AISTATS 2025 Accepted Papers

The full list of 583 papers accepted at AISTATS 2025 (International Conference on Artificial Intelligence and Statistics). 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. $\beta$-th order Acyclicity Derivatives for DAG LearningPoster
2. $\mathcal{I}$-trustworthy Models. A framework for trustworthiness evaluation of probabilistic classifiersPoster
3. $f$-PO: Generalizing Preference Optimization with $f$-divergence MinimizationPoster
4. $q\texttt{POTS}$: Efficient Batch Multiobjective Bayesian Optimization via Pareto Optimal Thompson SamplingPoster
5. A Bias-Variance Decomposition for Ensembles over Multiple Synthetic DatasetsPoster
6. A Causal Framework for Evaluating Deferring SystemsPoster
7. A Computation-Efficient Method of Measuring Dataset Quality based on the Coverage of the DatasetPoster
8. A Convex Relaxation Approach to Generalization Analysis for Parallel Positively Homogeneous NetworksPoster
9. A Differential Inclusion Approach for Learning Heterogeneous Sparsity in Neuroimaging AnalysisPoster
10. A Family of Distributions of Random Subsets for Controlling Positive and Negative DependencePoster
11. A Generalized Theory of Mixup for Structure-Preserving Synthetic DataPoster
12. A High Dimensional Statistical Model for Adversarial Training: Geometry and Trade-OffsPoster
13. A Likelihood Based Approach for Watermark DetectionPoster
14. A Multi-Armed Bandit Approach to Online Selection and Evaluation of Generative ModelsPoster
15. A Multi-Task Learning Approach to Linear Multivariate ForecastingPoster
16. A Novel Convex Gaussian Min Max Theorem for Repeated FeaturesOral
17. A Random Matrix Theory Perspective on the Spectrum of Learned Features and Asymptotic Generalization CapabilitiesOral
18. A Robust Kernel Statistical Test of Invariance: Detecting Subtle AsymmetriesOral
19. A Safe Bayesian Learning Algorithm for Constrained MDPs with Bounded Constraint ViolationPoster
20. A Safe Exploration Approach to Constrained Markov Decision ProcessesPoster
21. A Shapley-value Guided Rationale Editor for Rationale LearningPoster
22. A Shared Low-Rank Adaptation Approach to Personalized RLHFPoster
23. A Subquadratic Time Approximation Algorithm for Individually Fair k-CenterPoster
24. A Theoretical Framework for Preventing Class Collapse in Supervised Contrastive LearningPoster
25. A Theoretical Understanding of Chain-of-Thought: Coherent Reasoning and Error-Aware DemonstrationPoster
26. A Tight Regret Analysis of Non-Parametric Repeated Contextual BrokeragePoster
27. A Unified Evaluation Framework for Epistemic PredictionsPoster
28. A Unifying Framework for Action-Conditional Self-Predictive Reinforcement LearningPoster
29. A graphical global optimization framework for parameter estimation of statistical models with nonconvex regularization functionsPoster
30. A primer on linear classification with missing dataPoster
31. ADEPT: Hierarchical Bayes Approach to Personalized Federated Unsupervised LearningPoster
32. Accelerated Methods for Riemannian Min-Max Optimization Ensuring Bounded Geometric PenaltiesPoster
33. Accuracy on the wrong line: On the pitfalls of noisy data for out-of-distribution generalisationPoster
34. Achieving $\widetilde{\mathcal{O}}(\sqrt{T})$ Regret in Average-Reward POMDPs with Known Observation ModelsPoster
35. Active Bipartite Ranking with Smooth Posterior DistributionsPoster
36. Active Feature Acquisition for Personalised Treatment AssignmentPoster
37. Adapting to Online Distribution Shifts in Deep Learning: A Black-Box ApproachPoster
38. Adaptive Convergence Rates for Log-Concave Maximum LikelihoodPoster
39. Adaptive Extragradient Methods for Root-finding Problems under Relaxed AssumptionsPoster
40. Adaptive RKHS Fourier Features for Compositional Gaussian Process ModelsPoster
41. Additive Model Boosting: New Insights and Path(ologie)sOral
42. Advancing Fairness in Precision Medicine: A Universal Framework for Optimal Treatment Estimation in Censored DataPoster
43. Adversarial Training in High-Dimensional Regression: Generated Data and Neural NetworksPoster
44. Adversarial Vulnerabilities in Large Language Models for Time Series ForecastingPoster
45. Adversarially-Robust TD Learning with Markovian Data: Finite-Time Rates and Fundamental LimitsPoster
46. Algorithmic Accountability in Small Data: Sample-Size-Induced Bias Within Classification MetricsPoster
47. All models are wrong, some are useful: Model Selection with Limited LabelsPoster
48. All or None: Identifiable Linear Properties of Next-Token Predictors in Language ModelingPoster
49. AlleNoise - large-scale text classification benchmark dataset with real-world label noisePoster
50. Almost linear time differentially private release of synthetic graphsOral
51. Amortized Probabilistic Conditioning for Optimization, Simulation and InferencePoster
52. An Adaptive Method for Weak Supervision with Drifting DataPoster
53. An Empirical Bernstein Inequality for Dependent Data in Hilbert Spaces and ApplicationsPoster
54. An Iterative Algorithm for Rescaled Hyperbolic Functions RegressionPoster
55. Analysis of Two-Stage Rollout Designs with Clustering for Causal Inference under Network InterferencePoster
56. Analyzing Generative Models by Manifold Entropic MetricsPoster
57. Analyzing the Role of Permutation Invariance in Linear Mode ConnectivityPoster
58. Ant Colony Sampling with GFlowNets for Combinatorial OptimizationPoster
59. Anytime-Valid A/B Testing of Counting ProcessesPoster
60. Application of Structured State Space Models to High energy physics with locality sensitive hashingPoster
61. Approximate Equivariance in Reinforcement LearningPoster
62. Approximate Global Convergence of Independent Learning in Multi-Agent SystemsPoster
63. Approximate information maximization for bandit gamesPoster
64. Approximating the Total Variation Distance between GaussiansPoster
65. Asynchronous Decentralized Optimization with Constraints: Achievable Speeds of Convergence for Directed GraphsPoster
66. Automatically Adaptive Conformal Risk ControlPoster
67. Axiomatic Explainer Globalness via Optimal TransportPoster
68. AxlePro: Momentum-Accelerated Batched Training of Kernel MachinesPoster
69. Balls-and-Bins Sampling for DP-SGDOral
