– 12/30/19: A decade of fun and learning

– 10/5/19: Convex body chasing, Steiner point, Sellke point

– 6/10/19: Amazing progress in adversarially robust stochastic multi-armed bandits

– 1/9/19: Nemirovski’s acceleration

– 11/21/18: A short proof of Nesterov’s momentum

– 09/18/18: How to generalize (algorithmically)

– 12/16/17: k-server, part 1, part 2, part 3

– 09/28/17: Michael B. Cohen

– 07/05/17: Smooth distributed convex optimization

– 06/03/17: COLT 2017 accepted papers

– 03/07/17: New journal: Mathematical Statistics and Learning

– 02/26/17: STOC 2017 accepted papers

– 11/13/16: Geometry of linearized neural networks

– 10/24/16: Local max-cut in smoothed polynomial time

– 08/06/16: Kernel-based methods for bandit convex optimization, part 1 and part 2 and part 3

– 05/11/16: Bandit theory, part 1 and part 2

– 05/05/16: COLT 2016 accepted papers

– 03/14/16: Notes on least squares, part 1 and part 2

– 01/27/16: AlphaGo is born

– 12/13/15: On the spirit of NIPS 2015 and OpenAI

– 10/30/15: Convex Optimization: Algorithms and Complexity

– 10/22/15: Crash course on learning theory part 1, and part 2

– 08/31/15: Some stuff I learned this week (negative association, learning on networks, and new results on the stochastic block model)

– 07/24/15: A solution to bandit convex optimization

– 06/30/15: Revisiting Nesterov’s Acceleration

– 05/04/15: COLT 2015 accepted papers and some cool videos

– 03/20/15: Deep stuff about deep learning?

– 01/30/15: Some pictures in geometric probability

– 12/04/14: The entropic barrier: a simple and optimal universal self-concordant barrier

– 11/20/14: What’s the (hi)story of my networks?

– 08/19/14: Komlos conjecture, Gaussian correlation conjecture, and a bit of machine learning

– 06/25/14: A zest of number theory

– 06/21/14: Probability in high dimensions

– 05/16/14: Theory of convex optimization for machine learning

– 04/21/14: COLT 2014 accepted papers

– 03/30/14: On the influence of the seed graph in the preferential attachment model

– 03/06/14: Nesterov’s Accelerated Gradient Descent for Smooth and Strongly Convex Optimization

– 02/01/14: short review of ITCS 2014

– 01/14/14: One year of blogging

– 12/12/13: A good NIPS!

– 11/23/13: The hunter and the rabbit

– 10/21/13: 5 announcements

– 09/22/13: First Big Data workshop at the Simons Institute

– 09/02/13: First week of activity at the Simons Institute

– 08/27/13: Random-Approx 2013

– 07/18/13: Two summer readings on Big Data and Deep Learning

– 06/22/13: ICML and isotropic position of a convex body

– 06/17/13: COLT (with bombs, deep learning and other exciting stuff)

– 06/08/13: Embeddings of finite metric spaces in Hilbert space

– 02/05-05/17/13: ORF523: The complexities of optimization (series of posts)

– 01/19/13: Clique number of random geometric graphs in high dimension (see also Proof of the lower bound)

– 01/13/13: Welcome (with random graphs) – gives the size of the largest clique in an Erdös-Rényi (see also An unpleasant calculation?)

## By Magna Dijital Pazarlama Ajansı January 18, 2021 - 5:59 am

As a digital Marketing Agency, we’ll be happy to share our knowledge as guest posts.

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