mtybilly/Instruct2DS huggingface dataset:
Integer Discrete Flows and Lossless Compression
Authors: Emiel Hoogeboom, Jorn Peters, Rianne van den Berg, Max Welling
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: Lossless compression methods shorten the expected representation size of data without loss of information, using a statistical model. Flow-based models are attractive in this setting because they admit exact likelihood optimization, which is equivalent to minimizing the expected number of bits per message. However, conventional flows assume continuous data, which may lead to reconstruction errors when quantized for compression. For that reason, we introduce a flow-based generative model for ordinal discrete data called Integer Discrete Flow (IDF): a bijective integer map that can learn rich transformations on high-dimensional data. As building blocks for IDFs, we introduce a flexible transformation layer called integer discrete coupling. Our experiments show that IDFs are competitive with other flow-based generative models. Furthermore, we demonstrate that IDF based compression achieves state-of-the-art lossless compression rates on CIFAR10, ImageNet32, and ImageNet64. To the best of our knowledge, this is the first lossless compression method that uses invertible neural networks.
Generative Well-intentioned Networks
Authors: Justin Cosentino, Jun Zhu
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: We propose Generative Well-intentioned Networks (GWINs), a novel framework for increasing the accuracy of certainty-based, closed-world classifiers. A conditional generative network recovers the distribution of observations that the classifier labels correctly with high certainty. We introduce a reject option to the classifier during inference, allowing the classifier to reject an observation instance rather than predict an uncertain label. These rejected observations are translated by the generative network to high-certainty representations, which are then relabeled by the classifier. This architecture allows for any certainty-based classifier or rejection function and is not limited to multilayer perceptrons. The capability of this framework is assessed using benchmark classification datasets and shows that GWINs significantly improve the accuracy of uncertain observations.
An Embedding Framework for Consistent Polyhedral Surrogates
Authors: Jessica Finocchiaro, Rafael Frongillo, Bo Waggoner
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: We formalize and study the natural approach of designing convex surrogate loss functions via embeddings for problems such as classification or ranking. In this approach, one embeds each of the finitely many predictions (e.g. classes) as a point in \reals^d, assigns the original loss values to these points, and convexifies the loss in some way to obtain a surrogate.  We prove that this approach is equivalent, in a strong sense, to working with polyhedral (piecewise linear convex) losses.  Moreover, given any polyhedral loss L, we give a construction of a link function through which L is a consistent surrogate for the loss it embeds.  We go on to illustrate the power of this embedding framework with succinct proofs of consistency or inconsistency of various polyhedral surrogates in the literature.
The Normalization Method for Alleviating Pathological Sharpness in Wide Neural Networks
Authors: Ryo Karakida, Shotaro Akaho, Shun-ichi Amari
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: Normalization methods play an important role in enhancing the performance of deep learning while their theoretical understandings have been limited. To theoretically elucidate the effectiveness of normalization, we quantify the geometry of the parameter space determined by the Fisher information matrix (FIM), which also corresponds to the local shape of the loss landscape under certain conditions. We analyze deep neural networks with random initialization, which is known to suffer from a pathologically sharp shape of the landscape when the network becomes sufficiently wide. We reveal that batch normalization in the last layer contributes to drastically decreasing such pathological sharpness if the width and sample number satisfy a specific condition. In contrast, it is hard for batch normalization in the middle hidden layers to alleviate pathological sharpness in many settings. We also found that layer normalization cannot alleviate pathological sharpness either. Thus, we can conclude that batch normalization in the last layer significantly contributes to decreasing the sharpness induced by the FIM.
Re-randomized Densification for One Permutation Hashing and Bin-wise Consistent Weighted Sampling
Authors: Ping Li, Xiaoyun Li, Cun-Hui Zhang
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: Jaccard similarity is widely used as a distance measure in many machine learning
and search applications. Typically, hashing methods are essential for the use of
Jaccard similarity to be practical in large-scale settings. For hashing binary (0/1)
data, the idea of one permutation hashing (OPH) with densification significantly
accelerates traditional minwise hashing algorithms while providing unbiased and
accurate estimates. In this paper, we propose a strategy named “re-randomization”
in the process of densification that could achieve the smallest variance among all
densification schemes. The success of this idea naturally inspires us to generalize
one permutation hashing to weighted (non-binary) data, which results in the socalled “bin-wise consistent weighted sampling (BCWS)” algorithm. We analyze the
behavior of BCWS and compare it with a recent alternative. Extensive experiments
on various datasets illustrates the effectiveness of our proposed methods.
