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Found 18 papers, 6 papers with code
Synthetic Data Applications in Finance
Synthetic data has made tremendous strides in various commercial settings including finance, healthcare, and virtual reality.
Fair Wasserstein Coresets
Data distillation and coresets have emerged as popular approaches to generate a smaller representative set of samples for downstream learning tasks to handle large-scale datasets.
FairWASP: Fast and Optimal Fair Wasserstein Pre-processing
FairWASP can therefore be used to construct datasets which can be fed into any classification method, not just methods which accept sample weights.
Deep Gaussian Mixture Ensembles
This work introduces a novel probabilistic deep learning technique called deep Gaussian mixture ensembles (DGMEs), which enables accurate quantification of both epistemic and aleatoric uncertainty.
Fast Learning of Multidimensional Hawkes Processes via Frank-Wolfe
Hawkes processes have recently risen to the forefront of tools when it comes to modeling and generating sequential events data.
Online Learning for Mixture of Multivariate Hawkes Processes
Online learning of Hawkes processes has received increasing attention in the last couple of years especially for modeling a network of actors.
Differentially Private Learning of Hawkes Processes
Hawkes processes have recently gained increasing attention from the machine learning community for their versatility in modeling event sequence data.
Fair When Trained, Unfair When Deployed: Observable Fairness Measures are Unstable in Performative Prediction Settings
These measures are sensitive to distribution shift: a predictor which is trained to satisfy one of these fairness definitions may become unfair if the distribution changes.
Likelihood-Free Frequentist Inference: Bridging Classical Statistics and Machine Learning for Reliable Simulator-Based Inference
In this work, we propose a modular inference framework that bridges classical statistics and modern machine learning to provide (i) a practical approach for constructing confidence sets with near finite-sample validity at any value of the unknown parameters, and (ii) interpretable diagnostics for estimating empirical coverage across the entire parameter space.
HECT: High-Dimensional Ensemble Consistency Testing for Climate Models
Climate models play a crucial role in understanding the effect of environmental and man-made changes on climate to help mitigate climate risks and inform governmental decisions.
Structural Forecasting for Tropical Cyclone Intensity Prediction: Providing Insight with Deep Learning
Tropical cyclone (TC) intensity forecasts are ultimately issued by human forecasters.
Confidence Sets and Hypothesis Testing in a Likelihood-Free Inference Setting
In this paper, we present $\texttt{ACORE}$ (Approximate Computation via Odds Ratio Estimation), a frequentist approach to LFI that first formulates the classical likelihood ratio test (LRT) as a parametrized classification problem, and then uses the equivalence of tests and confidence sets to build confidence regions for parameters of interest.
Explicit Group Sparse Projection with Applications to Deep Learning and NMF
Instead, in our approach we set the sparsity level for the whole set explicitly and simultaneously project a group of vectors with the sparsity level of each vector tuned automatically.
Robust Learning Rate Selection for Stochastic Optimization via Splitting Diagnostic
This paper proposes SplitSGD, a new dynamic learning rate schedule for stochastic optimization.
Conditional Density Estimation Tools in Python and R with Applications to Photometric Redshifts and Likelihood-Free Cosmological Inference
We provide sample code in $\texttt{Python}$ and $\texttt{R}$ as well as examples of applications to photometric redshift estimation and likelihood-free cosmological inference via CDE.
A Flexible Pipeline for Prediction of Tropical Cyclone Paths
Hurricanes and, more generally, tropical cyclones (TCs) are rare, complex natural phenomena of both scientific and public interest.
Applications
Validation of Approximate Likelihood and Emulator Models for Computationally Intensive Simulations
At the heart of our approach is a two-sample test that quantifies the quality of the fit at fixed parameter values, and a global test that assesses goodness-of-fit across simulation parameters.
Clarifying the Hubble constant tension with a Bayesian hierarchical model of the local distance ladder
Estimates of the Hubble constant, $H_0$, from the distance ladder and the cosmic microwave background (CMB) differ at the $\sim$3-$\sigma$ level, indicating a potential issue with the standard $\Lambda$CDM cosmology.
Cosmology and Nongalactic Astrophysics
