pwc-archive/papers-with-abstracts huggingface dataset:
A Simple Proximal Stochastic Gradient Method for Nonsmooth Nonconvex Optimization
Authors: Zhize Li, Jian Li
Date: 2018-02-13 00:00:00
Proceeding: NeurIPS 2018 12

We analyze stochastic gradient algorithms for optimizing nonconvex, nonsmooth
finite-sum problems. In particular, the objective function is given by the
summation of a differentiable (possibly nonconvex) component, together with a
possibly non-differentiable but convex component. We propose a proximal
stochastic gradient algorithm based on variance reduction, called ProxSVRG+.
Our main contribution lies in the analysis of ProxSVRG+. It recovers several
existing convergence results and improves/generalizes them (in terms of the
number of stochastic gradient oracle calls and proximal oracle calls). In
particular, ProxSVRG+ generalizes the best results given by the SCSG algorithm,
recently proposed by [Lei et al., 2017] for the smooth nonconvex case.
ProxSVRG+ is also more straightforward than SCSG and yields simpler analysis.
Moreover, ProxSVRG+ outperforms the deterministic proximal gradient descent
(ProxGD) for a wide range of minibatch sizes, which partially solves an open
problem proposed in [Reddi et al., 2016b]. Also, ProxSVRG+ uses much less
proximal oracle calls than ProxSVRG [Reddi et al., 2016b]. Moreover, for
nonconvex functions satisfied Polyak-\L{}ojasiewicz condition, we prove that
ProxSVRG+ achieves a global linear convergence rate without restart unlike
ProxSVRG. Thus, it can \emph{automatically} switch to the faster linear
convergence in some regions as long as the objective function satisfies the PL
condition locally in these regions. ProxSVRG+ also improves ProxGD and
ProxSVRG/SAGA, and generalizes the results of SCSG in this case. Finally, we
conduct several experiments and the experimental results are consistent with
the theoretical results.
Alpha-Beta Divergence For Variational Inference
Authors: Jean-Baptiste Regli, Ricardo Silva
Date: 2018-05-02 00:00:00
Tasks: Variational Inference

This paper introduces a variational approximation framework using direct
optimization of what is known as the {\it scale invariant Alpha-Beta
divergence} (sAB divergence). This new objective encompasses most variational
objectives that use the Kullback-Leibler, the R{\'e}nyi or the gamma
divergences. It also gives access to objective functions never exploited before
in the context of variational inference. This is achieved via two easy to
interpret control parameters, which allow for a smooth interpolation over the
divergence space while trading-off properties such as mass-covering of a target
distribution and robustness to outliers in the data. Furthermore, the sAB
variational objective can be optimized directly by repurposing existing methods
for Monte Carlo computation of complex variational objectives, leading to
estimates of the divergence instead of variational lower bounds. We show the
advantages of this objective on Bayesian models for regression problems.
TVM: An Automated End-to-End Optimizing Compiler for Deep Learning
Authors: Tianqi Chen, Thierry Moreau, Ziheng Jiang, Lianmin Zheng, Eddie Yan, Meghan Cowan, Haichen Shen, Leyuan Wang, Yuwei Hu, Luis Ceze, Carlos Guestrin, Arvind Krishnamurthy
Date: 2018-02-12 00:00:00
Tasks: CPU, Deep Learning, Diversity, GPU

There is an increasing need to bring machine learning to a wide diversity of
hardware devices. Current frameworks rely on vendor-specific operator libraries
and optimize for a narrow range of server-class GPUs. Deploying workloads to
new platforms -- such as mobile phones, embedded devices, and accelerators
(e.g., FPGAs, ASICs) -- requires significant manual effort. We propose TVM, a
compiler that exposes graph-level and operator-level optimizations to provide
performance portability to deep learning workloads across diverse hardware
back-ends. TVM solves optimization challenges specific to deep learning, such
as high-level operator fusion, mapping to arbitrary hardware primitives, and
memory latency hiding. It also automates optimization of low-level programs to
hardware characteristics by employing a novel, learning-based cost modeling
method for rapid exploration of code optimizations. Experimental results show
that TVM delivers performance across hardware back-ends that are competitive
with state-of-the-art, hand-tuned libraries for low-power CPU, mobile GPU, and
server-class GPUs. We also demonstrate TVM's ability to target new accelerator
back-ends, such as the FPGA-based generic deep learning accelerator. The system
is open sourced and in production use inside several major companies.
Bayesian Regularization for Graphical Models with Unequal Shrinkage
Authors: Lingrui Gan, Naveen N. Narisetty, Feng Liang
Date: 2018-05-06 00:00:00

