I'll put my code, presentation slides, useful links, and various other useful resources here.


Selected recent talk slides

  • Talk on Watermarking LLMs at UCLA [slides]
  • Talk on Permute-and-Flip Decoding and Watermarking for LLMs [slides]
  • Open problem for DP-ERM at TTIC [slides]
  • Talk on watermarking generative AI at ICML/KDD [slides]
  • Talk on deep learning theory at UCSB Statistics/GeorgiaTech/USC/MBZUAI [slides]
  • Talk on Offline and Low-Adaptive RL at INFORMS[slides]
  • Talk on Universal Dynamic Regret at Princeton [slides]
  • Talk on privacy accounting at Rutgers/LinkedIn/MIT [slides]
  • Talk on the Local Adaptivity of Deep Neural Networks at Rice [slides]
  • Talk on Harnessing Nonstationarity at Genetech[slides]
  • Talk on dynamic pricing at UCSB [slides]
  • Talk on online forecasting at JSM-2021 [slides]
  • RL Theory Talk on Uniform OPE[slides]
  • UCSD Talk on Online Forecasting[slides]
  • Berkeley Simons Institute Talk on Subsampled RDP [slides]
  • Policy learning talk at Caltech MCS / UCSB Statistics [slides]
  • Talk on Per-Instance DP at UC Santa Cruz's PiG workshop [slides]
  • My PhD thesis defense slides [slides]
  • CMU and UCSB talks "SignSGD / Signum optimizers and deep learning with Gluon" [slides]
  • NIPS'16 "What-If“ workshop Talk on "Optimal and Adaptive Off-policy Evaluation" [slides]
  • ICML'16 TPDP workshop Talk on "Generalization and Learnability under Differential Privacy and its variants" [slides]
  • Columbia talk on GTF and "Total variation class beyond 1D" [slides]
  • ML Lunch talk on "Falling factorials" and "Graph Trend Filtering" [slides]
  • "Privacy for free talk at ICML'15" [slides]

Code and software

Some really old journal club/discussion class presentations back in Singapore

  • "Stochastic Subgradient Descent for Nuclear Norm Regularization" [slides]
  • Non-negative Matrix Factorization(NMF) and "A-Optimal Non-Negative Projection for Image Representation" [slides]
  • Differential privacy tutorial [slides]
  • Deep Learning and "Sparse modeling of human actions from motion imagery" [slides]
  • Subspace Clustering with missing data: "High rank matrix completion" [slides]
  • Discussion of Wiberg L1 (CVPR10 Best Paper) [slides]

Learning deep learning with Mxnet Gluon

A book made of jupyter notebooks on github. All examples are runnable. mxnet Gluon is highly flexible and very suitable for researchers. 中文版

Adventures in Data Land

Alex's blog with many ideas and practical tricks for using ML.

Csaba Szepesvari's blog on Bandits

Mostly stochastic and adversarial linear bandits.

Larry Wasserman's blog: Normal Deviate

Cool blog from the CMU statistician on statistics and machine learning topics.

John Langford's blog:

A renowned machine learning theory blog. A few good/interesting posts per month. To have a flavor, check out the article: Adversarial Academia.

Matrix Factorization Jungle

A comprehensive site that keeps updating the state-of-the-art algorithms, theory and evaluations in MF related fields, including: Matrix Completion, Matrix Recovery(RPCA), Compressive Sensing, Dictionary learning, Non-negative Matrix Factorization and etc.

Nuit Blanche blog on Compressive Sensing and Matrix Factorization

The maintainer of Matrix Factorization Jungle (Igor Carron), articles are faster than updates on the summary site.

Compressive sensing resources

An almost thorough list of compressive sensing papers, reviews and tutorials.

Ma Yi's Low-rank matrix recovery & completion page

A list of papers on nuclear norm based convex methods for low-rank matrix. Useful code samples of Augmented Lagrange Multiplier methods for RPCA.