961 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- The University of Texas at AustinUS8 papers
- Cornell UniversityUS6 papers
- University of BristolGB6 papers
- Princeton UniversityUS5 papers
- University of California, BerkeleyUS5 papers
- Google (United States)US4 papers
- University of Southern CaliforniaUS4 papers
- Columbia UniversityUS3 papers
- Massachusetts Institute of TechnologyUS3 papers
- Pennsylvania State UniversityUS3 papers
- Purdue University West LafayetteUS3 papers
- Stanford UniversityUS3 papers
7 papers · 1 filter
Relax: Composable Abstractions for End-to-End Dynamic Machine Learning
Ruihang Lai, Junru Shao, Siyuan Feng +16
Dynamic shape computations have become critical in modern machine learning workloads, especially in emerging large language models. The success of these models has driven the deman…
Counterfactual Evaluation of Slate Recommendations with Sequential Reward Interactions
James McInerney, Brian Brost, Praveen Chandar +2
Users of music streaming, video streaming, news recommendation, and e-commerce services often engage with content in a sequential manner. Providing and evaluating good sequences of…
More Efficient Off-Policy Evaluation through Regularized Targeted Learning
Aurélien F. Bibaut, Ivana Malenica, Nikos Vlassis +1
We study the problem of off-policy evaluation (OPE) in Reinforcement Learning (RL), where the aim is to estimate the performance of a new policy given historical data that may have…
Correlated Variational Auto-Encoders
Da Tang, Dawen Liang, Tony Jebara +1
Variational Auto-Encoders (VAEs) are capable of learning latent representations for high dimensional data. However, due to the i.i.d. assumption, VAEs only optimize the singleton v…
Explaining Deep Learning Models - A Bayesian Non-parametric Approach
Wenbo Guo, Sui Huang, Yunzhe Tao +2
Understanding and interpreting how machine learning (ML) models make decisions have been a big challenge. While recent research has proposed various technical approaches to provide…
Session-based Recommendations with Recurrent Neural Networks
Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas +1
We apply recurrent neural networks (RNN) on a new domain, namely recommender systems. Real-life recommender systems often face the problem of having to base recommendations only on…
MLlib: Machine Learning in Apache Spark
Xiangrui Meng, Joseph Bradley, Burak Yavuz +13
Apache Spark is a popular open-source platform for large-scale data processing that is well-suited for iterative machine learning tasks. In this paper we present MLlib, Spark's ope…