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This workshop will address algorithms, systems, and real-world problem domains related to large-scale machine learning (“Big Learning”). With active research spanning machine learning, databases, parallel and distributed systems, parallel architectures, programming languages and abstractions, and even the sciences, Big Learning has attracted intense interest. This workshop will bring together experts across these diverse communities to discuss recent progress, share tools and software, identify pressing new challenges, and to exchange new ideas. Topics of interest include (but are not limited to):
- Big Data: Methods for managing large, unstructured, and/or streaming data; cleaning, visualization, interactive platforms for data understanding and interpretation; sketching and summarization techniques; sources of large datasets.
- Models & Algorithms: Machine learning algorithms for parallel, distributed, GPGPUs, or other novel architectures; theoretical analysis; distributed online algorithms; implementation and experimental evaluation; methods for distributed fault tolerance.
- Applications of Big Learning: Practical application studies and challenges of real-world system building; insights on end-users, common data characteristics (stream or batch); trade-offs between labeling strategies (e.g., curated or crowd-sourced).
- Tools, Software & Systems: Languages and libraries for large-scale parallel or distributed learning which leverage cloud computing, scalable storage (e.g. RDBMs, NoSQL, graph databases), and/or specialized hardware.
Author Information
Sameer Singh (University of California, Irvine)
John Duchi (UC Berkeley)
Yucheng Low (Apple)
Joseph E Gonzalez (Carnegie Mellon University)
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2021 : PYLON: A PyTorch Framework for Learning with Constraints »
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2020 Tutorial: (Track2) Explaining Machine Learning Predictions: State-of-the-art, Challenges, and Opportunities Q&A »
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2019 Workshop: KR2ML - Knowledge Representation and Reasoning Meets Machine Learning »
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2019 Demonstration: AllenNLP Interpret: Explaining Predictions of NLP Models »
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2015 Workshop: Machine Learning Systems »
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2014 Workshop: 3rd NIPS Workshop on Probabilistic Programming »
Daniel Roy · Josh Tenenbaum · Thomas Dietterich · Stuart J Russell · YI WU · Ulrik R Beierholm · Alp Kucukelbir · Zenna Tavares · Yura Perov · Daniel Lee · Brian Ruttenberg · Sameer Singh · Michael Hughes · Marco Gaboardi · Alexey Radul · Vikash Mansinghka · Frank Wood · Sebastian Riedel · Prakash Panangaden -
2014 Workshop: 4th Workshop on Automated Knowledge Base Construction (AKBC) »
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2014 Demonstration: A Visual and Interactive IDE for Probabilistic Programming »
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2013 Workshop: Big Learning : Advances in Algorithms and Data Management »
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2013 Poster: Information-theoretic lower bounds for distributed statistical estimation with communication constraints »
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2013 Oral: Information-theoretic lower bounds for distributed statistical estimation with communication constraints »
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2013 Poster: Local Privacy and Minimax Bounds: Sharp Rates for Probability Estimation »
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2013 Poster: Estimation, Optimization, and Parallelism when Data is Sparse »
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2012 Poster: Privacy Aware Learning »
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2012 Poster: Communication-Efficient Algorithms for Statistical Optimization »
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2012 Demonstration: GraphLab: A Framework For Machine Learning in the Cloud »
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2012 Oral: Privacy Aware Learning »
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2012 Poster: Finite Sample Convergence Rates of Zero-Order Stochastic Optimization Methods »
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2011 Workshop: Big Learning: Algorithms, Systems, and Tools for Learning at Scale »
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2011 Poster: Distributed Delayed Stochastic Optimization »
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2010 Workshop: Learning on Cores, Clusters, and Clouds »
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2010 Spotlight: Distributed Dual Averaging In Networks »
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2010 Poster: Distributed Dual Averaging In Networks »
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2009 Poster: Efficient Learning using Forward-Backward Splitting »
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2009 Poster: FACTORIE: Probabilistic Programming via Imperatively Defined Factor Graphs »
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2009 Oral: Efficient Learning using Forward-Backward Splitting »
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2009 Poster: Training Factor Graphs with Reinforcement Learning for Efficient MAP Inference »
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2006 Poster: Using Combinatorial Optimization within Max-Product Belief Propagation »
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