Customer-obsessed science


Research areas
-
July 29, 2025New cost-to-serve-software metric that accounts for the full software development lifecycle helps determine which software development innovations provide quantifiable value.
Featured news
-
2024Natural language understanding over tabular data is crucial for data discovery tasks such as joinable and unionable table search. State-of-the-art approaches adopt large language models (LLMs) trained over massive text corpora to assess the table semantic relatedness, typically following a pretrain-and-finetune paradigm with labeled tabular data. Recent studies in-corporate auxiliary tasks such as entity
-
2024Chain-of-thought (CoT) prompting is a popular in-context learning (ICL) approach for large language models (LLMs), especially when tackling complex reasoning tasks. Traditional ICL approaches construct prompts using examples that contain questions similar to the input question. However, CoT prompting, which includes crucial intermediate reasoning steps (rationales) within its examples, necessitates selecting
-
RecSys 2024 Workshop on Design, Evaluation and Deployment of Robust Recommender Systems2024In the realm of sequential recommender systems, understanding users’ preferences based on their past actions is paramount. Yet, the susceptibility of these models to input perturbations has limited their practicality. Addressing this, we present an innovative approach to mitigate the impact of missing input items, a challenge that has been overlooked. Our method involves a novel training process that anticipates
-
2024We study the differences arising from merging predictors in the causal and anti-causal directions using the same data. In particular we study the asymmetries that arise in a simple model where we merge the predictors using one binary variable as target and two continuous variables as predictors. We use Causal Maximum Entropy (CMAXENT) as inductive bias to merge the predictors, however, we expect similar
-
2024The ability to construct transferable descriptors for molecular and biological systems has broad applications in drug discovery, molecular dynamics, and protein analysis. Geometric graph neural networks (Geom-GNNs) utilizing all-atom information have revolutionized atomistic simulations by enabling the prediction of interatomic potentials and molecular properties. Despite these advances, the application
Academia
View allWhether you're a faculty member or student, there are number of ways you can engage with Amazon.
View all