Toggle navigation. This tutorial will survey some of the key challenges in this context and then focus on the topic of adversarial robustness: the widespread vulnerability of state-of-the-art deep learning models to adversarial misclassification aka adversarial examples. We will discuss the practical as well as theoretical aspects of this phenomenon, with an emphasis on recent verification-based approaches to establishing formal robustness guarantees. Our treatment will go beyond viewing adversarial robustness solely as a security question. In particular, we will touch on the role it plays as a regularizer and its relation to generalization.
The most well-known examples of negatively dependent distributions are perhaps the Determinantal Point Processes DPPswhich have already found numerous ML applications. See nips clips Haqq. Distributed stochastic gradient descent is an important subroutine in distributed learning. Learn how your comment data is See nips clips. It can also be viewed as clkps a new kernel optimized for gradient descent. Neural networks are a powerful class of nonlinear functions that can be trained end-to-end on various applications. We cast interpretability of black-box classifiers as a combinatorial maximization Lynda baron nude pics and propose an efficient streaming algorithm to solve it subject to cardinality constraints. Brit-Brit suffered a similar fate in February when one of her dancers disassembled her leotard by mistake.
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Toggle navigation. Optimal transport OT provides a powerful and flexible way to compare probability measures, discrete and continuous, which includes therefore point clouds, histograms, datasets, parametric and generative models.
OT recently has reached the machine learning community, because it can tackle challenging learning scenarios including dimensionality reduction, structured prediction problems that involve histogram outputs, and estimation of generative models such as GANs in highly degenerate, high-dimensional problems.
Despite very recent successes bringing OT from theory to practice, OT remains challenging for the machine learning community because of its mathematical formality. This tutorial will introduce in an approachable way crucial theoretical, computational, algorithmic and practical aspects of OT needed for machine learning applications. Deep Learning has become an essential toolbox which is used in a wide variety of applications, research labs, industry, etc.
In this tutorial, we will provide a set of guidelines which will help newcomers to the field understand the most recent and advanced models, their application to diverse data modalities such as images, videos, waveforms, sequences, graphs, and to complex tasks such as learning to learn from a few examples, or generating molecules. There has been recent very exciting advances in deep reinforcement learning, particularly in the areas of games and robotics.
Yet perhaps the largest impact could come when reinforcement learning systems interact with people. In this tutorial we will discuss work on reinforcement learning for helping and assisting people, and frameworks and approaches for enabling people helping reinforcement learning.
We will cover Background on reinforcement learning. Reinforcement learning for people-focused applications Approaches for enabling people to assist reinforcement learners A number of the ideas presented here will also be relevant to many high stakes reinforcement learning systems.
Target audience: The majority of the tutorial will be aimed at an audience who has a basic machine learning background e. Learning objectives: Know some of the key technical challenges that arise for reinforcement learning in people-focusing domains; understand some of the algorithms and approaches that have been developed to address these challenges; become familiar with some of the other application areas that have also or can also benefit from reinforcement learning.
Over the past few years, fairness has emerged as a matter of serious concern within machine learning. There is growing recognition that even models developed with the best of intentions may exhibit discriminatory biases, perpetuate inequality, or perform less well for historically disadvantaged groups.
Considerable work is already underway within and outside machine learning to both characterize and address these problems. This tutorial will take a novel approach to parsing the topic, adopting three perspectives: statistics, causality, and measurement.
Each viewpoint will shed light on different facets of the problem and help explain matters of continuing technical and normative debate. Rather than attempting to resolve questions of fairness within a single technical framework, the tutorial aims to equip the audience with a coherent toolkit to critically examine the many ways that machine learning implicates fairness.
Neural network models are algorithmically simple, but mathematically complex. Gaussian process models are mathematically simple, but algorithmically complex. In this tutorial we will explore Deep Gaussian Process models. They bring advantages in their mathematical simplicity but are challenging in their algorithmic complexity. We will give an overview of Gaussian processes and highlight the algorithmic approximations that allow us to stack Gaussian process models: they are based on variational methods.
