Alexandra Johnson, Software Engineer, SigOpt at MLconf ATL 2017

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Best Practices for Hyperparameter Optimization: All machine learning and artificial intelligence pipelines – from reinforcement agents to deep neural nets – have tunable hyperparameters. Optimizing these hyperparameters provides tremendous performance gains, but only if the optimization is done correctly. This presentation will discuss topics including selecting performance criteria, why you should always use cross validation, and choosing between state of the art optimization methods.

Best Practices for Hyperparameter
Optimization
Alexandra Johnson
@alexandraj777

Example: Beating Vegas
Scott Clark. Using Model Tuning to Beat Vegas.

Terminology
● Optimization
● Hyperparameter
Optimization
● Hyperparameter
tuning
● Model tuning

Tune the Whole Pipeline

Optimize all Parameters at Once
TensorFlow Playground

Include Feature Parameters

Include Feature Parameters

Choosing a Metric
● Balance long-term
and short-term goals
● Question underlying
assumptions
● Example from
Microsoft

Composite Metric
Example: Lifetime Value
clicks*wclicks + likes*wlikes + views*wviews

Choose Multiple Metrics
● Balance competing
metrics
● Explore entire result
space
Image from PhD Comics

Avoiding Overfitting

Get A Suggestion

Shuffle and Split Data

Train the Model

Test the Performance

Repeat
Shuffle Train Evaluate

Report An Observation

Repeat the Entire Process
Shuffle Train Evaluate

Optimization Methods

Hand Tuning
● Hand tuning is time
consuming and
expensive
● Algorithms can
quickly and cheaply
beat expert tuning

Grid Search Random Search Bayesian Optimization
Alternatives to Hand Tuning

No Grid Search
Hyper-
parameters
Model
Evaluations
2 100
3 1,000
4 10,000
5 100,000

No Random Search
● Theoretically more
effective than grid
search
● Large variance in
results
● No intelligence

Bayesian Optimization
● Explore/exploit
● Ideal for "expensive"
optimization
● No requirements on:
convex,
differentiable,
continuous

Alternatives to Bayesian Optimization
Genetic algorithms
Particle-based methods
Convex optimizers
Simulated annealing
To name a few...

Takeaways
●Optimize the entire pipeline
●Ensure generalization
●Use Bayesian optimization

Thank You!
blog.sigopt.com
sigopt.com/research

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Ashrith Barthur, Security Scientist, H2o.ai, at MLconf 2017

Natural Language Understanding @ Facebook Scale: At Facebook, text understanding is the key to surfacing content that’s relevant and personalized, plus enabling new experiences like social recommendations and Marketplace suggestions. In this talk, I will introduce you to DeepText, Facebook’s platform for text understanding, and discuss the various models it supports. Bio: Rushin Shah is an engineering leader at Facebook, currently working on natural language understanding and dialog. Previously, he was at Siri at Apple for 5 years, where he built and headed the natural language understanding group, which included teams dedicated to modeling, engineering and data science. He also worked at the query understanding group at Yahoo. He has worked on a broad range of problems in the NLP area including parsing, information extraction, dialog and question answering. He holds degrees in language technologies and computer science from Carnegie Mellon and IIT Kharagpur.

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Rushin Shah, Engineering Manager, Facebook at MLconf SF 2017

Rushin Shah, Engineering Manager, Facebook at MLconf SF 2017

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Michael Alcorn, Sr. Software Engineer, Red Hat Inc. at MLconf SF 2017

Michael Alcorn, Sr. Software Engineer, Red Hat Inc. at MLconf SF 2017

Counter Intuitive Machine Learning for the Industrial Internet of Things: The Industrial Internet of Things (IIoT) is the infrastructure and data flow built around the world’s most valuable things like airplane engines, medical scanners, nuclear power plants, and oil pipelines. These machines and systems require far greater uptime, security, governance, and regulation than the IoT landscape based around consumer activity. In the IIoT the cost of being wrong can be the catastrophic loss of life on a massive scale. Nevertheless, given the growing scale through the digitalization of industrial assets, there is clearly a growing role for machine learning to help augment and automate human decision making. It is against this backdrop that traditional machine learning techniques must be adapted and need based innovations created. We see industrial machine learning as distinct from consumer machine learning and in this talk we will cover the counterintuitive changes of featurization, metrics for model performance, and human-in-the-loop design changes for using machine learning in an industrial environment. Bio: June Andrews is a Principal Data Scientist at Wise.io, From GE Digital working on a machine learning and data science platform for the Industrial Internet of Things, which includes aviation, trains, and power plants. Previously, she worked at Pinterest spearheading the Data Trustworthiness and Signals Program to create a healthy data ecosystem for machine learning. She has also lead efforts at LinkedIn on growth, engagement, and social network analysis to increase economic opportunity for professionals. June holds degrees in applied mathematics, computer science, and electrical engineering from UC Berkeley and Cornell.