Beyond Online Balanced Descent: An Optimal Algorithm for Smoothed Online Optimization
Authors: Gautam Goel, Yiheng Lin, Haoyuan Sun, Adam Wierman
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: We study online convex optimization in a setting where the learner seeks to minimize the sum of a per-round hitting cost and a movement cost which is incurred when changing decisions between rounds.  We prove a new lower bound on the competitive ratio of any online algorithm in the setting where the costs are $m$-strongly convex and the movement costs are the squared $\ell_2$ norm. This lower bound shows that no algorithm can achieve a competitive ratio that is  $o(m^{-1/2})$ as $m$ tends to zero.  No existing algorithms have competitive ratios matching this bound, and we show that the state-of-the-art algorithm, Online Balanced Decent (OBD), has a competitive ratio that is $\Omega(m^{-2/3})$. We additionally propose two new algorithms, Greedy OBD (G-OBD) and Regularized OBD (R-OBD) and prove that both algorithms have an $O(m^{-1/2})$ competitive ratio. The result for G-OBD holds when the hitting costs are quasiconvex and the movement costs are the squared $\ell_2$ norm, while the result for R-OBD holds when the hitting costs are $m$-strongly convex and the movement costs are Bregman Divergences.  Further, we show that R-OBD simultaneously achieves constant, dimension-free competitive ratio and sublinear regret when hitting costs are strongly convex.
Reconciling λ-Returns with Experience Replay
Authors: Brett Daley, Christopher Amato
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: Modern deep reinforcement learning methods have departed from the incremental learning required for eligibility traces, rendering the implementation of the λ-return difficult in this context. In particular, off-policy methods that utilize experience replay remain problematic because their random sampling of minibatches is not conducive to the efficient calculation of λ-returns. Yet replay-based methods are often the most sample efficient, and incorporating λ-returns into them is a viable way to achieve new state-of-the-art performance. Towards this, we propose the first method to enable practical use of λ-returns in arbitrary replay-based methods without relying on other forms of decorrelation such as asynchronous gradient updates. By promoting short sequences of past transitions into a small cache within the replay memory, adjacent λ-returns can be efficiently precomputed by sharing Q-values. Computation is not wasted on experiences that are never sampled, and stored λ-returns behave as stable temporal-difference (TD) targets that replace the target network. Additionally, our method grants the unique ability to observe TD errors prior to sampling; for the first time, transitions can be prioritized by their true significance rather than by a proxy to it. Furthermore, we propose the novel use of the TD error to dynamically select λ-values that facilitate faster learning. We show that these innovations can enhance the performance of DQN when playing Atari 2600 games, even under partial observability. While our work specifically focuses on λ-returns, these ideas are applicable to any multi-step return estimator.
Sinkhorn Barycenters with Free Support via Frank-Wolfe Algorithm
Authors: Giulia Luise, Saverio Salzo, Massimiliano Pontil, Carlo Ciliberto
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: We present a novel algorithm to estimate the barycenter of arbitrary probability distributions with respect to the Sinkhorn divergence. Based on a Frank-Wolfe optimization strategy, our approach proceeds by populating the support of the barycenter incrementally, without requiring any pre-allocation. We consider discrete as well as continuous distributions, proving convergence rates of the proposed algorithm in both settings. Key elements of our analysis are a new result showing that the Sinkhorn divergence on compact domains has Lipschitz continuous gradient with respect to the Total Variation and a characterization of the sample complexity of Sinkhorn potentials. Experiments validate the effectiveness of our method in practice.
Finite-Sample Analysis for SARSA with Linear Function Approximation
Authors: Shaofeng Zou, Tengyu Xu, Yingbin Liang
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: SARSA is an on-policy algorithm to learn a Markov decision process policy in reinforcement learning. We investigate the SARSA algorithm with linear function approximation under the non-i.i.d.\ setting, where a single sample trajectory is available. With a Lipschitz continuous policy improvement operator that is smooth enough, SARSA has been shown to converge asymptotically. However, its non-asymptotic analysis is challenging and remains unsolved due to the non-i.i.d.  samples, and the fact that the behavior policy changes dynamically with time. In this paper, we develop a novel technique to explicitly characterize the stochastic bias of a type of stochastic approximation procedures with time-varying Markov transition kernels. Our approach enables non-asymptotic convergence analyses of this type of stochastic approximation algorithms, which may be of independent interest. Using  our bias characterization technique and a  gradient descent type of analysis, we further provide the finite-sample analysis on the  mean square error of the SARSA algorithm.  In the end, we  present a fitted SARSA algorithm, which includes the original SARSA algorithm and its variant as special cases. This fitted SARSA algorithm provides a framework for \textit{iterative} on-policy fitted policy iteration, which is more memory and computationally efficient. For this fitted SARSA algorithm, we also present its finite-sample analysis.