In the last part of the tutorial will explore a use case exemplar: uncertainty quantification. We end with open questions. This tutorial will provide a gentle introduction into the foundations of statistical relational artificial intelligence, and will realize this by introducing the foundations of logic, of probability, of learning, and their respective combinations.
Both predicate logic and probability theory extend propositional logic, one by adding relations, individuals and quantified variables, the other by allowing for measures over possible worlds and conditional queries. While logical and probabilistic approaches have often been studied and used independently within artificial intelligence, they are not in conflict with each other but they are synergistic.
This explains why there has been a considerable body of research in combining first-order logic and probability over the last 25 years, evolving into what has come to be called Statistical Relational Artificial Intelligence StarAI.
Relational probabilistic models — we use this term in the broad sense, meaning any models that combine relations and probabilities — form the basis of StarAI, and can be seen as combinations of probability and predicate calculus that allow for individuals and relations as well as probabilities.
In building on top of relational models, StarAI goes far beyond reasoning, optimization, learning and acting optimally in terms of a fixed number of features or variables, as it is typically studied in machine learning, constraint satisfaction, probabilistic reasoning, and other areas of AI.
Since StarAI draws upon ideas developed within many different fields, however, it can also be quite challenging for newcomers to get started and our tutorial precisely aims to provide this background.
Differential privacy has emerged as one of the de-facto standards for measuring privacy risk when performing computations on sensitive data and disseminating the results. Algorithms that guarantee differential privacy are randomized, which causes a loss in performance, or utility. Managing the privacy-utility tradeoff becomes easier with more data. Many machine learning algorithms can be made differentially private through the judicious introduction of randomization, usually through noise, within the computation.
In this tutorial we will describe the basic framework of differential privacy, key mechanisms for guaranteeing privacy, and how to find differentially private approximations to several contemporary machine learning tools: convex optimization, Bayesian methods, and deep learning.
In the past years, deep learning methods have achieved unprecedented performance on a broad range of problems in various fields from computer vision to speech recognition. So far research has mainly focused on developing deep learning methods for Euclidean-structured data, while many important applications have to deal with non-Euclidean structured data, such as graphs and manifolds.
Such geometric data are becoming increasingly important in computer graphics and 3D vision, sensor networks, drug design, biomedicine, recommendation systems, and web applications. The adoption of deep learning in these fields has been lagging behind until recently, primarily since the non-Euclidean nature of objects dealt with makes the very definition of basic operations used in deep networks rather elusive.
The purpose of the proposed tutorial is to introduce the emerging field of geometric deep learning on graphs and manifolds, overview existing solutions and applications for this class of problems, as well as key difficulties and future research directions. Recent successes in computer vision, natural language processing and other areas of artificial intelligence have been largely driven by methods for sophisticated pattern recognition — most prominently deep neural networks. But human intelligence is more than just pattern recognition.
We will talk about prospects for reverse-engineering these capacities at the heart of human intelligence, and using what we learn to make machines smarter in more human-like ways. We introduce basic concepts and techniques of probabilistic programs, inference programming and program induction, which together with tools from deep learning and modern video game engines provide an approach to capturing many aspects of everyday intelligence.
Specific units in our tutorial will show how: 1 Defining probabilistic programs over algorithms and representations drawn from modern video game engines — graphics engines, physics engines, and planning engines — allows us to capture how people can perceive rich three-dimensional structure in visual scenes and objects, perceive and predict objects' motion based on their physical characteristics, and infer the mental states of other people from observing their actions.
These programs can learn new concepts from just one or a few examples. These languages provide powerful tools for robotics, interactive data analysis, and scientific discovery. My goal is to let everyone on Earth be able to use the same amount of energy per year as the average U.
To reach this goal by will require 0. How can human civilization obtain this much energy without flooding the atmosphere with carbon dioxide? To answer this question, I'll first dive into the economics of electricity, in order to understand the limits of current zero-carbon technologies. These limits cause us to investigate zero-carbon technologies that are still being developed, such as fusion energy.
For fusion, I'll show why it's been a tough problem for almost 70 years, and why there may be a solution in the near future. I'll also explain how we've been using machine learning and optimization to accelerate fusion research. We propose a framework that learns a representation transferable across different domains and tasks in a data efficient manner.