70. Bandit Pareto Set Identification in a Multi-Output Linear ModelPoster
71. Batch, match, and patch: low-rank approximations for score-based variational inferencePoster
72. Bayes without Underfitting: Fully Correlated Deep Learning Posteriors via Alternating ProjectionsPoster
73. Bayesian Circular Regression with von Mises Quasi-ProcessesPoster
74. Bayesian Decision Theory on Decision Trees: Uncertainty Evaluation and InterpretabilityPoster
75. Bayesian Gaussian Process ODEs via Double Normalizing FlowsPoster
76. Bayesian Inference in Recurrent Explicit Duration Switching Linear Dynamical SystemsPoster
77. Bayesian Off-Policy Evaluation and Learning for Large Action SpacesPoster
78. Bayesian Principles Improve Prompt Learning In Vision-Language ModelsPoster
79. Behavior-Inspired Neural Networks for Relational InferencePoster
80. Best-Arm Identification in Unimodal BanditsPoster
81. Beyond Discretization: Learning the Optimal Solution PathPoster
82. Beyond Size-Based Metrics: Measuring Task-Specific Complexity in Symbolic RegressionPoster
83. Bilevel Reinforcement Learning via the Development of Hyper-gradient without Lower-Level ConvexityPoster
84. Black-Box Uniform Stability for Non-Euclidean Empirical Risk MinimizationPoster
85. Bridging Domains with Approximately Shared FeaturesPoster
86. Bridging Multiple Worlds: Multi-marginal Optimal Transport for Causal Partial-identification ProblemPoster
87. Bridging the Theoretical Gap in Randomized SmoothingPoster
88. BudgetIV: Optimal Partial Identification of Causal Effects with Mostly Invalid InstrumentsPoster
89. Bypassing the Exponential Dependency: Looped Transformers Efficiently Learn In-context by Multi-step Gradient DescentPoster
90. Calibrated Computation-Aware Gaussian ProcessesPoster
91. Calm Composite Losses: Being Improper Yet Proper CompositePoster
92. Causal Discovery on Dependent Binary DataPoster
93. Causal Discovery-Driven Change Point Detection in Time SeriesPoster
94. Causal Representation Learning from General Environments under Nonparametric MixingPoster
95. Causal Temporal Regime Structure LearningPoster
96. Causal discovery in mixed additive noise modelsOral
97. Certifiably Quantisation-Robust training and inference of Neural NetworksOral
98. Change Point Detection in Hadamard Spaces by Alternating MinimizationPoster
99. Changepoint Estimation in Sparse Dynamic Stochastic Block Models under Near-Optimal Signal StrengthPoster
100. Characterizing the Accuracy-Communication-Privacy Trade-off in Distributed Stochastic Convex OptimizationPoster
101. Choice is what matters after AttentionPoster
102. ChronosX: Adapting Pretrained Time Series Models with Exogenous VariablesPoster
103. Class Imbalance in Anomaly Detection: Learning from an Exactly Solvable ModelPoster
104. Classification of High-dimensional Time Series in Spectral Domain Using Explainable Features with Applications to Neuroimaging DataPoster
105. ClusterSC: Advancing Synthetic Control with Donor SelectionPoster
106. Clustered Invariant Risk MinimizationPoster
107. Clustering Context in Off-Policy EvaluationPoster
108. Collaborative non-parametric two-sample testingPoster
109. Common Learning Constraints Alter Interpretations of Direct Preference OptimizationPoster
110. Composition and Control with Distilled Energy Diffusion Models and Sequential Monte CarloPoster
111. Computation-Aware Kalman Filtering and SmoothingPoster
112. Computing high-dimensional optimal transport by flow neural networksPoster
113. Conditional Generative Learning from Invariant Representations in Multi-Source: Robustness and EfficiencyPoster
114. Conditional Prediction ROC Bands for Graph ClassificationPoster
115. Conditional diffusions for amortized neural posterior estimationPoster
116. Conditional simulation via entropic optimal transport: Toward non-parametric estimation of conditional Brenier mapsPoster
117. Conditioning diffusion models by explicit forward-backward bridgingPoster
118. Conformal Prediction Under Generalized Covariate Shift with Posterior DriftPoster
119. Consistent Amortized Clustering via Generative Flow NetworksPoster
120. Consistent Validation for Predictive Methods in Spatial SettingsPoster
121. Constrained Multi-objective Bayesian Optimization through Optimistic Constraints EstimationPoster
122. Continuous Structure Constraint Integration for Robust Causal DiscoveryPoster
123. Contractivity and linear convergence in bilinear saddle-point problems: An operator-theoretic approachPoster
124. Convergence Analysis for General Probability Flow ODEs of Diffusion Models in Wasserstein DistancesPoster
125. Copula Based Trainable Calibration Error Estimator of Multi-Label Classification with Label InterdependenciesPoster
126. Corruption Robust Offline Reinforcement Learning with Human FeedbackOral
127. Cost-Aware Optimal Pairwise Pure ExplorationPoster
128. Cost-aware simulation-based inferencePoster
129. Counting Graphlets of Size k under Local Differential PrivacyPoster
130. Covariance Selection over NetworksPoster
131. Credal Two-Sample Tests of Epistemic UncertaintyPoster
132. Credibility-Aware Multimodal Fusion Using Probabilistic CircuitsPoster
133. Cross Validation for Correlated Data in Classification ModelsPoster
134. Cross-Modal Imputation and Uncertainty Estimation for Spatial TranscriptomicsPoster
135. Cross-modality Matching and Prediction of Perturbation Responses with Labeled Gromov-Wasserstein Optimal TransportPoster
136. Cubic regularized subspace Newton for non-convex optimizationOral
137. DDEQs: Distributional Deep Equilibrium Models through Wasserstein Gradient FlowsPoster
138. DPFL: Decentralized Personalized Federated LearningPoster
139. Data Reconstruction Attacks and Defenses: A Systematic EvaluationPoster
140. Data-Driven Upper Confidence Bounds with Near-Optimal Regret for Heavy-Tailed BanditsPoster
141. DeCaf: A Causal Decoupling Framework for OOD Generalization on Node ClassificationPoster
142. Decision from Suboptimal Classifiers: Excess Risk Pre- and Post-CalibrationPoster
143. Decision-Point Guided Safe Policy ImprovementPoster
144. Decoupling epistemic and aleatoric uncertainties with possibility theoryPoster
145. Deep Clustering via Probabilistic Ratio-Cut OptimizationPoster
