mtybilly/Instruct2DS huggingface dataset:
The Point Where Reality Meets Fantasy: Mixed Adversarial Generators for Image Splice Detection
Authors: Vladimir V. Kniaz, Vladimir Knyaz, Fabio Remondino
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: Modern photo editing tools allow creating realistic manipulated images easily. While fake images can be quickly generated, learning models for their detection is challenging due to the high variety of tampering artifacts and the lack of large labeled datasets of manipulated images. In this paper, we propose a new framework for training of discriminative segmentation model via an adversarial process. We simultaneously train four models: a generative retouching model GR that translates manipulated image to the real image domain, a generative annotation model GA that estimates the pixel-wise probability of image patch being either real or fake, and two discriminators DR and DA that qualify the output of GR and GA. The aim of model GR is to maximize the probability of model GA making a mistake. Our method extends the generative adversarial networks framework with two main contributions: (1) training of a generative model GR against a deep semantic segmentation network GA that learns rich scene semantics for manipulated region detection, (2) proposing per class semantic loss that facilitates semantically consistent image retouching by the G_R. We collected large-scale manipulated image dataset to train our model. The dataset includes 16k real and fake images with pixel-level annotations of manipulated areas. The dataset also provides ground truth pixel-level object annotations. We validate our approach on several modern manipulated image datasets, where quantitative results and ablations demonstrate that our method achieves and surpasses the state-of-the-art in manipulated image detection. We made our code and dataset publicly available.
Self-Supervised Deep Learning on Point Clouds by Reconstructing Space
Authors: Jonathan Sauder, Bjarne Sievers
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: Point clouds provide a flexible and natural representation usable in countless applications such as robotics or self-driving cars. Recently, deep neural networks operating on raw point cloud data have shown promising results on supervised learning tasks such as object classification and semantic segmentation. While massive point cloud datasets can be captured using modern scanning technology, manually labelling such large 3D point clouds for supervised learning tasks is a cumbersome process. This necessitates methods that can learn from unlabelled data to significantly reduce the number of annotated samples needed in supervised learning. We propose a self-supervised learning task for deep learning on raw point cloud data in which a neural network is trained to reconstruct point clouds whose parts have been randomly rearranged. While solving this task, representations that capture semantic properties of the point cloud are learned. Our method is agnostic of network architecture and outperforms current unsupervised learning approaches in downstream object classification tasks. We show experimentally, that pre-training with our method before supervised training improves the performance of state-of-the-art models and significantly improves sample efficiency.
Outlier-Robust High-Dimensional Sparse Estimation via Iterative Filtering
Authors: Ilias Diakonikolas, Daniel Kane, Sushrut Karmalkar, ecprice, Alistair Stewart
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: We study high-dimensional sparse estimation tasks in a robust setting where a constant fraction
of the dataset is adversarially corrupted. Specifically, we focus on the fundamental problems of robust
sparse mean estimation and robust sparse PCA.
We give the first practically viable robust estimators for these problems. 
In more detail, our algorithms are sample and computationally efficient 
and achieve near-optimal robustness guarantees. 
In contrast to prior provable algorithms which relied on the ellipsoid method, 
our algorithms use spectral techniques to iteratively remove outliers from the dataset. 
Our experimental evaluation on synthetic data shows that our algorithms are scalable and 
significantly outperform a range of previous approaches, nearly matching the best error rate without corruptions.
ODE2VAE: Deep generative second order ODEs with Bayesian neural networks
Authors: Cagatay Yildiz, Markus Heinonen, Harri Lahdesmaki
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: We present Ordinary Differential Equation Variational Auto-Encoder (ODE2VAE), a latent second order ODE model for high-dimensional sequential data. Leveraging the advances in deep generative models, ODE2VAE can simultaneously learn the embedding of high dimensional trajectories and infer arbitrarily complex continuous-time latent dynamics. Our model explicitly decomposes the latent space into momentum and position components and solves a second order ODE system, which is in contrast to recurrent neural network (RNN) based time series models and recently proposed black-box ODE techniques. In order to account for uncertainty, we propose probabilistic latent ODE dynamics parameterized by deep Bayesian neural networks. We demonstrate our approach on motion capture, image rotation, and bouncing balls datasets. We achieve state-of-the-art performance in long term motion prediction and imputation tasks.
