research
∙
09/07/2023
Uncovering Drift in Textual Data: An Unsupervised Method for Detecting and Mitigating Drift in Machine Learning Models
Drift in machine learning refers to the phenomenon where the statistical...
share
research
∙
05/23/2022
Improving language models fine-tuning with representation consistency targets
Fine-tuning contextualized representations learned by pre-trained langua...
research
∙
03/27/2022
Pyramid-BERT: Reducing Complexity via Successive Core-set based Token Selection
Transformer-based language models such as BERT have achieved the state-o...
research
∙
11/26/2021
Amazon SageMaker Model Monitor: A System for Real-Time Insights into Deployed Machine Learning Models
With the increasing adoption of machine learning (ML) models and systems...
research
∙
07/23/2021
Improving Early Sepsis Prediction with Multi Modal Learning
Sepsis is a life-threatening disease with high morbidity, mortality and ...
research
∙
12/15/2020
Amazon SageMaker Autopilot: a white box AutoML solution at scale
AutoML systems provide a black-box solution to machine learning problems...
research
∙
12/11/2020
TabTransformer: Tabular Data Modeling Using Contextual Embeddings
We propose TabTransformer, a novel deep tabular data modeling architectu...
research
∙
11/11/2020
GANMEX: One-vs-One Attributions using GAN-based Model Explainability
Attribution methods have been shown as promising approaches for identify...
research
∙
07/27/2020
Practical and sample efficient zero-shot HPO
Zero-shot hyperparameter optimization (HPO) is a simple yet effective us...
research
∙
06/21/2020
An Empirical Process Approach to the Union Bound: Practical Algorithms for Combinatorial and Linear Bandits
This paper proposes near-optimal algorithms for the pure-exploration lin...
research
∙
05/22/2020
PruneNet: Channel Pruning via Global Importance
Channel pruning is one of the predominant approaches for accelerating de...
research
∙
05/09/2020
schuBERT: Optimizing Elements of BERT
Transformers <cit.> have gradually become a key component for many state...
research
∙
04/03/2020
Relative Error Streaming Quantiles
Approximating ranks, quantiles, and distributions over streaming data is...
research
∙
06/29/2019
Streaming Quantiles Algorithms with Small Space and Update Time
Approximating quantiles and distributions over streaming data has been s...
research
∙
06/22/2019
Asymmetric Random Projections
Random projections (RP) are a popular tool for reducing dimensionality w...
research
∙
06/11/2019
Discrepancy, Coresets, and Sketches in Machine Learning
This paper defines the notion of class discrepancy for families of funct...
research
∙
05/20/2019
DARC: Differentiable ARchitecture Compression
In many learning situations, resources at inference time are significant...
research
∙
07/05/2016
One-Shot Session Recommendation Systems with Combinatorial Items
In recent years, content recommendation systems in large websites (or co...
research
∙
06/29/2016
How Many Folders Do You Really Need?
Email classification is still a mostly manual task. Consequently, most W...
research
∙
12/21/2013
Volumetric Spanners: an Efficient Exploration Basis for Learning
Numerous machine learning problems require an exploration basis - a mech...
research
∙
11/19/2013