146. Deep Generative Quantile BayesPoster
147. Deep Optimal Sensor Placement for Black Box Stochastic SimulationsPoster
148. Density Ratio Estimation via Sampling along Generalized Geodesics on Statistical ManifoldsPoster
149. Density Ratio-based Proxy Causal Learning Without Density RatiosPoster
150. Density-Dependent Group TestingPoster
151. Differentiable Calibration of Inexact Stochastic Simulation Models via Kernel Score MinimizationPoster
152. Differentiable Causal Structure Learning with Identifiability by NOTIMEPoster
153. Differential Privacy in Distributed Learning: Beyond Uniformly Bounded Stochastic GradientsPoster
154. Differentially Private Continual Release of Histograms and Related QueriesPoster
155. Differentially Private Graph Data Release: Inefficiencies & UnfairnessPoster
156. Differentially Private Kernelized Contextual BanditsPoster
157. Differentially Private Range Queries with Correlated Input PerturbationPoster
158. Differentially private algorithms for linear queries via stochastic convex optimizationPoster
159. Diffusion Models as Constrained Samplers for Optimization with Unknown ConstraintsPoster
160. Diffusion Models under Group TransformationsPoster
161. Disentangling Interactions and Dependencies in Feature AttributionsPoster
162. Disentangling impact of capacity, objective, batchsize, estimators, and step-size on flow VIPoster
163. Dissecting the Impact of Model Misspecification in Data-Driven OptimizationPoster
164. Distance Estimation for High-Dimensional Discrete DistributionsPoster
165. Distribution-Aware Mean Estimation under User-level Local Differential PrivacyPoster
166. Distributional Adversarial LossPoster
167. Distributional Counterfactual Explanations With Optimal TransportOral
168. Distributional Off-policy Evaluation with Bellman Residual MinimizationPoster
169. Do Regularization Methods for Shortcut Mitigation Work As Intended?Poster
170. Domain Adaptation and Entanglement: an Optimal Transport PerspectivePoster
171. Double Debiased Machine Learning for Mediation Analysis with Continuous TreatmentsPoster
172. Dynamic DBSCAN with Euler Tour SequencesPoster
173. Effective Bayesian Causal Inference via Structural Marginalisation and Autoregressive OrdersPoster
174. Efficient Estimation of a Gaussian Mean with Local Differential PrivacyPoster
175. Efficient Exploitation of Hierarchical Structure in Sparse Reward Reinforcement LearningPoster
176. Efficient Optimization Algorithms for Linear Adversarial TrainingPoster
177. Efficient Trajectory Inference in Wasserstein Space Using Consecutive AveragingPoster
178. Efficient and Asymptotically Unbiased Constrained Decoding for Large Language ModelsPoster
179. Elastic Representation: Mitigating Spurious Correlations for Group RobustnessPoster
180. Emergence of Globally Attracting Fixed Points in Deep Neural Networks With Nonlinear ActivationsPoster
181. Empirical Error Estimates for Graph SparsificationPoster
182. Energy-consistent Neural Operators for Hamiltonian and Dissipative Partial Differential EquationsPoster
183. Enhanced Adaptive Gradient Algorithms for Nonconvex-PL Minimax OptimizationPoster
184. Enhancing Feature-Specific Data Protection via Bayesian Coordinate Differential PrivacyPoster
185. Entropic Matching for Expectation Propagation of Markov Jump ProcessesOral
186. Epistemic Uncertainty and Excess Risk in Variational InferencePoster
187. Estimating the Spectral Moments of the Kernel Integral Operator from Finite Sample MatricesPoster
188. Estimation of Large Zipfian Distributions with Sort and SnapPoster
189. Evaluating Prediction-based Interventions with Human Decision Makers In MindPoster
190. Every Call is Precious: Global Optimization of Black-Box Functions with Unknown Lipschitz ConstantsPoster
191. Evidential Uncertainty Probes for Graph Neural NetworksPoster
192. Explaining ViTs Using Information FlowPoster
193. Exposing Privacy Gaps: Membership Inference Attack on Preference Data for LLM AlignmentPoster
194. FLIPHAT: Joint Differential Privacy for High Dimensional Linear BanditsPoster
195. Factor Analysis with Correlated Topic Model for Multi-Modal DataPoster
196. Fair Resource Allocation in Weakly Coupled Markov Decision ProcessesPoster
197. Fairness Risks for Group-Conditionally Missing DemographicsPoster
198. Fast Convergence of Softmax Policy Mirror AscentPoster
199. Faster WIND: Accelerating Iterative Best-of-$N$ Distillation for LLM AlignmentPoster
200. Feasible LearningPoster
201. FedBaF: Federated Learning Aggregation Biased by a Foundation ModelPoster
202. Federated Causal Inference: Multi-Study ATE Estimation beyond Meta-AnalysisPoster
203. Federated Communication-Efficient Multi-Objective OptimizationPoster
204. Federated UCBVI: Communication-Efficient Federated Regret Minimization with Heterogeneous AgentsPoster
205. Fine-Tuning with Uncertainty-Aware Priors Makes Vision and Language Foundation Models More ReliablePoster
206. Fixed-Budget Change Point Identification in Piecewise Constant BanditsPoster
207. Flexible Copula-Based Mixed Models in Deep Learning: A Scalable Approach to Arbitrary MarginalsPoster
208. Flexible and Efficient Probabilistic PDE Solvers through Gaussian Markov Random FieldsPoster
209. Fourier Circuits in Neural Networks and Transformers: A Case Study of Modular Arithmetic with Multiple InputsPoster
210. FreqMoE: Enhancing Time Series Forecasting through Frequency Decomposition Mixture of ExpertsPoster
211. From Deep Additive Kernel Learning to Last-Layer Bayesian Neural Networks via Induced Prior ApproximationPoster
212. From Gradient Clipping to Normalization for Heavy Tailed SGDPoster
213. From Learning to Optimize to Learning Optimization AlgorithmsPoster
214. Fully Dynamic Adversarially Robust Correlation Clustering in Polylogarithmic Update TimePoster
215. Function-Space MCMC for Bayesian Wide Neural NetworksPoster
216. Functional Stochastic Gradient MCMC for Bayesian Neural NetworksPoster
217. Fundamental Limits of Perfect Concept ErasurePoster
218. Fundamental computational limits of weak learnability in high-dimensional multi-index modelsPoster
219. Gated Recurrent Neural Networks with Weighted Time-Delay FeedbackPoster
220. Gaussian Mean Testing under TruncationPoster