Aligning Visual Regions and Textual Concepts for Semantic-Grounded Image Representations
Authors: Fenglin Liu, Yuanxin Liu, Xuancheng Ren, Xiaodong He, Xu Sun
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: In vision-and-language grounding problems, fine-grained representations of the image are considered to be of paramount importance. Most of the current systems incorporate visual features and textual concepts as a sketch of an image. However, plainly inferred representations are usually undesirable in that they are composed of separate components, the relations of which are elusive. In this work, we aim at representing an image with a set of integrated visual regions and corresponding textual concepts, reflecting certain semantics. To this end, we build the Mutual Iterative Attention (MIA) module, which integrates correlated visual features and textual concepts, respectively, by aligning the two modalities. We evaluate the proposed approach on two representative vision-and-language grounding tasks, i.e., image captioning and visual question answering. In both tasks, the semantic-grounded image representations consistently boost the performance of the baseline models under all metrics across the board. The results demonstrate that our approach is effective and generalizes well to a wide range of models for image-related applications. (The code is available at \url{https://github.com/fenglinliu98/MIA)
Network Pruning via Transformable Architecture Search
Authors: Xuanyi Dong, Yi Yang
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: Network pruning reduces the computation costs of an over-parameterized network without performance damage. Prevailing pruning algorithms pre-define the width and depth of the pruned networks, and then transfer parameters from the unpruned network to pruned networks. To break the structure limitation of the pruned networks, we propose to apply neural architecture search to search directly for a network with flexible channel and layer sizes. The number of the channels/layers is learned by minimizing the loss of the pruned networks. The feature map of the pruned network is an aggregation of K feature map fragments (generated by K networks of different sizes), which are sampled based on the probability distribution. The loss can be back-propagated not only to the network weights, but also to the parameterized distribution to explicitly tune the size of the channels/layers. Specifically, we apply channel-wise interpolation to keep the feature map with different channel sizes aligned in the aggregation procedure. The maximum probability for the size in each distribution serves as the width and depth of the pruned network, whose parameters are learned by knowledge transfer, e.g., knowledge distillation, from the original networks. Experiments on CIFAR-10, CIFAR-100 and ImageNet demonstrate the effectiveness of our new perspective of network pruning compared to traditional network pruning algorithms. Various searching and knowledge transfer approaches are conducted to show the effectiveness of the two components. Code is at: https://github.com/D-X-Y/NAS-Projects
Online Stochastic Shortest Path with Bandit Feedback and Unknown Transition Function
Authors: Aviv Rosenberg, Yishay Mansour
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: We  consider  online  learning  in  episodic loop-free Markov decision processes (MDPs), where the loss function can change arbitrarily between episodes. 
The transition function is fixed but unknown to the learner, and the learner only observes bandit feedback (not the entire loss function).
For this problem we develop no-regret algorithms that perform asymptotically as well as the best stationary policy in hindsight.
Assuming that all states are reachable with probability $\beta > 0$ under any policy, we give a regret bound of $\tilde{O} ( L|X|\sqrt{|A|T} / \beta )$, where $T$ is the number of episodes, $X$ is the state space, $A$ is the action space, and $L$ is the length of each episode.
When this assumption is removed we give a regret bound of $\tilde{O} ( L^{3/2} |X| |A|^{1/4} T^{3/4})$, that holds for an arbitrary transition function. 
To our knowledge these are the first algorithms that in our setting handle both bandit feedback and an unknown transition function.
Selective Sampling-based Scalable Sparse Subspace Clustering
Authors: Shin Matsushima, Maria Brbic
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: Sparse subspace clustering (SSC) represents each data point as a sparse linear combination of other data points in the dataset. In the representation learning step SSC finds a lower dimensional representation of data points, while in the spectral clustering step data points are clustered according to the underlying subspaces. However, both steps suffer from high computational and memory complexity, preventing the application of SSC to large-scale datasets. To overcome this limitation, we introduce Selective Sampling-based Scalable Sparse Subspace Clustering (S5C) algorithm which selects subsamples based on the approximated subgradients and linearly scales with the number of data points in terms of time and memory requirements. Along with the computational advantages, we derive theoretical guarantees for the correctness of S5C. Our theoretical result presents novel contribution for SSC in the case of limited number of subsamples. Extensive experimental results demonstrate effectiveness of our approach.
