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Author Information
Rishav Chourasia (National University of Singapore)
Yichong Xu (Carnegie Mellon University)
Corinna Cortes (Google Research)
Chien-Yi Chang (Stanford University)
Yoshihiro Nagano (The University of Tokyo)
So Yeon Min (MIT)
Benedikt Boecking (Carnegie Mellon University)
I'm a PhD student in Robotics at Carnegie Mellon University, where I'm a member of the Auton Lab advised by Artur Dubrawski. I am interested in the technical and theoretical aspects of how we engage domain experts in building and training Machine Learning models. In my current research projects I develop methods for data exploration (semi-supervised clustering) and label acquisition (active learning, interactive learning). In the past, I have also worked on algorithms, tools, and data analysis to help fight sex trafficking using deep web and dark web data.
Phi Vu Tran (Flyreel)
Machine Learning Scientist
Kamyar Ghasemipour (University of Toronto, Vector Institute)
Qianggang Ding (Peng Cheng Laboratory)
Shouvik Mani (C3.ai)
Vikram Voleti (Mila, University of Montreal)
I am a PhD candidate at Mila, University of Montreal, and a Research Intern at Meta AI. I work on generative models of images, 3D and video. My recent work is on score-based denoising diffusion model for video prediction, generation and interpolation.
Rasool Fakoor (Amazon)
Miao Xu (RIKEN AIP)
Kenneth Marino (Carnegie Mellon University)
Lisa Lee (Carnegie Mellon University)
Volker Tresp (Siemens AG)
Jean-Francois Kagy (Google)
Marvin Zhang (UC Berkeley)
Barnabas Poczos (Carnegie Mellon University)
Dinesh Khandelwal (Indian Institute of Technology Delhi)
am a PhD student in the Department of Computer Science and Engineering at IIT Delhi. My supervisors are Dr. Parag Singla and Dr. Chetan Arora. My broad interests lie in the area of machine learning. Specifically, I have worked in the applications of Deep Learning and Graphical Models to Computer Vision problems. My current work focuses on how to incorporate test time evidence to improve predictions of Deep Networks. In past, I worked on designing efficient algorithms for MAP inference and scaling up the parameter learning in Graphical Models. During my master's at IISc Bangalore, I have worked with Prof. Chiranjib Bhattacharyya
Adrien Bardes (Ecole Normale Superieure Paris-Saclay)
Evan Shelhamer (Adobe)
Jiacheng Zhu (Carnegie Mellon University)
Ziming Li (University of Amsterdam)
Xiaoyan Li (University of Ottawa)
Dmitrii Krasheninnikov (University of Amsterdam)
Ruohan Wang (Imperial College London)
Mayoore Jaiswal (IBM)
Emad Barsoum (Microsoft)
Emad Barsoum is an Architect at Microsoft AI Platform team. He leads the deep learning framework effort in Microsoft and help driving Microsoft strategy in AI. Prior to that Emad was Principal SDE and Applied Researcher in the Advance Technology Group at Microsoft Research. He was one of the core developer and researcher behind the Emotion Recognition algorithm used in MS Cognitive Service for both still image and video. Before that, He was one of the main Architects for NUI API on Xbox One, and the tech lead for the depth reconstruction pipeline for Kinect v2. Furthermore, He developed fitness algorithm from skeleton poses and helped develop real-time image segmentation algorithm for Kinect v2, both shipped as part of Kinect SDK. His current research focuses are in computer vision and deep learning algorithms, especially in the area of emotion recognition, activity detection/recognition and unsupervised learning. He has given numerous internal and external talks on Deep Learning and Computer Vision.