We consider a Bayesian framework for estimating a high-dimensional sparse
precision matrix, in which adaptive shrinkage and sparsity are induced by a
mixture of Laplace priors. Besides discussing our formulation from the Bayesian
standpoint, we investigate the MAP (maximum a posteriori) estimator from a
penalized likelihood perspective that gives rise to a new non-convex penalty
approximating the $\ell_0$ penalty. Optimal error rates for estimation
consistency in terms of various matrix norms along with selection consistency
for sparse structure recovery are shown for the unique MAP estimator under mild
conditions. For fast and efficient computation, an EM algorithm is proposed to
compute the MAP estimator of the precision matrix and (approximate) posterior
probabilities on the edges of the underlying sparse structure. Through
extensive simulation studies and a real application to a call center data, we
have demonstrated the fine performance of our method compared with existing
alternatives.
Online Structured Laplace Approximations For Overcoming Catastrophic Forgetting
Authors: Hippolyt Ritter, Aleksandar Botev, David Barber
Date: 2018-05-20 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Permuted-MNIST

We introduce the Kronecker factored online Laplace approximation for
overcoming catastrophic forgetting in neural networks. The method is grounded
in a Bayesian online learning framework, where we recursively approximate the
posterior after every task with a Gaussian, leading to a quadratic penalty on
changes to the weights. The Laplace approximation requires calculating the
Hessian around a mode, which is typically intractable for modern architectures.
In order to make our method scalable, we leverage recent block-diagonal
Kronecker factored approximations to the curvature. Our algorithm achieves over
90% test accuracy across a sequence of 50 instantiations of the permuted MNIST
dataset, substantially outperforming related methods for overcoming
catastrophic forgetting.
Testing Piecewise Functions
Authors: Steve Hanneke, Liu Yang
Date: 2017-06-23 00:00:00

This work explores the query complexity of property testing for general
piecewise functions on the real line, in the active and passive property
testing settings. The results are proven under an abstract zero-measure
crossings condition, which has as special cases piecewise constant functions
and piecewise polynomial functions. We find that, in the active testing
setting, the query complexity of testing general piecewise functions is
independent of the number of pieces. We also identify the optimal dependence on
the number of pieces in the query complexity of passive testing in the special
case of piecewise constant functions.
Statistical Learning under Nonstationary Mixing Processes
Authors: Steve Hanneke, Liu Yang
Date: 2015-12-26 00:00:00
Tasks: General Classification

We study a special case of the problem of statistical learning without the
i.i.d. assumption. Specifically, we suppose a learning method is presented with
a sequence of data points, and required to make a prediction (e.g., a
classification) for each one, and can then observe the loss incurred by this
prediction. We go beyond traditional analyses, which have focused on stationary
mixing processes or nonstationary product processes, by combining these two
relaxations to allow nonstationary mixing processes. We are particularly
interested in the case of $\beta$-mixing processes, with the sum of changes in
marginal distributions growing sublinearly in the number of samples. Under
these conditions, we propose a learning method, and establish that for bounded
VC subgraph classes, the cumulative excess risk grows sublinearly in the number
of predictions, at a quantified rate.
The Diverse Cohort Selection Problem
Authors: Candice Schumann, Samsara N. Counts, Jeffrey S. Foster, John P. Dickerson
Date: 2017-09-11 00:00:00
Tasks: Diversity