On the Expressive Power of Deep Polynomial Neural Networks
Authors: Joe Kileel, Matthew Trager, Joan Bruna
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: We study deep neural networks with polynomial activations, particularly their expressive power.  For a fixed architecture and activation degree, a polynomial neural network defines an algebraic map from weights to polynomials.  The image of this map is the functional space associated to the network, and it is an irreducible algebraic variety upon taking closure.  This paper proposes the dimension of this variety as a precise measure of the expressive power of polynomial neural networks.  We obtain several theoretical results regarding this dimension as a function of architecture, including an exact formula for high activation degrees, as well as upper and lower bounds on layer widths in order for deep polynomials networks to fill the ambient functional space. We also present computational evidence that it is profitable in terms of expressiveness for layer widths to increase monotonically and then decrease monotonically.  Finally, we link our study to favorable optimization properties when training weights, and we draw  intriguing connections with tensor and polynomial decompositions.
BehaveNet: nonlinear embedding and Bayesian neural decoding of behavioral videos
Authors: Eleanor Batty, Matthew Whiteway, Shreya Saxena, Dan Biderman, Taiga Abe, Simon Musall, Winthrop Gillis, Jeffrey Markowitz, Anne Churchland, John P. Cunningham, Sandeep R Datta, Scott Linderman, Liam Paninski
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: A fundamental goal of systems neuroscience is to understand the relationship between neural activity and behavior. Behavior has traditionally been characterized by low-dimensional, task-related variables such as movement speed or response times. More recently, there has been a growing interest in automated analysis of high-dimensional video data collected during experiments. Here we introduce a probabilistic framework for the analysis of behavioral video and neural activity. This framework provides tools for compression, segmentation, generation, and decoding of behavioral videos. Compression is performed using a convolutional autoencoder (CAE), which yields a low-dimensional continuous representation of behavior. We then use an autoregressive hidden Markov model (ARHMM) to segment the CAE representation into discrete "behavioral syllables." The resulting generative model can be used to simulate behavioral video data. Finally, based on this generative model, we develop a novel Bayesian decoding approach that takes in neural activity and outputs probabilistic estimates of the full-resolution behavioral video. We demonstrate this framework on two different experimental paradigms using distinct behavioral and neural recording technologies.
Accurate Layerwise Interpretable Competence Estimation
Authors: Vickram Rajendran, William LeVine
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: Estimating machine learning performance “in the wild” is both an important and
unsolved problem. In this paper, we seek to examine, understand, and predict the
pointwise competence of classification models. Our contributions are twofold:
First, we establish a statistically rigorous definition of competence that generalizes
the common notion of classifier confidence; second, we present the ALICE
(Accurate Layerwise Interpretable Competence Estimation) Score, a pointwise
competence estimator for any classifier. By considering distributional, data, and
model uncertainty, ALICE empirically shows accurate competence estimation in
common failure situations such as class-imbalanced datasets, out-of-distribution
datasets, and poorly trained models.
Our contributions allow us to accurately predict the competence of any classification model given any input and error function. We compare our score with state-of-the-art confidence estimators such as model confidence and Trust Score, and show significant improvements in competence prediction over these methods on datasets such as DIGITS, CIFAR10, and CIFAR100.
On the Global Convergence of (Fast) Incremental Expectation Maximization Methods
Authors: Belhal Karimi, Hoi-To Wai, Eric Moulines, Marc Lavielle
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: The EM algorithm is one of the most popular algorithm for inference in latent data models. The original formulation of the EM algorithm does not scale to large data set, because the whole data set is required at each iteration of the algorithm. To alleviate this problem, Neal and Hinton [1998] have proposed an incremental version of the EM (iEM) in which at each iteration the conditional expectation of the latent data (E-step) is updated only for a mini-batch of observations. Another approach has been proposed by Cappe and Moulines [2009] in which the E-step is replaced by a stochastic approximation step, closely related to stochastic gradient. In this paper, we analyze incremental and stochastic version of the EM algorithm as well as the variance reduced-version of [Chen et al., 2018] in a common unifying framework. We also introduce a new version incremental version, inspired by the SAGA algorithm by Defazio et al. [2014]. We establish non-asymptotic convergence bounds for global convergence. Numerical applications are presented in this article to illustrate our findings.
