A Research Blog
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)
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
Scribe notes by Praneeth Vepakomma Previous post: Unsupervised learning and generative models Next post: Inference and statistical physics. See also all seminar posts and course webpage. lecture slides (pdf) - lecture slides (Powerpoint with animation and annotation) - video In this blog post, we will focus on the topic of robustness - how well (or … Continue reading Robustness in train and test time
Shuchi Chawla, Madhur Tulsiani, and I are organizing a new summer school aimed at exposing undergraduate students to research directions in theoretical computer science and its applications. The school will take place from May 31 till June 4, 2021. This first iteration will be online, but we hope it will become a recurring and lasting event. … Continue reading New summer school in TCS
Scribe notes by Richard Xu Previous post: What do neural networks learn and when do they learn it Next post: TBD. See also all seminar posts and course webpage. lecture slides (pdf) - lecture slides (Powerpoint with animation and annotation) - video In this lecture, we move from the world of supervised learning to unsupervised … Continue reading Unsupervised Learning and generative models
Scribe notes by Manos Theodosis Previous post: A blitz through statistical learning theory Next post: Unsupervised learning and generative models. See also all seminar posts and course webpage. Lecture video - Slides (pdf) - Slides (powerpoint with ink and animation) In this lecture, we talk about what neural networks end up learning (in terms of … Continue reading What do deep networks learn and when do they learn it
Previous post: ML theory with bad drawings Next post: What do neural networks learn and when do they learn it, see also all seminar posts and course webpage. Lecture video (starts in slide 2 since I hit record button 30 seconds too late - sorry!) - slides (pdf) - slides (Powerpoint with ink and animation) … Continue reading A blitz through classical statistical learning theory
(Next post: Lecture 1 - a blitz through classical statistical learning theory . See also all seminar posts) This semester I am teaching a seminar on the theory of machine learning. For the first lecture, I would like to talk about what is the theory of machine learning. I decided to write this (very rough!) … Continue reading ML Theory with bad drawings
For many of the famous open problems of theoretical computer science, most researchers agree on what the answer is, but the challenge is to prove it. Most complexity theorists (with few notable exceptions) believe that P≠NP, but we don't know how to prove it. Similarly, most people working on matrix multiplication believe that there is … Continue reading Obfuscation: The season 4 Finale
[Announcement from Jelani Nelson --Boaz]TL;DR: https://tinyurl.com/tcs-connections A task force has been convened by CATCS to investigate possibleapproaches to modifying aspects of the TCS community, especially ourpublishing culture, to enhance connections with other areas of CS andbe as welcoming as possible to a broad range of contributions withintheory. This committee will collect and synthesize feedback from … Continue reading Making TCS more connected / less insular