221. Gaussian Smoothing in Saliency Maps: The Stability-Fidelity Trade-Off in Neural Network InterpretabilityPoster
222. General Staircase Mechanisms for Optimal Differential PrivacyPoster
223. Generalization Bounds for Dependent Data using Online-to-Batch Conversion.Poster
224. Generalization Lower Bounds for GD and SGD in Smooth Stochastic Convex OptimizationPoster
225. Generalized Criterion for Identifiability of Additive Noise Models Using MajorizationPoster
226. Geometric Collaborative Filtering with ConvergencePoster
227. Geometry-Aware Generative Autoencoders for Warped Riemannian Metric Learning and Generative Modeling on Data ManifoldsPoster
228. Get rid of your constraints and reparametrize: A study in NNLS and implicit biasPoster
229. Global Ground Metric Learning with Applications to scRNA dataPoster
230. Global Group Fairness in Federated Learning via Function TrackingPoster
231. Global Optimization of Gaussian Process Acquisition Functions Using a Piecewise-Linear Kernel ApproximationPoster
232. Graph Machine Learning based Doubly Robust Estimator for Network Causal EffectsPoster
233. Graph-based Complexity for Causal Effect by Empirical Plug-inPoster
234. HACSurv: A Hierarchical Copula-Based Approach for Survival Analysis with Dependent Competing RisksPoster
235. HAR-former: Hybrid Transformer with an Adaptive Time-Frequency Representation Matrix for Long-Term Series ForecastingPoster
236. HAVER: Instance-Dependent Error Bounds for Maximum Mean Estimation and Applications to Q-Learning and Monte Carlo Tree SearchPoster
237. HR-Bandit: Human-AI Collaborated Linear Recourse BanditPoster
238. Harnessing Causality in Reinforcement Learning with Bagged Decision TimesPoster
239. Harnessing the Power of Vicinity-Informed Analysis for Classification under Covariate ShiftPoster
240. Heterogeneous Graph Structure Learning through the Lens of Data-generating ProcessesPoster
241. Hierarchical Bias-Driven Stratification for Interpretable Causal Effect EstimationPoster
242. High Dimensional Bayesian Optimization using Lasso Variable SelectionPoster
243. High-Dimensional Differential Parameter Inference in Exponential Family using Time Score MatchingPoster
244. High-probability Convergence Bounds for Online Nonlinear Stochastic Gradient Descent under Heavy-tailed NoisePoster
245. How Well Can Transformers Emulate In-Context Newton's Method?Poster
246. Hybrid Transfer Reinforcement Learning: Provable Sample Efficiency from Shifted-Dynamics DataOral
247. Hyperbolic Prototypical Entailment Cones for Image ClassificationPoster
248. Hyperboloid GPLVM for Discovering Continuous Hierarchies via Nonparametric EstimationPoster
249. Hypernym Bias: Unraveling Deep Classifier Training Dynamics through the Lens of Class HierarchyPoster
250. Implicit Diffusion: Efficient optimization through stochastic samplingOral
251. Importance-weighted Positive-unlabeled Learning for Distribution Shift AdaptationOral
252. Improved dependence on coherence in eigenvector and eigenvalue estimation error boundsPoster
253. Improving N-Glycosylation and Biopharmaceutical Production Predictions Using AutoML-Built Residual Hybrid ModelsPoster
254. Improving Pre-trained Self-Supervised Embeddings Through Effective Entropy MaximizationPoster
255. Improving Stochastic Cubic Newton with MomentumPoster
256. Incremental Uncertainty-aware Performance Monitoring with Active Labeling InterventionPoster
257. Independent Learning in Performative Markov Potential GamesPoster
258. Infinite Width Limits of Self Supervised Neural NetworksPoster
259. Infinite-Horizon Reinforcement Learning with Multinomial Logit Function ApproximationPoster
260. Infinite-dimensional Diffusion Bridge Simulation via Operator LearningPoster
261. InfoNCE: Identifying the Gap Between Theory and PracticePoster
262. Information Transfer Across Clinical Tasks via Adaptive Parameter OptimisationOral
263. Information-Theoretic Causal Discovery in Topological OrderPoster
264. Information-Theoretic Measures on Lattices for Higher-Order InteractionsPoster
265. InnerThoughts: Disentangling Representations and Predictions in Large Language ModelsPoster
266. Integer Programming Based Methods and Heuristics for Causal Graph LearningPoster
267. Invariant Link Selector for Spatial-Temporal Out-of-Distribution ProblemPoster
268. Inverse Optimization with Prediction Market: A Characterization of Scoring Rules for Elciting System StatesPoster
269. Invertible Fourier Neural Operators for Tackling Both Forward and Inverse ProblemsPoster
270. Is Gibbs sampling faster than Hamiltonian Monte Carlo on GLMs?Poster
271. Is Merging Worth It? Securely Evaluating the Information Gain for Causal Dataset AcquisitionPoster
272. Is Prior-Free Black-Box Non-Stationary Reinforcement Learning Feasible?Poster
273. Keeping up with dynamic attackers: Certifying robustness to adaptive online data poisoningPoster
274. Kernel Single Proxy Control for Deterministic ConfoundingPoster
275. Knowledge Graph Completion with Mixed Geometry Tensor FactorizationPoster
276. Koopman-Equivariant Gaussian ProcessesPoster
277. LC-Tsallis-INF: Generalized Best-of-Both-Worlds Linear Contextual BanditsPoster
278. LITE: Efficiently Estimating Gaussian Probability of MaximalityPoster
279. LMEraser: Large Model Unlearning via Adaptive Prompt TuningPoster
280. Large Covariance Matrix Estimation With Nonnegative CorrelationsPoster
281. Learning Gaussian Multi-Index Models with Gradient Flow: Time Complexity and Directional ConvergencePoster
282. Learning Geometrically-Informed Lyapunov Functions with Deep Diffeomorphic RBF NetworksPoster
283. Learning Graph Node Embeddings by Smooth Pair SamplingOral
284. Learning High-dimensional Gaussians from Censored DataPoster
285. Learning Identifiable Structures Helps Avoid Bias in DNN-based Supervised Causal LearningPoster
286. Learning Infinite-Horizon Average-Reward Linear Mixture MDPs of Bounded SpanPoster
287. Learning Laplacian Positional Encodings for Heterophilous GraphsPoster
288. Learning Pareto manifolds in high dimensions: How can regularization help?Poster
289. Learning Stochastic Nonlinear Dynamics with Embedded Latent Transfer OperatorsPoster
290. Learning Visual-Semantic Subspace RepresentationsPoster
