How can you show what a Machine Learning model does once it’s trained?

In this talk, you are going to learn how to create Machine Learning apps and demos using Streamlit and Gradio, Python libraries for this purpose. Additionally, you’ll see how to share them with the rest of the Open Source ecosystem. Learning to create graphic interfaces for models is extremely useful for sharing with other people interesting them.

What is required to follow it?
👉 Basic knowledge of Python
👉 Conceptual knowledge of ML
👉 A google Colab account
👉 A Hugging Face Hub account

   Although machine learning and artificial intelligence have had many successes in the past years, many state-of-the-art methods still fail drastically in some real-life applications. One of the main reasons for that is the noisy and corrupted nature of real-life data. This failure phenomenon is what is typically known as overfitting.

In this talk, we study precisely what are the sources of overfitting in machine problems. The goal is to define formally what robustness properties are desired in machine learning algorithms to overcome this phenomenon and lead to stronger performance. We identify three overfitting sources naturally present in any dataset: (i) statistical error, as a result of working with finite sample data, (ii) noise, which occurs when the data points are measured only with finite precision, and finally (iii) data misspecification in which a small fraction of all data may be wholly corrupted. We show that existing machine learning formulations, such as LASSO and Ridge, are typically robust against one of these sources in isolation but do not provide protection against all overfitting sources simultaneously.

We design a novel machine learning formulation, which guarantees protection against all these sources of overfitting. We further show that this novel formulation provides “optimal” robustness. We finally show applications of our formulation to neural networks and show in experiments that the resulting novel robust neural networks considerably outperform state-of-art robust deep learning approaches.

Groundbreaking language-vision architectures like CLIP and DALL-E proved the utility of training on large amounts of noisy image-text data, without relying on expensive accurate labels used in standard vision uni-modal supervised learning.
The resulting models showed capabilities of strong text-guided image generation and transfer to downstream tasks, while performing remarkably at zero-shot classification with noteworthy out-of-distribution robustness.
Studying the training and capabilities of such models requires datasets containing billions of image-text pairs. Until now, no datasets of this size have been made openly available for the broader research community.
In this talk, I will present LAION-5B, a dataset consisting of 5.85 billion CLIP-filtered image-text pairs, and share our recent findings on training contrastive language-image models at a large scale, and how they perform on downstream tasks.

Language models trained on code have demonstrated remarkable code completion and synthesis abilities from natural language descriptions. However, the best-performing models are not publicly available, and critical information about their datasets, licensing, and evaluation is missing. BigCode project aims to build code models in an open and responsible approach with support from the ML community. We are working on addressing challenges related to dataset composition, model architecture, inference techniques, and evaluation. To this end, we released The Stack – the largest permissively licensed open-source dataset, with over 350 programming languages and an opt-out mechanism. We also developed SantaCoder – a 1.1B multilingual code model that outperforms larger open-source models in left-to-right code generation and infilling.

Uncertainty quantification is not an easy task. Its difficulty depends on various factors related to the available data, the application domain, and also the learned task. Having multiple outputs to predict simultaneously can be even more demanding, principally when these outputs are correlated.
This presentation focuses on producing confidence regions for such complex problems, mainly multi-target regression, by using conformal prediction: a theoretically proven method that can be added to any Machine Learning model to generate set predictions whose size and statistical guarantee depend on a user-defined error rate.

Explainable AI has gained a lot of attention from both legislators and the scientific community. There are many advantages of being able to explain the reasoning behind the decisions a model makes, top among them are fairness, accountability, and causality. More and more, explainability is used to improve both human and machine decision-making in a mutually reinforcing loop. This session specifically focuses on post hoc local explainability for transformer-based NLP through a practical industrial project : the oncall assistant. It first details the general and scientific methods for explaining BERT and then explores challenges for a real-world implementation.

Neural networks have achieved impressive performance in many applications such as image recognition and generation, and speech recognition. State-of-the-art performance is usually achieved via a series of engineered modifications to existing neural architectures and their training procedures. However, a common feature of these systems is their large-scale nature. Indeed, modern neural networks usually contain millions – if not billions – of trainable parameters, and empirical evaluations (generally) support the claim that increasing the scale of neural networks (e.g. width and depth) boosts the model performance. However, given a neural network model, it is not straightforward to address the crucial question `how do we scale the network?’. In this talk, I will discuss certain properties of large-scale residual neural networks and show how we can leverage different mathematical results to build robust residual networks with empirically confirmed benefits.

High Mountain Asia supplies freshwater to over one billion people via Asia’s largest rivers. In this area, rain and snowfall are the main drivers of river flow. However, the spatiotemporal distribution of precipitation is still poorly understood due to limited direct measurements from weather stations. Existing tools to fill in missing data or improve the resolution of coarser precipitation products produce biased results. In this talk, I will propose a method to generate more accurate high-resolution precipitation predictions over areas with sparse in situ data, called Multi-Fidelity Gaussian Processes (MFGPs). MFGP can combine multiple precipitation sources to increase the accuracy of precipitation estimates while providing principled uncertainties. This method can also make predictions in ungauged locations, away from the high-fidelity training distribution. Finally, MFGPs are simpler to implement and more applicable to small datasets than state-of-the-art machine learning models.

How can we acquire world models that veridically represent the outside world both in terms of what is there and in terms of how our actions affect it?
Can we state mathematical desiderata for their relationship with a posited reality existing outside our brains?
As machine learning is moving towards representations containing not just observational but also interventional knowledge, we study these problems using tools from representation learning and group theory.
We propose methods enabling an agent acting upon the world to learn internal representations of sensory information that are consistent with actions that modify it.
We present the Homomorphism AutoEncoder, an autoencoder equipped with a learnable group representation acting on its latent space, trained using an equivariance-derived loss to enforce a suitable “homomorphism” property on the group representation.

The success of modern-day machine learning models can be attributed to algorithms and systems that effectively leverage the large amounts of available distributed data. In fact, very large models cannot be trained nor stored on a single machine anymore, and it is hard to collect the required data centrally. This explains the recent popularity of distributed optimization algorithms and paradigms like Federated Learning. Unfortunately, the gains achieved using distributed methods come with fundamental trade-offs with important desiderata: robustness and privacy. Specifically, we overview the algorithmic and theoretical advances in making distributed machine learning methods robust to adversarial participants and poisoned data, as well as ensuring that they preserve the privacy of the participants’ data, and the resulting fundamental trade-offs.