Nonconvex Low-Rank Tensor Completion from Noisy Data
Authors: Changxiao Cai, Gen Li, H. Vincent Poor, Yuxin Chen
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: We study a completion problem of broad practical interest: the reconstruction of a low-rank symmetric tensor from highly incomplete and randomly corrupted observations of its entries.  While a variety of prior work has been dedicated to this problem,   prior algorithms either are computationally too expensive for large-scale applications, or come with sub-optimal statistical guarantees.  Focusing on  ``incoherent'' and well-conditioned tensors of a constant CP rank, we propose a two-stage nonconvex algorithm --- (vanilla) gradient descent following a rough initialization --- that  achieves the best of both worlds. Specifically, the proposed nonconvex algorithm faithfully completes the tensor and retrieves all low-rank tensor factors within nearly linear time, while at the same time enjoying near-optimal statistical guarantees (i.e.~minimal sample complexity and optimal $\ell_2$ and $\ell_{\infty}$ statistical accuracy). The insights conveyed through our analysis of nonconvex optimization might have implications for other tensor estimation problems.
Gossip-based Actor-Learner Architectures for Deep Reinforcement Learning
Authors: Mahmoud Assran, Joshua Romoff, Nicolas Ballas, Joelle Pineau, Michael Rabbat
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: Multi-simulator training has contributed to the recent success of Deep Reinforcement Learning (Deep RL) by stabilizing learning and allowing for higher training throughputs. In this work, we propose Gossip-based Actor-Learner Architectures (GALA) where several actor-learners (such as A2C agents) are organized in a peer-to-peer communication topology, and exchange information through asynchronous gossip in order to take advantage of a large number of distributed simulators. We prove that GALA agents remain within an epsilon-ball of one-another during training when using loosely coupled asynchronous communication. By reducing the amount of synchronization between agents, GALA is more computationally efficient and scalable compared to A2C, its fully-synchronous counterpart. GALA also outperforms A2C, being more robust and sample efficient. We show that we can run several loosely coupled GALA agents in parallel on a single GPU and achieve significantly higher hardware utilization and frame-rates than vanilla A2C at comparable power draws.
Fast and Accurate Stochastic Gradient Estimation
Authors: Beidi Chen, Yingchen Xu, Anshumali Shrivastava
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: Stochastic Gradient Descent or SGD is the most popular optimization algorithm for large-scale problems. SGD estimates the gradient by uniform sampling with sample size one. There have been several other works that suggest faster epoch-wise convergence by using weighted non-uniform sampling for better gradient estimates. Unfortunately, the per-iteration cost of maintaining this adaptive distribution for gradient estimation is more than calculating the full gradient itself, which we call the chicken-and-the-egg loop. As a result, the false impression of faster convergence in iterations, in reality, leads to slower convergence in time. In this paper, we break this barrier by providing the first demonstration of a scheme, Locality sensitive hashing (LSH) sampled Stochastic Gradient Descent (LGD), which leads to superior gradient estimation while keeping the sampling cost per iteration similar to that of the uniform sampling. Such an algorithm is possible due to the sampling view of LSH, which came to light recently. As a consequence of superior and fast estimation, we reduce the running time of all existing gradient descent algorithms, that relies on gradient estimates including Adam, Ada-grad, etc. We demonstrate the effectiveness of our proposal with experiments on linear models as well as the non-linear BERT, which is a recent popular deep learning based language representation model.
Learning Disentangled Representations for Recommendation
Authors: Jianxin Ma, Chang Zhou, Peng Cui, Hongxia Yang, Wenwu Zhu
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: User behavior data in recommender systems are driven by the complex interactions of many latent factors behind the users’ decision making processes. The factors are highly entangled, and may range from high-level ones that govern user intentions, to low-level ones that characterize a user’s preference when executing an intention. Learning representations that uncover and disentangle these latent factors can bring enhanced robustness, interpretability, and controllability. However, learning such disentangled representations from user behavior is challenging, and remains largely neglected by the existing literature. In this paper, we present the MACRo-mIcro Disentangled Variational Auto-Encoder (MacridVAE) for learning disentangled representations from user behavior. Our approach achieves macro disentanglement by inferring the high-level concepts associated with user intentions (e.g., to buy a shirt or a cellphone), while capturing the preference of a user regarding the different concepts separately. A micro-disentanglement regularizer, stemming from an information-theoretic interpretation of VAEs, then forces each dimension of the representations to independently reflect an isolated low-level factor (e.g., the size or the color of a shirt). Empirical results show that our approach can achieve substantial improvement over the state-of-the-art baselines. We further demonstrate that the learned representations are interpretable and controllable, which can potentially lead to a new paradigm for recommendation where users are given fine-grained control over targeted aspects of the recommendation lists.