Dr. June Andrews, Principal Data Scientist, Wise.io, From GE Digital at MLcon...

Dr. June Andrews, Principal Data Scientist, Wise.io, From GE Digital at MLcon...

Dr. June Andrews, Principal Data Scientist, Wise.io, From GE Digital at MLcon...

Large-scale Machine Learning: Deep, Distributed and Multi-Dimensional: Modern machine learning involves deep neural network architectures which yields state-of-art performance on multiple domains such as computer vision, natural language processing and speech recognition. As the data and models scale, it becomes necessary to have multiple processing units for both training and inference. Apache MXNet is an open-source framework developed for distributed deep learning. I will describe the underlying lightweight hierarchical parameter server architecture that results in high efficiency in distributed settings. Pushing the current boundaries of deep learning requires using multiple dimensions and modalities. These can be encoded into tensors, which are natural extensions of matrices. We present new deep learning architectures that preserve the multi-dimensional information in data end-to-end. We show that tensor contractions and regression layers are an effective replacement for fully connected layers in deep learning architectures. They result in significant space savings with negligible performance degradation. These functionalities are available in the Tensorly package with MXNet backend interface for large-scale efficient learning. Bio: Anima Anandkumar is a principal scientist at Amazon Web Services and a Bren professor at Caltech CMS department. Her research interests are in the areas of large-scale machine learning, non-convex optimization and high-dimensional statistics. In particular, she has been spearheading the development and analysis of tensor algorithms. She is the recipient of several awards such as the Alfred. P. Sloan Fellowship, Microsoft Faculty Fellowship, Google research award, ARO and AFOSR Young Investigator Awards, NSF Career Award, Early Career Excellence in Research Award at UCI, Best Thesis Award from the ACM Sigmetrics society, IBM Fran Allen PhD fellowship, and several best paper awards. She has been featured in a number of forums such as the yourstory, Quora ML session, O’Reilly media, and so on. She received her B.Tech in Electrical Engineering from IIT Madras in 2004 and her PhD from Cornell University in 2009. She was a postdoctoral researcher at MIT from 2009 to 2010, an assistant professor at U.C. Irvine between 2010 and 2016, and a visiting researcher at Microsoft Research New England in 2012 and 2014.

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Talha Obaid, Email Security, Symantec at MLconf ATL 2017

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The Importance of Modeling Data Collection Data sets used in machine learning are often collected in a systematically biased way - certain data points are more likely to be collected than others. We call this "observation bias". For example, in health care, we are more likely to see lab tests when the patient is feeling unwell than otherwise. Failing to account for observation bias can, of course, result in poor predictions on new data. By contrast, properly accounting for this bias allows us to make better use of the data we do have. In this presentation, we discuss practical and theoretical approaches to dealing with observation bias. When the nature of the bias is known, there are simple adjustments we can make to nonparametric function estimation techniques, such as Gaussian Process models. We also discuss the scenario where the data collection model is unknown. In this case, there are steps we can take to estimate it from observed data. Finally, we demonstrate that having a small subset of data points that are known to be collected at random - that is, in an unbiased way - can vastly improve our ability to account for observation bias in the rest of the data set. My hope is that attendees of this presentation will be aware of the perils of observation bias in their own work, and be equipped with tools to address it.

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The Uncanny Valley of ML Every so often, the conundrum of the Uncanny Valley re-emerges as advanced technologies evolve from clearly experimental products to refined accepted technologies. We have seen its effects in robotics, computer graphics, and page load times. The debate of how to handle the new technology detracts from its benefits. When machine learning is added to human decision systems a similar effect can be measured in increased response time and decreased accuracy. These systems include radiology, judicial assignments, bus schedules, housing prices, power grids and a growing variety of applications. Unfortunately, the Uncanny Valley of ML can be hard to detect in these systems and can lead to degraded system performance when ML is introduced, at great expense. Here, we'll introduce key design principles for introducing ML into human decision systems to navigate around the Uncanny Valley and avoid its pitfalls.

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Deep Learning Architectures for Semantic Relation Detection Tasks Recognizing and distinguishing specific semantic relations from other types of semantic relations is an essential part of language understanding systems. Identifying expressions with similar and contrasting meanings is valuable for NLP systems which go beyond recognizing semantic relatedness and require to identify specific semantic relations. In this talk, I will first present novel techniques for creating labelled datasets required for training deep learning models for classifying semantic relations between phrases. I will further present various neural network architectures that integrate morphological features into integrated path-based and distributional relation detection algorithms and demonstrate that this model outperforms state-of-the-art models in distinguishing semantic relations and is capable of efficiently handling multi-word expressions.