Our approach battles domain shift with a domain adversarial loss, and generalizes the embedding to novel task using a metric learning-based approach.
Our model is simultaneously optimized on labeled source data and unlabeled or sparsely labeled data in the target domain. Our method shows compelling results on novel classes within a new domain even when only a few labeled examples per class are available, outperforming the prevalent fine-tuning approach. In addition, we demonstrate the effectiveness of our framework on the transfer learning task from image object recognition to video action recognition. Many machine learning tasks require finding per-part correspondences between objects.
In this work we focus on low-level correspondences a highly ambiguous matching problem. We propose to use a hierarchical semantic representation of the objects, coming from a convolutional neural network, to solve this ambiguity. Training it for low-level correspondence prediction directly might not be an option in some domains where the ground-truth correspondences are hard to obtain. We show how transfer from recognition can be used to avoid such training.
Although the overall number of such paths is exponential in the number of layers, we propose a polynomial algorithm for aggregating all of them in a single backward pass.
The empirical validation is done on the task of stereo correspondence and demonstrates that we achieve competitive results among the methods which do not use labeled target domain data. We investigate an unsupervised generative approach for network embedding.
A multi-task Siamese neural network structure is formulated to connect embedding vectors and our objective to preserve the global node ranking and local proximity of nodes. We provide deeper analysis to connect the proposed proximity objective to link prediction and community detection in the network. We show our model can satisfy the following design properties: scalability, asymmetry, unity and simplicity. Experiment results not only verify the above design properties but also demonstrate the superior performance in learning-to-rank, classification, regression, and link prediction tasks.
For the purpose of learning on graphs, we hunt for a graph feature representation that exhibit certain uniqueness, stability and sparsity properties while also being amenable to fast computation. This leads to the discovery of family of graph spectral distances denoted as FGSD and their based graph feature representations, which we prove to possess most of these desired properties. To both evaluate the quality of graph features produced by FGSD and demonstrate their utility, we apply them to the graph classification problem.
Through extensive experiments, we show that a simple SVM based classification algorithm, driven with our powerful FGSD based graph features, significantly outperforms all the more sophisticated state-of-art algorithms on the unlabeled node datasets in terms of both accuracy and speed; it also yields very competitive results on the labeled datasets - despite the fact it does not utilize any node label information. We propose a novel adaptive approximation approach for test-time resource-constrained prediction motivated by Mobile, IoT, health, security and other applications, where constraints in the form of computation, communication, latency and feature acquisition costs arise.
We learn an adaptive low-cost system by training a gating and prediction model that limits utilization of a high-cost model to hard input instances and gates easy-to-handle input instances to a low-cost model.
Our method is based on adaptively approximating the high-cost model in regions where low-cost models suffice for making highly accurate predictions. We pose an empirical loss minimization problem with cost constraints to jointly train gating and prediction models. On a number of benchmark datasets our method outperforms state-of-the-art achieving higher accuracy for the same cost.
Obtaining enough labeled data to robustly train complex discriminative models is a major bottleneck in the machine learning pipeline. A popular solution is combining multiple sources of weak supervision using generative models. The structure of these models affects the quality of the training labels, but is difficult to learn without any ground truth labels. We instead rely on weak supervision sources having some structure by virtue of being encoded programmatically.
We present Coral, a paradigm that infers generative model structure by statically analyzing the code for these heuristics, thus significantly reducing the amount of data required to learn structure. We prove that Coral's sample complexity scales quasilinearly with the number of heuristics and number of relations identified, improving over the standard sample complexity, which is exponential in n for learning n-th degree relations.
Empirically, Coral matches or outperforms traditional structure learning approaches by up to 3. Using Coral to model dependencies instead of assuming independence results in better performance than a fully supervised model by 3. Learning a regression function using censored or interval-valued output data is an important problem in fields such as genomics and medicine.
The goal is to learn a real-valued prediction function, and the training output labels indicate an interval of possible values. Whereas most existing algorithms for this task are linear models, in this paper we investigate learning nonlinear tree models.
