pwc-archive/papers-with-abstracts huggingface dataset:
Privacy Preserving Off-Policy Evaluation
Authors: Tengyang Xie, Philip S. Thomas, Gerome Miklau
Date: 2019-02-01 00:00:00
Tasks: Off-policy evaluation, Privacy Preserving, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Many reinforcement learning applications involve the use of data that is
sensitive, such as medical records of patients or financial information.
However, most current reinforcement learning methods can leak information
contained within the (possibly sensitive) data on which they are trained. To
address this problem, we present the first differentially private approach for
off-policy evaluation. We provide a theoretical analysis of the
privacy-preserving properties of our algorithm and analyze its utility (speed
of convergence). After describing some results of this theoretical analysis, we
show empirically that our method outperforms previous methods (which are
restricted to the on-policy setting).
Dataset Culling: Towards Efficient Training Of Distillation-Based Domain Specific Models
Authors: Kentaro Yoshioka, Edward Lee, Simon Wong, Mark Horowitz
Date: 2019-02-01 00:00:00
Tasks: object-detection, Object Detection

Real-time CNN-based object detection models for applications like surveillance can achieve high accuracy but are computationally expensive. Recent works have shown 10 to 100x reduction in computation cost for inference by using domain-specific networks. However, prior works have focused on inference only. If the domain model requires frequent retraining, training costs can pose a significant bottleneck. To address this, we propose Dataset Culling: a pipeline to reduce the size of the dataset for training, based on the prediction difficulty. Images that are easy to classify are filtered out since they contribute little to improving the accuracy. The difficulty is measured using our proposed confidence loss metric with little computational overhead. Dataset Culling is extended to optimize the image resolution to further improve training and inference costs. We develop fixed-angle, long-duration video datasets across several domains, and we show that the dataset size can be culled by a factor of 300x to reduce the total training time by 47x with no accuracy loss or even with slight improvement. Codes are available: https://github.com/kentaroy47/DatasetCulling
CESI: Canonicalizing Open Knowledge Bases using Embeddings and Side Information
Authors: Shikhar Vashishth, Prince Jain, Partha Talukdar
Date: 2019-02-01 00:00:00
Tasks: Clustering, Feature Engineering, Noun Phrase Canonicalization, Open Information Extraction, Open Knowledge Graph Canonicalization, Relation

Open Information Extraction (OpenIE) methods extract (noun phrase, relation
phrase, noun phrase) triples from text, resulting in the construction of large
Open Knowledge Bases (Open KBs). The noun phrases (NPs) and relation phrases in
such Open KBs are not canonicalized, leading to the storage of redundant and
ambiguous facts. Recent research has posed canonicalization of Open KBs as
clustering over manuallydefined feature spaces. Manual feature engineering is
expensive and often sub-optimal. In order to overcome this challenge, we
propose Canonicalization using Embeddings and Side Information (CESI) - a novel
approach which performs canonicalization over learned embeddings of Open KBs.
CESI extends recent advances in KB embedding by incorporating relevant NP and
relation phrase side information in a principled manner. Through extensive
experiments on multiple real-world datasets, we demonstrate CESI's
effectiveness.
DREAM: A Challenge Dataset and Models for Dialogue-Based Reading Comprehension
Authors: Kai Sun, Dian Yu, Jianshu Chen, Dong Yu, Yejin Choi, Claire Cardie
Date: 2019-02-01 00:00:00
Tasks: Dialogue Understanding, Multiple-choice, Reading Comprehension, Sentence, World Knowledge