Cross-Domain Transferability of Adversarial Perturbations
Authors: Muhammad Muzammal Naseer, Salman H Khan, Muhammad Haris Khan, Fahad Shahbaz Khan, Fatih Porikli
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: Adversarial examples reveal the blind spots of deep neural networks (DNNs) and represent a major concern for security-critical applications. The transferability of adversarial examples makes real-world attacks possible in black-box settings, where the attacker is forbidden to access the internal parameters of the model. The underlying assumption in most adversary generation methods, whether learning an instance-specific or an instance-agnostic perturbation, is the direct or indirect reliance on the original domain-specific data distribution. In this work, for the first time, we demonstrate the existence of domain-invariant adversaries, thereby showing common adversarial space among different datasets and models. To this end, we propose a framework capable of launching highly transferable attacks that crafts adversarial patterns to mislead networks trained on wholly different domains. For instance, an adversarial function learned on Paintings, Cartoons or Medical images can successfully perturb ImageNet samples to fool the classifier, with success rates as high as $\sim$99\% ($\ell_{\infty} \le 10$). The core of our proposed adversarial function is a generative network that is trained using a relativistic supervisory signal that enables domain-invariant perturbations. Our approach sets the new state-of-the-art for fooling rates, both under the white-box and black-box scenarios. Furthermore, despite being an instance-agnostic perturbation function, our attack outperforms the conventionally much stronger instance-specific attack methods.
Thresholding Bandit with Optimal Aggregate Regret
Authors: Chao Tao, Saúl Blanco, Jian Peng, Yuan Zhou
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: We consider the thresholding bandit problem,  whose goal is to find arms of mean rewards above a given threshold $\theta$, with a fixed budget of $T$ trials. We introduce LSA, a new, simple and anytime algorithm that aims to minimize the aggregate regret (or the expected number of mis-classified arms). We prove that our algorithm is instance-wise asymptotically optimal. We also provide comprehensive empirical results to demonstrate the algorithm's superior performance over existing algorithms under a variety of different scenarios.
Recovering Bandits
Authors: Ciara Pike-Burke, Steffen Grunewalder
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: We study the recovering bandits problem, a variant of the stochastic multi-armed bandit problem where the expected reward of each arm varies according to some unknown function of the time since the arm was last played. While being a natural extension of the classical bandit problem that arises in many real-world settings, this variation is accompanied by significant difficulties. In particular, methods need to plan ahead and estimate many more quantities than in the classical bandit setting. In this work, we explore the use of Gaussian processes to tackle the estimation and planing problem. We also discuss different regret definitions that let us quantify the performance of the methods. To improve computational efficiency of the methods, we provide an optimistic planning approximation. We complement these discussions with regret bounds and empirical studies.
A neurally plausible model for online recognition and postdiction in a dynamical environment
Authors: Li Kevin Wenliang, Maneesh Sahani
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: Humans and other animals are frequently near-optimal in their ability to integrate noisy and ambiguous sensory data to form robust percepts---which are informed both by sensory evidence and by prior expectations about the structure of the environment. It is suggested that the brain does so using the statistical structure provided by an internal model of how latent, causal factors produce the observed patterns. In dynamic environments, such integration often takes the form of \emph{postdiction}, wherein later sensory evidence affects inferences about earlier percepts.  As the brain must operate in current time, without the luxury of acausal propagation of information, how does such postdictive inference come about? Here, we propose a general framework for neural probabilistic inference in dynamic models based on the distributed distributional code (DDC) representation of uncertainty, naturally extending the underlying encoding to incorporate implicit probabilistic beliefs about both present and past. We show that, as in other uses of the DDC, an inferential model can be learnt efficiently using samples from an internal model of the world. Applied to stimuli used in the context of psychophysics experiments, the framework provides an online and plausible mechanism for inference, including postdictive effects.
Limits of Private Learning with Access to Public Data
Authors: Noga Alon, Raef Bassily, Shay Moran
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: We consider learning problems where the training set consists of two types of examples: private and public. The goal is to design a learning algorithm that satisfies differential privacy only with respect to the private examples. This setting interpolates between private learning (where all examples are private) and classical learning (where all examples are public). 

We study the limits of learning in this setting in terms of private and public sample complexities. We show that any hypothesis class of VC-dimension $d$ can be agnostically learned up to an excess error of $\alpha$ using only (roughly) $d/\alpha$ public examples and $d/\alpha^2$ private labeled examples. This result holds even when the public examples are unlabeled. This gives a quadratic improvement over the standard $d/\alpha^2$ upper bound on the public sample complexity (where private examples can be ignored altogether if the public examples are labeled). Furthermore, we give a nearly matching lower bound, which we prove via a generic reduction from this setting to the one of private learning without public data.