We propose to learn a tree by minimizing a margin-based discriminative objective function, and we provide a dynamic programming algorithm for computing the optimal solution in log-linear time.
We show empirically that this algorithm achieves state-of-the-art speed and prediction accuracy in a benchmark of several data sets. Spectral methods of moments provide a powerful tool for learning the parameters of latent variable models. Despite their theoretical appeal, the applicability of these methods to real data is still limited due to a lack of robustness to model misspecification.
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Toggle navigation. This tutorial will survey some of the key challenges in this context and then focus on the topic of adversarial robustness: the widespread vulnerability of state-of-the-art deep learning models to adversarial misclassification aka adversarial examples.
We will discuss the practical as well as theoretical aspects of this phenomenon, with an emphasis on recent verification-based approaches to establishing formal robustness guarantees. Our treatment will go beyond viewing adversarial robustness solely as a security question. In particular, we will touch on the role it plays as a regularizer and its relation to generalization. Visualization is a powerful way to understand and interpret machine learning--as well as a promising area for ML researchers to investigate.
This tutorial will provide an introduction to the landscape of ML visualizations, organized by types of users and their goals.
We'll discuss how each stage of the ML research and development pipeline lends itself to different visualization techniques: analyzing training data, understanding the internals of a model, and testing performance. The tutorial will also include a brief introduction to key techniques from the fields of graphic design and human-computer interaction that are relevant in designing data displays. These ideas are helpful whether refining existing visualizations, or inventing entirely new visual techniques.
This tutorial will provide a practical overview of state-of-the-art approaches for analyzing massive data sets using Bayesian statistical methods. The first focus area will be on algorithms for very large sample size data large n , and the second focus area will be on approaches for very high-dimensional data large p.
A particular emphasis will be on maintaining a valid characterization of uncertainty, ruling out many popular methods, such as most variational approximations and approaches for maximum a posteriori estimation. I will briefly review classical large sample approximations to posterior distributions e.
The focus is on making posterior computation much faster to implement for huge datasets while maintaining accuracy guarantees. Some useful classes of algorithms having increasing theoretical and practical support include embarrassingly parallel EP MCMC, approximate MCMC, stochastic approximation, hybrid optimization and sampling, and modularization.
Applications to computational advertising, genomics, neurosciences and other areas will provide a concrete motivation. Code and notes will be made available, and research problems of ongoing interest highlighted.
Unsupervised learning looks set to play an ever more important role for deep neural networks, both as a way of harnessing vast quantities of unlabelled data, and as a means of learning representations that can rapidly generalise to new tasks and situations.
The central challenge is how to determine what the objective function should be, when by definition we do not have an explicit target in mind. However, we will also survey a range of other techniques, including un-normalized energy-based models, self-supervised algorithms and purely generative models such as GANs. Time allowing, we will extend our discussion to the reinforcement learning setting, where the natural analogue of unsupervised learning is intrinsic motivation, and notions such as curiosity, empowerment and compression progress are invoked as drivers of learning.
As machine learning becomes increasingly important in everyday life, researchers have examined its relationship to people and society to answer calls for more responsible uses of data-driven technologies. Much work has focused on fairness, accountability, and transparency as well as on explanation and interpretability. However, these terms have resisted definition by computer scientists: while many definitions of each have been put forward, several capturing natural intuitions, these definitions do not capture everything that is meant by associated concept, causing friction with other disciplines and the public.
Worse, sometimes different properties conflict explicitly or cannot be satisfied simultaneously. Drawing on our research on the meanings of these terms and the concepts they refer to across different disciplines e. For example, it is often axiomatic that producing machine learning explanations automatically makes the outputs of a model more understandable, but this is hardly if ever the case.
Similarly, defining fairness as a statistical property of the distribution of model outputs ignores the many procedural requirements supporting fairness in policymaking and the operation of the law. We describe how to integrate the rich meanings of these concepts into machine learning research and practice, enabling attendees to engage with disparate communities of research and practice and to recognize when terms are being overloaded, thereby avoiding speaking to people from other disciplines at cross purposes.