How should a firm allocate its limited interviewing resources to select the
optimal cohort of new employees from a large set of job applicants? How should
that firm allocate cheap but noisy resume screenings and expensive but in-depth
in-person interviews? We view this problem through the lens of combinatorial
pure exploration (CPE) in the multi-armed bandit setting, where a central
learning agent performs costly exploration of a set of arms before selecting a
final subset with some combinatorial structure. We generalize a recent CPE
algorithm to the setting where arm pulls can have different costs and return
different levels of information. We then prove theoretical upper bounds for a
general class of arm-pulling strategies in this new setting. We apply our
general algorithm to a real-world problem with combinatorial structure:
incorporating diversity into university admissions. We take real data from
admissions at one of the largest US-based computer science graduate programs
and show that a simulation of our algorithm produces a cohort with hiring
overall utility while spending comparable budget to the current admissions
process at that university.
Constrained Policy Improvement for Safe and Efficient Reinforcement Learning
Authors: Elad Sarafian, Aviv Tamar, Sarit Kraus
Date: 2018-05-20 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We propose a policy improvement algorithm for Reinforcement Learning (RL) which is called Rerouted Behavior Improvement (RBI). RBI is designed to take into account the evaluation errors of the Q-function. Such errors are common in RL when learning the $Q$-value from finite past experience data. Greedy policies or even constrained policy optimization algorithms which ignore these errors may suffer from an improvement penalty (i.e. a negative policy improvement). To minimize the improvement penalty, the RBI idea is to attenuate rapid policy changes of low probability actions which were less frequently sampled. This approach is shown to avoid catastrophic performance degradation and reduce regret when learning from a batch of past experience. Through a two-armed bandit with Gaussian distributed rewards example, we show that it also increases data efficiency when the optimal action has a high variance. We evaluate RBI in two tasks in the Atari Learning Environment: (1) learning from observations of multiple behavior policies and (2) iterative RL. Our results demonstrate the advantage of RBI over greedy policies and other constrained policy optimization algorithms as a safe learning approach and as a general data efficient learning algorithm. An anonymous Github repository of our RBI implementation is found at https://github.com/eladsar/rbi.
Network Learning with Local Propagation
Authors: Dimche Kostadinov, Behrooz Razeghi, Sohrab Ferdowsi, Slava Voloshynovskiy
Date: 2018-05-20 00:00:00

This paper presents a locally decoupled network parameter learning with local
propagation. Three elements are taken into account: (i) sets of nonlinear
transforms that describe the representations at all nodes, (ii) a local
objective at each node related to the corresponding local representation goal,
and (iii) a local propagation model that relates the nonlinear error vectors at
each node with the goal error vectors from the directly connected nodes. The
modeling concepts (i), (ii) and (iii) offer several advantages, including (a) a
unified learning principle for any network that is represented as a graph, (b)
understanding and interpretation of the local and the global learning dynamics,
(c) decoupled and parallel parameter learning, (d) a possibility for learning
in infinitely long, multi-path and multi-goal networks. Numerical experiments
validate the potential of the learning principle. The preliminary results show
advantages in comparison to the state-of-the-art methods, w.r.t. the learning
time and the network size while having comparable recognition accuracy.
A Hierarchical Structured Self-Attentive Model for Extractive Document Summarization (HSSAS)
Authors: Kamal Al-Sabahi, Zhang Zuping, Mohammed Nadher
Date: 2018-05-20 00:00:00
Tasks: Document Summarization, Extractive Document Summarization, Extractive Text Summarization, Representation Learning, Sentence, Text Summarization

The recent advance in neural network architecture and training algorithms
have shown the effectiveness of representation learning. The neural
network-based models generate better representation than the traditional ones.
They have the ability to automatically learn the distributed representation for
sentences and documents. To this end, we proposed a novel model that addresses
several issues that are not adequately modeled by the previously proposed
models, such as the memory problem and incorporating the knowledge of document
structure. Our model uses a hierarchical structured self-attention mechanism to
create the sentence and document embeddings. This architecture mirrors the
hierarchical structure of the document and in turn enables us to obtain better
feature representation. The attention mechanism provides extra source of
information to guide the summary extraction. The new model treated the
summarization task as a classification problem in which the model computes the
respective probabilities of sentence-summary membership. The model predictions
are broken up by several features such as information content, salience,
novelty and positional representation. The proposed model was evaluated on two
well-known datasets, the CNN / Daily Mail, and DUC 2002. The experimental
results show that our model outperforms the current extractive state-of-the-art
by a considerable margin.
One Formalization of Virtue Ethics via Learning
Authors: Naveen Sundar Govindarajulu, Selmer Bringjsord, Rikhiya Ghosh
Date: 2018-05-20 00:00:00
Tasks: Ethics