We present DREAM, the first dialogue-based multiple-choice reading
comprehension dataset. Collected from English-as-a-foreign-language
examinations designed by human experts to evaluate the comprehension level of
Chinese learners of English, our dataset contains 10,197 multiple-choice
questions for 6,444 dialogues. In contrast to existing reading comprehension
datasets, DREAM is the first to focus on in-depth multi-turn multi-party
dialogue understanding. DREAM is likely to present significant challenges for
existing reading comprehension systems: 84% of answers are non-extractive, 85%
of questions require reasoning beyond a single sentence, and 34% of questions
also involve commonsense knowledge.
  We apply several popular neural reading comprehension models that primarily
exploit surface information within the text and find them to, at best, just
barely outperform a rule-based approach. We next investigate the effects of
incorporating dialogue structure and different kinds of general world knowledge
into both rule-based and (neural and non-neural) machine learning-based reading
comprehension models. Experimental results on the DREAM dataset show the
effectiveness of dialogue structure and general world knowledge. DREAM will be
available at https://dataset.org/dream/.
Lift-the-flap: what, where and when for context reasoning
Authors: Mengmi Zhang, Claire Tseng, Karla Montejo, Joseph Kwon, Gabriel Kreiman
Date: 2019-02-01 00:00:00
Tasks: Diagnostic, General Classification, Object Recognition, Semantic Segmentation

Context reasoning is critical in a wide variety of applications where current inputs need to be interpreted in the light of previous experience and knowledge. Both spatial and temporal contextual information play a critical role in the domain of visual recognition. Here we investigate spatial constraints (what image features provide contextual information and where they are located), and temporal constraints (when different contextual cues matter) for visual recognition. The task is to reason about the scene context and infer what a target object hidden behind a flap is in a natural image. To tackle this problem, we first describe an online human psychophysics experiment recording active sampling via mouse clicks in lift-the-flap games and identify clicking patterns and features which are diagnostic for high contextual reasoning accuracy. As a proof of the usefulness of these clicking patterns and visual features, we extend a state-of-the-art recurrent model capable of attending to salient context regions, dynamically integrating useful information, making inferences, and predicting class label for the target object over multiple clicks. The proposed model achieves human-level contextual reasoning accuracy, shares human-like sampling behavior and learns interpretable features for contextual reasoning.
Compressing GANs using Knowledge Distillation
Authors: Angeline Aguinaldo, Ping-Yeh Chiang, Alex Gain, Ameya Patil, Kolten Pearson, Soheil Feizi
Date: 2019-02-01 00:00:00
Tasks: Knowledge Distillation, Super-Resolution, Synthetic Data Generation

Generative Adversarial Networks (GANs) have been used in several machine
learning tasks such as domain transfer, super resolution, and synthetic data
generation. State-of-the-art GANs often use tens of millions of parameters,
making them expensive to deploy for applications in low SWAP (size, weight, and
power) hardware, such as mobile devices, and for applications with real time
capabilities. There has been no work found to reduce the number of parameters
used in GANs. Therefore, we propose a method to compress GANs using knowledge
distillation techniques, in which a smaller "student" GAN learns to mimic a
larger "teacher" GAN. We show that the distillation methods used on MNIST,
CIFAR-10, and Celeb-A datasets can compress teacher GANs at ratios of 1669:1,
58:1, and 87:1, respectively, while retaining the quality of the generated
image. From our experiments, we observe a qualitative limit for GAN's
compression. Moreover, we observe that, with a fixed parameter budget,
compressed GANs outperform GANs trained using standard training methods. We
conjecture that this is partially owing to the optimization landscape of
over-parameterized GANs which allows efficient training using alternating
gradient descent. Thus, training an over-parameterized GAN followed by our
proposed compression scheme provides a high quality generative model with a
small number of parameters.
Towards Generating Long and Coherent Text with Multi-Level Latent Variable Models
Authors: Dinghan Shen, Asli Celikyilmaz, Yizhe Zhang, Liqun Chen, Xin Wang, Jianfeng Gao, Lawrence Carin
Date: 2019-02-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Decoder, Sentence, Text Generation

Variational autoencoders (VAEs) have received much attention recently as an end-to-end architecture for text generation with latent variables. In this paper, we investigate several multi-level structures to learn a VAE model to generate long, and coherent text. In particular, we use a hierarchy of stochastic layers between the encoder and decoder networks to generate more informative latent codes. We also investigate a multi-level decoder structure to learn a coherent long-term structure by generating intermediate sentence representations as high-level plan vectors. Empirical results demonstrate that a multi-level VAE model produces more coherent and less repetitive long text compared to the standard VAE models and can further mitigate the posterior-collapse issue.
Deep Triplet Quantization
Authors: Bin Liu, Yue Cao, Mingsheng Long, Jian-Min Wang, Jingdong Wang
Date: 2019-02-01 00:00:00
Tasks: Deep Hashing, Image Retrieval, Quantization, Retrieval, Triplet

