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2,500 | Ensembles of Random Sphere Cover Classifiers | cs.LG | We propose and evaluate alternative ensemble schemes for a new instance based
learning classifier, the Randomised Sphere Cover (RSC) classifier. RSC fuses
instances into spheres, then bases classification on distance to spheres rather
than distance to instances. The randomised nature of RSC makes it ideal for use
in en... | computer science |
2,501 | Tight Regret Bounds for Stochastic Combinatorial Semi-Bandits | cs.LG | A stochastic combinatorial semi-bandit is an online learning problem where at
each step a learning agent chooses a subset of ground items subject to
constraints, and then observes stochastic weights of these items and receives
their sum as a payoff. In this paper, we close the problem of computationally
and sample effi... | computer science |
2,502 | On the Computational Efficiency of Training Neural Networks | cs.LG | It is well-known that neural networks are computationally hard to train. On
the other hand, in practice, modern day neural networks are trained efficiently
using SGD and a variety of tricks that include different activation functions
(e.g. ReLU), over-specification (i.e., train networks which are larger than
needed), a... | computer science |
2,503 | Trend Filtering on Graphs | stat.ML | We introduce a family of adaptive estimators on graphs, based on penalizing
the $\ell_1$ norm of discrete graph differences. This generalizes the idea of
trend filtering [Kim et al. (2009), Tibshirani (2014)], used for univariate
nonparametric regression, to graphs. Analogous to the univariate case, graph
trend filteri... | computer science |
2,504 | Diversifying Sparsity Using Variational Determinantal Point Processes | cs.LG | We propose a novel diverse feature selection method based on determinantal
point processes (DPPs). Our model enables one to flexibly define diversity
based on the covariance of features (similar to orthogonal matching pursuit) or
alternatively based on side information. We introduce our approach in the
context of Bayes... | computer science |
2,505 | Pattern Decomposition with Complex Combinatorial Constraints:
Application to Materials Discovery | cs.AI | Identifying important components or factors in large amounts of noisy data is
a key problem in machine learning and data mining. Motivated by a pattern
decomposition problem in materials discovery, aimed at discovering new
materials for renewable energy, e.g. for fuel and solar cells, we introduce
CombiFD, a framework ... | computer science |
2,506 | Hierarchical Mixture-of-Experts Model for Large-Scale Gaussian Process
Regression | stat.ML | We propose a practical and scalable Gaussian process model for large-scale
nonlinear probabilistic regression. Our mixture-of-experts model is
conceptually simple and hierarchically recombines computations for an overall
approximation of a full Gaussian process. Closed-form and distributed
computations allow for effici... | computer science |
2,507 | Example Selection For Dictionary Learning | cs.LG | In unsupervised learning, an unbiased uniform sampling strategy is typically
used, in order that the learned features faithfully encode the statistical
structure of the training data. In this work, we explore whether active example
selection strategies - algorithms that select which examples to use, based on
the curren... | computer science |
2,508 | Value Iteration with Options and State Aggregation | cs.AI | This paper presents a way of solving Markov Decision Processes that combines
state abstraction and temporal abstraction. Specifically, we combine state
aggregation with the options framework and demonstrate that they work well
together and indeed it is only after one combines the two that the full benefit
of each is re... | computer science |
2,509 | Structure Learning in Bayesian Networks of Moderate Size by Efficient
Sampling | cs.AI | We study the Bayesian model averaging approach to learning Bayesian network
structures (DAGs) from data. We develop new algorithms including the first
algorithm that is able to efficiently sample DAGs according to the exact
structure posterior. The DAG samples can then be used to construct estimators
for the posterior ... | computer science |
2,510 | An efficient algorithm for contextual bandits with knapsacks, and an
extension to concave objectives | cs.LG | We consider a contextual version of multi-armed bandit problem with global
knapsack constraints. In each round, the outcome of pulling an arm is a scalar
reward and a resource consumption vector, both dependent on the context, and
the global knapsack constraints require the total consumption for each resource
to be bel... | computer science |
2,511 | On the Prior Sensitivity of Thompson Sampling | cs.LG | The empirically successful Thompson Sampling algorithm for stochastic bandits
has drawn much interest in understanding its theoretical properties. One
important benefit of the algorithm is that it allows domain knowledge to be
conveniently encoded as a prior distribution to balance exploration and
exploitation more eff... | computer science |
2,512 | A hybrid algorithm for Bayesian network structure learning with
application to multi-label learning | stat.ML | We present a novel hybrid algorithm for Bayesian network structure learning,
called H2PC. It first reconstructs the skeleton of a Bayesian network and then
performs a Bayesian-scoring greedy hill-climbing search to orient the edges.
