269 citations · 410 across the 21 of their papers we have counts for
9 papers · 1 filter
Amazon SageMaker Autopilot: a white box AutoML solution at scale
Piali Das, Valerio Perrone, Nikita Ivkin +22
AutoML systems provide a black-box solution to machine learning problems by selecting the right way of processing features, choosing an algorithm and tuning the hyperparameters of…
Amazon SageMaker Automatic Model Tuning: Scalable Gradient-Free Optimization
Valerio Perrone, Huibin Shen, Aida Zolic +12
Tuning complex machine learning systems is challenging. Machine learning typically requires to set hyperparameters, be it regularization, architecture, or optimization parameters,…
Non-Stationary Latent Bandits
Joey Hong, Branislav Kveton, Manzil Zaheer +4
Users of recommender systems often behave in a non-stationary fashion, due to their evolving preferences and tastes over time. In this work, we propose a practical approach for fas…
Scalable Hierarchical Agglomerative Clustering
Nicholas Monath, Avinava Dubey, Guru Guruganesh +9
The applicability of agglomerative clustering, for inferring both hierarchical and flat clustering, is limited by its scalability. Existing scalable hierarchical clustering methods…
Unsupervised Abstractive Dialogue Summarization for Tete-a-Tetes
Xinyuan Zhang, Ruiyi Zhang, Manzil Zaheer +1
High-quality dialogue-summary paired data is expensive to produce and domain-sensitive, making abstractive dialogue summarization a challenging task. In this work, we propose the f…
Big Bird: Transformers for Longer Sequences
Manzil Zaheer, Guru Guruganesh, Avinava Dubey +8
Transformers-based models, such as BERT, have been one of the most successful deep learning models for NLP. Unfortunately, one of their core limitations is the quadratic dependency…
Latent Bandits Revisited
Joey Hong, Branislav Kveton, Manzil Zaheer +3
A latent bandit problem is one in which the learning agent knows the arm reward distributions conditioned on an unknown discrete latent state. The primary goal of the agent is to i…
Non-Stationary Off-Policy Optimization
Joey Hong, Branislav Kveton, Manzil Zaheer +2
Off-policy learning is a framework for evaluating and optimizing policies without deploying them, from data collected by another policy. Real-world environments are typically non-s…
Anchor & Transform: Learning Sparse Embeddings for Large Vocabularies
Paul Pu Liang, Manzil Zaheer, Yuan Wang +1
Learning continuous representations of discrete objects such as text, users, movies, and URLs lies at the heart of many applications including language and user modeling. When usin…