Deep hashing establishes efficient and effective image retrieval by
end-to-end learning of deep representations and hash codes from similarity
data. We present a compact coding solution, focusing on deep learning to
quantization approach that has shown superior performance over hashing
solutions for similarity retrieval. We propose Deep Triplet Quantization (DTQ),
a novel approach to learning deep quantization models from the similarity
triplets. To enable more effective triplet training, we design a new triplet
selection approach, Group Hard, that randomly selects hard triplets in each
image group. To generate compact binary codes, we further apply a triplet
quantization with weak orthogonality during triplet training. The quantization
loss reduces the codebook redundancy and enhances the quantizability of deep
representations through back-propagation. Extensive experiments demonstrate
that DTQ can generate high-quality and compact binary codes, which yields
state-of-the-art image retrieval performance on three benchmark datasets,
NUS-WIDE, CIFAR-10, and MS-COCO.
A dual Newton based preconditioned proximal point algorithm for exclusive lasso models
Authors: Meixia Lin, Defeng Sun, Kim-Chuan Toh, Yancheng Yuan
Date: 2019-02-01 00:00:00

The exclusive lasso (also known as elitist lasso) regularization has become popular recently due to its superior performance on group sparsity. Compared to the group lasso regularization which enforces the competition on variables among different groups, the exclusive lasso regularization also enforces the competition within each group. In this paper, we propose a highly efficient dual Newton based preconditioned proximal point algorithm (PPDNA) to solve machine learning models involving the exclusive lasso regularizer. As an important ingredient, we provide a rigorous proof for deriving the closed-form solution to the proximal mapping of the weighted exclusive lasso regularizer. In addition, we derive the corresponding HS-Jacobian to the proximal mapping and analyze its structure --- which plays an essential role in the efficient computation of the PPA subproblem via applying a semismooth Newton method on its dual. Various numerical experiments in this paper demonstrate the superior performance of the proposed PPDNA against other state-of-the-art numerical algorithms.
Domain Adaptation for Real-Time Student Performance Prediction
Authors: Byung-Hak Kim, Ethan Vizitei, Varun Ganapathi
Date: 2018-09-07 00:00:00
Tasks: Domain Adaptation, Prediction, Unsupervised Domain Adaptation

Increasingly fast development and update cycle of online course contents, and
diverse demographics of students in each online classroom, make student
performance prediction in real-time (before the course finishes) and/or on
curriculum without specific historical performance data available interesting
topics for both industrial research and practical needs. In this research, we
tackle the problem of real-time student performance prediction with on-going
courses in a domain adaptation framework, which is a system trained on
students' labeled outcome from one set of previous coursework but is meant to
be deployed on another. In particular, we first introduce recently-developed
GritNet architecture which is the current state of the art for student
performance prediction problem, and develop a new \emph{unsupervised} domain
adaptation method to transfer a GritNet trained on a past course to a new
course without any (students' outcome) label. Our results for real Udacity
students' graduation predictions show that the GritNet not only
\emph{generalizes} well from one course to another across different Nanodegree
programs, but enhances real-time predictions explicitly in the first few weeks
when accurate predictions are most challenging.
Bifidelity data-assisted neural networks in nonintrusive reduced-order modeling
Authors: Chuan Lu, Xueyu Zhu
Date: 2019-02-01 00:00:00

