Santosh Vempala, David Blei, Katherine Heller, John Langford and Le Song with the Simons Institute at UC Berkeley just organised a workshop on Computational Challenges in Machine Learning this week. The videos can be accessed by following each link.
The aim of this workshop is to bring together a broad set of researchers looking at algorithmic questions that arise in machine learning. The primary target areas will be large-scale learning, including algorithms for Bayesian estimation and variational inference, nonlinear and nonparametric function estimation, reinforcement learning, and stochastic processes including diffusion, point processes and MCMC. While many of these methods have been central to statistical modeling and machine learning, recent advances in their scope and applicability lead to basic questions about their computational efficiency. The latter is often linked to modeling assumptions and objectives. The workshop will examine progress and challenges and include a set of tutorials on the state of the art by leading experts.
- Variational Inference: Foundations and Innovations David Blei, Columbia University
- Representational and Optimization Properties of Deep Residual Networks Peter Bartlett, UC Berkeley
- Composing Graphical Models with Neural Networks for Structured Representations and Fast Inference David Duvenaud, University of Toronto
- Scaling Up Bayesian Inference for Big and Complex Data David Dunson, Duke University
- Unbiased Estimation of the Spectral Properties of Large Implicit Matrices Ryan Adams, Harvard University
- Stochastic Gradient MCMC for Independent and Dependent Data Sources Emily Fox, University of Washington
- On Gradient-Based Optimization: Accelerated, Distributed, Asynchronous and Stochastic Michael Jordan, UC Berkeley
- Sampling Polytopes: From Euclid to Riemann Yin Tat Lee, Microsoft Research and University of Washington
- Understanding Generalization in Adaptive Data Analysis Vitaly Feldman, IBM Almaden
- Gradient Descent Learns Linear Dynamical Systems Moritz Hardt, Google
- Your Neural Network Can't Learn Mine John Wilmes, Georgia Institute of Technology
- Efficient Distributed Deep Learning Using MXNet Anima Anandkumar, UC Irvine
- Machine Learning for Healthcare Data Katherine Heller, Duke University
- Machine Learning Combinatorial Optimization Algorithms Dorit Hochbaum, UC Berkeley
- A Cost Function for Similarity-Based Hierarchical Clustering Sanjoy Dasgupta, UC San Diego
- The Imitation Learning View of Structured Prediction Hal Daume, University of Maryland at College Park
- Exponential Computational Improvement by Reduction John Langford, Microsoft Research New York
- Simons Institute Open Lecture: "Does Computational Complexity Restrict Artificial Intelligence (AI) and Machine Learning? Sanjeev Arora, Princeton University
- Embedding as a Tool for Algorithm Design Le Song, Georgia Institute of Technology
- Robust Estimation of Mean and Covariance Anup Rao, Georgia Institute of Technology
- Computational Efficiency and Robust Statistics Ilias Diakonikolas, University of Southern California
- System and Algorithm Co-Design, Theory and Practice, for Distributed Machine Learning Eric Xing, Carnegie Mellon University
- The Polytope Learning Problem Navin Goyal, Microsoft Research
- Computational Challenges and the Future of ML Panel
- Computationally Tractable and Near Optimal Design of Experiments Aarti Singh, Carnegie Mellon University
- Learning from Untrusted Data Gregory Valiant, Stanford University
- The “Tell Me Something New” Model of Computation for Machine Learning Yoav Freund, UC San Diego
Credits: NASA/JPL-Caltech/SwRI/MSSS/Jason Major
This image, taken by the JunoCam imager on NASA’s Juno spacecraft, highlights a swirling storm just south of one of the white oval storms on Jupiter.
The image was taken on March 27, 2017, at 2:12 a.m. PDT (5:12 a.m. EDT), as the Juno spacecraft performed a close flyby of Jupiter. At the time the image was taken, the spacecraft was about 12,400 miles (20,000 kilometers) from the planet.
Citizen scientist Jason Major enhanced the color and contrast in this image, turning the picture into a Jovian work of art. He then cropped it to focus our attention on this beautiful example of Jupiter’s spinning storms.
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