How to Watch the Orionids Meteor Shower
This long-lasting meteor shower peaks in late October and is known for producing bright “fireball” shooting stars. Here’s what to know about Orionids and other major showers that will appear in 2025.
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How to Build an Advanced Agentic Retrieval-Augmented Generation (RAG) System with Dynamic Strategy and Smart Retrieval?
In this tutorial, we walk through the implementation of an Agentic Retrieval-Augmented Generation (RAG) system. We design it so that the agent does more than just retrieve documents; it actively decides when retrieval is needed, selects the best retrieval strategy,…
Max-Sliced Wasserstein Distance and its use for GANs
arXiv:1904.05877v2 Announce Type: replace-cross Abstract: Generative adversarial nets (GANs) and variational auto-encoders have significantly improved our distribution modeling capabilities, showing promise for dataset augmentation, image-to-image translation and feature learning. However, to model high-dimensional distributions, sequential training and stacked architectures are…
BaB-prob: Branch and Bound with Preactivation Splitting for Probabilistic Verification of Neural Networks
arXiv:2509.25647v1 Announce Type: cross Abstract: Branch-and-bound with preactivation splitting has been shown highly effective for deterministic verification of neural networks. In this paper, we extend this framework to the probabilistic setting. We propose BaB-prob that iteratively divides the original problem…
MIBoost: A Gradient Boosting Algorithm for Variable Selection After Multiple Imputation
arXiv:2507.21807v3 Announce Type: replace Abstract: Statistical learning methods for automated variable selection, such as LASSO, elastic nets, or gradient boosting, have become increasingly popular tools for building powerful prediction models. Yet, in practice, analyses are often complicated by missing data.…
Transformers through the lens of support-preserving maps between measures
arXiv:2509.25611v1 Announce Type: cross Abstract: Transformers are deep architectures that define “in-context maps” which enable predicting new tokens based on a given set of tokens (such as a prompt in NLP applications or a set of patches for a vision…
TADA: Improved Diffusion Sampling with Training-free Augmented Dynamics
arXiv:2506.21757v2 Announce Type: replace Abstract: Diffusion models have demonstrated exceptional capabilities in generating high-fidelity images but typically suffer from inefficient sampling. Many solver designs and noise scheduling strategies have been proposed to dramatically improve sampling speeds. In this paper, we…
Optimal Nuisance Function Tuning for Estimating a Doubly Robust Functional under Proportional Asymptotics
arXiv:2509.25536v1 Announce Type: cross Abstract: In this paper, we explore the asymptotically optimal tuning parameter choice in ridge regression for estimating nuisance functions of a statistical functional that has recently gained prominence in conditional independence testing and causal inference. Given…
Fast Likelihood-Free Parameter Estimation for L’evy Processes
arXiv:2505.01639v2 Announce Type: replace Abstract: L’evy processes are widely used in financial modeling due to their ability to capture discontinuities and heavy tails, which are common in high-frequency asset return data. However, parameter estimation remains a challenge when associated likelihoods…