pwc-archive/papers-with-abstracts huggingface dataset:
Coordinated hippocampal-entorhinal replay as structural inference
Authors: Talfan Evans, Neil Burgess
Date: 2019-12-01 00:00:00
Proceeding: NeurIPS 2019 12

Constructing and maintaining useful representations of sensory experience is essential for reasoning about ones environment. High-level associative (topological) maps can be useful for efficient planning and are easily constructed from experience. Conversely, embedding new experiences within a metric structure allows them to be integrated with existing ones and novel associations to be implicitly inferred. Neurobiologically, the synaptic associations between hippocampal place cells and entorhinal grid cells are thought to represent associative and metric structures, respectively. Learning the place-grid cell associations can therefore be interpreted as learning a mapping between these two spaces. Here, we show how this map could be constructed by probabilistic message-passing through the hippocampal-entorhinal system, where messages are scheduled to reduce the propagation of redundant information. We propose that this offline inference corresponds to coordinated hippocampal-entorhinal replay during sharp wave ripples. Our results also suggest that the metric map will contain local distortions that reflect the inferred structure of the environment according to associative experience, explaining observed grid deformations.
Cascaded Dilated Dense Network with Two-step Data Consistency for MRI Reconstruction
Authors: Hao Zheng, Faming Fang, Guixu Zhang
Date: 2019-12-01 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: compressed sensing, MRI Reconstruction

Compressed Sensing MRI (CS-MRI) aims at reconstrcuting de-aliased images from sub-Nyquist sampling k-space data to accelerate MR Imaging. Inspired by recent deep learning methods, we propose a Cascaded Dilated Dense Network (CDDN) for MRI reconstruction. Dense blocks with residual connection are used to restore clear images step by step and dilated convolution is introduced for expanding receptive field without taking more network parameters. After each sub-network, we use a novel two-step Data Consistency (DC) operation in k-space. We convert the complex result from first DC operation to real-valued images and applied another sampled \emph{k}-space data replacement. Extensive experiments demonstrate that the proposed CDDN with two-step DC achieves state-of-art result.
Better Exploration with Optimistic Actor Critic
Authors: Kamil Ciosek, Quan Vuong, Robert Loftin, Katja Hofmann
Date: 2019-12-01 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: continuous-control, Continuous Control, Efficient Exploration, Reinforcement Learning

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.
Catastrophic Forgetting Meets Negative Transfer: Batch Spectral Shrinkage for Safe Transfer Learning
Authors: Xinyang Chen, Sinan Wang, Bo Fu, Mingsheng Long, Jian-Min Wang
Date: 2019-12-01 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Transfer Learning

Before sufficient training data is available, fine-tuning neural networks pre-trained on large-scale datasets substantially outperforms training from random initialization. However, fine-tuning methods suffer from two dilemmas, catastrophic forgetting and negative transfer. While several methods with explicit attempts to overcome catastrophic forgetting have been proposed, negative transfer is rarely delved into. In this paper, we launch an in-depth empirical investigation into negative transfer in fine-tuning and find that, for the weight parameters and feature representations, transferability of their spectral components is diverse. For safe transfer learning, we present Batch Spectral Shrinkage (BSS), a novel regularization approach to penalizing smaller singular values so that untransferable spectral components are suppressed. BSS is orthogonal to existing fine-tuning methods and is readily pluggable to them. Experimental results show that BSS can significantly enhance the performance of representative methods, especially with limited training data.
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
Date: 2019-12-01 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Decoder, Diversity, Image Captioning

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.
Transferable Normalization: Towards Improving Transferability of Deep Neural Networks
Authors: Ximei Wang, Ying Jin, Mingsheng Long, Jian-Min Wang, Michael. I. Jordan
Date: 2019-12-01 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Domain Adaptation, Unsupervised Domain Adaptation