Optimizing Generalized PageRank Methods for Seed-Expansion Community Detection
Authors: Pan Li, I Chien, Olgica Milenkovic
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: Landing probabilities (LP) of random walks (RW) over graphs encode rich information regarding graph topology. Generalized PageRanks (GPR), which represent weighted sums of LPs of RWs, utilize the discriminative power of LP features to enable many graph-based learning studies. Previous work in the area has mostly focused on evaluating suitable weights for GPRs, and only a few studies so far have attempted to derive the optimal weights of GPRs for a given application. We take a fundamental step forward in this direction by using random graph models to better our understanding of the behavior of GPRs. In this context, we provide a rigorous non-asymptotic analysis for the convergence of LPs and GPRs to their mean-field values on edge-independent random graphs. Although our theoretical results apply to many problem settings, we focus on the task of seed-expansion community detection over stochastic block models. There, we find that the predictive power of LPs decreases significantly slower than previously reported based on asymptotic findings. Given this result, we propose a new GPR, termed Inverse PR (IPR), with LP weights that increase for the initial few steps of the walks. Extensive experiments on both synthetic and real, large-scale networks illustrate the superiority of IPR compared to other GPRs for seeded community detection.
Importance Resampling for Off-policy Prediction
Authors: Matthew Schlegel, Wesley Chung, Daniel Graves, Jian Qian, Martha White
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: Importance sampling (IS) is a common reweighting strategy for off-policy prediction in reinforcement learning.  While it is consistent and unbiased, it can result in high variance updates to the weights for the value function. In this work, we explore a resampling strategy as an alternative to reweighting. We propose Importance Resampling (IR) for off-policy prediction, which resamples experience from a replay buffer and applies standard on-policy updates. The approach avoids using importance sampling ratios in the update, instead correcting the distribution before the update. We characterize the bias and consistency of IR, particularly compared to Weighted IS (WIS). We demonstrate in several microworlds that IR has improved sample efficiency and lower variance updates, as compared to IS and several variance-reduced IS strategies, including variants of WIS and V-trace which clips IS ratios. We also provide a demonstration showing IR improves over IS for learning a value function from images in a racing car simulator.
A Condition Number for Joint Optimization of Cycle-Consistent Networks
Authors: Leonidas Guibas, Qixing Huang, Zhenxiao Liang
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: A recent trend in optimizing maps such as dense correspondences between objects or neural networks between pairs of domains is to optimize them jointly. In this context, there is a natural \textsl{cycle-consistency} constraint, which regularizes composite maps associated with cycles, i.e., they are forced to be identity maps. However, as there is an exponential number of cycles in a graph, how to sample a subset of cycles becomes critical for efficient and effective enforcement of the cycle-consistency constraint. This paper presents an algorithm that select a subset of weighted cycles to minimize a condition number of the induced joint optimization problem. Experimental results on benchmark datasets justify the effectiveness of our approach for optimizing dense correspondences between 3D shapes and neural networks for predicting dense image flows.
A Graph Theoretic Additive Approximation of Optimal Transport
Authors: Nathaniel Lahn, Deepika Mulchandani, Sharath Raghvendra
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: Transportation cost is an attractive similarity measure between probability distributions due to its many useful theoretical properties.  However, solving optimal transport exactly can be prohibitively expensive. Therefore, there has been significant effort towards the design of scalable approximation algorithms. Previous combinatorial results [Sharathkumar, Agarwal STOC '12, Agarwal, Sharathkumar STOC '14] have focused primarily on the design of near-linear time multiplicative approximation algorithms. There has also been an effort to design approximate solutions with additive errors [Cuturi NIPS '13, Altschuler \etal\ NIPS '17, Dvurechensky \etal\, ICML '18, Quanrud, SOSA '19]  within a time bound that is linear in the size of the cost matrix and polynomial in $C/\delta$; here $C$ is the largest value in the cost matrix and $\delta$ is the additive error.  
We present an adaptation of the classical graph algorithm of Gabow and Tarjan and provide a novel analysis of this algorithm that bounds its execution time by $\BigO(\frac{n^2 C}{\delta}+ \frac{nC^2}{\delta^2})$. Our algorithm is extremely simple and executes, for an arbitrarily small constant $\eps$, only $\lfloor \frac{2C}{(1-\eps)\delta}\rfloor + 1$ iterations, where each iteration consists only of a Dijkstra-type search followed by a depth-first search.  We also provide empirical results that suggest our algorithm is competitive with respect to a sequential implementation of the Sinkhorn algorithm in execution time. Moreover, our algorithm quickly computes a solution for very small values of $\delta$ whereas Sinkhorn algorithm slows down due to numerical instability.
