Scott Clark, CEO, SigOpt, at The AI Conference 2017

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Startup Showcase Presentation on SigOpt

AMPLIFY YOUR
MODELING

SigOpt optimizes...
● Machine Learning
● AI / Deep Learning
● Simulations
OPTIMIZATION AS A SERVICE

SigOpt optimizes...
● Machine Learning
● AI / Deep Learning
● Simulations
Resulting in...
● Better Results
● Faster Development
● Cheaper, Faster
Tuning
OPTIMIZATION AS A SERVICE

Photo: Joe Ross

TUNABLE PARAMETERS IN DEEP LEARNING

TUNABLE PARAMETERS IN DEEP LEARNING

STANDARD TUNING METHODS
Parameter
Configuration
?
Grid Search Random Search
Manual Search
- Architecture
- Learning Rates
- Window sizes
- Transformations
ML / AI
Model
Testing
Data
Cross
Validation
Training
Data

OPTIMIZATION FEEDBACK LOOP
Objective Metric
Better
Results
REST API
New configurations
ML / AI
Model
Testing
Data
Cross
Validation
Training
Data

SIMPLIFIED OPTIMIZATION
Client Libraries
● Python
● Java
● R
● Matlab
● And more...
Framework Integrations
● TensorFlow
● scikit-learn
● xgboost
● Keras
● Neon
● And more...
Live Demo

COMPARATIVE PERFORMANCE
● Better Results, Faster and Cheaper
Quickly get the most out of your models with our proven, peer-reviewed
ensemble of Bayesian and Global Optimization Methods
○ A Stratified Analysis of Bayesian Optimization Methods (ICML 2016)
○ Evaluation System for a Bayesian Optimization Service (ICML 2016)
○ Interactive Preference Learning of Utility Functions for Multi-Objective Optimization (NIPS 2016)
○ And more...
● Fully Featured
Tune any model in any pipeline
○ Scales to 100 continuous, integer, and categorical parameters and many thousands of evaluations
○ Parallel tuning support across any number of models
○ Simple integrations with many languages and libraries
○ Powerful dashboards for introspecting your models and optimization
○ Advanced features like multi-objective optimization, failure region support, and more
● Secure Black Box Optimization
Your data and models never leave your system

SIGOPT CUSTOMERS
SigOpt has successfully engaged
with globally recognized leaders
in insurance, credit card,
algorithmic trading and
consumer packaged goods
industries. Use cases include:
● Trading Strategies
● Complex Models
● Simulations / Backtests
● Machine Learning and AI
Select Customers

Contact us to set up an evaluation today
sales@sigopt.com

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Jeff Bradshaw, Founder, Adaptris

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Jeff Bradshaw, Founder, Adaptris

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Layla El Asri, Research Scientist, Maluuba

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Machine Learning with TensorFlow: TensorFlow has enabled cutting-edge machine learning research at the top AI labs in the world. At the same time it has made the technology accessible to a large audience leading to some amazing uses. TensorFlow is used for classification, recommendation, text parsing, sentiment analysis and more. This talk will go over the design that makes it fast, flexible, and easy to use, and describe how we continue to make it better.

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Local Search Optimization for Hyper-Parameter Tuning: Many machine learning algorithms are sensitive to their hyper-parameter settings, lacking good universal rule-of-thumb defaults. In this talk we discuss the use of black-box local search optimization (LSO) for machine learning hyper-parameter tuning. Viewed as a black-box objective function of hyper-parameters, machine learning algorithms create a difficult class of optimization problems. The corresponding objective functions involved tend to be nonsmooth, discontinuous, unpredictably computationally expensive, requiring support for both continuous, categorical, and integer variables. Further evaluations can fail for a variety of reasons such as early exits due to node failure or hitting max time. Additionally, not all hyper-parameter combinations are compatible (creating so called “hidden constraints”). In this context, we apply a parallel hybrid derivative-free optimization algorithm that can make progress despite these difficulties providing significantly improved results over default settings with minimal user interaction. Further, we will address efficient parallel paradigms for different types of machine learning problems, while exploring the importance of validation to avoid overfitting and emphasizing that even for small data problems, the need to perform cross validations can create computationally intense functions that benefit from a distributed/threaded environment.

Funda Gunes, Senior Research Statistician Developer & Patrick Koch, Principal...

Funda Gunes, Senior Research Statistician Developer & Patrick Koch, Principal...

Funda Gunes, Senior Research Statistician Developer & Patrick Koch, Principal...

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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.

Sneha Rajana - Deep Learning Architectures for Semantic Relation Detection Tasks

Sneha Rajana - Deep Learning Architectures for Semantic Relation Detection Tasks

Sneha Rajana - Deep Learning Architectures for Semantic Relation Detection Tasks

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.

Anoop Deoras - Building an Incrementally Trained, Local Taste Aware, Global D...

Anoop Deoras - Building an Incrementally Trained, Local Taste Aware, Global D...

Anoop Deoras - Building an Incrementally Trained, Local Taste Aware, Global D...

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.

Vito Ostuni - The Voice: New Challenges in a Zero UI World

Vito Ostuni - The Voice: New Challenges in a Zero UI World

Vito Ostuni - The Voice: New Challenges in a Zero UI World

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Roy Lowrance - Predicting Bond Prices: Regime Changes

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The action of the next cyber saga takes place in the mystical lands of the Asia-Pacific region, where the main characters began their digital activities in the middle of 2021 and qualitatively strengthened it in 2022. Corporate espionage, document theft, audio recordings, and data leaks from messaging platforms were all a matter of one day for Dark Pink. Their geographical focus may have started in the Asia-Pacific region, but their ambitions knew no bounds, targeting a European government ministry in a bold move to expand their portfolio. Their victim profile was as diverse as a UN meeting, targeting military organizations, government agencies, and even a religious organization. Because discrimination is not a fashionable agenda. In the world of cybercrime, they serve as a reminder that sometimes the most serious threats come in the most unassuming packages with a pink bow.

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Effective data discovery is crucial for maintaining compliance and mitigating risks in today's rapidly evolving privacy landscape. However, traditional manual approaches often struggle to keep pace with the growing volume and complexity of data. Join us for an insightful webinar where industry leaders from TrustArc and Privya will share their expertise on leveraging AI-powered solutions to revolutionize data discovery. You'll learn how to: - Effortlessly maintain a comprehensive, up-to-date data inventory - Harness code scanning insights to gain complete visibility into data flows leveraging the advantages of code scanning over DB scanning - Simplify compliance by leveraging Privya's integration with TrustArc - Implement proven strategies to mitigate third-party risks Our panel of experts will discuss real-world case studies and share practical strategies for overcoming common data discovery challenges. They'll also explore the latest trends and innovations in AI-driven data management, and how these technologies can help organizations stay ahead of the curve in an ever-changing privacy landscape.

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4. Photo: Joe Ross

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