Customer-obsessed science
Research areas
-
November 20, 20254 min readA new evaluation pipeline called FiSCo uncovers hidden biases and offers an assessment framework that evolves alongside language models.
-
October 2, 20253 min read
-
-
-
September 2, 20253 min read
Featured news
-
KDD 2023 Workshop on End-End Customer Journey Optimization2023Customer service is often the most time-consuming aspect for e-commerce websites, with each contact typically taking 10-15 minutes. Effectively routing customers to appropriate agents without transfers is therefore crucial for e-commerce success. To this end, we have developed a machine learning framework that predicts the complexity of customer contacts and routes them to appropriate agents accordingly
-
ACL Findings 20232023We investigate semi-structured document classification in a zero-shot setting. Classification of semi-structured documents is more challenging than that of standard unstructured documents, as positional, layout, and style information play a vital role in interpreting such documents. The standard classification setting where categories are fixed during both training and testing falls short in dynamic environments
-
KDD 2023 Workshop on Deep Learning on Graphs2023Encoder-decoder deep neural networks have been increasingly studied for multi-horizon time series forecasting, especially in real-world applications. However, to forecast accurately, these sophisticated neural forecasters typically rely on a large number of time series examples with substantial history. A rapidly growing topic of interest is forecasting time series which lack sufficient historical data—often
-
ECOOP 20232023Reliable storage systems must be crash consistent – guaranteed to recover to a consistent state after a crash. Crash consistency is non-trivial as it requires maintaining complex invariants about persistent data structures in the presence of caching, reordering, and system failures. Current programming models offer little support for implementing crash consistency, forcing storage system developers to roll
-
KDD 2023 International Workshop on Mining and Learning from Time Series (MileTS)2023Ensembles have long been a cornerstone in improving performance by integrating black-box base learners. The primary approach, “stacked generalization” is inherently static, lacking adaptability to changes in input data post-training. This limitation is more pronounced in time series forecasting, where these methods struggle to manage temporal correlations or non-stationarity. Given base learners may perform
Collaborations
View allWhether you're a faculty member or student, there are number of ways you can engage with Amazon.
View all