MaxGap Bandit: Adaptive Algorithms for Approximate Ranking
Authors: Sumeet Katariya, Ardhendu Tripathy, Robert Nowak
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: This paper studies the problem of adaptively sampling from K distributions (arms) in order to identify the largest gap between any two adjacent means. We call this the MaxGap-bandit problem. This problem arises naturally in approximate ranking, noisy sorting, outlier detection, and top-arm identification in bandits.  The key novelty of the MaxGap bandit problem is that it aims to adaptively determine the natural partitioning of the distributions into a subset with larger means and a subset with smaller means, where the split is determined by the largest gap rather than a pre-specified rank or threshold. Estimating an arm’s gap requires sampling its neighboring arms in addition to itself, and this dependence results in a novel hardness parameter that characterizes the sample complexity of the problem. We propose elimination and UCB-style algorithms and show that they are minimax optimal. Our experiments show that the UCB-style algorithms require 6-8x fewer samples than non-adaptive sampling to achieve the same error.
Regret Bounds for Learning State Representations in Reinforcement Learning
Authors: Ronald Ortner, Matteo Pirotta, Alessandro Lazaric, Ronan Fruit, Odalric-Ambrym Maillard
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: We consider the problem of online reinforcement learning when several state representations (mapping histories to a discrete state space) are available to the learning agent. At least one of these representations is assumed to induce a Markov decision process (MDP), and the performance of the agent is measured in terms of cumulative regret against the optimal policy giving the highest average reward in this MDP representation. We propose an algorithm (UCB-MS) with O(sqrt(T)) regret in any communicating Markov decision process. The regret bound shows that UCB-MS automatically adapts to the Markov model. This improves over the currently known best results in the literature that gave regret bounds of order O(T^(2/3)).
Exact Rate-Distortion in Autoencoders via Echo Noise
Authors: Rob Brekelmans, Daniel Moyer, Aram Galstyan, Greg Ver Steeg
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: Compression is at the heart of effective representation learning. However, lossy compression is typically achieved through simple parametric models like Gaussian noise to preserve analytic tractability, and the limitations this imposes on learning are largely unexplored.  Further, the Gaussian prior assumptions in models such as variational autoencoders (VAEs) provide only an upper bound on the compression rate in general.  We introduce a new noise channel, Echo noise, that admits a simple, exact expression for mutual information for arbitrary input distributions.  The noise is constructed in a data-driven fashion that does not require restrictive distributional assumptions.  With its complex encoding mechanism and exact rate regularization, Echo leads to improved bounds on log-likelihood and dominates beta-VAEs across the achievable range of rate-distortion trade-offs. Further, we show that Echo noise can outperform flow-based methods without the need to train additional distributional transformations.
AutoAssist: A Framework to Accelerate Training of Deep Neural Networks
Authors: Jiong Zhang, Hsiang-Fu Yu, Inderjit S Dhillon
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: Deep neural networks have yielded superior performance in many contemporary
applications. However, the gradient computation in a deep model with millions of
instances leads to a lengthy training process even with modern
GPU/TPU hardware acceleration.  In this paper, we propose AutoAssist, a
simple framework to accelerate training of a deep neural network.
Typically, as the training procedure evolves, the amount of improvement by a
stochastic gradient update varies dynamically with the choice of instances in the mini-batch.
In AutoAssist, we utilize this fact and design an instance shrinking operation that is
used to filter out instances with relatively low marginal improvement to the
current model; thus the computationally intensive gradient computations are
performed on informative instances as much as possible.
Specifically, we train
a very lightweight Assistant model jointly with the original deep network, which we refer to as Boss.
The Assistant model is designed to gauge the importance of a given
instance with respect to the current Boss such that the shrinking operation can
be applied in the batch generator. With careful design, we train the Boss and
Assistant in a nonblocking and asynchronous fashion such that
overhead is minimal.
To demonstrate the effectiveness of AutoAssist, we conduct experiments on two
contemporary applications: image classification using ResNets with varied number
of layers, and neural machine translation using LSTMs, ConvS2S and
Transformer models. For each application, we verify that AutoAssist leads to
significant reduction in training time; in particular, 30% to 40% of the total operation count can be reduced which leads to
faster convergence and a corresponding decrease in training time.
