Customer-obsessed science


-
March 28, 2023“Branch-and-bound” method rules out nonoptimal solutions to mixed-integer nonlinear-programming problems.
-
March 24, 2023By leveraging neural vocoding, Amazon Chime SDK’s new deep-redundancy (DRED) technology can reconstruct long sequences of lost packets with little bandwidth overhead.
-
March 21, 2023Tailoring neighborhood sizes and sampling probability to nodes’ degree of connectivity improves the utility of graph-neural-network embeddings by as much as 230%.
-
-
April 30 - May 4, 2023
-
May 1 - 5, 2023
-
May 3 - 5, 2023
-
-
March 31, 2023Attendees explored new avenues of research in areas including robotics and conversational AI via roundtables moderated by researchers from Amazon.
-
March 27, 2023Initiative will advance artificial intelligence and machine learning research within speech, language, and multimodal-AI domains.
-
March 23, 2023The center will support UIUC researchers in their development of novel approaches to conversational AI systems.
-
March 23, 2023How Amazon is shaping a set of initiatives to enable academia-based talent to harmonize their passions, life stations, and career ambitions.
-
2023Hyperparameter optimization (HPO) and neural architecture search (NAS) are methods of choice to obtain the best-in-class machine learning models, but in practice they can be costly to run. When models are trained on large datasets, tuning them with HPO or NAS rapidly becomes prohibitively expensive for practitioners, even when efficient multi-fidelity methods are employed. We propose an approach to tackle
-
2023We develop a meta-learning framework for simple regret minimization in bandits. In this framework, a learning agent interacts with a sequence of bandit tasks, which are sampled i.i.d. from an unknown prior distribution, and learns its meta-parameters to perform better on future tasks. We propose the first Bayesian and frequentist meta-learning algorithms for this setting. The Bayesian algorithm has access
-
AISTATS 20232023A contextual bandit is a popular framework for online learning to act under uncertainty. In practice, the number of actions is huge and their expected rewards are correlated. In this work, we introduce a general framework for capturing such correlations through a mixed-effect model where actions are related through multiple shared effect parameters. To explore efficiently using this structure, we propose
-
2023Self-supervised representation learning approaches have grown in popularity due to the ability to train models on large amounts of unlabeled data and have demonstrated success in diverse fields such as natural language processing, computer vision, and speech. Previous self-supervised work in the speech domain has disentangled multiple attributes of speech such as linguistic content, speaker identity, and
-
GrAPL 20232023GNN models on heterogeneous graphs have achieved state-of-the-art (SOTA) performance in various graph tasks such as link prediction and node classification. Despite their success in providing SOTA results, popular GNN libraries, such as PyG and DGL, fail to provide fast and efficient solutions for heterogeneous GNN models. One common key bottlenecks of models like RGAT, RGCN, and HGT is relation-specific
-
March 15, 2023The submission period opens March 15 and closes on April 26.
-
March 08, 2023This year’s cohort is researching, among other topics, online changepoint detection algorithms and automated reasoning.
-
March 01, 2023New fellows include PhD candidates in operations research and computer science.
Working at Amazon
View allMeet the people driving the innovation essential to being the world’s most customer-centric company.
View all
-
March 14, 2023Ren Zhang and her team tackle the interesting science challenges behind surfacing the most relevant offerings.
-
February 28, 2023How the former astrobiology professor is charting new territory as a scientist for Amazon Flex.
-
February 08, 2023How her background helps her manage a team charged with assisting internal partners to answer questions about the economic impacts of their decisions.