Algorithms for Logistic Contextual Slate Bandits with Bandit Feedbackaccepted
76. Efficiently Escaping Saddle Points for Policy Optimizationaccepted
77. Enhanced Equilibria-Solving via Private Information Pre-Branch Structure in Adversarial Team Gamesaccepted
78. Enhancing Uncertainty Quantification in Large Language Models through Semantic Graph Densityaccepted
79. Enumerating Optimal Cost-Constrained Adjustment Setsaccepted
80. Epistemic Uncertainty in Conformal Scores: A Unified Approachaccepted
81. Error Bounds for Physics-Informed Neural Networks in Fokker-Planck PDEsaccepted
82. Evasion Attacks Against Bayesian Predictive Modelsaccepted
83. Experimentation under Treatment Dependent Network Interferenceaccepted
84. Expert-In-The-Loop Causal Discovery: Iterative Model Refinement Using Expert Knowledgeaccepted
85. Explaining Negative Classifications of AI Models in Tumor Diagnosisaccepted
86. Exploring Exploration in Bayesian Optimizationaccepted
87. FALCON: Adaptive Cross-Domain APT Attack Investigation with Federated Causal Learningaccepted
88. FDR-SVM: A Federated Distributionally Robust Support Vector Machine via a Mixture of Wasserstein Balls Ambiguity Setaccepted
89. Fast Calculation of Feature Contributions in Boosting Treesaccepted
90. Fast Non-convex Matrix Sensing with Optimal Sample Complexityaccepted
91. FeDCM: Federated Learning of Deep Causal Generative Modelsaccepted
92. FedSPD: A Soft-clustering Approach for Personalized Decentralized Federated Learningaccepted
93. Federated Rényi Fair Inference in Federated Heterogeneous Systemaccepted
94. Finding Interior Optimum of Black-box Constrained Objective with Bayesian Optimizationaccepted
95. Flat Posterior Does Matter For Bayesian Model Averagingaccepted
96. FlightPatchNet: Multi-Scale Patch Network with Differential Coding for Short-Term Flight Trajectory Predictionaccepted
97. Flow-Based Delayed Hawkes Processaccepted
98. Full Network Capacity Framework for Sample-Efficient Deep Reinforcement Learningaccepted
99. Generalised Probabilistic Modelling and Improved Uncertainty Estimation in Comparative LLM-as-a-judgeaccepted
100. Generative Uncertainty in Diffusion Modelsaccepted
101. Geodesic Slice Sampler for Multimodal Distributions with Strong Curvatureaccepted
102. Group-Agent Reinforcement Learning with Heterogeneous Agentsaccepted
103. Guaranteed Prediction Sets for Functional Surrogate Modelsaccepted
104. Guiding Time-Varying Generative Models with Natural Gradients on Exponential Family Manifoldaccepted
105. HDP-Flow: Generalizable Bayesian Nonparametric Model for Time Series State Discoveryaccepted
106. Hindsight Merging: Diverse Data Generation with Language Modelsaccepted
107. How Likely Are Two Voting Rules Different?accepted
108. Hybrid Bernstein Normalizing Flows for Flexible Multivariate Density Regression with Interpretable Marginalsaccepted
109. Improved Uncertainty Quantification in Physics-Informed Neural Networks Using Error Bounds and Solution Bundlesaccepted
110. Improved Variational Inference in Discrete VAEs using Error Correcting Codesaccepted
111. Improving Adversarial Transferability via Decision Boundary Adaptationaccepted
112. Improving Graph Contrastive Learning with Community Structureaccepted
113. InfoDPCCA: Information-Theoretic Dynamic Probabilistic Canonical Correlation Analysisaccepted
114. Informative Synthetic Data Generation for Thorax Disease Classificationaccepted
115. Instance-Wise Monotonic Calibration by Constrained Transformationaccepted
116. Just Trial Once: Ongoing Causal Validation of Machine Learning Modelsaccepted
117. Label Distribution Learning using the Squared Neural Family on the Probability Simplexaccepted
118. Learning Algorithms for Multiple Instance Regressionaccepted
119. Learning Causal Response Representations through Direct Effect Analysisaccepted
120. Learning Multi-interest Embedding with Dynamic Graph Cluster for Sequention Recommendationaccepted
121. Learning Robust XGBoost Ensembles for Regression Tasksaccepted
122. Learning from Label Proportions and Covariate-shifted Instancesaccepted
123. Learning to Sample in Stochastic Optimizationaccepted
124. Learning to Stabilize Unknown LTI Systems on a Single Trajectory under Stochastic Noiseaccepted
125. Learning with Confidenceaccepted
126. Letting Uncertainty Guide Your Multimodal Machine Translationaccepted