BIVA: A Very Deep Hierarchy of Latent Variables for Generative Modeling
Authors: Lars Maaløe, Marco Fraccaro, Valentin Liévin, Ole Winther
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: With the introduction of the variational autoencoder (VAE), probabilistic latent variable models have received renewed attention as powerful generative models. However, their performance in terms of test likelihood and quality of generated samples has been surpassed by autoregressive models without stochastic units. Furthermore, flow-based models have recently been shown to be an attractive alternative that scales well to high-dimensional data. In this paper we close the performance gap by constructing VAE models that can effectively utilize a deep hierarchy of stochastic variables and model complex covariance structures. We introduce the Bidirectional-Inference Variational Autoencoder (BIVA), characterized by a skip-connected generative model and an inference network formed by a bidirectional stochastic inference path. We show that BIVA reaches state-of-the-art test likelihoods, generates sharp and coherent natural images, and uses the hierarchy of latent variables to capture different aspects of the data distribution. We observe that BIVA, in contrast to recent results, can be used for anomaly detection. We attribute this to the hierarchy of latent variables which is able to extract high-level semantic features. Finally, we extend BIVA to semi-supervised classification tasks and show that it performs comparably to state-of-the-art results by generative adversarial networks.
Multiway clustering via tensor block models
Authors: Miaoyan Wang, Yuchen Zeng
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: We consider the problem of identifying multiway block structure from a large noisy tensor. Such problems arise frequently in applications such as genomics, recommendation system, topic modeling, and sensor network localization. We propose a tensor block model, develop a unified least-square estimation, and obtain the theoretical accuracy guarantees for multiway clustering. The statistical convergence of the estimator is established, and we show that the associated clustering procedure achieves partition consistency. A sparse regularization is further developed for identifying important blocks with elevated means. The proposal handles a broad range of data types, including binary, continuous, and hybrid observations. Through simulation and application to two real datasets, we demonstrate the outperformance of our approach over previous methods.
Bridging Machine Learning and Logical Reasoning by Abductive Learning
Authors: Wang-Zhou Dai, Qiuling Xu, Yang Yu, Zhi-Hua Zhou
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: Perception and reasoning are two representative abilities of intelligence that are integrated seamlessly during human problem-solving processes. In the area of artificial intelligence (AI), the two abilities are usually realised by machine learning and logic programming, respectively. However, the two categories of techniques were developed separately throughout most of the history of AI. In this paper, we present the abductive learning targeted at unifying the two AI paradigms in a mutually beneficial way, where the machine learning model learns to perceive primitive logic facts from data, while logical reasoning can exploit symbolic domain knowledge and correct the wrongly perceived facts for improving the machine learning models. Furthermore, we propose a novel approach to optimise the machine learning model and the logical reasoning model jointly. We demonstrate that by using abductive learning, machines can learn to recognise numbers and resolve unknown mathematical operations simultaneously from images of simple hand-written equations. Moreover, the learned models can be generalised to longer equations and adapted to different tasks, which is beyond the capability of state-of-the-art deep learning models.
Variational Structured Semantic Inference for Diverse Image Captioning
Authors: Fuhai Chen, Rongrong Ji, Jiayi Ji, Xiaoshuai Sun, Baochang Zhang, Xuri Ge, Yongjian Wu, Feiyue Huang, Yan Wang
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: Despite the exciting progress in image captioning, generating diverse captions for a given image remains as an open problem. Existing methods typically apply generative models such as Variational Auto-Encoder to diversify the captions, which however neglect two key factors of diverse expression, i.e., the lexical diversity and the syntactic diversity. To model these two inherent diversities in image captioning, we propose a Variational Structured Semantic Inferring model (termed VSSI-cap) executed in a novel structured encoder-inferer-decoder schema. VSSI-cap mainly innovates in a novel structure, i.e., Variational Multi-modal Inferring tree (termed VarMI-tree). In particular, conditioned on the visual-textual features from the encoder, the VarMI-tree models the lexical and syntactic diversities by inferring their latent variables (with variations) in an approximate posterior inference guided by a visual semantic prior. Then, a reconstruction loss and the posterior-prior KL-divergence are jointly estimated to optimize the VSSI-cap model. Finally, diverse captions are generated upon the visual features and the latent variables from this structured encoder-inferer-decoder model. Experiments on the benchmark dataset show that the proposed VSSI-cap achieves significant improvements over the state-of-the-arts.
Input-Output Equivalence of Unitary and Contractive RNNs
Authors: Melikasadat Emami, Mojtaba Sahraee Ardakan, Sundeep Rangan, Alyson K. Fletcher
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: Unitary recurrent neural networks (URNNs) have been proposed as a method to overcome the vanishing and exploding gradient problem in modeling data with long-term dependencies. A basic question is how restrictive is the unitary constraint on the possible input-output mappings of such a network? This works shows that for any contractive RNN with ReLU activations, there is a URNN with at most twice the number of hidden states and the identical input-output mapping.  Hence, with ReLU activations, URNNs are as expressive as general RNNs.  In contrast, for certain smooth activations, it is shown that the input-output mapping of an RNN cannot be matched with a URNN, even with an arbitrary number of states.  The theoretical results are supported by experiments on modeling of slowly-varying dynamical systems.
