PEQ-Net jointly estimates multiple longitudinal treatment policies via a shared policy encoder and kernel mean embeddings to constrain second-order bias after LTMLE correction.
Borgwardt and Malte J
8 Pith papers cite this work. Polarity classification is still indexing.
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TabKDE generates synthetic tabular data using copula transformations followed by kernel density estimation, matching prior accuracy with negligible training time and reduced storage via coresets.
A unified framework for exponential tilting in diffusion and flow models that includes bias-variance decompositions showing finite gradient variance for some methods, norm bounds on adjoint ODEs, and adapted losses with new Crooks and Jarzynski identities.
KBSE learns policies and barrier functions iteratively via conditional mean embeddings to bound unsafe state reachability probabilities during exploration in deep RL.
APEX is an assumption-free image quality metric using Sliced Wasserstein Distance on CLIP and DINOv2 embeddings that claims superior robustness to degradations and cross-dataset stability.
Establishes CMMD as a family of kernel-based metrics for differences between conditional distributions, with levels 0-2 and general s, plus a doubly robust estimator consistent if at least one model is correct.
MIRA is a new analytic score for conditional distribution accuracy derived from equal probability mass assignment, enabling Bayesian model comparison via direct posterior validation.
A tractable estimator for functional KL divergence provides a coherent way to compare trajectory inference methods and reveal discrepancies in inferred dynamics from snapshot data.
citing papers explorer
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Smooth Multi-Policy Causal Effect Estimation in Longitudinal Settings
PEQ-Net jointly estimates multiple longitudinal treatment policies via a shared policy encoder and kernel mean embeddings to constrain second-order bias after LTMLE correction.
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TabKDE: Simple and Scalable Tabular Data Generation with Kernel Density Estimates
TabKDE generates synthetic tabular data using copula transformations followed by kernel density estimation, matching prior accuracy with negligible training time and reduced storage via coresets.
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A unified perspective on fine-tuning and sampling with diffusion and flow models
A unified framework for exponential tilting in diffusion and flow models that includes bias-variance decompositions showing finite gradient variance for some methods, norm bounds on adjoint ODEs, and adapted losses with new Crooks and Jarzynski identities.
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Kernel-Based Safe Exploration in Deep Reinforcement Learning
KBSE learns policies and barrier functions iteratively via conditional mean embeddings to bound unsafe state reachability probabilities during exploration in deep RL.
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APEX: Assumption-free Projection-based Embedding eXamination Metric for Image Quality Assessment
APEX is an assumption-free image quality metric using Sliced Wasserstein Distance on CLIP and DINOv2 embeddings that claims superior robustness to degradations and cross-dataset stability.
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Measuring Differences between Conditional Distributions using Kernel Embeddings
Establishes CMMD as a family of kernel-based metrics for differences between conditional distributions, with levels 0-2 and general s, plus a doubly robust estimator consistent if at least one model is correct.
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MIRA: A Score for Conditional Distribution Accuracy and Model Comparison
MIRA is a new analytic score for conditional distribution accuracy derived from equal probability mass assignment, enabling Bayesian model comparison via direct posterior validation.
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Relative Entropy Estimation in Function Space: Theory and Applications to Trajectory Inference
A tractable estimator for functional KL divergence provides a coherent way to compare trajectory inference methods and reveal discrepancies in inferred dynamics from snapshot data.