291. Learning a Single Index Model from Anisotropic Data with Vanilla Stochastic Gradient DescentPoster
292. Learning from biased positive-unlabeled data via threshold calibrationOral
293. Learning in Herding Mean Field Games: Single-Loop Algorithm with Finite-Time Convergence AnalysisPoster
294. Learning signals defined on graphs with optimal transport and Gaussian process regressionPoster
295. Learning the Distribution Map in Reverse Causal Performative PredictionPoster
296. Learning the Pareto Front Using Bootstrapped Observation SamplesPoster
297. Learning to Forget: Bayesian Time Series Forecasting using Recurrent Sparse Spectrum Signature Gaussian ProcessesPoster
298. Learning to Negotiate via Voluntary CommitmentPoster
299. Learning-Augmented Algorithms for Online Concave Packing and Convex Covering ProblemsPoster
300. Legitimate ground-truth-free metrics for deep uncertainty classification scoringPoster
301. Level Set Teleportation: An Optimization PerspectivePoster
302. Leveraging Frozen Batch Normalization for Co-Training in Source-Free Domain AdaptationPoster
303. Linear Submodular Maximization with Bandit FeedbackPoster
304. Linearized Wasserstein Barycenters: Synthesis, Analysis, Representational Capacity, and ApplicationsPoster
305. Local Stochastic Sensitivity Analysis For Dynamical SystemsPoster
306. Locally Optimal Descent for Dynamic Stepsize SchedulingPoster
307. Locally Private Estimation with Public FeaturesPoster
308. Locally Private Sampling with Public DataPoster
309. Logarithmic Neyman Regret for Adaptive Estimation of the Average Treatment EffectPoster
310. Looped ReLU MLPs May Be All You Need as Practical Programmable ComputersPoster
311. Loss Gradient Gaussian Width based Generalization and Optimization GuaranteesOral
312. Lower Bounds for Time-Varying Kernelized BanditsPoster
313. M$^2$AD: Multi-Sensor Multi-System Anomaly Detection through Global Scoring and Calibrated ThresholdingPoster
314. M-HOF-Opt: Multi-Objective Hierarchical Output Feedback Optimization via Multiplier Induced Loss Landscape SchedulingPoster
315. MDP Geometry, Normalization and Reward Balancing SolversPoster
316. MEDUSA: Medical Data Under Shadow Attacks via Hybrid Model InversionPoster
317. MING: A Functional Approach to Learning Molecular Generative ModelsPoster
318. MODL: Multilearner Online Deep LearningPoster
319. Max-Rank: Efficient Multiple Testing for Conformal PredictionPoster
320. Mean-Field Microcanonical Gradient DescentPoster
321. Memorization in Attention-only TransformersPoster
322. Memory-Efficient Optimization with Factorized Hamiltonian DescentPoster
323. Meta-learning Task-specific Regularization Weights for Few-shot Linear RegressionPoster
324. Meta-learning from Heterogeneous Tensors for Few-shot Tensor CompletionPoster
325. Microfoundation inference for strategic predictionPoster
326. Minimum Empirical Divergence for Sub-Gaussian Linear BanditsPoster
327. Mixed-Feature Logistic Regression Robust to Distribution ShiftsPoster
328. Model Evaluation in the Dark: Robust Classifier Metrics with Missing LabelsPoster
329. Model selection for behavioral learning data and applications to contextual banditsPoster
330. Models That Are Interpretable But Not TransparentPoster
331. Multi-Agent Credit Assignment with Pretrained Language ModelsPoster
332. Multi-Player Approaches for Dueling BanditsPoster
333. Multi-agent Multi-armed Bandit Regret Complexity and OptimalityPoster
334. Multi-level Advantage Credit Assignment for Cooperative Multi-Agent Reinforcement LearningPoster
335. Multi-marginal Schrödinger Bridges with Iterative Reference RefinementOral
336. Multimodal Learning with Uncertainty Quantification based on Discounted Belief FusionPoster
337. Narrowing the Gap between Adversarial and Stochastic MDPs via Policy OptimizationPoster
338. Natural Language Counterfactual Explanations for Graphs Using Large Language ModelsPoster
339. Near-Optimal Algorithm for Non-Stationary Kernelized BanditsOral
340. Near-Optimal Sample Complexity for Iterated CVaR Reinforcement Learning with a Generative ModelPoster
341. Near-Optimal Sample Complexity in Reward-Free Kernel-based Reinforcement LearningPoster
342. Near-Polynomially Competitive Active Logistic RegressionPoster
343. Near-optimal algorithms for private estimation and sequential testing of collision probabilityPoster
344. Neural Point Processes for Pixel-wise RegressionPoster
345. New User Event Prediction Through the Lens of Causal InferencePoster
346. No-Regret Bayesian Optimization with Stochastic Observation FailuresPoster
347. Noise-Aware Differentially Private Variational InferencePoster
348. Noisy Low-Rank Matrix Completion via Transformed $L_1$ Regularization and its Theoretical PropertiesPoster
349. Nonparametric Distributional Regression via Quantile RegressionPoster
350. Nonparametric Factor Analysis and BeyondPoster
351. Nonparametric estimation of Hawkes processes with RKHSsPoster
352. Nyström Kernel Stein DiscrepancyPoster
353. Offline Multi-task Transfer RL with Representational PenalizationPoster
354. Offline RL via Feature-Occupancy Gradient AscentPoster
355. On Distributional Discrepancy for Experimental Design with General Assignment ProbabilitiesOral
356. On Local Posterior Structure in Deep EnsemblesPoster
357. On Preference-based Stochastic Linear Contextual Bandits with KnapsacksPoster
358. On Subjective Uncertainty Quantification and Calibration in Natural Language GenerationPoster
359. On Tractability of Learning Bayesian Networks with Ancestral ConstraintsPoster
360. On Tradeoffs in Learning-Augmented AlgorithmsPoster
361. On adaptivity and minimax optimality of two-sided nearest neighborsPoster
362. On the Asymptotic Mean Square Error Optimality of Diffusion ModelsPoster
363. On the Computational Tractability of the (Many) Shapley ValuesPoster
364. On the Consistent Recovery of Joint Distributions from ConditionalsPoster
365. On the Convergence of Continual Federated Learning Using Incrementally Aggregated GradientsPoster
366. On the Convergence of Locally Adaptive and Scalable Diffusion-Based Sampling Methods for Deep Bayesian Neural Network PosteriorsPoster
367. On the Difficulty of Constructing a Robust and Publicly-Detectable WatermarkPoster
