AI News, Titanic – Machine Learning From Distaster with Vowpal Wabbit

Titanic – Machine Learning From Distaster with Vowpal Wabbit

Good clicklog datasets are hard to come by.

Now is your chance to play around with online learning, the hash trick, adaptive learning and logistic loss and get a score of ~0.46902 on the public leaderboard. FastML

tinrtgu posted a very cool benchmark on the forums that uses only standard Python libraries and under 200MB of memory.

Now is your chance to play around with online learning, the hash trick, adaptive learning and logistic loss and get a score of ~0.46902 on the public leaderboard.

The competition of optimizing online advertisements with machine learning is like strawberries with chocolate and vanilla: You have large amounts of data, an almost endless variety of features to engineer, and profitable patterns waiting to be discovered.

Predicting CTR with online machine learning

Now is your chance to play around with online learning, the hash trick, adaptive learning and logistic loss and get a score of ~0.46902 on the public leaderboard. FastML

Now is your chance to play around with online learning, the hash trick, adaptive learning and logistic loss and get a score of ~0.46902 on the public leaderboard.

The competition of optimizing online advertisements with machine learning is like strawberries with chocolate and vanilla: You have large amounts of data, an almost endless variety of features to engineer, and profitable patterns waiting to be discovered.

Identifying (and serving) those ads that have a higher probability of a click, translates into more profit and a higher quality: Behavorial retargeting is a form of online advertising where the advertisements are targeted according  to previous user behavior, often in the case where a visit did not result in a sale or conversion.

The engine is capable of predicting effective personalized advertisements on a web scale: real-time optimization of ad campaigns, click-through rates and conversion.

behavioral numerical feature may be the count of previous purchases. A behavioral categorical feature may be the product ID that was added to a shopping cart, but not purchased.

The engineering blog also shows how Criteo is creating web graphs (without using web crawlers) to engineer new features.

The web as seen by Criteo [from the Criteo Engineering blog] For this contest Criteo’s R&D division, CriteoLabs, has released a week’s worth of click data.

We have 13 columns of integer features (mostly count features), and 26 columns with hashed categorical features.

Though the exact nature of the features is unknown to us, according to a competition admin (Olivier Chapelle), they fall in the following categories: Our task now is to create a model that will predict the probability of a click.

think that sticking with your favorite machine learning tool or algorithm for all classification and regression problems, is like picking a chess opening and only playing that against all opponents.

It won’t perform the best on all competitions, though it will perform in most (Multiclass Classification, Regression, online LDA, Matrix Factorization, Structured Prediction, Neural network reduction, Feature interactions), and is a robust addition to many ensembles.

The collected click data is huge (often far larger than fits into memory) or unbounded (you may constantly collect new categorical feature values).

That the competition metric really is logaritmic loss means we gain a massive amount of information even by training a model with Vowpal Wabbit: With its holdout functionality one-in-ten samples will be used to calculate and report on the average loss.

(Edit: Thanks to Anuj for spotting that I forgot to specify the model when testing, code above updated.) After running the above command we should have our predictions in a text file (~100MB).

The process for creating baseline benchmark was: We beat the logistic regression benchmark with our first submission. Vowpal Wabbit truly is an industry-ready tool for machine learning on large and high dimensional datasets.

Titanic – Machine Learning From Distaster with Vowpal Wabbit

Good clicklog datasets are hard to come by.

Now is your chance to play around with online learning, the hash trick, adaptive learning and logistic loss and get a score of ~0.46902 on the public leaderboard. FastML

tinrtgu posted a very cool benchmark on the forums that uses only standard Python libraries and under 200MB of memory.

Now is your chance to play around with online learning, the hash trick, adaptive learning and logistic loss and get a score of ~0.46902 on the public leaderboard.

The competition of optimizing online advertisements with machine learning is like strawberries with chocolate and vanilla: You have large amounts of data, an almost endless variety of features to engineer, and profitable patterns waiting to be discovered.

Retro Contest: Results

The first run of our Retro Contest — exploring the development of algorithms that can generalize from previous experience — is now complete.

These results provide validation that our Sonic benchmark is a good problem for the community to double down on: the winning solutions are general machine learning approaches rather than competition-specific hacks, suggesting that one can't cheat through this problem.

Our automated evaluation systems conducted a total of 4,448 evaluations of submitted algorithms, corresponding to about 20 submissions per team.

The contestants got to see their scores rise on the leaderboard, which was based on a test set of five low-quality levels that we created using a level editor.

as users' leaderboard scores are based on their latest entry the maximum score can drop if the top entry is replaced with a lower scoring entry from the same user.

Because contestants got feedback about their submission in the form of a score and a video of the agent being tested on a level, they could easily overfit to the leaderboard test set.

Once submissions closed, we took the latest submission from the top 10 entrants and tested their agents against 11 custom Sonic levels made by skilled level designers.

(From top to bottom, ordered by score on the level: Dharmaraja, aborg and mistake) The top 5 scoring teams are: Dharmaraja topped the scoreboard during the contest, and the lead remained on the final evaluation;

Learning curves of the top three teams for all 11 levels are as follows (showing the standard error computed from three runs).

As we will describe in more detail below, these two teams fine-tuned (using PPO) from a pre-trained network, whereas mistake trained from scratch (using Rainbow DQN).

In recent years, they have studied how to apply reinforcement learning to real world problems, especially in an e-commerce setting, together with Yang Yu, who is an Associate Professor of the Department of Computer Science at Nanjing University, Nanjing, China.

third, it uses an augmented reward function, which rewards the agent for visiting new states (as judged by a perceptual hash of the screen).

As both a video game and machine learning enthusiast, he spends most of his free time studying deep reinforcement learning, which led him to take part in the OpenAI Retro Contest.

He got into machine learning after watching a video of a genetic algorithm learning how to play Mario a year and a half ago.

Contests have the potential to overhaul the prevailing consensus on what works the best, since contestants will try a diverse set of different approaches and the best one will win.

We hope and expect that some of the more off-beat approaches will be successful in this second round, now that people know what to expect and have begun to think deeply about the problems of fast learning and generalization in reinforcement learning.

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