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The 35th International Conference on Machine Learning (ICML) 2018, took place on July 10, 2018 – July 15, 2018 in Stockholm, Sweden. ICML is one of the most anticipated conferences for every data scientist and ML practitioner and features some of the best ML researchers who come to talk about their research and discuss new ideas.

It won’t be wrong to say that Deep learning and its subsets were the showstopper of this conference with a large number of research papers and AI professionals implementing it in their methods. These included sessions and paper presentations on, Gaussian Processes, -Networks and Relational Learning, Time-Series Analysis, Deep Bayesian Non-parametric Tracking, Generative Models, etc. Also, other deep learning subsets such as Representation Learning, Ranking and Preference Learning, Supervised Learning, Transfer and Multi-Task Learning, etc were heavily featured.

The conference consisted of one day of tutorials (July 10), followed by three days of main conference sessions (July 11-13), followed by two days of workshops (July 14-15).

Best Talks and Seminars of ICML 2018

ICML 2018 featured two informative talks dealing with the applications of Artificial Intelligence in other domains. Day 1 was inaugurated by an invited talk from Prof. Dawn Song on “AI and Security: Lessons, Challenges and Future Directions’’. She talked about the impact of AI in computer security, differential privacy techniques, and the synergy between AI, computer security, and blockchain. She also gave an overview of challenges and new techniques to enable privacy-preserving machine learning.

Day 3 featured an inaugural talk by Max Welling on “Intelligence per  Kilowatt hour”, focusing on the connection between physics and AI. According to Max, in the coming future, companies will find it too expensive to run energy absorbing ML tools to power their AI engines, or the heat dissipation in edge devices will be too high to be safe. So the next frontier of AI is going to be finding the most energy efficient combination of hardware and algorithms.

There were also two plenary talks. Language to Action: towards Interactive Task Learning with Physical Agents, by Joyce Chai and Building Machines that Learn and Think Like People by Josh Tenenbaum.

Best Research Papers of ICML 2018

Among the many interesting research papers that were submitted to the ICML 2018 conference, here are the winners.

Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples by Anish Athalye, Nicholas Carlini, and David Wagner was lauded and bestowed with the Best Paper award. The paper identifies obfuscated gradients, a kind of gradient masking, as a phenomenon that leads to a false sense of security in defenses against adversarial examples. They identify the three different types of obfuscated gradients and develop attack techniques to overcome them.

Delayed Impact of Fair Machine Learning by Lydia T. Liu, Sarah Dean, Esther Rolf, and Max Simchowitz also got the Best Paper award. This paper examines the circumstances where fairness criteria promotes the long-term well-being of disadvantaged groups, measured in terms of a temporal variable of interest. The paper also introduces a one-step feedback model of decision-making that exposes how decisions change the underlying population over time.

Bonus: The Test of Time award

Day 4 witnessed Facebook researchers Ronan Collobert and Jason Weston receiving the honorary ‘Test of Time award’ for their 2008 ICML paper, A Unified Architecture for Natural Language Processing: Deep Neural Networks with Multitask Learning. The paper proposed a single convolutional neural network that takes a sentence and outputs it’s language processing predictions. So the network can identify and distinguish part-of-speech tags, chunks, named entity tags, semantic roles, semantically similar words and the likelihood that the sentence makes sense (grammatically and semantically) using a language model.

At the time of the paper publishing there was almost no neural networks research in Natural Language Processing. The paper’s use of word embeddings and how they are trained, the use of auxiliary tasks and multitasking, and the use of convolutional neural nets in NLP, really inspired the neural networks of today.

For instance, Facebook’s recent machine translation and summarization tool Fairseq uses CNNs for language. AllenNLP’s Elmo learns improved word embeddings via a neural net language model and applies them to a large number of NLP tasks.

Featured Tutorials at ICML 2018

The ICML 2018 featured a total of 9 tutorials in sets of 3 each. All the tutorials took place on Day 1. These included:

  1. Imitation Learning by Yisong Yue and Hoang M Le where they gave a broad overview of imitation learning techniques and its recent applications.
  2. Learning with Temporal Point Processes by Manuel Gomez Rodriguez and Isabel Valera. They talk about temporal point processes in machine learning from basics to advanced concepts such as marks and dynamical systems with jumps.
  3. Machine Learning in Automated Mechanism Design for Pricing and Auctions by Nina Balcan, Tuomas Sandholm, and Ellen Vitercik. This tutorial covered automated mechanism design for revenue maximization.
  4. Toward Theoretical Understanding of Deep Learning by Sanjeev Arora where he explained about what kind of theory may ultimately arise for deep learning with examples.
  5. Defining and Designing Fair Algorithms by Sam Corbett-Davies and Sharad Goel. They illustrated the problems that lie at the foundation of algorithmic fairness, drawing on ideas from machine learning, economics, and legal theory.
  6. Understanding your Neighbors: Practical Perspectives From Modern Analysis by Sanjoy Dasgupta and Samory Kpotufe. This tutorial aimed to cover new perspectives on k-NN, and translate new theoretical insights to a broader audience.
  7. Variational Bayes and Beyond: Bayesian Inference for Big Data by Tamara Broderick where she covered modern tools for fast, approximate Bayesian inference at scale.
  8. Machine Learning for Personalised Health by Danielle Belgrave and Konstantina Palla. This tutorial evaluated the current drivers of machine learning in healthcare and present machine learning strategies for personalised health.
  9. Optimization Perspectives on Learning to Control by Benjamin Recht where he showed how to learn models of dynamical systems, how to use data to achieve objectives in a timely fashion, how to balance model specification etc.

Workshops at ICML 2018

Day 5 and 6 of the ICML 2018 conference were dedicated entirely for Workshops based on topics ranging from AI in health to AI in computational psychology to Humanizing AI to AI for Wildlife Conservation. Some other workshops included

  • Bridging the Gap between Human and Automated Reasoning
  • Data Science meets Optimization
  • Domain Adaptation for Visual Understanding
  • Eighth International Workshop on Statistical Relational AI
  • Enabling Reproducibility in Machine Learning MLTrain@RML
  • Engineering Multi-Agent Systems
  • Exploration in Reinforcement Learning
  • Federated AI for Robotics Workshop (F-Rob-2018)

This is just a brief overview of the ICML conference, where we have handpicked a select few paper presentations and invited talks. You can see the full schedule along with the list of selected research papers at the ICML website.

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Content Marketing Editor at Packt Hub. I blog about new and upcoming tech trends ranging from Data science, Web development, Programming, Cloud & Networking, IoT, Security and Game development.


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