368. On the Geometry and Optimization of Polynomial Convolutional NetworksPoster
369. On the Identifiability of Causal AbstractionsPoster
370. On the Inherent Privacy of Zeroth-Order Projected Gradient DescentPoster
371. On the Power of Adaptive Weighted Aggregation in Heterogeneous Federated Learning and BeyondPoster
372. On the Power of Multitask Representation Learning with Gradient DescentPoster
373. On the Relationship Between Robustness and Expressivity of Graph Neural NetworksPoster
374. On the Sample Complexity of Next-Token PredictionPoster
375. Online Assortment and Price Optimization Under Contextual Choice ModelsPoster
376. Online Student-$t$ Processes with an Overall-local Scale Structure for Modelling Non-stationary DataPoster
377. Online-to-PAC generalization bounds under graph-mixing dependenciesPoster
378. Optimal Multi-Objective Best Arm Identification with Fixed ConfidencePoster
379. Optimal Stochastic Trace Estimation in Generative ModelingPoster
380. Optimal Time Complexity Algorithms for Computing General Random Walk Graph Kernels on Sparse GraphsPoster
381. Optimal downsampling for Imbalanced Classification with Generalized Linear ModelsPoster
382. Optimal estimation of linear non-Gaussian structure equation modelsPoster
383. Optimising Clinical Federated Learning through Mode Connectivity-based Model AggregationPoster
384. Optimistic Safety for Online Convex Optimization with Unknown Linear ConstraintsPoster
385. Optimizing Neural Network Training and Quantization with Rooted Logistic ObjectivesPoster
386. Order-Optimal Regret in Distributed Kernel Bandits using Uniform Sampling with Shared RandomnessPoster
387. Order-Optimal Regret with Novel Policy Gradient Approaches in Infinite-Horizon Average Reward MDPsPoster
388. Ordered $\mathcal{V}$-information Growth: A Fresh Perspective on Shared InformationPoster
389. Out-of-distribution robustness for multivariate analysis via causal regularisationPoster
390. Parabolic Continual LearningPoster
391. Parallel Backpropagation for Inverse of a Convolution with Application to Normalizing FlowsPoster
392. Parameter estimation in state space models using particle importance samplingPoster
393. Pareto Set Identification With Posterior SamplingPoster
394. Partial Information Decomposition for Data Interpretability and Feature SelectionPoster
395. Paths and Ambient Spaces in Neural Loss LandscapesPoster
396. Perfect Recovery for Random Geometric Graph Matching with Shallow Graph Neural NetworksPoster
397. Performative Prediction on Games and Mechanism DesignPoster
398. Performative Reinforcement Learning with Linear Markov Decision ProcessPoster
399. Permutation Invariant Functions: Statistical Testing, Density Estimation, and Metric EntropyPoster
400. Personalized Convolutional Dictionary Learning of Physiological Time SeriesPoster
401. Personalizing Low-Rank Bayesian Neural Networks Via Federated LearningPoster
402. Pick-to-Learn and Self-Certified Gaussian Process ApproximationsOral
403. Planning and Learning in Risk-Aware Restless Multi-Arm BanditsPoster
404. Poisoning Bayesian Inference via Data Deletion and ReplicationPoster
405. Policy Teaching via Data Poisoning in Learning from Human PreferencesPoster
406. Post-processing for Fair Regression via Explainable SVDPoster
407. Posterior Mean Matching: Generative Modeling through Online Bayesian InferencePoster
408. Powerful batch conformal prediction for classificationPoster
409. Prediction-Centric Uncertainty Quantification via MMDPoster
410. Prepacking: A Simple Method for Fast Prefilling and Increased Throughput in Large Language ModelsPoster
411. Primal-Dual Spectral Representation for Off-policy EvaluationPoster
412. Prior-Dependent Allocations for Bayesian Fixed-Budget Best-Arm Identification in Structured BanditsPoster
413. Prior-Fitted Networks Scale to Larger Datasets When Treated as Weak LearnersPoster
414. Privacy in Metalearning and Multitask Learning: Modeling and SeparationsPoster
415. Protein Fitness Landscape: Spectral Graph Theory PerspectivePoster
416. Provable Benefits of Task-Specific Prompts for In-context LearningPoster
417. Proximal Sampler with Adaptive Step SizePoster
418. Pure Exploration with Feedback GraphsOral
419. Q-function Decomposition with Intervention Semantics for Factored Action SpacesPoster
420. Q-learning for Quantile MDPs: A Decomposition, Performance, and Convergence AnalysisPoster
421. QuACK: A Multipurpose Queuing Algorithm for Cooperative $k$-Armed BanditsPoster
422. Quantifying Knowledge Distillation using Partial Information DecompositionPoster
423. Quantifying the Optimization and Generalization Advantages of Graph Neural Networks Over Multilayer PerceptronsPoster
424. Quantile Additive Trend FilteringPoster
425. ROTI-GCV: Generalized Cross-Validation for right-ROTationally Invariant DataPoster
426. RTD-Lite: Scalable Topological Analysis for Comparing Weighted Graphs in Learning TasksPoster
427. Randomized Iterative Solver as Iterative Refinement: A Simple Fix Towards Backward StabilityPoster
428. Rate of Model Collapse in Recursive TrainingPoster
429. Recurrent Neural Goodness-of-Fit Test for Time SeriesPoster
430. Recursive Learning of Asymptotic Variational ObjectivesPoster
431. Refined Analysis of Constant Step Size Federated Averaging and Federated Richardson-Romberg ExtrapolationPoster
432. Regularity in Canonicalized Models: A Theoretical PerspectivePoster
433. Reinforcement Learning for Adaptive MCMCPoster
434. Reinforcement Learning for Infinite-Horizon Average-Reward Linear MDPs via Approximation by Discounted-Reward MDPsPoster
435. Reinforcement Learning with Intrinsically Motivated Feedback Graph for Lost-sales Inventory ControlPoster
436. Relating Piecewise Linear Kolmogorov Arnold Networks to ReLU NetworksPoster
437. Reliable and Scalable Variable Importance Estimation via Warm-start and Early StoppingPoster
438. Representer Theorems for Metric and Preference Learning: Geometric Insights and AlgorithmsPoster
439. Restructuring Tractable Probabilistic CircuitsOral
440. Rethinking Neural-based Matrix Inversion: Why can't, and Where canPoster
441. RetroDiff: Retrosynthesis as Multi-stage Distribution InterpolationPoster
