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Sets and Partitions
Nicholas Monath · Manzil Zaheer · Andrew McCallum · Ari Kobren · Junier Oliva · Barnabas Poczos · Ruslan Salakhutdinov

Sat Dec 08:00 AM -- 06:00 PM PST @ West 215 + 216
Event URL: https://www.sets.parts »

Classic problems for which the input and/or output is set-valued are ubiquitous in machine learning. For example, multi-instance learning, estimating population statistics, and point cloud classification are all problem domains in which the input is set-valued. In multi-label classification the output is a set of labels, and in clustering, the output is a partition. New tasks that take sets as input are also rapidly emerging in a variety of application areas including: high energy physics, cosmology, crystallography, and art. As a natural means of succinctly capturing large collections of items, techniques for learning representations of sets and partitions have significant potential to enhance scalability, capture complex dependencies, and improve interpretability. The importance and potential of improved set processing has led to recent work on permutation invariant and equivariant representations (Ravanbakhsh et al, 2016; Zaheer et al, 2017; Ilse et al, 2018; Hartford et al, 2018; Lee et al, 2019, Cotter et al, 2019, Bloom-Reddy & Teh, 2019, and more) and continuous representations of set-based outputs and partitions (Tai and Lin, 2012; Belanger & McCallum, 2015; Wiseman et al, 2016; Caron et al, 2018; Zhang et al, 2019; Vikram et al 2019).

The goal of this workshop is to explore:
- Permutation invariant and equivariant representations; empirical performance, limitations, implications, inductive biases of proposed representations of sets and partitions, as well as rich models of interaction among set elements;
- Inference methods for predicting sets or clusterings; approaches based on gradient-descent, continuous representations, amenable to end-to-end optimization with other models;
- New applications of set and partition-based models.

The First Workshop on Sets and Partitions, to be held as a part of the NeurIPS 2019 conference, focuses on models for tasks with set-based inputs/outputs as well as models of partitions and novel clustering methodology. The workshop welcomes both methodological and theoretical contributions, and also new applications. Connections to related problems in optimization, algorithms, theory as well as investigations of learning approaches to set/partition problems are also highly relevant to the workshop. We invite both paper submissions and submissions of open problems. We hope that the workshops will inspire further progress in this important field.

Organizing Committee:
Andrew McCallum, UMass Amherst
Ruslan Salakhutdinov, CMU
Barnabas Poczos, CMU
Junier Oliva, UNC Chapel Hill
Manzil Zaheer, Google Research
Ari Kobren, UMass Amherst
Nicholas Monath, UMass Amherst
with senior advisory support from Alex Smola.

Invited Speakers:
Siamak Ravanbakhsh
Abhishek Khetan
Eunsu Kang
Amr Ahmed
Stefanie Jegelka

08:45 AM Opening Remarks (Talk)|| Manzil Zaheer, Nicholas Monath, Ari Kobren, Junier Oliva, Barnabas Poczos, Ruslan Salakhutdinov, Andrew McCallum
09:00 AM Invited Talk - Stefanie Jegelka - Set Representations in Graph Neural Networks and Reasoning (Talk)|| Stefanie Jegelka
09:45 AM Coffee Break & Poster Session 1 (Poster Session)||
Yan Zhang, Jonathon Hare, Adam Prugel-Bennett, Alex Leung, Patrick Flaherty, Pitchaya Wiratchotisatian, Alessandro Epasto, Silvio Lattanzi, Sergei Vassilvitskii, Morteza Zadimoghaddam, Theja Tulabandhula, Fabian Fuchs, Adam Kosiorek, Ingmar Posner, William Hang, Anna Goldie, Sujith Ravi, Azalia Mirhoseini, Yuwen Xiong, Mengye Ren, Renjie Liao, Raquel Urtasun, Haici Zhang, Michele Borassi, Shengda Luo, Andy Trapp, Geoffroy Dubourg-Felonneau, Yasmeen Kussad, Chris Bender, Manzil Zaheer, Junier Oliva, Michał Stypułkowski, Maciej Zieba, Austin Dill, Chun-Liang Li, Songwei Ge, Eunsu Kang, Oiwi Parker Jones, Kelvin Ka Wing Wong, Josh Payne, Yang Li, Azade Nazi, Erkut Erdem, Aykut Erdem, Kevin O'Connor, Juan J Garcia, Maciej Zamorski, Jan Chorowski, Deeksha Sinha, Harry Clifford, John W Cassidy
10:30 AM Invited Talk - Siamak Ravanbakhsh - Equivariant Multilayer Perceptrons (Talk)|| Siamak Ravanbakhsh
11:15 AM Contributed Talk - Towards deep amortized clustering (Talk)|| Juho Lee, Yoonho Lee, Yee Whye Teh
11:30 AM Contributed Talk - Fair Hierarchical Clustering (Talk)|| Sara Ahmadian, Alessandro Epasto, Marina Knittel, Ravi Kumar, Mohammad Mahdian, Philip Pham
11:45 AM Invited Talk - Abhishek Khetan - Molecular geometries as point clouds: Learning physico-chemical properties using DeepSets (Talk)|| Abhishek Khetan
12:30 PM Lunch Break (on your own) (Break)||
02:00 PM Contributed Talk - Limitations of Deep Learning on Point Clouds (Talk)|| Christian Bueno
02:15 PM Contributed Talk - Chirality Nets: Exploiting Structure in Human Pose Regression (Talk)|| Raymond Yeh, Yuan-Ting Hu, Alex Schwing
02:30 PM Invited Talk - Eunsu Kang - Sets for Arts (Talk)|| Eunsu Kang
03:15 PM Coffee Break & Poster Session 2 (Poster Session)||
Juho Lee, Yoonho Lee, Yee Whye Teh, Raymond Yeh, Yuan-Ting Hu, Alex Schwing, Sara Ahmadian, Alessandro Epasto, Marina Knittel, Ravi Kumar, Mohammad Mahdian, Christian Bueno, Aditya Sanghi, Pradeep Kumar Jayaraman, Ignacio Arroyo-Fernández, Andrew Hryniowski, Vinayak Mathur, Sanjay Singh, Shahrzad Haddadan, Vasco Portilheiro, Luna Zhang, Mert Yuksekgonul, Jhosimar Arias Figueroa, Deepak Maurya, Balaraman Ravindran, Frank NIELSEN, Philip Pham, Justin Payan, Andrew McCallum, Jinesh Mehta, Ke Sun
04:15 PM Invited Talk - Alexander J. Smola - Sets and symmetries (Talk)|| Alex Smola
05:00 PM Panel Discussion
05:40 PM Closing Remarks (Talk)||

Author Information

Nicholas Monath (University of Massachusetts Amherst)
Manzil Zaheer (Google)
Andrew McCallum (UMass Amherst)
Ari Kobren (UMass Amherst)
Junier Oliva (UNC - Chapel Hill)
Barnabas Poczos (Carnegie Mellon University)
Ruslan Salakhutdinov (Carnegie Mellon University)

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