Releasing DP synthetic data built from treatment-arm and outcome moments, plus noise-aware multiple imputation, gives calibrated ATE intervals at strict privacy budgets.
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A General Framework for Updating Belief Distributions
14 Pith papers cite this work, alongside 268 external citations. Polarity classification is still indexing.
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A Bayesian global Fréchet regression method is introduced via a Fréchet Bayes rule that reduces the problem to scalar tasks, allows prior-data interpolation, and remains valid under moment conditions using weak conditional expectations.
Federated martingale posterior sampling lets clients share data embeddings for central predictive Bayesian sampling, matching centralized performance and improving calibration on MNIST, CIFAR-10, and CIFAR-100.
Susceptibilities applied to regret in deep RL agents reveal stagewise internal development in parameter space of a gridworld model that policy inspection alone cannot detect, validated via activation steering.
A Bayesian predictive model adaptively selects martingale factors to construct asymptotically log-optimal confidence sequences for bounded means while preserving anytime validity under misspecification.
Newton's recursive mixture estimator is a discrete gradient flow on the Fisher-Rao manifold of probability measures.
GPLFR jointly learns compression and GP regression for high-dimensional outputs, outperforming PCA-GP under structured noise and enabling a spatially resolved rocky-exoplanet climate emulator.
A Bayesian hierarchical model integrates coherence penalization and level-specific focus into forecasting estimation, yielding improved predictive accuracy on simulated and Australian tourism data.
New discrete-time approximations to SG(L)D enable accurate non-asymptotic predictions of covariance and integrated autocorrelation time for practical tuning in large-batch or misspecified regimes.
Derives exact marginal likelihood under finite-support Huber contamination via Dirichlet-Beta priors and dynamic programming over count allocations.
Two hybrid Bayesian surrogate training approaches integrate simulation and real-world data via a weighting strategy independent of surrogate family, shown in synthetic and real case studies to improve accuracy and diagnose simulation issues.
Relative plausibility theory supplies a computational-level account of comparing explanations against evidence in legal proof, while probabilistic methods supply algorithmic-level implementations, and the two correspond when plausibility judgments meet basic coherence conditions.
HyRe personalizes reward models at test time by reweighting an ensemble of heads trained on aggregate preferences, using few target examples to outperform uniform averaging and prior methods on RewardBench and 32 tasks.
citing papers explorer
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Workload-Preserving Differentially Private Synthetic Data for Causal Inference via Maximum-Entropy Calibration
Releasing DP synthetic data built from treatment-arm and outcome moments, plus noise-aware multiple imputation, gives calibrated ATE intervals at strict privacy budgets.
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Bayesian Global Fr\'echet Regression via Weak Conditional Expectations
A Bayesian global Fréchet regression method is introduced via a Fréchet Bayes rule that reduces the problem to scalar tasks, allows prior-data interpolation, and remains valid under moment conditions using weak conditional expectations.
-
Federated Martingale Posterior Samping
Federated martingale posterior sampling lets clients share data embeddings for central predictive Bayesian sampling, matching centralized performance and improving calibration on MNIST, CIFAR-10, and CIFAR-100.
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Interpreting Reinforcement Learning Agents with Susceptibilities
Susceptibilities applied to regret in deep RL agents reveal stagewise internal development in parameter space of a gridworld model that policy inspection alone cannot detect, validated via activation steering.
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Asymptotically Log-Optimal Bayes-Assisted Confidence Sequences for Bounded Means
A Bayesian predictive model adaptively selects martingale factors to construct asymptotically log-optimal confidence sequences for bounded means while preserving anytime validity under misspecification.
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Newton's Algorithm as a Gradient Flow: A Geometric Framework for Recursive Mixture Estimation
Newton's recursive mixture estimator is a discrete gradient flow on the Fisher-Rao manifold of probability measures.
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Gaussian Process Latent Factor Regression for Low-Data, High-Dimensional Output Problems
GPLFR jointly learns compression and GP regression for high-dimensional outputs, outperforming PCA-GP under structured noise and enabling a spatially resolved rocky-exoplanet climate emulator.
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Hierarchical Bayes meets hierarchical forecasting: A flexible framework for level-focused forecasts
A Bayesian hierarchical model integrates coherence penalization and level-specific focus into forecasting estimation, yielding improved predictive accuracy on simulated and Australian tourism data.
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Accurate Large-sample Uncertainty Quantification using Stochastic Gradient Markov Chain Monte Carlo
New discrete-time approximations to SG(L)D enable accurate non-asymptotic predictions of covariance and integrated autocorrelation time for practical tuning in large-batch or misspecified regimes.
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Marginal likelihoods for finite-support Huber contamination
Derives exact marginal likelihood under finite-support Huber contamination via Dirichlet-Beta priors and dynamic programming over count allocations.
-
Bayesian Surrogate Training on Multiple Data Sources: A Hybrid Modeling Strategy
Two hybrid Bayesian surrogate training approaches integrate simulation and real-world data via a weighting strategy independent of surrogate family, shown in synthetic and real case studies to improve accuracy and diagnose simulation issues.
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Relative plausibility versus probabilism: A level-of-analysis error in juridical proof
Relative plausibility theory supplies a computational-level account of comparing explanations against evidence in legal proof, while probabilistic methods supply algorithmic-level implementations, and the two correspond when plausibility judgments meet basic coherence conditions.
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Test-Time Alignment via Hypothesis Reweighting
HyRe personalizes reward models at test time by reweighting an ensemble of heads trained on aggregate preferences, using few target examples to outperform uniform averaging and prior methods on RewardBench and 32 tasks.
- On Asymptotic Outlier Rejection in Bayesian Mixed Poisson Regression Models Under Extreme Target and Covariate Values