442. Revisiting LocalSGD and SCAFFOLD: Improved Rates and Missing AnalysisPoster
443. Revisiting Online Learning Approach to Inverse Linear Optimization: A Fenchel–Young Loss Perspective and Gap-Dependent Regret AnalysisPoster
444. Reward Maximization for Pure Exploration: Minimax Optimal Good Arm Identification for Nonparametric Multi-Armed BanditsPoster
445. Riemann$^2$: Learning Riemannian Submanifolds from Riemannian DataPoster
446. Risk-sensitive Bandits: Arm Mixture Optimality and Regret-efficient AlgorithmsPoster
447. Robust Classification by Coupling Data Mollification with Label SmoothingPoster
448. Robust Estimation in metric spaces: Achieving Exponential Concentration with a Fr\'echet MedianPoster
449. Robust Fair Clustering with Group Membership Uncertainty SetsPoster
450. Robust Gradient Descent for Phase RetrievalPoster
451. Robust Kernel Hypothesis Testing under Data CorruptionOral
452. Robust Multi-fidelity Bayesian Optimization with Deep Kernel and PartitionPoster
453. Robust Offline Policy Learning with Observational Data from Multiple SourcesPoster
454. Robust Score MatchingPoster
455. S-CFE: Simple Counterfactual ExplanationsPoster
456. SINE: Scalable MPE Inference for Probabilistic Graphical Models using Advanced Neural EmbeddingsPoster
457. SNAP: Sequential Non-Ancestor Pruning for Targeted Causal Effect Estimation With an Unknown GraphPoster
458. Safe exploration in reproducing kernel Hilbert spacesPoster
459. Safety in the Face of Adversity: Achieving Zero Constraint Violation in Online Learning with Slowly Changing ConstraintsPoster
460. Sample Compression Unleashed: New Generalization Bounds for Real Valued LossesPoster
461. Sampling From Multiscale Densities With Delayed Rejection Generalized Hamiltonian Monte CarloPoster
462. Sampling from Bayesian Neural Network Posteriors with Symmetric Minibatch Splitting Langevin DynamicsPoster
463. Sampling from the Random Linear Model via Stochastic Localization Up to the AMP ThresholdPoster
464. Sampling in High-Dimensions using Stochastic Interpolants and Forward-Backward Stochastic Differential EquationsPoster
465. Scalable Implicit Graphon LearningPoster
466. Scalable Inference for Bayesian Multinomial Logistic-Normal Dynamic Linear ModelsPoster
467. Scalable Out-of-Distribution Robustness in the Presence of Unobserved ConfoundersPoster
468. Scalable spectral representations for multiagent reinforcement learning in network MDPsPoster
469. Score matching for bridges without learning time-reversalsPoster
470. ScoreFusion: Fusing Score-based Generative Models via Kullback–Leibler BarycentersOral
471. Selecting the Number of Communities for Weighted Degree-Corrected Stochastic Block ModelsPoster
472. Semiparametric conformal predictionPoster
473. SemlaFlow -- Efficient 3D Molecular Generation with Latent Attention and Equivariant Flow MatchingPoster
474. Separation-Based Distance Measures for Causal GraphsPoster
475. Sequential Kernelized Stein DiscrepancyPoster
476. Signal Recovery from Random Dot-Product Graphs under Local Differential PrivacyPoster
477. Signature Isolation ForestPoster
478. Signed Graph Autoencoder for Explainable and Polarization-Aware Network EmbeddingsPoster
479. Sketch-and-Project Meets Newton Method: Global $O(1/k^2)$ Convergence with Low-Rank UpdatesPoster
480. Some Targets Are Harder to Identify than Others: Quantifying the Target-dependent Membership LeakageOral
481. Sparse Activations as Conformal PredictorsPoster
482. Sparse Causal Effect Estimation using Two-Sample Summary Statistics in the Presence of Unmeasured ConfoundingPoster
483. Spectral Differential Network Analysis for High-Dimensional Time SeriesPoster
484. Spectral Representation for Causal Estimation with Hidden ConfoundersPoster
485. StableMDS: A Novel Gradient Descent-Based Method for Stabilizing and Accelerating Weighted Multidimensional ScalingPoster
486. Statistical Guarantees for Lifelong Reinforcement Learning using PAC-Bayes TheoryPoster
487. Statistical Guarantees for Unpaired Image-to-Image Cross-Domain Analysis using GANsPoster
488. Statistical Inference for Feature Selection after Optimal Transport-based Domain AdaptationPoster
489. Statistical Learning of Distributionally Robust Stochastic Control in Continuous State SpacesOral
490. Statistical Test for Auto Feature Engineering by Selective InferencePoster
491. Steering No-Regret Agents in MFGs under Model UncertaintyPoster
492. Stein Boltzmann Sampling: A Variational Approach for Global OptimizationPoster
493. SteinDreamer: Variance Reduction for Text-to-3D Score Distillation via Stein IdentityPoster
494. Steinmetz Neural Networks for Complex-Valued DataPoster
495. Stochastic Approximation with Unbounded Markovian Noise: A General-Purpose TheoremPoster
496. Stochastic Compositional Minimax Optimization with Provable Convergence GuaranteesPoster
497. Stochastic Gradient Descent for Bézier Simplex Representation of Pareto Set in Multi-Objective OptimizationPoster
498. Stochastic Rounding for LLM Training: Theory and PracticePoster
499. Stochastic Weight Sharing for Bayesian Neural NetworksPoster
500. Strategic Conformal PredictionPoster
501. Strong Screening Rules for Group-based SLOPE ModelsPoster
502. Structure based SAT dataset for analysing GNN generalisationPoster
503. SubSearch: Robust Estimation and Outlier Detection for Stochastic Block Models via Subgraph SearchPoster
504. Subspace Recovery in Winsorized PCA: Insights into Accuracy and RobustnessPoster
505. Superiority of Multi-Head Attention: A Theoretical Study in Shallow Transformers in In-Context Linear RegressionPoster
506. Survival Models: Proper Scoring Rule and Stochastic Optimization with Competing RisksPoster
507. Symmetry-Based Structured Matrices for Efficient Approximately Equivariant NetworksOral
508. Synthesis and Analysis of Data as Probability Measures With Entropy-Regularized Optimal TransportPoster
509. Synthetic Potential Outcomes and Causal Mixture IdentifiabilityPoster
510. TRADE: Transfer of Distributions between External Conditions with Normalizing FlowsPoster
511. TVineSynth: A Truncated C-Vine Copula Generator of Synthetic Tabular Data to Balance Privacy and UtilityPoster
512. Tamed Langevin sampling under weaker conditionsPoster