In this paper, we present a new nonintrusive reduced basis method when a
cheap low-fidelity model and expensive high-fidelity model are available. The
method relies on proper orthogonal decomposition (POD) to generate the
high-fidelity reduced basis and a shallow multilayer perceptron to learn the
high-fidelity reduced coefficients. In contrast to other methods, one distinct
feature of the proposed method is to incorporate the features extracted from
the low-fidelity data as the input feature, this approach not only improves the
predictive capability of the neural network but also enables the decoupling the
high-fidelity simulation from the online stage. Due to its nonintrusive nature,
it is applicable to general parameterized problems. We also provide several
numerical examples to illustrate the effectiveness and performance of the
proposed method.
Agnostic Federated Learning
Authors: Mehryar Mohri, Gary Sivek, Ananda Theertha Suresh
Date: 2019-02-01 00:00:00
Tasks: Cloud Computing, Domain Adaptation, Fairness, Federated Learning, Stochastic Optimization

A key learning scenario in large-scale applications is that of federated
learning, where a centralized model is trained based on data originating from a
large number of clients. We argue that, with the existing training and
inference, federated models can be biased towards different clients. Instead,
we propose a new framework of agnostic federated learning, where the
centralized model is optimized for any target distribution formed by a mixture
of the client distributions. We further show that this framework naturally
yields a notion of fairness. We present data-dependent Rademacher complexity
guarantees for learning with this objective, which guide the definition of an
algorithm for agnostic federated learning. We also give a fast stochastic
optimization algorithm for solving the corresponding optimization problem, for
which we prove convergence bounds, assuming a convex loss function and
hypothesis set. We further empirically demonstrate the benefits of our approach
in several datasets. Beyond federated learning, our framework and algorithm can
be of interest to other learning scenarios such as cloud computing, domain
adaptation, drifting, and other contexts where the training and test
distributions do not coincide.
Graph Resistance and Learning from Pairwise Comparisons
Authors: Julien M. Hendrickx, Alex Olshevsky, Venkatesh Saligrama
Date: 2019-02-01 00:00:00

We consider the problem of learning the qualities of a collection of items by performing noisy comparisons among them. Following the standard paradigm, we assume there is a fixed "comparison graph" and every neighboring pair of items in this graph is compared $k$ times according to the Bradley-Terry-Luce model (where the probability than an item wins a comparison is proportional the item quality). We are interested in how the relative error in quality estimation scales with the comparison graph in the regime where $k$ is large. We prove that, after a known transition period, the relevant graph-theoretic quantity is the square root of the resistance of the comparison graph. Specifically, we provide an algorithm that is minimax optimal. The algorithm has a relative error decay that scales with the square root of the graph resistance, and provide a matching lower bound (up to log factors). The performance guarantee of our algorithm, both in terms of the graph and the skewness of the item quality distribution, outperforms earlier results.
Passed & Spurious: Descent Algorithms and Local Minima in Spiked Matrix-Tensor Models
Authors: Stefano Sarao Mannelli, Florent Krzakala, Pierfrancesco Urbani, Lenka Zdeborová
Date: 2019-02-01 00:00:00