Deep neural networks (DNNs) excel at learning representations when trained on large-scale datasets. Pre-trained DNNs also show strong transferability when fine-tuned to other labeled datasets. However, such transferability becomes weak when the target dataset is fully unlabeled as in Unsupervised Domain Adaptation (UDA). We envision that the loss of transferability may stem from the intrinsic limitation of the architecture design of DNNs. In this paper, we delve into the components of DNN architectures and propose Transferable Normalization (TransNorm) in place of existing normalization techniques. TransNorm is an end-to-end trainable layer to make DNNs more transferable across domains. As a general method, TransNorm can be easily applied to various deep neural networks and domain adaption methods, without introducing any extra hyper-parameters or learnable parameters. Empirical results justify that TransNorm not only improves classification accuracies but also accelerates convergence for mainstream DNN-based domain adaptation methods.
CNN^{2}: Viewpoint Generalization via a Binocular Vision
Authors: Wei-Da Chen, Shan-Hung (Brandon) Wu
Date: 2019-12-01 00:00:00
Proceeding: NeurIPS 2019 12

The Convolutional Neural Networks (CNNs) have laid the foundation for many techniques in various applications. Despite achieving remarkable performance in some tasks, the 3D viewpoint generalizability of CNNs is still far behind humans visual capabilities. Although recent efforts, such as the Capsule Networks, have been made to address this issue, these new models are either hard to train and/or incompatible with existing CNN-based techniques specialized for different applications. Observing that humans use binocular vision to understand the world, we study in this paper whether the 3D viewpoint generalizability of CNNs can be achieved via a binocular vision. We propose CNN^{2}, a CNN that takes two images as input, which resembles the process of an object being viewed from the left eye and the right eye. CNN^{2} uses novel augmentation, pooling, and convolutional layers to learn a sense of three-dimensionality in a recursive manner. Empirical evaluation shows that CNN^{2} has improved viewpoint generalizability compared to vanilla CNNs. Furthermore, CNN^{2} is easy to implement and train, and is compatible with existing CNN-based specialized techniques for different applications.
Numerically Accurate Hyperbolic Embeddings Using Tiling-Based Models
Authors: Tao Yu, Christopher M. De Sa
Date: 2019-12-01 00:00:00
Proceeding: NeurIPS 2019 12

Hyperbolic embeddings achieve excellent performance when embedding hierarchical data structures like synonym or type hierarchies, but they can be limited by numerical error when ordinary floating-point numbers are used to represent points in hyperbolic space. Standard models such as the Poincar{\'e} disk and the Lorentz model have unbounded numerical error as points get far from the origin.
To address this, we propose a new model which uses an integer-based tiling to represent \emph{any} point in hyperbolic space with provably bounded numerical error. This allows us to learn high-precision embeddings without using BigFloats, and enables us to store the resulting embeddings with fewer bits. We evaluate our tiling-based model empirically, and show that it can both compress hyperbolic embeddings (down to $2\%$ of a Poincar{\'e} embedding on WordNet Nouns) and learn more accurate embeddings on real-world datasets.
Discrimination in Online Markets: Effects of Social Bias on Learning from Reviews and Policy Design
Authors: Faidra Georgia Monachou, Itai Ashlagi
Date: 2019-12-01 00:00:00
Proceeding: NeurIPS 2019 12

The increasing popularity of online two-sided markets such as ride-sharing, accommodation and freelance labor platforms, goes hand in hand with new socioeconomic challenges.  One major issue remains the existence of bias and discrimination against certain social groups. We study this problem using  a two-sided large market model with  employers and workers mediated by a platform. Employers who seek to hire  workers face uncertainty about a candidate worker's  skill level. Therefore, they base their hiring decision  on learning from past reviews about an individual worker as well as on their (possibly misspecified)  prior beliefs about the ability level of the social group the worker belongs to. Drawing upon the  social learning literature with bounded rationality and limited information, uncertainty combined with social bias leads to  unequal hiring opportunities between workers of different social groups. Although the effect of social bias decreases as the number of reviews increases (consistent with empirical findings), minority workers still receive lower expected  payoffs. Finally, we  consider a simple directed matching policy (DM), which combines learning and matching to make better matching decisions for minority workers. Under this policy, there exists a steady-state equilibrium, in which  DM reduces the discrimination gap.
Online Stochastic Shortest Path with Bandit Feedback and Unknown Transition Function
Authors: Aviv Rosenberg, Yishay Mansour
Date: 2019-12-01 00:00:00
Proceeding: NeurIPS 2019 12

