arXiv:2501.06376v2 Announce Type: replace-cross Abstract: In Reward Learning (ReL), we are given feedback on an unknown target reward, and the goal is to use this information to recover it in order to carry out some downstream application, e.g., planning. When…
arXiv:2509.11381v1 Announce Type: cross Abstract: Recursive decision trees have emerged as a leading methodology for heterogeneous causal treatment effect estimation and inference in experimental and observational settings. These procedures are fitted using the celebrated CART (Classification And Regression Tree) algorithm…
arXiv:2504.03228v4 Announce Type: replace-cross Abstract: A triangular structural panel data model with additive separable individual-specific effects is used to model the causal effect of a covariate on an outcome variable when there are unobservable confounders with some of them time-invariant.…
arXiv:2509.11397v1 Announce Type: cross Abstract: Recovering signals from low-order moments is a fundamental yet notoriously difficult task in inverse problems. This recovery process often reduces to solving ill-conditioned systems of polynomial equations. In this work, we propose a new framework…
arXiv:2508.10447v2 Announce Type: replace-cross Abstract: We present BKP, a user-friendly and extensible R package that implements the Beta Kernel Process (BKP) — a fully nonparametric and computationally efficient framework for modeling spatially varying binomial probabilities. The BKP model combines localized…
arXiv:2509.11426v1 Announce Type: cross Abstract: This paper develops a general approach to characterize the long-time trajectory behavior of nonconvex gradient descent in generalized single-index models in the large aspect ratio regime. In this regime, we show that for each iteration…
arXiv:2509.11486v1 Announce Type: cross Abstract: Composite optimization problems involve minimizing the composition of a smooth map with a convex function. Such objectives arise in numerous data science and signal processing applications, including phase retrieval, blind deconvolution, and collaborative filtering. The…
In this tutorial, we take a hands-on approach to building an advanced convolutional neural network for DNA sequence classification. We focus on simulating real biological tasks, such as promoter prediction, splice site detection, and regulatory element identification. By combining one-hot…
OpenAI has just released GPT-5-Codex, a version of GPT-5 further optimized for “agentic coding” tasks within the Codex ecosystem. The goal: improve reliability, speed, and autonomous behavior so that Codex acts more like a teammate, not just a prompt-executor. Codex…
