Kiran Vodrahalli

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Technical Reviews

Class Notes

For now, I have individual class notes for some classes I’ve taken. Sometimes, lectures are missing or the rest of the notes have been taken by other people — in these events, I direct you to the website of the course, if it exists. If not, take a look at my links page for a list of other resources. At some point in the future, I plan to put together a big document of notes organized by subject.

Seminar Notes

  • Yann LeCun on The Power and Limits of Deep Learning and AI [pdf]
  • Yisong Yue on New Frontiers in Imitation Learning [pdf]
  • Tim Roughgarden on How does Computer Science Inform Modern Auction Theory [pdf]
  • Yuchen Zhang on Two Approaches to Non-Convex Machine Learning [pdf]
  • Ben Recht on What can Deep Learning learn from Linear Regression [pdf]
  • Nicholas Boumal on ManOpt: Manifold Optimization [pdf]
  • Michael Jordan on Computational Thinking, Inferential Thinking, and Data Science [pdf]
  • Joel Tropp on Universality Laws for Randomized Dimension Reduction [pdf]
  • Joel Tropp on Finding Structure With Randomness [pdf]
  • Michael Jordan on Nonparametric Bayesian and Combinatorial Stochastic Processes [pdf]
  • Jacob Steinhardt on Unsupervised Risk Estimation with Only Structural Assumptions [pdf]
  • Yuanzhi Li on Weighted Low-Rank Matrix Approximation [pdf]
  • Zeyuan Allen-Zhu on Linear Coupling Framework of Gradient and Mirror Descent [pdf]
  • Naman Agarwal and Brian Bullins on LiSSA: A Linear Time Second-Order Stochastic Algorithm [pdf]
  • Andrew Barron on Computationally Feasible Greedy Algorithms for Neural Networks [pdf]
  • Samy Bengio on Neural Image Captioning [pdf]
  • Zaid Harchaoui on Convolutional Kernel Neural Networks [pdf]
  • Lester Mackey on Divide-and-Conquer Matrix Completion [pdf]
  • Gillat Kol on Interactive Information Theory [pdf]
  • Sanjeev Arora on Reversible Deep Nets [pdf]
  • Santosh Vempala on The Complexity of Detecting Planted Solutions [pdf]
  • Amir Ali Ahmadi on Optimizing over Nonnegative Polynomials [pdf]
  • Francisco Pereira on Decoding Generic Representations of fMRI [pdf]
  • Elad Hazan on Simulated Annealing and Interior Point Methods [pdf]
  • Dana Lahat on Joint Independent Subspace Analysis and Blind Source Separation [pdf]
  • Barbara Engelhardt on Bayesian Structured Sparsity Using Gaussian Fields [pdf]
  • Dimitris Bertsimas on Statistics and Machine Learning from a Modern Optimization Lens [pdf]
  • Sébastien Bubeck on Optimal Regret Bounds for the General Convex Multi-Armed Bandit Setting [pdf]
  • Han Liu on Nonparametric Graphical Models [pdf]
  • Mehryar Mohri on Deep Boosting [pdf]
  • Percy Liang on Learning Hidden Computational Processes [pdf]
  • Young Kun Ko on The Hardness of Sparse PCA [pdf]
  • Tom Griffiths on Rationality, Heuristics, and the Cost of Computation [pdf]
  • Anna Choromanska on Optimization for Large-Scale Machine Learning [pdf]

Symposia Notes

  • MIT Deep Learning and Nonconvex Optimization (MIFODS MIT Workshop) (Jan. 2019) [page]
  • MIT Sublinear Algorithms, Local Algorithms, and Robust Statistics (MIFODS MIT Workshop) (Jun. 2018) [page]
  • Bridging Optimization Theory, Information Theory, and Data Science at Princeton (May. 2018) [pdf]
  • Mathematics of Deep Learning at Princeton (Mar. 2018) [pdf]
  • NYU Mini-Theory Symposium (Feb. 2018) [pdf]

Conference Notes

  • NeurIPS 2017 Workshop: Deep Learning Theory [pdf]
  • COLT 2016 Keynote by Ravi Kannan: Linear Algebra in Machine Learning [pdf]