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.
Partitioning Structure Learning for Segmented Linear Regression Trees
Authors: Xiangyu Zheng, Song Xi Chen
Date: 2019-12-01 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: regression

This paper proposes a partitioning structure learning method for segmented linear regression trees (SLRT), which assigns linear predictors over the terminal nodes. The recursive partitioning process is driven by an adaptive split selection algorithm that maximizes, at each node, a criterion function based on a conditional Kendall’s τ statistic that measures the rank dependence between the regressors and the fit- ted linear residuals. Theoretical analysis shows that the split selection algorithm permits consistent identification and estimation of the unknown segments. A suffi- ciently large tree is induced by applying the split selection algorithm recursively. Then the minimal cost-complexity tree pruning procedure is applied to attain the right-sized tree, that ensures (i) the nested structure of pruned subtrees and (ii) consistent estimation to the number of segments. Implanting the SLRT as the built-in base predictor, we obtain the ensemble predictors by random forests (RF) and the proposed weighted random forests (WRF). The practical performance of the SLRT and its ensemble versions are evaluated via numerical simulations and empirical studies. The latter shows their advantageous predictive performance over a set of state-of-the-art tree-based models on well-studied public datasets.
Disentangled behavioural representations
Authors: Amir Dezfouli, Hassan Ashtiani, Omar Ghattas, Richard Nock, Peter Dayan, Cheng Soon Ong
Date: 2019-12-01 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Decision Making

Individual characteristics in human decision-making are often
  quantified by fitting a parametric cognitive model to subjects'
  behavior and then studying differences between them in the associated
  parameter space.  However, these models often fit behavior more poorly
  than recurrent neural networks (RNNs), which are more flexible and
  make fewer assumptions about the underlying decision-making processes.
  Unfortunately, the parameter and latent activity spaces of RNNs are
  generally high-dimensional and uninterpretable, making it hard to use
  them to study individual differences.  Here, we
  show how to benefit from the flexibility of RNNs while representing
  individual differences in a low-dimensional and interpretable space.
  To achieve this, we propose a novel end-to-end learning framework in
  which an encoder is trained to map the behavior of subjects into a
  low-dimensional latent space. These low-dimensional representations
  are used to generate the parameters of individual RNNs corresponding
  to the decision-making process of each subject.  We introduce terms
  into the loss function that ensure that the latent dimensions are
  informative and disentangled, i.e.,
encouraged to have distinct effects on behavior. This allows them to
align with separate facets of
  individual differences. We illustrate the performance
   of our framework on synthetic data as well as a dataset including the behavior
   of patients with psychiatric disorders.
Integrating Bayesian and Discriminative Sparse Kernel Machines for Multi-class Active Learning
Authors: Weishi Shi, Qi Yu
Date: 2019-12-01 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Active Learning

We propose a novel active learning (AL) model that integrates Bayesian and discriminative kernel machines for fast and accurate multi-class data sampling. By joining a sparse Bayesian model and a maximum margin machine under a unified kernel machine committee (KMC), the proposed model is able to identify a small number of data samples that best represent the overall data space while accurately capturing the decision boundaries. The integration is conducted using the  maximum entropy discrimination framework, resulting in a joint objective function that contains generalized entropy as a regularizer. Such a property allows the proposed AL model to choose data samples that more effectively handle non-separable classification problems. Parameter learning is achieved through a principled optimization framework that leverages  convex duality and sparse structure of KMC to efficiently optimize the joint objective function. Key model parameters are used to design a novel sampling function  to choose data samples that can simultaneously improve multiple decision boundaries, making it an effective sampler for problems with a large number of classes. Experiments conducted over both synthetic and real data and comparison with competitive AL methods demonstrate the effectiveness of the proposed model.
Perceiving the arrow of time in autoregressive motion
Authors: Kristof Meding, Dominik Janzing, Bernhard Schölkopf, Felix A. Wichmann
Date: 2019-12-01 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Causal Inference, Time Series Analysis

