Keane, "Amortized Inference for Correlated Discrete Choice Models via Equivariant Neural Networks," NBER Working Paper 35037 (2026), ...

For years, co-founder and chief executive officer Jensen Huang and other higher-ups at Nvidia have been banging on the ...

Alex Lew is an Assistant Professor of Computer Science at Yale. His research aims to automate and scale up principled probabilistic reasoning, drawing on techniques from programming languages, machine ...

In the era of A.I. agents, many Silicon Valley programmers are now barely programming. Instead, what they’re doing is deeply, deeply weird. Credit...Illustration by Pablo Delcan and Danielle Del Plato ...

ABSTRACT: This paper introduces a methodology that enables the relational learning framework to incorporate quantitative data derived from experimental studies in microbial ecology. The focus of using ...

This paper presents a valuable software package, named "Virtual Brain Inference" (VBI), that enables faster and more efficient inference of parameters in dynamical system models of whole-brain ...

Not revised: This Reviewed Preprint includes the authors’ original preprint (without revision), an eLife assessment, public reviews, and a provisional response from the authors. This work provides a ...

We study machine learning formulations of inductive program synthesis; given input-output examples, we try to synthesize source code that maps inputs to corresponding outputs. Our aims are to develop ...

Probabilistic Programming is a way of defining probabilistic models by overloading the operations in standard programming language to have probabilistic meanings. The goal is to specify probabilistic ...

Power grid icing is a severe natural hazard that threatens the safe and stable operation of power systems. With the expansion of ultra-high voltage (UHV) power grids, systematic assessment of icing ...

Probabilistic programming languages (PPLs) have emerged as a transformative tool for expressing complex statistical models and automating inference procedures. By integrating probability theory into ...

Abstract: Post-training quantization (PTQ) is an effective solution for deploying deep neural networks on edge devices with limited resources. PTQ is especially attractive because it does not require ...