Customer-obsessed science
Research areas
-
July 30, 20268 min readInstead of compromising among parameter updates dictated by different training objectives, ControlG allocates computational capacity to objectives sequentially and dynamically. -
-
July 9, 202610 min read
-
Featured news
The Build on Trainium program is a $110 million credit initiative supporting AI research at 30 universities including Stanford, UC Berkeley, UIUC, UCLA, CMU, and MIT, with a focus on Responsible AI.
The Antibody Developability Benchmark is powered by one of the most diverse antibody datasets in public literature, enabling transparent performance evaluation for AI-guided antibody design.
Challenge pushes teams to demonstrate measurable gains in secure-coding performance while building AI agents that advance real-world utility and reliability at scale.
Initiative will fund over 100 doctoral students researching machine learning, computer vision, and natural-language processing at nine universities.
-
ECML PKDD 20232023Timestamped graphs find applications in critical business problems like user classification, fraud detection, etc. This is due to the inherent nature of the data generation process, in which relationships between nodes are observed at defined timestamps. Deployment-focused GNN models should be trained on point-in-time information about node features and neighborhood, similar to the data ingestion process
-
ECML PKDD 20232023Causal Impact (CI) measurement is broadly used across the industry to inform both short- and long-term investment decisions of various types. In this paper, we apply the double machine learning (DML) methodology to estimate average and conditional average treatment effects across 100s of customer action types for e-commerce and digital businesses and 100s of millions of customers that can be used in decisions
-
ACL Findings 20232023There has been great progress in unifying various table-to-text tasks using a single encoder-decoder model trained via multi-task learning (Xie et al., 2022). However, existing methods typically encode task information with a simple dataset name as a prefix to the encoder. This not only limits the effectiveness of multitask learning, but also hinders the model’s ability to generalize to new domains or tasks
-
SIGIR 2023 Workshop on eCommerce2023Learning-to-rank models are mostly evaluated based on how good it is able to estimate the user behaviour. Output metrics like NDCG become the obvious choice for the purpose. A model is considered to have a good performance if it is able to predict the correct ranked ordering, else it is considered to be of poor quality. However the performance of a model is not only dependent on the prediction power of
-
ACM IMX 20232023Virtual Product Placement (VPP) is an advertising technique that digitally places branded objects into movie or TV show scenes. Despite being a billion-dollar industry, current ad rendering techniques are time-consuming, costly, and executed manually with the help of visual effects (VFX) artists. In this paper, we present a fully automated and generalized framework for placing 2D ads in any linear TV cooking
Collaborations
View allWhether you're a faculty member or student, there are number of ways you can engage with Amazon.
View all
Gretchen Ertl
The program offers unrestricted funds and other resources to support research at academic institutions and non-profit organizations in areas that align with our mission.
A global university competition to drive secure innovation in generative AI technology, which focuses on responsible AI and large language model coding security.
Credit: Wolfram Scheible
We partner with particular academic organizations across the world for deep and sustained collaborations in multiple research areas of mutual interest.
Courtesy of Pai-Ling Yin
We hire world-class academics to work on large-scale technical challenges, while they continue to teach and conduct research at their universities.
