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Alexandre Passos's ML blog

Alexandre Passos's ML blog. NIPS 2013 reading list. As usual, here is my nips 2013 reading list. It’s incomplete, and only has papers I could find online as of a couple of weeks ago. Accelerating Stochastic Gradient Descent using Predictive Variance Reduction;. R Johnson, T. Zhang. A cool thing is that this lets you implement gradient updates for sparse feature vectors efficiently, since you can keep a multiplier on the full gradient you keep around, as the information that varyes from sample to sample i...

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Alexandre Passos's ML blog | atpassos.me Reviews

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Alexandre Passos's ML blog. NIPS 2013 reading list. As usual, here is my nips 2013 reading list. It’s incomplete, and only has papers I could find online as of a couple of weeks ago. Accelerating Stochastic Gradient Descent using Predictive Variance Reduction;. R Johnson, T. Zhang. A cool thing is that this lets you implement gradient updates for sparse feature vectors efficiently, since you can keep a multiplier on the full gradient you keep around, as the information that varyes from sample to sample i...

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Alexandre Passos's ML blog | atpassos.me Reviews

https://atpassos.me

Alexandre Passos's ML blog. NIPS 2013 reading list. As usual, here is my nips 2013 reading list. It’s incomplete, and only has papers I could find online as of a couple of weeks ago. Accelerating Stochastic Gradient Descent using Predictive Variance Reduction;. R Johnson, T. Zhang. A cool thing is that this lets you implement gradient updates for sparse feature vectors efficiently, since you can keep a multiplier on the full gradient you keep around, as the information that varyes from sample to sample i...

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Alexandre Passos's ML blog NIPS 2013 reading list

http://atpassos.me/post/67560831508/nips-2013-reading-list

Alexandre Passos's ML blog. NIPS 2013 reading list. As usual, here is my nips 2013 reading list. It’s incomplete, and only has papers I could find online as of a couple of weeks ago. Accelerating Stochastic Gradient Descent using Predictive Variance Reduction;. R Johnson, T. Zhang. A cool thing is that this lets you implement gradient updates for sparse feature vectors efficiently, since you can keep a multiplier on the full gradient you keep around, as the information that varyes from sample to sample i...

2

Alexandre Passos's ML blog My thesis: Combinatorial algorithms and linear programming for inference in natural language processing.

http://atpassos.me/post/62998319598/my-thesis-combinatorial-algorithms-and-linear

Alexandre Passos's ML blog. My thesis: Combinatorial algorithms and linear programming for inference in natural language processing. I recently defended my phd thesis. In UNICAMP, and I’ve decided to revive this blog (in a new address) with a version of the text I used in my defense. Linear programs have really nice properties. The big one for our cases is duality, which is that for any linear program with a well-defined optimum you can construct a lower-bounding linear program called the dual program.

3

Alexandre Passos's ML blog Passive-aggressive perceptrons and trust-region methods

http://atpassos.me/post/44900083269/passive-aggressive-perceptrons-and-trust-regi-12708

Alexandre Passos's ML blog. Passive-aggressive perceptrons and trust-region methods. Recently I’ve been reading up on some optimization things, and I think I saw an interesting connection between trust-region methods for nonlinear optimization (things like Levenberg-Marquadt. PA) family of online learning algorithms (as well as many proximal-gradient methods). To do so I think I’ll first explain trust-region methods, then PA, and then the connection. This problem is commonly solved by looking at its dual...

4

Alexandre Passos's ML blog My #NIPS2012 reading list, part 2 of 3

http://atpassos.me/post/44900076125/my-nips-2012-reading-list-part-2-of-3

Alexandre Passos's ML blog. My #NIPS2012 reading list, part 2 of 3. FastEx: Fast Clustering with Exponential Families. A Ahmed, S. Ravi, S. Narayanamurthy, A. Smola. Fast Variational Inference in the Conjugate Exponential Family. J Hensman, M. Rattray, N. Lawrence. Hamming Distance Metric Learning. M Norouzi, R. Salakhutdinov, D. Fleet. Identifiability and Unmixing of Latent Parse Trees. P Liang, S. Kakade, D. Hsu. Interpreting prediction markets: a stochastic approach. T Jebara, A. Choromanska.

5

Alexandre Passos's ML blog My #NIPS2012 reading list, part 3 of 3

http://atpassos.me/post/44900074631/my-nips-2012-reading-list-part-3-of-3

Alexandre Passos's ML blog. My #NIPS2012 reading list, part 3 of 3. MAP Inference in Chains using Column Generation. D Belanger, A. Passos, S. Riedel, A. McCallum. Disclaimer: this is my paper! Practical Bayesian Optimization of Machine Learning Algorithms. J Snoek, H. Larochelle, R. Adams. Small-Variance Asymptotics for Exponential Family Dirichlet Process Mixture Models. K Jiang, B. Kulis, M. Jordan. Truncation-free Online Variational Inference for Bayesian Nonparametric Models. C Wang, D. Blei.

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Alexandre Passos's ML blog

Alexandre Passos's ML blog. NIPS 2013 reading list. As usual, here is my nips 2013 reading list. It’s incomplete, and only has papers I could find online as of a couple of weeks ago. Accelerating Stochastic Gradient Descent using Predictive Variance Reduction;. R Johnson, T. Zhang. A cool thing is that this lets you implement gradient updates for sparse feature vectors efficiently, since you can keep a multiplier on the full gradient you keep around, as the information that varyes from sample to sample i...

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