Suvansh Sanjeev (UC Berkeley)
Theeraphol Wattanavekin (Google)
Qizhe Xie (CMU, Google Brain)
Sifan Wu (Tsinghua University)
Yuki Yoshida (The University of Tokyo)
David Kanaa (Ecole Polytechnique Montreal)
Sina Khoshfetrat Pakazad (C3.ai)
Mehdi Maasoumy (C3.ai)
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2019 : Poster Session »
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2019 : Opening Remarks »
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2019 : Poster Spotlights B (13 posters) »
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2019 Workshop: Learning with Rich Experience: Integration of Learning Paradigms »
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2019 Poster: Nonparametric Density Estimation & Convergence Rates for GANs under Besov IPM Losses »
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2019 Poster: Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels »
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2019 Poster: Regularized Gradient Boosting »
Corinna Cortes · Mehryar Mohri · Dmitry Storcheus -
2019 Poster: SMILe: Scalable Meta Inverse Reinforcement Learning through Context-Conditional Policies »
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2019 Poster: Learning Local Search Heuristics for Boolean Satisfiability »
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2018 : Spotlights »
Guangneng Hu · Ke Li · Aviral Kumar · Phi Vu Tran · Samuel G. Fadel · Rita Kuznetsova · Bong-Nam Kang · Behrouz Haji Soleimani · Jinwon An · Nathan de Lara · Anjishnu Kumar · Tillman Weyde · Melanie Weber · Kristen Altenburger · Saeed Amizadeh · Xiaoran Xu · Yatin Nandwani · Yang Guo · Maria Pacheco · William Fedus · Guillaume Jaume · Yuka Yoneda · Yunpu Ma · Yunsheng Bai · Berk Kapicioglu · Maximilian Nickel · Fragkiskos Malliaros · Beier Zhu · Aleksandar Bojchevski · Joshua Joseph · Gemma Roig · Esma Balkir · Xander Steenbrugge -
2018 : second authors »
Evan Shelhamer · Daniel Freeman · Tyler (Lixuan) Zhu -
2018 Poster: Nonparametric Density Estimation under Adversarial Losses »
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2018 Poster: Efficient Gradient Computation for Structured Output Learning with Rational and Tropical Losses »
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2017 : Distribution Regression and its Applications. »
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2017 : Poster Session Speech: source separation, enhancement, recognition, synthesis »
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2017 Poster: Deep Sets »
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2017 Poster: Gradient Descent Can Take Exponential Time to Escape Saddle Points »
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Simon Du · Chi Jin · Jason D Lee · Michael Jordan · Aarti Singh · Barnabas Poczos -
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2016 Poster: Variance Reduction in Stochastic Gradient Langevin Dynamics »
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2016 Poster: Proximal Stochastic Methods for Nonsmooth Nonconvex Finite-Sum Optimization »
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2016 Poster: Gaussian Process Bandit Optimisation with Multi-fidelity Evaluations »
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2015 Poster: Nonparametric von Mises Estimators for Entropies, Divergences and Mutual Informations »
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2015 Poster: On Variance Reduction in Stochastic Gradient Descent and its Asynchronous Variants »
Sashank J. Reddi · Ahmed Hefny · Suvrit Sra · Barnabas Poczos · Alexander Smola -
2014 Poster: Reducing the Rank in Relational Factorization Models by Including Observable Patterns »
Maximilian Nickel · Xueyan Jiang · Volker Tresp -
2014 Poster: Exponential Concentration of a Density Functional Estimator »
Shashank Singh · Barnabas Poczos -
2014 Spotlight: Reducing the Rank in Relational Factorization Models by Including Observable Patterns »
Maximilian Nickel · Xueyan Jiang · Volker Tresp -
2013 Poster: Learning Kernels Using Local Rademacher Complexity »
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2013 Spotlight: Learning Kernels Using Local Rademacher Complexity »
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2013 Session: Oral Session 6 »
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2012 Poster: Accuracy at the Top »
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2011 Workshop: Domain Adaptation Workshop: Theory and Application »
John Blitzer · Corinna Cortes · Afshin Rostamizadeh -
2011 Poster: Group Anomaly Detection using Flexible Genre Models »
Liang Xiong · Barnabas Poczos · Jeff Schneider -
2010 Poster: Learning Bounds for Importance Weighting »
Corinna Cortes · Yishay Mansour · Mehryar Mohri -
2010 Poster: Estimation of Renyi Entropy and Mutual Information Based on Generalized Nearest-Neighbor Graphs »
David Pal · Barnabas Poczos · Csaba Szepesvari -
2009 Poster: Learning Non-Linear Combinations of Kernels »
Corinna Cortes · Mehryar Mohri · Afshin Rostamizadeh -
2009 Poster: Polynomial Semantic Indexing »
Bing Bai · Jason E Weston · David Grangier · Ronan Collobert · Kunihiko Sadamasa · Yanjun Qi · Corinna Cortes · Mehryar Mohri -
2008 Workshop: Kernel Learning: Automatic Selection of Optimal Kernels »
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2007 Workshop: Efficient Machine Learning - Overcoming Computational Bottlenecks in Machine Learning (Part 2) »
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2007 Workshop: Efficient Machine Learning - Overcoming Computational Bottlenecks in Machine Learning (Part 1) »
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2006 Poster: On Transductive Regression »
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2006 Poster: Gaussian Process Models for Discriminative Link Prediction »
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