Understanding the principles of causal inference in the visual system has a long history at least since the seminal studies by Albert Michotte. Many cognitive and machine learning scientists believe that intelligent behavior requires agents to possess causal models of the world. Recent ML algorithms exploit the dependence structure of additive noise terms for inferring causal structures from observational data, e.g. to detect the direction of time series; the arrow of time. This raises the question whether the subtle asymmetries between the time directions can also be perceived by humans. Here we show that human observers can indeed discriminate forward and backward autoregressive motion with non-Gaussian additive independent noise, i.e. they appear sensitive to subtle asymmetries between the time directions. We employ a so-called frozen noise paradigm enabling us to compare human performance with four different algorithms on a trial-by-trial basis: A causal inference algorithm exploiting the dependence structure of additive noise terms, a neurally inspired network, a Bayesian ideal observer model as well as a simple heuristic. Our results suggest that all human observers use similar cues or strategies to solve the arrow of time motion discrimination task, but the human algorithm is significantly different from the three machine algorithms we compared it to. In fact, our simple heuristic appears most similar to our human observers.
Gradient Information for Representation and Modeling
Authors: Jie Ding, Robert Calderbank, Vahid Tarokh
Date: 2019-12-01 00:00:00
Proceeding: NeurIPS 2019 12

Motivated by Fisher divergence, in this paper we present a new set of information quantities which we refer to as gradient information.  These measures serve as surrogates for classical information measures such as those based on logarithmic loss, Kullback-Leibler divergence, directed Shannon information, etc.  in many data-processing scenarios of interest, and often provide significant computational advantage, improved stability and robustness.  As an example, we apply these measures to the Chow-Liu tree algorithm, and demonstrate remarkable performance and significant computational reduction using both synthetic and real data.
SpiderBoost and Momentum: Faster Variance Reduction Algorithms
Authors: Zhe Wang, Kaiyi Ji, Yi Zhou, Yingbin Liang, Vahid Tarokh
Date: 2019-12-01 00:00:00
Proceeding: NeurIPS 2019 12

SARAH and SPIDER are two recently developed stochastic variance-reduced algorithms, and SPIDER has been shown to achieve a near-optimal first-order oracle complexity in smooth nonconvex optimization. However, SPIDER uses an accuracy-dependent stepsize that slows down the convergence in practice, and cannot handle objective functions that involve nonsmooth regularizers. In this paper, we propose SpiderBoost as an improved scheme, which allows to use a much larger constant-level stepsize while maintaining the same near-optimal oracle complexity, and can be extended with proximal mapping to handle composite optimization (which is nonsmooth and nonconvex) with provable convergence guarantee. In particular, we show that proximal SpiderBoost achieves an oracle complexity of  O(min{n^{1/2}\epsilon^{-2},\epsilon^{-3}})  in composite nonconvex optimization, improving the state-of-the-art result by a factor of  O(min{n^{1/6},\epsilon^{-1/3}}). We further develop a novel momentum scheme to accelerate SpiderBoost for composite optimization, which achieves the near-optimal oracle complexity in theory and substantial improvement in experiments.
Minimax Optimal Estimation of Approximate Differential Privacy on Neighboring Databases
Authors: Xiyang Liu, Sewoong Oh
Date: 2019-12-01 00:00:00
Proceeding: NeurIPS 2019 12

Differential privacy has become a widely accepted notion of privacy, leading to the introduction and deployment of numerous privatization mechanisms. However, ensuring the privacy guarantee is an error-prone process, both in designing mechanisms and in implementing those mechanisms. Both types of errors will be greatly reduced, if we have a data-driven approach to verify privacy guarantees, from a black-box access to a mechanism. We pose it as a property estimation problem, and study the fundamental trade-offs involved in the accuracy in estimated privacy guarantees and the number of samples required. We introduce a novel estimator that uses polynomial approximation of a carefully chosen degree to optimally trade-off bias and variance. With n samples, we show that this estimator achieves performance of a straightforward plug-in estimator with n*log(n) samples, a phenomenon referred to as effective sample size amplification. The minimax optimality of the proposed estimator is proved by comparing it to a matching fundamental lower bound.