Latent Weights Do Not Exist: Rethinking Binarized Neural Network Optimization
Authors: Koen Helwegen, James Widdicombe, Lukas Geiger, Zechun Liu, Kwang-Ting Cheng, Roeland Nusselder
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: Optimization of Binarized Neural Networks (BNNs) currently relies on real-valued latent weights to accumulate small update steps. In this paper, we argue that these latent weights cannot be treated analogously to weights in real-valued networks. Instead their main role is to provide inertia during training. We interpret current methods in terms of inertia and provide novel insights into the optimization of BNNs. We subsequently introduce the first optimizer specifically designed for BNNs, Binary Optimizer (Bop), and demonstrate its performance on CIFAR-10 and ImageNet. Together, the redefinition of latent weights as inertia and the introduction of Bop enable a better understanding of BNN optimization and open up the way for further improvements in training methodologies for BNNs.
Nonparametric Regressive Point Processes Based on Conditional Gaussian Processes
Authors: Siqi Liu, Milos Hauskrecht
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: Real-world event sequences consist of complex mixtures of different types of events occurring in time. An event may depend on past events of the same type, as well as, the other types. Point processes define a general class of models for event sequences. ``Regressive point processes'' refer to point processes that directly model the dependency between an event and any past event, an example of which is a Hawkes process. In this work, we propose and develop a new nonparametric regressive point process model based on Gaussian processes. We show that our model can represent better many commonly observed real-world event sequences and capture the dependencies between events that are difficult to model using existing nonparametric Hawkes process variants. We demonstrate the improved predictive performance of our model against state-of-the-art baselines on multiple synthetic and real-world datasets.
Continuous-time Models for Stochastic Optimization Algorithms
Authors: Antonio Orvieto, Aurelien Lucchi
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: We propose new continuous-time formulations for first-order stochastic optimization algorithms such as mini-batch gradient descent and variance-reduced methods. We exploit these continuous-time models, together with simple Lyapunov analysis as well as tools from stochastic calculus, in order to derive convergence bounds for various types of non-convex functions. Guided by such analysis, we show that the same Lyapunov arguments hold in discrete-time, leading to matching rates. In addition, we use these models and Ito calculus to infer novel insights on the dynamics of SGD, proving that a decreasing learning rate acts as time warping or, equivalently, as landscape stretching.
Differentiable Convex Optimization Layers
Authors: Akshay Agrawal, Brandon Amos, Shane Barratt, Stephen Boyd, Steven Diamond, J. Zico Kolter
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: Recent work has shown how to embed differentiable optimization problems (that is, problems whose solutions can be backpropagated through) as layers within deep learning architectures. This method provides a useful inductive bias for certain problems, but existing software for differentiable optimization layers is rigid and difficult to apply to new settings. In this paper, we propose an approach to differentiating through disciplined convex programs, a subclass of convex optimization problems used by domain-specific languages (DSLs) for convex optimization. We introduce disciplined parametrized programming, a subset of disciplined convex programming, and we show that every disciplined parametrized program can be represented as the composition of an affine map from parameters to problem data, a solver, and an affine map from the solver’s solution to a solution of the original problem (a new form we refer to as affine-solver-affine form). We then demonstrate how to efficiently differentiate through each of these components, allowing for end-to-end analytical differentiation through the entire convex program. We implement our methodology in version 1.1 of CVXPY, a popular Python-embedded DSL for convex optimization, and additionally implement differentiable layers for disciplined convex programs in PyTorch and TensorFlow 2.0. Our implementation significantly lowers the barrier to using convex optimization problems in differentiable programs. We present applications in linear machine learning models and in stochastic control, and we show that our layer is competitive (in execution time) compared to specialized differentiable solvers from past work.
A Zero-Positive Learning Approach for Diagnosing Software Performance Regressions
Authors: Mejbah Alam, Justin Gottschlich, Nesime Tatbul, Javier S Turek, Tim Mattson, Abdullah Muzahid
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: The field of machine programming (MP), the automation of the development of software, is making notable research advances. This is, in part, due to the emergence of a wide range of novel techniques in machine learning. In this paper, we apply MP to the automation of software performance regression testing. A performance regression is a software performance degradation caused by a code change. We present AutoPerf – a novel approach to automate regression testing that utilizes three core techniques: (i) zero-positive learning, (ii) autoencoders, and (iii) hardware telemetry. We demonstrate AutoPerf’s generality and efficacy against 3 types of performance regressions across 10 real performance bugs in 7 benchmark and open-source programs. On average, AutoPerf exhibits 4% profiling
overhead and accurately diagnoses more performance bugs than prior state-of-the-art approaches. Thus far, AutoPerf has produced no false negatives.