In this work we analyse quantitatively the interplay between the loss landscape and performance of descent algorithms in a prototypical inference problem, the spiked matrix-tensor model. We study a loss function that is the negative log-likelihood of the model. We analyse the number of local minima at a fixed distance from the signal/spike with the Kac-Rice formula, and locate trivialization of the landscape at large signal-to-noise ratios. We evaluate in a closed form the performance of a gradient flow algorithm using integro-differential PDEs as developed in physics of disordered systems for the Langevin dynamics. We analyze the performance of an approximate message passing algorithm estimating the maximum likelihood configuration via its state evolution. We conclude by comparing the above results: while we observe a drastic slow down of the gradient flow dynamics even in the region where the landscape is trivial, both the analyzed algorithms are shown to perform well even in the part of the region of parameters where spurious local minima are present.
Tsallis Reinforcement Learning: A Unified Framework for Maximum Entropy Reinforcement Learning
Authors: Kyungjae Lee, Sungyub Kim, Sungbin Lim, Sungjoon Choi, Songhwai Oh
Date: 2019-01-31 00:00:00
Tasks: MuJoCo, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this paper, we present a new class of Markov decision processes (MDPs),
called Tsallis MDPs, with Tsallis entropy maximization, which generalizes
existing maximum entropy reinforcement learning (RL). A Tsallis MDP provides a
unified framework for the original RL problem and RL with various types of
entropy, including the well-known standard Shannon-Gibbs (SG) entropy, using an
additional real-valued parameter, called an entropic index. By controlling the
entropic index, we can generate various types of entropy, including the SG
entropy, and a different entropy results in a different class of the optimal
policy in Tsallis MDPs. We also provide a full mathematical analysis of Tsallis
MDPs, including the optimality condition, performance error bounds, and
convergence. Our theoretical result enables us to use any positive entropic
index in RL. To handle complex and large-scale problems, we propose a
model-free actor-critic RL method using Tsallis entropy maximization. We
evaluate the regularization effect of the Tsallis entropy with various values
of entropic indices and show that the entropic index controls the exploration
tendency of the proposed method. For a different type of RL problems, we find
that a different value of the entropic index is desirable. The proposed method
is evaluated using the MuJoCo simulator and achieves the state-of-the-art
performance.
initKmix -- A Novel Initial Partition Generation Algorithm for Clustering Mixed Data using k-means-based Clustering
Authors: Amir Ahmad, Shehroz S. Khan
Date: 2019-01-31 00:00:00
Tasks: Clustering

Mixed datasets consist of both numeric and categorical attributes. Various k-means-based clustering algorithms have been developed for these datasets. Generally, these algorithms use random partition as a starting point, which tends to produce different clustering results for different runs. In this paper, we propose, initKmix, a novel algorithm for finding an initial partition for k-means-based clustering algorithms for mixed datasets. In the initKmix algorithm, a k-means-based clustering algorithm is run many times, and in each run, one of the attributes is used to create initial clusters for that run. The clustering results of various runs are combined to produce the initial partition. This initial partition is then used as a seed to a k-means-based clustering algorithm to cluster mixed data. Experiments with various categorical and mixed datasets showed that initKmix produced accurate and consistent results, and outperformed the random initial partition method and other state-of-the-art initialization methods. Experiments also showed that k-means-based clustering for mixed datasets with initKmix performed similar to or better than many state-of-the-art clustering algorithms for categorical and mixed datasets.
US-net for robust and efficient nuclei instance segmentation
Authors: Zhaoyang Xu, Faranak Sobhani, Carlos Fernandez Moro, Qianni Zhang
Date: 2019-01-31 00:00:00
Tasks: Instance Segmentation, Segmentation, Semantic Segmentation

We present a novel neural network architecture, US-Net, for robust nuclei
instance segmentation in histopathology images. The proposed framework
integrates the nuclei detection and segmentation networks by sharing their
outputs through the same foundation network, and thus enhancing the performance
of both. The detection network takes into account the high-level semantic cues
with contextual information, while the segmentation network focuses more on the
low-level details like the edges. Extensive experiments reveal that our
proposed framework can strengthen the performance of both branch networks in an
integrated architecture and outperforms most of the state-of-the-art nuclei
detection and segmentation networks.
Learning to Make Analogies by Contrasting Abstract Relational Structure
Authors: Felix Hill, Adam Santoro, David G. T. Barrett, Ari S. Morcos, Timothy Lillicrap
Date: 2019-01-31 00:00:00
Proceeding: ICLR 2019 5

Analogical reasoning has been a principal focus of various waves of AI
research. Analogy is particularly challenging for machines because it requires
relational structures to be represented such that they can be flexibly applied
across diverse domains of experience. Here, we study how analogical reasoning
can be induced in neural networks that learn to perceive and reason about raw
visual data. We find that the critical factor for inducing such a capacity is
not an elaborate architecture, but rather, careful attention to the choice of
data and the manner in which it is presented to the model. The most robust
capacity for analogical reasoning is induced when networks learn analogies by
contrasting abstract relational structures in their input domains, a training
method that uses only the input data to force models to learn about important
abstract features. Using this technique we demonstrate capacities for complex,
visual and symbolic analogy making and generalisation in even the simplest
neural network architectures.