Given that there exist many different formal and precise treatments of
deontologi- cal and consequentialist ethics, we turn to virtue ethics and
consider what could be a formalization of virtue ethics that makes it amenable
to automation. We present an embroyonic formalization in a cognitive calculus
(which subsumes a quantified first-order logic) that has been previously used
to model robust ethical principles, in both the deontological and
consequentialist traditions.
Machine Learning: Basic Principles
Authors: Alexander Jung
Date: 2018-05-14 00:00:00
Tasks: BIG-bench Machine Learning

This tutorial is based on the lecture notes for, and the plentiful student
feedback received from, the courses "Machine Learning: Basic Principles" and
"Artificial Intelligence", which I have co-taught since 2015 at Aalto
University. The aim is to provide an accessible introduction to some of the
main concepts and methods within machine learning. Many of the current systems
which are considered as (artificially) intelligent are based on combinations of
few basic machine learning methods. After formalizing the main building blocks
of a machine learning problem, some popular algorithmic design patterns for
machine learning methods are discussed in some detail.
Conditional Inference in Pre-trained Variational Autoencoders via Cross-coding
Authors: Ga Wu, Justin Domke, Scott Sanner
Date: 2018-05-20 00:00:00
Proceeding: ICLR 2019 5

Variational Autoencoders (VAEs) are a popular generative model, but one in
which conditional inference can be challenging. If the decomposition into query
and evidence variables is fixed, conditional VAEs provide an attractive
solution. To support arbitrary queries, one is generally reduced to Markov
Chain Monte Carlo sampling methods that can suffer from long mixing times. In
this paper, we propose an idea we term cross-coding to approximate the
distribution over the latent variables after conditioning on an evidence
assignment to some subset of the variables. This allows generating query
samples without retraining the full VAE. We experimentally evaluate three
variations of cross-coding showing that (i) they can be quickly optimized for
different decompositions of evidence and query and (ii) they quantitatively and
qualitatively outperform Hamiltonian Monte Carlo.
Model Aggregation via Good-Enough Model Spaces
Authors: Neel Guha, Virginia Smith
Date: 2018-05-20 00:00:00
Tasks: Distributed Optimization, model, Sentiment Analysis

In many applications, the training data for a machine learning task is partitioned across multiple nodes, and aggregating this data may be infeasible due to communication, privacy, or storage constraints. Existing distributed optimization methods for learning global models in these settings typically aggregate local updates from each node in an iterative fashion. However, these approaches require many rounds of communication between nodes, and assume that updates can be synchronously shared across a connected network. In this work, we present Good-Enough Model Spaces (GEMS), a novel framework for learning a global model by carefully intersecting the sets of "good-enough" models across each node. Our approach utilizes minimal communication and does not require sharing of data between nodes. We present methods for learning both convex models and neural networks within this framework and discuss how small samples of held-out data can be used for post-learning fine-tuning. In experiments on image and medical datasets, our approach on average improves upon other baseline aggregation techniques such as ensembling or model averaging by as much as 15 points (accuracy).
Unsupervised Video Object Segmentation for Deep Reinforcement Learning
Authors: Vik Goel, Jameson Weng, Pascal Poupart
Date: 2018-05-20 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Atari Games, Decision Making, Deep Reinforcement Learning, Object, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Semantic Segmentation, Unsupervised Video Object Segmentation, Video Object Segmentation, Video Semantic Segmentation