Learning Latent Process from High-Dimensional Event Sequences via Efficient Sampling
Authors: Qitian Wu, Zixuan Zhang, Xiaofeng Gao, Junchi Yan, Guihai Chen
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: We target modeling latent dynamics in high-dimension marked event sequences without any prior knowledge about marker relations. Such problem has been rarely studied by previous works which would have fundamental difficulty to handle the arisen challenges: 1) the high-dimensional markers and unknown relation network among them pose intractable obstacles for modeling the latent dynamic process; 2) one observed event sequence may concurrently contain several different chains of interdependent events; 3) it is hard to well define the distance between two high-dimension event sequences. To these ends, in this paper, we propose a seminal adversarial imitation learning framework for high-dimension event sequence generation which could be decomposed into: 1) a latent structural intensity model that estimates the adjacent nodes without explicit networks and learns to capture the temporal dynamics in the latent space of markers over observed sequence; 2) an efficient random walk based generation model that aims at imitating the generation process of high-dimension event sequences from a bottom-up view; 3) a discriminator specified as a seq2seq network optimizing the rewards to help the generator output event sequences as real as possible. Experimental results on both synthetic and real-world datasets demonstrate that the proposed method could effectively detect the hidden network among markers and make decent prediction for future marked events, even when the number of markers scales to million level.
Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty
Authors: Dan Hendrycks, Mantas Mazeika, Saurav Kadavath, Dawn Song
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: Self-supervision provides effective representations for downstream tasks without requiring labels. However, existing approaches lag behind fully supervised training and are often not thought beneficial beyond obviating or reducing the need for annotations. We find that self-supervision can benefit robustness in a variety of ways, including robustness to adversarial examples, label corruption, and common input corruptions. Additionally, self-supervision greatly benefits out-of-distribution detection on difficult, near-distribution outliers, so much so that it exceeds the performance of fully supervised methods. These results demonstrate the promise of self-supervision for improving robustness and uncertainty estimation and establish these tasks as new axes of evaluation for future self-supervised learning research.
Space and Time Efficient Kernel Density Estimation in High Dimensions
Authors: Arturs Backurs, Piotr Indyk, Tal Wagner
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: Recently, Charikar and Siminelakis (2017) presented a framework for kernel density estimation in provably sublinear query time, for kernels that possess a certain hashing-based property. However, their data structure requires a significantly increased super-linear storage space, as well as super-linear preprocessing time. These limitations inhibit the practical applicability of their approach on large datasets.
In this work, we present an improvement to their framework that retains the same query time, while requiring only linear space and linear preprocessing time. We instantiate our framework with the Laplacian and Exponential kernels, two popular kernels which possess the aforementioned property. Our experiments on various datasets verify that our approach attains accuracy and query time similar to Charikar and Siminelakis (2017), with significantly improved space and preprocessing time.
Random Projections with Asymmetric Quantization
Authors: Xiaoyun Li, Ping Li
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: The method of random projection has been a popular tool for data compression,
similarity search, and machine learning. In many practical scenarios, applying
quantization on randomly projected data could be very helpful to further reduce
storage cost and facilitate more efficient retrievals, while only suffering from
little loss in accuracy. In real-world applications, however, data collected from
different sources may be quantized under different schemes, which calls for a need to study the asymmetric quantization problem. In this paper, we investigate the cosine similarity estimators derived in such setting under the Lloyd-Max (LM)
quantization scheme. We thoroughly analyze the biases and variances of a series of estimators including the basic simple estimators, their normalized versions, and
their debiased versions. Furthermore, by studying the monotonicity, we show that
the expectation of proposed estimators increases with the true cosine similarity,
on a broader family of stair-shaped quantizers. Experiments on nearest neighbor
search justify the theory and illustrate the effectiveness of our proposed estimators.
Scalable Deep Generative Relational Model with High-Order Node Dependence
Authors: Xuhui Fan, Bin Li, Caoyuan Li, Scott SIsson, Ling Chen
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: In this work, we propose a probabilistic framework for relational data modelling and latent structure exploring. Given the possible feature information for the nodes in a network, our model builds up a deep architecture that can approximate to the possible nonlinear mappings between the nodes' feature information and latent representations. For each node, we incorporate all its neighborhoods' high-order structure information to generate latent representation, such that these latent representations are ``smooth'' in terms of the network. Since the latent representations are generated from Dirichlet distributions, we further develop a data augmentation trick to enable efficient Gibbs sampling for Ber-Poisson likelihood with Dirichlet random variables. Our model can be ready to apply to large sparse network as its computations cost scales to the number of positive links in the networks. The superior performance of our model is demonstrated through improved link prediction performance on a range of real-world datasets.