127. Limit-sure Reachability for Small Memory Policies in POMDPs is NP-completeaccepted
128. LoSAM: Local Search in Additive Noise Models with Mixed Mechanisms and General Noise for Global Causal Discoveryaccepted
129. Lower Bound on Howard Policy Iteration for Deterministic Markov Decision Processesaccepted
130. Lower Bounds on the Size of Markov Equivalence Classesaccepted
131. MOHITO: Multi-Agent Reinforcement Learning using Hypergraphs for Task-Open Systemsaccepted
132. MSCGrapher: Learning Multi-Scale Dynamic Correlations for Multivariate Time Series Forecastingaccepted
133. MSP-SR: Multi-Stage Probabilistic Generative Super Resolution with Scarce High-Resolution Dataaccepted
134. Measuring IIA Violations in Similarity Choices with Bayesian Modelsaccepted
135. Metric Learning in an RKHSaccepted
136. MindFlayer SGD: Efficient Parallel SGD in the Presence of Heterogeneous and Random Worker Compute Timesaccepted
137. Minimax Optimal Nonsmooth Nonparametric Regression via Fractional Laplacian Eigenmapsaccepted
138. Mixup Regularization: A Probabilistic Perspectiveaccepted
139. Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalizationaccepted
140. Moments of Causal Effectsaccepted
141. Multi-Cost-Bounded Reachability Analysis of POMDPsaccepted
142. Multi-Label Bayesian Active Learning with Inter-Label Relationshipsaccepted
143. Multi-armed Bandits with Missing Outcomesaccepted
144. Multi-group Uncertainty Quantification for Long-form Text Generationaccepted
145. Multiple Wasserstein Gradient Descent Algorithm for Multi-Objective Distributional Optimizationaccepted
146. MutualNeRF: Improve the Performance of NeRF under Limited Samples with Mutual Information Theoryaccepted
147. NRFlow: Towards Noise-Robust Generative Modeling via High-Order Mechanismaccepted
148. Near-Optimal Regret Bounds for Federated Multi-armed Bandits with Fully Distributed Communicationaccepted
149. Nearly Optimal Differentially Private ReLU Regressionaccepted
150. Nonlinear Causal Discovery for Grouped Dataaccepted
151. Nonparametric Bayesian Multi-Facet Clustering for Longitudinal Dataaccepted
152. Nonparametric Bayesian inference of item-level features in classifier combinationaccepted
153. ODD: Overlap-aware Estimation of Model Performance under Distribution Shiftaccepted
154. Off-policy Predictive Control with Causal Sensitivity Analysisaccepted
155. Offline Changepoint Detection With Gaussian Processesaccepted
156. On Constant Regret for Low-Rank MDPsaccepted
157. On Continuous Monitoring of Risk Violations under Unknown Shiftaccepted
158. On Information-Theoretic Measures of Predictive Uncertaintyaccepted
159. On the Privacy Risks of Spiking Neural Networks: A Membership Inference Analysisaccepted
160. Online Generalized Magician’s Problem with Multiple Workersaccepted
161. Online Learning with Stochastically Partitioning Expertsaccepted
162. Optimal Submanifold Structure in Log-linear Modelsaccepted
163. Optimal Transport Alignment of User Preferences from Ratings and Textsaccepted
164. Optimal Transport for Probabilistic Circuitsaccepted
165. Optimal Zero-shot Regret Minimization for Selective Classification with Out-of-Distribution Detectionaccepted
166. Order-Optimal Global Convergence for Actor-Critic with General Policy and Neural Critic Parametrizationaccepted
167. Out-of-distribution Robust Optimizationaccepted
168. Over the Top-1: Uncertainty-Aware Cross-Modal Retrieval with CLIPaccepted
169. Partial-Label Learning with Conformal Candidate Cleaningaccepted
170. Periodical Moving Average Accelerates Gradient Accumulation for Post-Trainingaccepted
171. Privacy-Preserving Neural Processes for Probabilistic User Modelingaccepted
172. Probabilistic Embeddings for Frozen Vision-Language Models: Uncertainty Quantification with Gaussian Process Latent Variable Modelsaccepted
173. Probabilistic Explanations for Regression Modelsaccepted
174. Probabilistic Graph Circuits: Deep Generative Models for Tractable Probabilistic Inference over Graphsaccepted
175. Probabilistic Semantics Guided Discovery of Approximate Functional Dependenciesaccepted
176. Probability-Raising Causality for Uncertain Parametric Markov Decision Processes with PAC Guaranteesaccepted
177. Provably Adaptive Average Reward Reinforcement Learning for Metric Spacesaccepted
178. Proximal Interacting Particle Langevin Algorithmsaccepted
179. Proxy-informed Bayesian transfer learning with unknown sourcesaccepted