This tutorial provides an introduction to a rapidly evolving topic: the theory of negative dependence and its numerous ramifications in machine learning. Indeed, negatively dependent probability measures provide a powerful tool for modeling non-i. The most well-known examples of negatively dependent distributions are perhaps the Determinantal Point Processes DPPs , which have already found numerous ML applications.
But DPPs are just the tip of the iceberg; the class of negatively dependent measures is much broader, and given the vast web of mathematical connections it enjoys, its holds great promise as a tool for machine learning. This tutorial exposes the ML audience to this rich mathematical toolbox, while outlining key theoretical ideas and motivating fundamental applications.
Tasks that profit from negative dependence include anomaly detection, information maximization, experimental design, validation of black-box systems, architecture learning, fast MCMC sampling, dataset summarization, interpretable learning. The success of machine learning crucially relies on human machine learning experts, who construct appropriate features and workflows, and select appropriate machine learning paradigms, algorithms, neural architectures, and their hyperparameters.
Automatic machine learning AutoML is an emerging research area that targets the progressive automation of machine learning, which uses machine learning and optimization to develop off-the-shelf machine learning methods that can be used easily and without expert knowledge. It covers a broad range of subfields, including hyperparameter optimization, neural architecture search, meta-learning, and transfer learning.
This tutorial will cover the methods underlying the current state of the art in this fast-paced field. The tutorial will showcase what statistical learning theory aims to assess about and hence deliver for learning systems.
We will highlight how algorithms can piggy back on its results to improve the performances of learning algorithms as well as to understand their limitations. The tutorial is aimed at those wishing to gain an understanding of the value and role of statistical learning theory in order to hitch a ride on its results.
This tutorial will review the literature that brings together recent developments in machine learning with methods for counterfactual inference. The tutorial will consider two strands of the literature. The first strand attempts to estimate causal effects of a single intervention, like a drug or a price change.
The goal can be to estimate the average counterfactual effect of applying the treatment to everyone; or the conditional average treatment effect, which is the effect of applying the treatment to an individual conditional on covariates. We will also consider the problem of estimating an optimal treatment assignment policy mapping features to assignments under constraints on the nature of the policy, such as budget constraints.
We look at applications to assigning unemployed workers to re-employment services. We finish by considering the case with multiple alternative treatments, as well as the link between this literature and the literature on contextual bandits.
We look at applications to consumer choice behavior, and analyze counterfactuals around price changes. We discuss how models such as these can be tuned when the goal is counterfactual estimation rather than predicting outcomes. AI and Machine Learning are already having a big impact on the world. Policymakers have noticed, and they are starting to formulate laws and regulations, and to convene conversations, about how society will govern the development of these technologies.
We define the capacity of a learning machine to be the logarithm of the number or volume of the functions it can implement. We review known results, and derive new results, estimating the capacity of several neuronal models: linear and polynomial threshold gates, linear and polynomial threshold gates with constrained weights binary weights, positive weights , and ReLU neurons.
We also derive capacity estimates and bounds for fully recurrent networks and layered feedforward networks. As is common in many imaging problems, previous methodologies have considered natural signals as being sparse with respect to a known basis, resulting in the decision to enforce a generic sparsity prior. This formulation for structured phase retrieval thus benefits from two effects: generative priors can more tightly represent natural signals than sparsity priors, and this empirical risk formulation can exploit those generative priors at an information theoretically optimal sample complexity, unlike for a sparsity prior.
We corroborate these results with experiments showing that exploiting generative models in phase retrieval tasks outperforms both sparse and general phase retrieval methods. Neural networks have many successful applications, while much less theoretical understanding has been gained.
Towards bridging this gap, we study the problem of learning a two-layer overparameterized ReLU neural network for multi-class classification via stochastic gradient descent SGD from random initialization.
In the overparameterized setting, when the data comes from mixtures of well-separated distributions, we prove that SGD learns a network with a small generalization error, albeit the network has enough capacity to fit arbitrary labels.
Furthermore, the analysis provides interesting insights into several aspects of learning neural networks and can be verified based on empirical studies on synthetic data and on the MNIST dataset. Recent research has shown that word embedding spaces learned from text corpora of different languages can be aligned without any parallel data supervision.