Better Exploration with Optimistic Actor Critic
Authors: Kamil Ciosek, Quan Vuong, Robert Loftin, Katja Hofmann
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: Actor-critic methods, a type of model-free Reinforcement Learning, have been successfully applied to challenging tasks in continuous control, often achieving state-of-the art performance. However, wide-scale adoption of these methods in real-world domains is made difficult by their poor sample efficiency. We address this problem both theoretically and empirically. On the theoretical side, we identify two phenomena preventing efficient exploration in existing state-of-the-art algorithms such as Soft Actor Critic. First, combining a greedy actor update with a pessimistic estimate of the critic leads to the avoidance of actions that the agent does not know about, a phenomenon we call pessimistic underexploration. Second, current algorithms are directionally uninformed, sampling actions with equal probability in opposite directions from the current mean. This is wasteful, since we typically need actions taken along certain directions much more than others. To address both of these phenomena, we introduce a new algorithm, Optimistic Actor Critic, which approximates a lower and upper confidence bound on the state-action value function. This allows us to apply the principle of optimism in the face of uncertainty to perform directed exploration using the upper bound while still using the lower bound to avoid overestimation. We evaluate OAC in several challenging continuous control tasks, achieving state-of the art sample efficiency.
Algorithmic Analysis and Statistical Estimation of SLOPE via Approximate Message Passing
Authors: Zhiqi Bu, Jason Klusowski, Cynthia Rush, Weijie Su
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: SLOPE is a relatively new convex optimization procedure for high-dimensional linear regression via the sorted $\ell_1$ penalty: the larger the rank of the fitted coefficient, the larger the penalty. This non-separable penalty renders many existing techniques invalid or inconclusive in analyzing the SLOPE solution. In this paper, we develop an asymptotically exact characterization of the SLOPE solution under Gaussian random designs through solving the SLOPE problem using approximate message passing (AMP). This algorithmic approach allows us to approximate the SLOPE solution via the much more amenable AMP iterates. Explicitly, we characterize the asymptotic dynamics of the AMP iterates relying on a recently developed state evolution analysis for non-separable penalties, thereby overcoming the difficulty caused by the sorted $\ell_1$ penalty. Moreover, we prove that the AMP iterates converge to the SLOPE solution in an asymptotic sense, and numerical simulations show that the convergence is surprisingly fast. Our proof rests on a novel technique that specifically leverages the SLOPE problem. In contrast to prior literature, our work not only yields an asymptotically sharp analysis but also offers an algorithmic, flexible, and constructive approach to understanding the SLOPE problem.
Multi-objects Generation with Amortized Structural Regularization
Authors: Taufik Xu, Chongxuan LI, Jun Zhu, Bo Zhang
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: Deep generative models (DGMs) have shown promise in image generation. However, most of the existing methods learn a model by simply optimizing a divergence between the marginal distributions of the model and the data, and often fail to capture rich structures, such as attributes of objects and their relationships, in an image.
Human knowledge is a crucial element to the success of DGMs to infer these structures, especially in unsupervised learning.
In this paper, we propose amortized structural regularization (ASR), which adopts posterior regularization (PR) to embed human knowledge into DGMs via a set of structural constraints.
We derive a lower bound of the regularized log-likelihood in PR and adopt the amortized inference technique to jointly optimize the generative model and an auxiliary recognition model for inference efficiently.
Empirical results show that ASR outperforms the DGM baselines in terms of inference performance and sample quality.
A Family of Robust Stochastic Operators for Reinforcement Learning
Authors: Yingdong Lu, Mark Squillante, Chai Wah Wu
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: We consider a new family of stochastic operators for reinforcement learning with the goal of alleviating negative effects and becoming more robust to approximation or estimation errors. Various theoretical results are established, which include showing that our family of operators preserve optimality and increase the action gap in a stochastic sense. Our empirical results illustrate the strong benefits of our robust stochastic operators, significantly outperforming the classical Bellman operator and recently proposed operators.
One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizers
Authors: Ari Morcos, Haonan Yu, Michela Paganini, Yuandong Tian
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: The success of lottery ticket initializations (Frankle and Carbin, 2019) suggests that small, sparsified networks can be trained so long as the network is initialized appropriately. Unfortunately, finding these "winning ticket'' initializations is computationally expensive. One potential solution is to reuse the same winning tickets across a variety of datasets and optimizers. However, the generality of winning ticket initializations remains unclear. Here, we attempt to answer this question by generating winning tickets for one training configuration (optimizer and dataset) and evaluating their performance on another configuration. Perhaps surprisingly, we found that, within the natural images domain, winning ticket initializations generalized across a variety of datasets, including Fashion MNIST, SVHN, CIFAR-10/100, ImageNet, and Places365, often achieving performance close to that of winning tickets generated on the same dataset. Moreover, winning tickets generated using larger datasets consistently transferred better than those generated using smaller datasets. We also found that winning ticket initializations generalize across optimizers with high performance. These results suggest that winning ticket initializations generated by sufficiently large datasets contain inductive biases generic to neural networks more broadly which improve training across many settings and provide hope for the development of better initialization methods.