We present a new technique for deep reinforcement learning that automatically
detects moving objects and uses the relevant information for action selection.
The detection of moving objects is done in an unsupervised way by exploiting
structure from motion. Instead of directly learning a policy from raw images,
the agent first learns to detect and segment moving objects by exploiting flow
information in video sequences. The learned representation is then used to
focus the policy of the agent on the moving objects. Over time, the agent
identifies which objects are critical for decision making and gradually builds
a policy based on relevant moving objects. This approach, which we call
Motion-Oriented REinforcement Learning (MOREL), is demonstrated on a suite of
Atari games where the ability to detect moving objects reduces the amount of
interaction needed with the environment to obtain a good policy. Furthermore,
the resulting policy is more interpretable than policies that directly map
images to actions or values with a black box neural network. We can gain
insight into the policy by inspecting the segmentation and motion of each
object detected by the agent. This allows practitioners to confirm whether a
policy is making decisions based on sensible information.
Variational Continual Learning
Authors: Cuong V. Nguyen, Yingzhen Li, Thang D. Bui, Richard E. Turner
Date: 2017-10-29 00:00:00
Proceeding: ICLR 2018 1
Tasks: Continual Learning, Variational Inference

This paper develops variational continual learning (VCL), a simple but
general framework for continual learning that fuses online variational
inference (VI) and recent advances in Monte Carlo VI for neural networks. The
framework can successfully train both deep discriminative models and deep
generative models in complex continual learning settings where existing tasks
evolve over time and entirely new tasks emerge. Experimental results show that
VCL outperforms state-of-the-art continual learning methods on a variety of
tasks, avoiding catastrophic forgetting in a fully automatic way.
Edge Attention-based Multi-Relational Graph Convolutional Networks
Authors: Chao Shang, Qinqing Liu, Ko-Shin Chen, Jiangwen Sun, Jin Lu, Jin-Feng Yi, Jinbo Bi
Date: 2018-02-14 00:00:00
Tasks: Attribute

Graph convolutional network (GCN) is generalization of convolutional neural
network (CNN) to work with arbitrarily structured graphs. A binary adjacency
matrix is commonly used in training a GCN. Recently, the attention mechanism
allows the network to learn a dynamic and adaptive aggregation of the
neighborhood. We propose a new GCN model on the graphs where edges are
characterized in multiple views or precisely in terms of multiple
relationships. For instance, in chemical graph theory, compound structures are
often represented by the hydrogen-depleted molecular graph where nodes
correspond to atoms and edges correspond to chemical bonds. Multiple attributes
can be important to characterize chemical bonds, such as atom pair (the types
of atoms that a bond connects), aromaticity, and whether a bond is in a ring.
The different attributes lead to different graph representations for the same
molecule. There is growing interests in both chemistry and machine learning
fields to directly learn molecular properties of compounds from the molecular
graph, instead of from fingerprints predefined by chemists. The proposed GCN
model, which we call edge attention-based multi-relational GCN (EAGCN), jointly
learns attention weights and node features in graph convolution. For each bond
attribute, a real-valued attention matrix is used to replace the binary
adjacency matrix. By designing a dictionary for the edge attention, and forming
the attention matrix of each molecule by looking up the dictionary, the EAGCN
exploits correspondence between bonds in different molecules. The prediction of
compound properties is based on the aggregated node features, which is
independent of the varying molecule (graph) size. We demonstrate the efficacy
of the EAGCN on multiple chemical datasets: Tox21, HIV, Freesolv, and
Lipophilicity, and interpret the resultant attention weights.
Internal node bagging
Authors: Shun Yi
Date: 2018-05-01 00:00:00
Tasks: Ensemble Learning

We introduce a novel view to understand how dropout works as an inexplicit
ensemble learning method, which doesn't point out how many and which nodes to
learn a certain feature. We propose a new training method named internal node
bagging, it explicitly forces a group of nodes to learn a certain feature in
training time, and combine those nodes to be one node in inference time. It
means we can use much more parameters to improve model's fitting ability in
training time while keeping model small in inference time. We test our method
on several benchmark datasets and find it performs significantly better than
dropout on small models.