513. Task Shift: From Classification to Regression in Overparameterized Linear ModelsPoster
514. Task-Driven Discrete Representation LearningPoster
515. TempTest: Local Normalization Distortion and the Detection of Machine-generated TextPoster
516. Tensor Network Based Feature Learning ModelPoster
517. Tensor Network-Constrained Kernel Machines as Gaussian ProcessesPoster
518. Testing Conditional Independence with Deep Neural Network Based Binary Expansion Testing (DeepBET)Poster
519. The Hardness of Validating Observational Studies with Experimental DataPoster
520. The Local Learning Coefficient: A Singularity-Aware Complexity MeasurePoster
521. The Pivoting Framework: Frank-Wolfe Algorithms with Active Set Size ControlOral
522. The Polynomial Iteration Complexity for Variance Exploding Diffusion Models: Elucidating SDE and ODE SamplersPoster
523. The Sample Complexity of Stackelberg GamesOral
524. The Size of Teachers as a Measure of Data Complexity: PAC-Bayes Excess Risk Bounds and Scaling LawsPoster
525. The Strong Product Model for Network Inference without Independence AssumptionsPoster
526. The Uniformly Rotated Mondrian KernelPoster
527. The VampPrior Mixture ModelPoster
528. The cost of local and global fairness in Federated LearningPoster
529. Theoretical Analysis of Leave-one-out Cross Validation for Non-differentiable Penalties under High-dimensional SettingsPoster
530. Theoretical Convergence Guarantees for Variational AutoencodersPoster
531. Theoretically Grounded Pruning of Large Ground Sets for Constrained, Discrete OptimizationPoster
532. Theory of Agreement-on-the-Line in Linear Models and Gaussian DataPoster
533. Tight Analysis of Difference-of-Convex Algorithm (DCA) Improves Convergence Rates for Proximal Gradient DescentPoster
534. Tighter Confidence Bounds for Sequential Kernel RegressionPoster
535. Time-series attribution maps with regularized contrastive learningPoster
536. Time-varying Gaussian Process Bandits with Unknown PriorPoster
537. To Give or Not to Give? The Impacts of Strategically Withheld RecoursePoster
538. Towards Cost Sensitive Decision MakingPoster
539. Towards Fair Graph Learning without Demographic InformationPoster
540. Towards Regulatory-Confirmed Adaptive Clinical Trials: Machine Learning Opportunities and SolutionsPoster
541. Towards a mathematical theory for consistency training in diffusion modelsPoster
542. Training LLMs with MXFP4Poster
543. Training Neural Samplers with Reverse Diffusive KL DivergencePoster
544. Transfer Learning for High-dimensional Reduced Rank Time Series ModelsPoster
545. Transfer Neyman-Pearson Algorithm for Outlier DetectionPoster
546. Transformers are Provably Optimal In-context Estimators for Wireless CommunicationsPoster
547. Truncated Inverse-Lévy Measure Representation of the Beta ProcessPoster
548. Trustworthy assessment of heterogeneous treatment effect estimator via analysis of relative errorPoster
549. Two-Timescale Linear Stochastic Approximation: Constant Stepsizes Go a Long WayPoster
550. Type Information-Assisted Self-Supervised Knowledge Graph DenoisingPoster
551. UNHaP: Unmixing Noise from Hawkes ProcessesPoster
552. Unbiased Quantization of the $L_1$ Ball for Communication-Efficient Distributed Mean EstimationPoster
553. Unbiased and Sign Compression in Distributed Learning: Comparing Noise Resilience via SDEsOral
554. Unconditionally Calibrated Priors for Beta Mixture Density NetworksPoster
555. Understanding Expert Structures on Minimax Parameter Estimation in Contaminated Mixture of ExpertsPoster
556. Understanding GNNs and Homophily in Dynamic Node ClassificationPoster
557. Understanding Inverse Reinforcement Learning under Overparameterization: Non-Asymptotic Analysis and Global OptimalityPoster
558. Understanding the Effect of GCN Convolutions in Regression TasksPoster
559. Understanding the Learning Dynamics of LoRA: A Gradient Flow Perspective on Low-Rank Adaptation in Matrix FactorizationPoster
560. Unifying Feature-Based Explanations with Functional ANOVA and Cooperative Game TheoryPoster
561. Unveiling the Role of Randomization in Multiclass Adversarial Classification: Insights from Graph TheoryPoster
562. Variance-Aware Linear UCB with Deep Representation for Neural Contextual BanditsPoster
563. Variance-Dependent Regret Bounds for Nonstationary Linear BanditsPoster
564. Variation Due to Regularization Tractably Recovers Bayesian Deep Learning UncertaintyOral
565. Variational Adversarial Training Towards Policies with Improved RobustnessPoster
566. Variational Combinatorial Sequential Monte Carlo for Bayesian Phylogenetics in Hyperbolic SpacePoster
567. Variational Inference in Location-Scale Families: Exact Recovery of the Mean and Correlation MatrixOral
568. Variational Inference on the Boolean Hypercube with the Quantum EntropyPoster
569. Variational Schr\"odinger Momentum DiffusionPoster
570. Vecchia Gaussian Process Ensembles on Internal Representations of Deep Neural NetworksPoster
571. Visualizing token importance for black-box language modelsPoster
572. Wasserstein Distributionally Robust Bayesian Optimization with Continuous ContextPoster
573. Wasserstein Gradient Flow over Variational Parameter Space for Variational InferencePoster
574. Weighted Euclidean Distance Matrices over Mixed Continuous and Categorical Inputs for Gaussian Process ModelsPoster
575. Weighted Sum of Gaussian Process Latent Variable ModelsPoster
576. What Ails Generative Structure-based Drug Design: Expressivity is Too Little or Too Much?Oral
577. What and How does In-Context Learning Learn? Bayesian Model Averaging, Parameterization, and GeneralizationPoster
578. When Can We Solve the Weighted Low Rank Approximation Problem in Truly Subquadratic Time?Poster
579. When the Universe is Too Big: Bounding Consideration Probabilities for Plackett-Luce RankingsPoster
580. Your Finetuned Large Language Model is Already a Powerful Out-of-distribution DetectorPoster
581. Your copula is a classifier in disguise: classification-based copula density estimationPoster
582. Zero-Shot Action Generalization with Limited ObservationsPoster
583. posteriordb: Testing, Benchmarking and Developing Bayesian Inference AlgorithmsOral

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