Workshop on Local Algorithms (Guest post by Ronitt Rubinfeld)

The fifth WOLA (Workshop on Local Algorithms) will be virtual, and take place June 14-15. Registration is free, but required: please fill this form by June 10th to attend. Local algorithms — that is, algorithms that compute and make decisions on parts of the output considering only a portion of the input — have been studied in a number of … Continue reading Workshop on Local Algorithms (Guest post by Ronitt Rubinfeld)

Google Research Workshop on Deep Learning Theory

[Guest post from Pranjal Awasthi and Rina Panigrahy - workshop looks great! --Boaz] Please join us for a virtual Google workshop on “Conceptual Understanding of Deep Learning”  When: May 17th 9am-4pm. Where: Live over Youtube, Goal: How does the Brain/Mind (perhaps even an artificial one) work at an algorithmic level? While deep learning has produced tremendous technological … Continue reading Google Research Workshop on Deep Learning Theory

Towards a Theory of Generalization in Reinforcement Learning: guest lecture by Sham Kakade

Scribe notes by Hamza Chaudhry and Zhaolin Ren Previous post: Natural Language Processing - guest lecture by Sasha Rush Next post: TBD. See also all seminar posts and course webpage. See also video of lecture. Lecture slides: Original form: main / bandit analysis. Annotated: main / bandit analysis. Sham Kakade is a professor in the … Continue reading Towards a Theory of Generalization in Reinforcement Learning: guest lecture by Sham Kakade

Natural Language Processing (guest lecture by Sasha Rush)

Scribe notes by Benjamin Basseri and Richard Xu Previous post: Inference and statistical physics Next post: TBD. See also all seminar posts and course webpage. Alexander (Sasha) Rush is a professor at Cornell working in in Deep Learning / NLP. He applies machine learning to problems of text generation, summarizing long documents, and interactions between … Continue reading Natural Language Processing (guest lecture by Sasha Rush)

Inference and statistical physics

Scribe notes by Franklyn Wang Previous post: Robustness in train and test time Next post: Natural Language Processing (guest lecture by Sasha Rush). See also all seminar posts and course webpage. lecture slides (pdf) - lecture slides (Powerpoint with animation and annotation) - video Digression: Frequentism vs Bayesianism Before getting started, we'll discuss the difference … Continue reading Inference and statistical physics