notes
Technical Reviews
- What Can ResNets Learn Efficiently? [pdf] (for Simons Summer 2019 Reading Group on Generalization Theory)
- Geometry of Optimal Transport [pdf] (for Simons Summer 2019 Reading Group on Optimal Transport)
- Learning to Plan and Hierarchical, Multi-Task Reinforcement Learning [pdf] (for IEOR 8100)
- Generalization from Margins: Connecting Boosting Theory to Neural Nets [pdf–in progress!] (at Columbia DSI Seminar)
- Learning Sparse Polynomials [pdf] (for COMS 6998-5)
- Interactive Clustering [pdf] (for COMS 6998-4)
- Convex Optimization and Randomness [pdf] (for Dave Blei’s Reading Group)
- Tensor Decompositions [pdf] (for ELE 538B)
- Rate Distortion and Unsupervised Learning [pdf] (for COS 598E)
- Matrix Concentration and Applications [pdf] (for ORF 550)
- On Lipschitz Extensions from Finite Sets [pdf] (with Mikhail Khodak, for MAT 529)
- The Structure Theorem for Finitely-Generated Modules over PIDs [pdf] (for MAT 346: Algebra II)
- The Representation of Language in the Brain [pdf] (at Algorithms-Machine Learning Reading Group)
- A Brief Survey on Expander Graphs [pdf] (for Junior Seminar with Zeev Dvir)
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.
- COMS 6998-9: Algorithms for Massive Data [pdf] [course website]
- COMS 4995-2: Unsupervised Learning [pdf] [course website]
- STAT 8301: Topics in Probability Theory (taught by Roman Vershynin) [pdf–in progress!] [all notes]
- IEOR 8100: Reinforcement Learning [pdf] [course website]
- STAT 8101: High-dimensional Statistical Inference [pdf–in progress!] [course website]
- COMS 6998-5: Algorithms with a Geometric Lens [scribe notes] [course website]
- COMS 6998-4: Interactive Learning [scribe notes] [course website]
- COS 598E: Unsupervised Learning [scribe notes] [course website]
- ORF 550: Probability in High Dimension [scribe notes] [syllabus] [all notes] (with Xinyi Chen)
- ORF 524: Statistical Theory and Methods [scribe notes] [syllabus]
- MAT 529: Metric Embeddings and Geometric Inequalities (taught by Assaf Naor) [pdf] (with Holden Lee)
- MAT 597/PHY521: Mathematical Physics (taught by Michael Aizenman) [pdf] (with Holden Lee)
- APC 529: Coding Theory and Random Graphs (taught by Emmanuel Abbe) [pdf]
- MAT 340: Applied Algebra (taught by Mark McConnell) [pdf]
- CS 224d: Deep Learning for Natural Language Processing (taught by Richard Socher) [scribe notes]
- COS 511: Theoretical Machine Learning [scribe notes] (with others)
- COS 510: Programming Languages (taught by David Walker) [scribe notes]
- APC 486: Transmission and Compression of Information (taught by Emmanuel Abbe) [scribe notes]
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]