180. Pure and Strong Nash Equilibrium Computation in Compactly Representable Aggregate Gamesaccepted
181. Quantum Speedups for Bayesian Network Structure Learningaccepted
182. RCAP: Robust, Class-Aware, Probabilistic Dynamic Dataset Pruningaccepted
183. RDI: An adversarial robustness evaluation metric for deep neural networks based on model statistical featuresaccepted
184. RL, but don’t do anything I wouldn’t doaccepted
185. Relational Causal Discovery with Latent Confoundersaccepted
186. Reparameterizing Hybrid Markov Logic Networks to handle Covariate-Shift in Representationsaccepted
187. Residual Reweighted Conformal Prediction for Graph Neural Networksaccepted
188. Revisiting the Berkeley Admissions data: Statistical Tests for Causal Hypothesesaccepted
189. Revisiting the Equivalence of Bayesian Neural Networks and Gaussian Processes: On the Importance of Learning Activationsaccepted
190. Robust Optimization with Diffusion Models for Green Securityaccepted
191. Root Cause Analysis of Failures from Partial Causal Structuresaccepted
192. SALSA: A Secure, Adaptive and Label-Agnostic Scalable Algorithm for Machine Unlearningaccepted
193. SPvR: Structured Pruning via Rankingaccepted
194. STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learningaccepted
195. Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximationaccepted
196. Scalable Bayesian Low-Rank Adaptation of Large Language Models via Stochastic Variational Subspace Inferenceaccepted
197. Scaling Probabilistic Circuits via Data Partitioningaccepted
198. Selective Blocking for Message-Passing Neural Networks on Heterophilic Graphsaccepted
199. Simulation-Free Differential Dynamics Through Neural Conservation Lawsaccepted
200. Simulation-based Inference for High-dimensional Data using Surjective Sequential Neural Likelihood Estimationaccepted
201. Sparse Structure Exploration and Re-optimization for Vision Transformeraccepted
202. SpinSVAR: Estimating Structural Vector Autoregression Assuming Sparse Inputaccepted
203. Statistical Significance of Feature Importance Rankingsaccepted
204. Stein Variational Evolution Strategiesaccepted
205. Stochastic Embeddings : A Probabilistic and Geometric Analysis of Out-of-Distribution Behavioraccepted
206. Symbiotic Local Search for Small Decision Tree Policies in MDPsaccepted
207. Targeted Learning for Variable Importanceaccepted
208. Temperature Optimization for Bayesian Deep Learningaccepted
209. Testing Generalizability in Causal Inferenceaccepted
210. The Causal Information Bottleneck and Optimal Causal Variable Abstractionsaccepted
211. The Consistency Hypothesis in Uncertainty Quantification for Large Language Modelsaccepted
212. The Relativity of Causal Knowledgeaccepted
213. Toward Universal Laws of Outlier Propagationaccepted
214. Towards Provably Efficient Learning of Imperfect Information Extensive-Form Games with Linear Function Approximationaccepted
215. Trading Off Voting Axioms for Privacyaccepted
216. Transparent Trade-offs between Properties of Explanationsaccepted
217. Truthful Elicitation of Imprecise Forecastsaccepted
218. Tuning Algorithmic and Architectural Hyperparameters in Graph-Based Semi-Supervised Learning with Provable Guaranteesaccepted
219. Tuning-Free Coreset Markov Chain Monte Carlo via Hot DoGaccepted
220. Unsupervised Attributed Dynamic Network Embedding with Stability Guaranteesaccepted
221. Using Submodular Optimization to Approximate Minimum-Size Abductive Path Explanations for Tree-Based Modelsaccepted
222. VADIS: Investigating Inter-View Representation Biases for Multi-View Partial Multi-Label Learningaccepted
223. Valid Bootstraps for Network Embeddings with Applications to Network Visualisationaccepted
224. Variational Learning of Gaussian Process Latent Variable Models through Stochastic Gradient Annealed Importance Samplingaccepted
225. Weak to Strong Learning from Aggregate Labelsaccepted
226. Well-Defined Function-Space Variational Inference in Bayesian Neural Networks via Regularized KL-Divergenceaccepted
227. What is the Right Notion of Distance between Predict-then-Optimize Tasks?accepted
228. When Extragradient Meets PAGE: Bridging Two Giants to Boost Variational Inequalitiesaccepted
229. i$^2$VAE: Interest Information Augmentation with Variational Regularizers for Cross-Domain Sequential Recommendationaccepted

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