Learning Distributions Generated by One-Layer ReLU Networks
Authors: Shanshan Wu, Alexandros G Dimakis, Sujay Sanghavi
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: We consider the problem of estimating the parameters of a $d$-dimensional rectified Gaussian distribution from i.i.d. samples. A rectified Gaussian distribution is defined by passing a standard Gaussian distribution through a one-layer ReLU neural network. We give a simple algorithm to estimate the parameters (i.e., the weight matrix and bias vector of the ReLU neural network) up to an error $\eps\norm{W}_F$ using $\widetilde{O}(1/\eps^2)$ samples and $\widetilde{O}(d^2/\eps^2)$ time (log factors are ignored for simplicity). This implies that we can estimate the distribution up to $\eps$ in total variation distance using $\widetilde{O}(\kappa^2d^2/\eps^2)$ samples, where $\kappa$ is the condition number of the covariance matrix. Our only assumption is that the bias vector is non-negative. Without this non-negativity assumption, we show that estimating the bias vector within any error requires the number of samples at least exponential in the infinity norm of the bias vector. Our algorithm is based on the key observation that vector norms and pairwise angles can be estimated separately. We use a recent result on learning from truncated samples. We also prove two sample complexity lower bounds: $\Omega(1/\eps^2)$ samples are required to estimate the parameters up to error $\eps$, while $\Omega(d/\eps^2)$ samples are necessary to estimate the distribution up to $\eps$ in total variation distance. The first lower bound implies that our algorithm is optimal for parameter estimation. Finally, we show an interesting connection between learning a two-layer generative model and non-negative matrix factorization. Experimental results are provided to support our analysis.
Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules
Authors: Niklas Gebauer, Michael Gastegger, Kristof Schütt
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: Deep learning has proven to yield fast and accurate predictions of quantum-chemical properties to accelerate the discovery of novel molecules and materials. As an exhaustive exploration of the vast chemical space is still infeasible, we require generative models that guide our search towards systems with desired properties. While graph-based models have previously been proposed, they are restricted by a lack of spatial information such that they are unable to recognize spatial isomerism and non-bonded interactions. Here, we introduce a generative neural network for 3d point sets that respects the rotational invariance of the targeted structures. We apply it to the generation of molecules and demonstrate its ability to approximate the distribution of equilibrium structures using spatial metrics as well as established measures from chemoinformatics. As our model is able to capture the complex relationship between 3d geometry and electronic properties, we bias the distribution of the generator towards molecules with a small HOMO-LUMO gap - an important property for the design of organic solar cells.
Quantum Entropy Scoring for Fast Robust Mean Estimation and Improved Outlier Detection
Authors: Yihe Dong, Samuel Hopkins, Jerry Li
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: We study two problems in high-dimensional robust statistics: \emph{robust mean estimation} and \emph{outlier detection}.
In robust mean estimation the goal is to estimate the mean $\mu$ of a distribution on $\mathbb{R}^d$ given $n$ independent samples, an $\epsilon$-fraction of which have been corrupted by a malicious adversary.
In outlier detection the goal is to assign an \emph{outlier score} to each element of a data set such that elements more likely to be outliers are assigned higher scores.
Our algorithms for both problems are based on a new outlier scoring method we call QUE-scoring based on \emph{quantum entropy regularization}.
For robust mean estimation, this yields the first algorithm with optimal error rates and nearly-linear running time $\tilde{O}(nd)$ in all parameters, improving on the previous fastest running time $\tilde{O}(\min(nd/\e^6, nd^2))$.
For outlier detection, we evaluate the performance of QUE-scoring via extensive experiments on synthetic and real data, and demonstrate that it often performs better than previously proposed algorithms.
Semi-flat minima and saddle points by embedding neural networks to overparameterization
Authors: Kenji Fukumizu, Shoichiro Yamaguchi, Yoh-ichi Mototake, Mirai Tanaka
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: We theoretically study the landscape of the training error for neural networks in overparameterized cases.  We consider three basic methods for embedding a network into a wider one with more hidden units, and discuss whether a minimum point of the narrower network gives a minimum or saddle point of the wider one. Our results show that the networks with smooth and ReLU activation have different partially flat landscapes around the embedded point.  We also relate these results to a difference of their generalization abilities in overparameterized realization.