Partially Encrypted Deep Learning using Functional Encryption
Authors: Théo Ryffel, David Pointcheval, Francis Bach, Edouard Dufour-Sans, Romain Gay
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: Machine learning on encrypted data has received a lot of attention thanks to recent breakthroughs in homomorphic encryption and secure multi-party computation. It allows outsourcing computation to untrusted servers without sacrificing privacy of sensitive data. We propose a practical framework to perform partially encrypted and privacy-preserving predictions which combines adversarial training and functional encryption. We first present a new functional encryption scheme to efficiently compute quadratic functions so that the data owner controls what can be computed but is not involved in the calculation: it provides a decryption key which allows one to learn a specific function evaluation of some encrypted data. We then show how to use it in machine learning to partially encrypt neural networks with quadratic activation functions at evaluation time and we provide a thorough analysis of the information leaks based on indistinguishability of data items of the same label. Last, since several encryption schemes cannot deal with the last thresholding operation used for classification, we propose a training method to prevent selected sensitive features from leaking which adversarially optimizes the network against an adversary trying to identify these features. This is of great interest for several existing works using partially encrypted machine learning as it comes with almost no cost on the model's accuracy and significantly improves data privacy.
Graph Transformer Networks
Authors: Seongjun Yun, Minbyul Jeong, Raehyun Kim, Jaewoo Kang, Hyunwoo J Kim
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: Graph neural networks (GNNs) have been widely used in representation learning on graphs and achieved state-of-the-art performance in tasks such as node classification and link prediction. However, most existing GNNs are designed to learn node representations on the fixed and homogeneous graphs. The limitations especially become problematic when learning representations on a misspecified graph or a heterogeneous graph that consists of various types of nodes and edges. In this paper, we propose Graph Transformer Networks (GTNs) that are capable of generating new graph structures, which involve identifying useful connections between unconnected nodes on the original graph, while learning effective node representation on the new graphs in an end-to-end fashion. Graph Transformer layer, a core layer of GTNs, learns a soft selection of edge types and composite relations for generating useful multi-hop connections so-call meta-paths. Our experiments show that GTNs learn new graph structures, based on data and tasks without domain knowledge, and yield powerful node representation via convolution on the new graphs. Without domain-specific graph preprocessing, GTNs achieved the best performance in all three benchmark node classification tasks against the state-of-the-art methods that require pre-defined meta-paths from domain knowledge.
Non-normal Recurrent Neural Network (nnRNN): learning long time dependencies while improving expressivity with transient dynamics
Authors: Giancarlo Kerg, Kyle Goyette, Maximilian Puelma Touzel, Gauthier Gidel, Eugene Vorontsov, Yoshua Bengio, Guillaume Lajoie
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: A recent strategy to circumvent the exploding and vanishing gradient problem in RNNs, and to allow the stable propagation of signals over long time scales, is to constrain recurrent connectivity matrices to be orthogonal or unitary. This ensures eigenvalues with unit norm and thus stable dynamics and training. However this comes at the cost of reduced expressivity due to the limited variety of orthogonal transformations. We propose a novel connectivity structure based on the Schur decomposition and a splitting of the Schur form into normal and non-normal parts. 
This allows to parametrize matrices with unit-norm eigenspectra without orthogonality constraints on eigenbases. The resulting architecture ensures access to a larger space of spectrally constrained matrices, of which orthogonal matrices are a subset. 
This crucial difference retains the stability advantages and training speed of orthogonal RNNs while enhancing expressivity, especially on tasks that require computations over ongoing input sequences.
Large Memory Layers with Product Keys
Authors: Guillaume Lample, Alexandre Sablayrolles, Marc'Aurelio Ranzato, Ludovic Denoyer, Herve Jegou
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: This paper introduces a structured memory which can be easily integrated into a neural network. The memory is very large by design and significantly increases the capacity of the architecture, by up to a billion parameters with a negligible computational overhead.
Its design and access pattern is based on product keys, which enable fast and exact nearest neighbor search. The ability to increase the number of parameters while keeping the same computational budget lets the overall system strike a better trade-off between prediction accuracy and computation efficiency both at training and test time. This memory layer allows us to tackle very large scale language modeling tasks. In our experiments we consider a dataset with up to 30 billion words, and we plug our memory layer in a state-of-the-art transformer-based architecture. In particular, we found that a memory augmented model with only 12 layers outperforms a baseline transformer model with 24 layers, while being twice faster at inference time. We release our code for reproducibility purposes.
