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Author Information
Laurent Condat (KAUST)
Tiffany Vlaar (University of Edinburgh)
Ohad Shamir (Weizmann Institute of Science)
Mohammadi Zaki (Indian Institute of Science Bangalore)
Zhize Li (King Abdullah University of Science and Technology (KAUST))
Guan-Horng Liu (Georgia Institute of Technology)
Samuel Horváth (King Abdullah University of Science and Technology)
Mher Safaryan (KAUST)
Yoni Choukroun (Toga networks)
Kumar Shridhar (TU Kaiserslautern)
Nabil Kahale (ESCP Business School)
Nabil Kahalé is an associate professor at ESCP Business School in Paris. He graduated from Ecole Polytechnique with a B.S. in Engineering in 1987 and received his Ph.D. in theoretical Computer Science from MIT in 1993. Nabil Kahalé’s current research and teaching address risk management, the pricing of derivative securities, Monte Carlo simulation, and machine learning.
Jikai Jin (Peking University)
Pratik Kumar Jawanpuria (Microsoft)
Gaurav Kumar Yadav (Indian Institute of Technology, Madras)
Gaurav Kumar is a research scholar in the department of mechanical engineering at IIT Madras. He is currently working on the applications of Machine learning in the solution of Fluid-flow and heat transfer problem, under the guidance of Dr. Balaji Srinivasan.
Kazuki Koyama (NTT Communications Corp.)
Junyoung Kim (Department of Industrial Engineering, Seoul National University)
Xiao Li (The Chinese University of Hong Kong, Shenzhen)
Saugata Purkayastha (Assam Don Bosco University)
I completed my Ph.D. in Mathematics from Gauhati University in 2015. Presently I am working as an Assistant Professor in the department of Mathematics, Assam Don Bosco University, Assam, India. My main research interests are Algebraic structures, optimization theory in Machine Learning.
Adil Salim (KAUST)
Dighanchal Banerjee (Tata Consultancy Services)
Peter Richtarik (KAUST)
Lakshman Mahto (Indian Institute of Information Technology Dharwad)
I am working as an Assistant Professor (Mathematics) at Indian Institute of Information Technology Dharwad since August, 2016. My current research interests lie in the interface of optimization, statistical learning, and control with understanding and development of efficient optimization algorithms for machine intelligence, system dynamics and control. This often requires conceptual, innovations, and technical (for scientific inference and transforming a large amount of data-sets into useful information with better decisions in the face of uncertainty) breakthroughs along three different dimensions: 1. Optimization of convex and non-convex problems 2. Scalable algorithms that leverage a statistical model to improve its decision making and learning. 3. Data-driven control and optimization of dynamical processes
Tian Ye (Tsinghua University)
Bamdev Mishra (Microsoft)
Huikang Liu (Imperial College London)
Jiajie Zhu (Max Planck Institute for Intelligent Systems)
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2020 : Contributed talks in Session 2 (Zoom) »
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2020 : Contributed Video: Adaptivity of Stochastic Gradient Methods for Nonconvex Optimization, Samuel Horvath »
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Guan-Horng Liu -
2020 : Contributed talks in Session 1 (Zoom) »
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2020 : Contributed Video: Constraint-Based Regularization of Neural Networks, Tiffany Vlaar »
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2020 : Contributed Video: Can We Find Near-Approximately-Stationary Points of Nonsmooth Nonconvex Functions?, Ohad Shamir »
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2020 : Contributed Video: Employing No Regret Learners for Pure Exploration in Linear Bandits, Mohammadi Zaki »
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2020 : Contributed Video: Distributed Proximal Splitting Algorithms with Rates and Acceleration, Laurent Condat »
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2020 : Contributed Video: PAGE: A Simple and Optimal Probabilistic Gradient Estimator for Nonconvex Optimization, Zhize Li »
Zhize Li -
2020 Poster: Improved Analysis of Clipping Algorithms for Non-convex Optimization »
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2020 Poster: A Non-Asymptotic Analysis for Stein Variational Gradient Descent »
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2020 Poster: Neural Networks with Small Weights and Depth-Separation Barriers »
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2020 Poster: Primal Dual Interpretation of the Proximal Stochastic Gradient Langevin Algorithm »
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2020 Poster: The Wasserstein Proximal Gradient Algorithm »
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2020 Poster: Linearly Converging Error Compensated SGD »
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2020 Poster: Statistical Optimal Transport posed as Learning Kernel Embedding »
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2020 Poster: Random Reshuffling: Simple Analysis with Vast Improvements »
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2020 Spotlight: Linearly Converging Error Compensated SGD »
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2019 Poster: A unified variance-reduced accelerated gradient method for convex optimization »
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2019 Poster: On the Power and Limitations of Random Features for Understanding Neural Networks »
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2018 Poster: Stochastic Spectral and Conjugate Descent Methods »
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2016 Poster: Dimension-Free Iteration Complexity of Finite Sum Optimization Problems »
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2016 Poster: Without-Replacement Sampling for Stochastic Gradient Methods »
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2016 Oral: Without-Replacement Sampling for Stochastic Gradient Methods »
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2015 Poster: Quartz: Randomized Dual Coordinate Ascent with Arbitrary Sampling »
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