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Coffee Break & Poster Session 2
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

Sat Dec 14 03:15 PM -- 04:15 PM (PST) @

Poster Session 2 Paper Titles & Authors:

Towards deep amortized clustering. Juho Lee, Yoonho Lee, Yee Whye Teh

Chirality Nets: Exploiting Structure in Human Pose Regression. Raymond Yeh, Yuan-Ting Hu, Alexander Schwing

Fair Hierarchical Clustering. Sara Ahmadian, Alessandro Epasto, Marina Knittel, Ravi Kumar, Mohammad Mahdian, Philip Pham

Limitations of Deep Learning on Point Clouds. Christian Bueno, Alan G. Hylton

How Powerful Are Randomly Initialized Pointcloud Set Functions? Aditya Sanghi, Pradeep Kumar Jayaraman

On the Possibility of Rewarding Structure Learning Agents: Mutual Information on Linguistic Random Sets. Ignacio Arroyo-Fernández, Mauricio Carrasco-Ruiz, José Anibal Arias-Aguilar

Modelling Convolution as a Finite Set of Operations Through Transformation Semigroup Theory. Andrew Hryniowski, Alexander Wong

HCA-DBSCAN: HyperCube Accelerated Density Based Spatial Clustering for Applications with Noise. Vinayak Mathur, Jinesh Mehta, Sanjay Singh

Finding densest subgraph in probabilistically evolving graphs. Sara Ahmadian, Shahrzad Haddadan

Representation Learning with Multisets. Vasco Portilheiro

PairNets: Novel Fast Shallow Artificial Neural Networks on Partitioned Subspaces. Luna Zhang

Fair Correlation Clustering. Sara Ahmadian, Alessandro Epasto, Ravi Kumar, Mohammad Mahdian

Learning Maximally Predictive Prototypes in Multiple Instance Learning. Mert Yuksekgonul, Ozgur Emre Sivrikaya, Mustafa Gokce Baydogan

Deep Clustering using MMD Variational Autoencoder and Traditional Clustering Algorithms. Jhosimar Arias

Hypergraph Partitioning using Tensor Eigenvalue Decomposition. Deepak Maurya, Balaraman Ravindran, Shankar Narasimhan

Information Geometric Set Embeddings: From Sets to Distributions. Ke Sun, Frank Nielsen

Document Representations using Fine-Grained Topics. Justin Payan, Andrew McCallum

Author Information

Juho Lee (University of Oxford)
Yoonho Lee (Kakao Corporation)
Yee Whye Teh (University of Oxford, DeepMind)

I am a Professor of Statistical Machine Learning at the Department of Statistics, University of Oxford and a Research Scientist at DeepMind. I am also an Alan Turing Institute Fellow and a European Research Council Consolidator Fellow. I obtained my Ph.D. at the University of Toronto (working with Geoffrey Hinton), and did postdoctoral work at the University of California at Berkeley (with Michael Jordan) and National University of Singapore (as Lee Kuan Yew Postdoctoral Fellow). I was a Lecturer then a Reader at the Gatsby Computational Neuroscience Unit, UCL, and a tutorial fellow at University College Oxford, prior to my current appointment. I am interested in the statistical and computational foundations of intelligence, and works on scalable machine learning, probabilistic models, Bayesian nonparametrics and deep learning. I was programme co-chair of ICML 2017 and AISTATS 2010.

Raymond Yeh (University of Illinois at Urbana–Champaign)
Yuan-Ting Hu (University of Illinois Urbana-Champaign)
Alex Schwing (University of Illinois at Urbana-Champaign)
Sara Ahmadian (Google)
Alessandro Epasto (Google)

I am a senior research scientist at Google, New York working in the Google Research Algorithms and Optimization team lead by Vahab Mirrokni. I received a Ph.D in computer science from Sapienza University of Rome, where I was advised by Professor Alessandro Panconesi and supported by the Google Europe Ph.D. Fellowship in Algorithms, 2011. I was also a post-doc at the department of computer science of Brown University in Providence (RI), USA where I was advised by Professor Eli Upfal. My research interests include algorithmic problems in machine learning and data mining, in particular in the areas of clustering, and large scale graphs analysis.

Marina Knittel (University of Maryland, College Park)
Ravi Kumar (Google)
Mohammad Mahdian (Google Research)
Christian Bueno (University of California, Santa Barbara)
Aditya Sanghi (Autodesk)
Pradeep Kumar Jayaraman (Autodesk)
Ignacio Arroyo-Fernández (Universidad Tecnológica de la Mixteca)
Andrew Hryniowski (DarwinAI \ University of Waterloo)
Vinayak Mathur (EBSCO)

I like solving impactful natural language understanding problems. As a ML Data Scientist at EBSCO, I am working on extracting knowledge from millions of academic publishings and making scientific knowledge more accessible to the world. I gained my chops working with Prof. Andrew McCallum and the IESL lab at the University of Massachusetts Amherst – resolving polysemy and inducing lexical frames. Before moving to UMass I worked on my bachelor dissertation at the Machine and Language Learning Lab at the Indian Institute of Science under the guidance of Dr Partha P Talukdar. When not praying to the optimization Gods, you can find me sailing on the Charles river trying to practise my broken Italian.

Sanjay Singh (Manipal Institute of Technology)
Shahrzad Haddadan (Sapienza University, Rome, Italy)
Vasco Portilheiro (Stanford University)
Luna Zhang (BigBear, Inc.)
Mert Yuksekgonul (Bogazici University)
Jhosimar Arias Figueroa (Independent)
Deepak Maurya (Indian Institute of Technology Madras)

I am currently an MS scholar at CSE dept, IIT Madras. My research is focused on spectral hypergraph theory and system identification.

Balaraman Ravindran (Indian Institute of Technology Madras)
Frank NIELSEN (Ecole Polytechniqe)
Philip Pham (Google)

SWE @Waymo

Justin Payan (UMass Amherst)
Andrew McCallum (UMass Amherst)
Jinesh Mehta (Manipal Institute of Technology K)

I am a CS grad who likes to work on solving daily life problems using ML and AI

Ke SUN (Data61)

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