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Building an Incrementally Trained, Local Taste Aware, Global Deep Learned Recommender System Model At Netflix, our main goal is to maximize our members’ enjoyment of the selected show by minimizing the amount of time it takes for them to find it. We try to achieve this goal by personalizing almost all the aspects of our product -- from what shows to recommend, to how to present these shows and construct their home-pages to what images to select per show, among many other things. Everything is recommendations for us and as an applied Machine Learning group, we spend our time building models for personalization that will eventually increase the joy and satisfaction of our members. In this talk we will primarily focus our attention on a) making a global deep learned recommender model that is regional tastes and popularity aware and b) adapting this model to changing taste preferences as well as dynamic catalog availability. We will first go through some standard recommender system models that use Matrix Factorization and Topic Models and then compare and contrast them with more powerful and higher capacity deep learning based models such as sequence models that use recurrent neural networks. We will show what it entails to build a global model that is aware of regional taste preferences and catalog availability. We will show how models that are built on simple Maximum Likelihood principle fail to do that. We will then describe one solution that we have employed in order to enable the global deep learned models to focus their attention on capturing regional taste preferences and changing catalog.In the latter half of the talk, we will discuss how we do incremental learning of deep learned recommender system models. Why do we need to do that ? Everything changes with time. Users’ tastes change with time. What’s available on Netflix and what’s popular also change over time. Therefore, updating or improving recommendation systems over time is necessary to bring more joy to users. In addition to how we apply incremental learning, we will discuss some of the challenges we face involving large-scale data preparation, infrastructure setup for incremental model training as well as pipeline scheduling. The incremental training enables us to serve fresher models trained on fresher and larger amounts of data. This helps our recommender system to nicely and quickly adapt to catalog and users’ taste changes, and improve overall performance.

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Vito Ostuni - The Voice: New Challenges in a Zero UI World The adoption of voice-enabled devices has seen an explosive growth in the last few years and music consumption is among the most popular use cases. Music personalization and recommendation plays a major role at Pandora in providing a daily delightful listening experience for millions of users. In turn, providing the same perfectly tailored listening experience through these novel voice interfaces brings new interesting challenges and exciting opportunities. In this talk we will describe how we apply personalization and recommendation techniques in three common voice scenarios which can be defined in terms of request types: known-item, thematic, and broad open-ended. We will describe how we use deep learning slot filling techniques and query classification to interpret the user intent and identify the main concepts in the query. We will also present the differences and challenges regarding evaluation of voice powered recommendation systems. Since pure voice interfaces do not contain visual UI elements, relevance labels need to be inferred through implicit actions such as play time, query reformulations or other types of session level information. Another difference is that while the typical recommendation task corresponds to recommending a ranked list of items, a voice play request translates into a single item play action. Thus, some considerations about closed feedback loops need to be made. In summary, improving the quality of voice interactions in music services is a relatively new challenge and many exciting opportunities for breakthroughs still remain. There are many new aspects of recommendation system interfaces to address to bring a delightful and effortless experience for voice users. We will share a few open challenges to solve for the future.

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1. Best Practices for Hyperparameter Optimization Alexandra Johnson @alexandraj777

2. Example: Beating Vegas Scott Clark. Using Model Tuning to Beat Vegas.

3. Terminology ● Optimization ● Hyperparameter Optimization ● Hyperparameter tuning ● Model tuning

4. Tune the Whole Pipeline

5. Optimize all Parameters at Once TensorFlow Playground

6. Include Feature Parameters

7. Include Feature Parameters

8. Choosing a Metric ● Balance long-term and short-term goals ● Question underlying assumptions ● Example from Microsoft

9. Composite Metric Example: Lifetime Value clicks*wclicks + likes*wlikes + views*wviews

10. Choose Multiple Metrics ● Balance competing metrics ● Explore entire result space Image from PhD Comics

11. Avoiding Overfitting

12. Get A Suggestion

13. Shuffle and Split Data

14. Train the Model

15. Test the Performance

16. Repeat Shuffle Train Evaluate

17. Report An Observation

18. Repeat the Entire Process Shuffle Train Evaluate

19. Optimization Methods

20. Hand Tuning ● Hand tuning is time consuming and expensive ● Algorithms can quickly and cheaply beat expert tuning

21. Grid Search Random Search Bayesian Optimization Alternatives to Hand Tuning

22. No Grid Search Hyper- parameters Model Evaluations 2 100 3 1,000 4 10,000 5 100,000

23. No Random Search ● Theoretically more effective than grid search ● Large variance in results ● No intelligence

24. Bayesian Optimization ● Explore/exploit ● Ideal for "expensive" optimization ● No requirements on: convex, differentiable, continuous

25. Alternatives to Bayesian Optimization Genetic algorithms Particle-based methods Convex optimizers Simulated annealing To name a few...

26. Takeaways ●Optimize the entire pipeline ●Ensure generalization ●Use Bayesian optimization

27. Thank You! blog.sigopt.com sigopt.com/research

Editor's Notes

Hyperparameter Optimization was the difference between making money and losing money