Computing Full Conformal Prediction Set with Approximate Homotopy
Authors: Eugene Ndiaye, Ichiro Takeuchi
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: If you are predicting the label $y$ of a new object with $\hat y$, how confident are you that $y = \hat y$? Conformal prediction methods provide an elegant framework for answering such question by building a $100 (1 - \alpha)\%$ confidence region without assumptions on the distribution of the data. It is based on a refitting procedure that parses all the possibilities for $y$ to select the most likely ones. Although providing strong coverage guarantees, conformal set is impractical to compute exactly for many regression problems. We propose efficient algorithms to compute conformal prediction set using approximated solution of (convex) regularized empirical risk minimization. Our approaches rely on a new homotopy continuation technique for tracking the solution path with respect to sequential changes of the observations. We also provide a detailed analysis quantifying its complexity.
AttentionXML: Label Tree-based Attention-Aware Deep Model for High-Performance Extreme Multi-Label Text Classification
Authors: Ronghui You, Zihan Zhang, Ziye Wang, Suyang Dai, Hiroshi Mamitsuka, Shanfeng Zhu
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: Extreme multi-label text classification (XMTC) is an important problem in the 
era of {\it big data}, for tagging a given text with the most relevant multiple 
labels from an extremely large-scale label set. XMTC can be found in many 
applications, such as item categorization, web page tagging, and news 
annotation.
Traditionally most methods used bag-of-words (BOW) as inputs, ignoring word 
context as well as deep semantic information. Recent attempts to overcome the 
problems of BOW by deep learning still suffer from 1) failing to capture the 
important subtext for each label and 2) lack of scalability against the huge 
number of labels.
We propose a new label tree-based deep learning model for XMTC, called 
AttentionXML, with two unique features: 1) a multi-label attention mechanism 
with raw text as input, which allows to capture the most relevant part of text 
to each label; and 2) a shallow and wide probabilistic label tree (PLT), which 
allows to handle millions of labels, especially for "tail labels".
We empirically compared the performance of AttentionXML with those of eight 
state-of-the-art methods over six benchmark datasets, including Amazon-3M with 
around 3 million labels. AttentionXML outperformed all competing methods 
under all experimental settings.
Experimental results also show that AttentionXML achieved the best performance 
against tail labels among label tree-based methods. The code and datasets are 
available at \url{http://github.com/yourh/AttentionXML} .
Policy Learning for Fairness in Ranking
Authors: Ashudeep Singh, Thorsten Joachims
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: Conventional Learning-to-Rank (LTR) methods optimize the utility of the rankings to the users, but they are oblivious to their impact on the ranked items. However, there has been a growing understanding that the latter is important to consider for a wide range of ranking applications (e.g. online marketplaces, job placement, admissions). To address this need, we propose a general LTR framework that can optimize a wide range of utility metrics (e.g. NDCG) while satisfying fairness of exposure constraints with respect to the items. This framework expands the class of learnable ranking functions to stochastic ranking policies, which provides a language for rigorously expressing fairness specifications. Furthermore, we provide a new LTR algorithm called Fair-PG-Rank for directly searching the space of fair ranking policies via a policy-gradient approach. Beyond the theoretical evidence in deriving the framework and the algorithm, we provide empirical results on simulated and real-world datasets verifying the effectiveness of the approach in individual and group-fairness settings.
Regret Minimization for Reinforcement Learning by Evaluating the Optimal Bias Function
Authors: Zihan Zhang, Xiangyang Ji
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: We present an algorithm based on the \emph{Optimism in the Face of Uncertainty} (OFU) principle which is able to learn Reinforcement Learning (RL) modeled by Markov decision process (MDP) with finite state-action space efficiently. 
By evaluating the state-pair difference of the optimal bias function $h^{*}$, the proposed algorithm achieves a regret bound of $\tilde{O}(\sqrt{SATH})$\footnote{The symbol $\tilde{O}$ means $O$ with log factors ignored. } for MDP with S states and A actions, in the case that an upper bound $H$ on the span of $h^{*}$, i.e., $sp(h^{*})$ is known. 
This result outperforms the best previous regret bounds $\tilde{O}(HS\sqrt{AT})$\cite{bartlett2009regal} by a factor of $\sqrt{SH}$. 
Furthermore, this regret bound matches the lower bound of $\Omega(\sqrt{SATH})$\cite{jaksch2010near} up to a logarithmic factor. As a consequence,  we show that there is a near optimal regret bound of $\tilde{O}(\sqrt{DSAT})$ for MDPs with finite diameter $D$ compared to the lower bound of $\Omega(\sqrt{DSAT})$\cite{jaksch2010near}.