Inspired by the success in unsupervised cross-lingual word embeddings, in this paper we target learning a cross-modal alignment between the embedding spaces of speech and text learned from corpora of their respective modalities in an unsupervised fashion.
The proposed framework learns the individual speech and text embedding spaces, and attempts to align the two spaces via adversarial training, followed by a refinement procedure. We show how our framework could be used to perform the tasks of spoken word classification and translation, and the experimental results on these two tasks demonstrate that the performance of our unsupervised alignment approach is comparable to its supervised counterpart.
Our framework is especially useful for developing automatic speech recognition ASR and speech-to-text translation systems for low- or zero-resource languages, which have little parallel audio-text data for training modern supervised ASR and speech-to-text translation models, but account for the majority of the languages spoken across the world.
Our theoretical findings are complemented by numerical experiments, which demonstrate superior performance of the proposed approach over the previous methods. This paper investigates the ability of generative networks to convert their input noise distributions into other distributions.
We show this construction is optimal by analyzing the number of affine pieces in functions computed by multivariate ReLU networks. Lastly, we indicate how high dimensional distributions can be efficiently transformed into low dimensional distributions.
Textual network embedding leverages rich text information associated with the network to learn low-dimensional vectorial representations of vertices. Rather than using typical natural language processing NLP approaches, recent research exploits the relationship of texts on the same edge to graphically embed text.
However, these models neglect to measure the complete level of connectivity between any two texts in the graph. We present diffusion maps for textual network embedding DMTE , integrating global structural information of the graph to capture the semantic relatedness between texts, with a diffusion-convolution operation applied on the text inputs. In addition, a new objective function is designed to efficiently preserve the high-order proximity using the graph diffusion.
Experimental results show that the proposed approach outperforms state-of-the-art methods on the vertex-classification and link-prediction tasks. In recent years, unfolding iterative algorithms as neural networks has become an empirical success in solving sparse recovery problems.
However, its theoretical understanding is still immature, which prevents us from fully utilizing the power of neural networks. We introduce a weight structure that is necessary for asymptotic convergence to the true sparse signal. Furthermore, we propose to incorporate thresholding in the network to perform support selection, which is easy to implement and able to boost the convergence rate both theoretically and empirically.
Extensive simulations, including sparse vector recovery and a compressive sensing experiment on real image data, corroborate our theoretical results and demonstrate their practical usefulness.
Deep learning has seen remarkable developments over the last years, many of them inspired by neuroscience. However, the main learning mechanism behind these advances — error backpropagation — appears to be at odds with neurobiology. Here, we introduce a multilayer neuronal network model with simplified dendritic compartments in which error-driven synaptic plasticity adapts the network towards a global desired output.
In contrast to previous work our model does not require separate phases and synaptic learning is driven by local dendritic prediction errors continuously in time. Such errors originate at apical dendrites and occur due to a mismatch between predictive input from lateral interneurons and activity from actual top-down feedback.
Through the use of simple dendritic compartments and different cell-types our model can represent both error and normal activity within a pyramidal neuron. We demonstrate the learning capabilities of the model in regression and classification tasks, and show analytically that it approximates the error backpropagation algorithm. Moreover, our framework is consistent with recent observations of learning between brain areas and the architecture of cortical microcircuits.
Overall, we introduce a novel view of learning on dendritic cortical circuits and on how the brain may solve the long-standing synaptic credit assignment problem. We give a polynomial-time algorithm for learning latent-state linear dynamical systems without system identification, and without assumptions on the spectral radius of the system's transition matrix. The algorithm extends the recently introduced technique of spectral filtering, previously applied only to systems with a symmetric transition matrix, using a novel convex relaxation to allow for the efficient identification of phases.
Generative adversarial network GAN is a minimax game between a generator mimicking the true model and a discriminator distinguishing the samples produced by the generator from the real training samples. Given an unconstrained discriminator able to approximate any function, this game reduces to finding the generative model minimizing a divergence measure, e.
However, in practice the discriminator is constrained to be in a smaller class F such as neural nets. Then, a natural question is how the divergence minimization interpretation changes as we constrain F.