The algorithm is based on divide-and-conquer constraint-based subroutines to
learn the ... | computer science |
2,513 | Skopus: Mining top-k sequential patterns under leverage | cs.AI | This paper presents a framework for exact discovery of the top-k sequential
patterns under Leverage. It combines (1) a novel definition of the expected
support for a sequential pattern - a concept on which most interestingness
measures directly rely - with (2) SkOPUS: a new branch-and-bound algorithm for
the exact disc... | computer science |
2,514 | Exact and approximate inference in graphical models: variable
elimination and beyond | stat.ML | Probabilistic graphical models offer a powerful framework to account for the
dependence structure between variables, which is represented as a graph.
However, the dependence between variables may render inference tasks
intractable. In this paper we review techniques exploiting the graph structure
for exact inference, b... | computer science |
2,515 | Fast Cross-Validation for Incremental Learning | stat.ML | Cross-validation (CV) is one of the main tools for performance estimation and
parameter tuning in machine learning. The general recipe for computing CV
estimate is to run a learning algorithm separately for each CV fold, a
computationally expensive process. In this paper, we propose a new approach to
reduce the computa... | computer science |
2,516 | An Empirical Evaluation of True Online TD(λ) | cs.AI | The true online TD({\lambda}) algorithm has recently been proposed (van
Seijen and Sutton, 2014) as a universal replacement for the popular
TD({\lambda}) algorithm, in temporal-difference learning and reinforcement
learning. True online TD({\lambda}) has better theoretical properties than
conventional TD({\lambda}), an... | computer science |
2,517 | FRULER: Fuzzy Rule Learning through Evolution for Regression | cs.LG | In regression problems, the use of TSK fuzzy systems is widely extended due
to the precision of the obtained models. Moreover, the use of simple linear TSK
models is a good choice in many real problems due to the easy understanding of
the relationship between the output and input variables. In this paper we
present FRU... | computer science |
2,518 | A Gauss-Newton Method for Markov Decision Processes | cs.AI | Approximate Newton methods are a standard optimization tool which aim to
maintain the benefits of Newton's method, such as a fast rate of convergence,
whilst alleviating its drawbacks, such as computationally expensive calculation
or estimation of the inverse Hessian. In this work we investigate approximate
Newton meth... | computer science |
2,519 | Clustering With Side Information: From a Probabilistic Model to a
Deterministic Algorithm | stat.ML | In this paper, we propose a model-based clustering method (TVClust) that
robustly incorporates noisy side information as soft-constraints and aims to
seek a consensus between side information and the observed data. Our method is
based on a nonparametric Bayesian hierarchical model that combines the
probabilistic model ... | computer science |
2,520 | Semi-described and semi-supervised learning with Gaussian processes | stat.ML | Propagating input uncertainty through non-linear Gaussian process (GP)
mappings is intractable. This hinders the task of training GPs using uncertain
and partially observed inputs. In this paper we refer to this task as
"semi-described learning". We then introduce a GP framework that solves both,
the semi-described and... | computer science |
2,521 | Quantization based Fast Inner Product Search | cs.AI | We propose a quantization based approach for fast approximate Maximum Inner
Product Search (MIPS). Each database vector is quantized in multiple subspaces
via a set of codebooks, learned directly by minimizing the inner product
quantization error. Then, the inner product of a query to a database vector is
approximated ... | computer science |
2,522 | Optimization of anemia treatment in hemodialysis patients via
reinforcement learning | stat.ML | Objective: Anemia is a frequent comorbidity in hemodialysis patients that can
be successfully treated by administering erythropoiesis-stimulating agents
(ESAs). ESAs dosing is currently based on clinical protocols that often do not
account for the high inter- and intra-individual variability in the patient's
response. ... | computer science |
2,523 | Variational Information Maximisation for Intrinsically Motivated
Reinforcement Learning | stat.ML | The mutual information is a core statistical quantity that has applications
in all areas of machine learning, whether this is in training of density models
over multiple data modalities, in maximising the efficiency of noisy
transmission channels, or when learning behaviour policies for exploration by
artificial agents... | computer science |
2,524 | Client Profiling for an Anti-Money Laundering System | cs.LG | We present a data mining approach for profiling bank clients in order to
support the process of detection of anti-money laundering operations. We first
present the overall system architecture, and then focus on the relevant
component for this paper. We detail the experiments performed on real world
data from a financia... | computer science |
2,525 | Context-Aware Bandits | cs.LG | We propose an efficient Context-Aware clustering of Bandits (CAB) algorithm,
which can capture collaborative effects. CAB can be easily deployed in a
real-world recommendation system, where multi-armed bandits have been shown to
perform well in particular with respect to the cold-start problem. CAB utilizes
a context-a... | computer science |
2,526 | Holographic Embeddings of Knowledge Graphs | cs.AI | Learning embeddings of entities and relations is an efficient and versatile
method to perform machine learning on relational data such as knowledge graphs.
In this work, we propose holographic embeddings (HolE) to learn compositional
vector space representations of entire knowledge graphs. The proposed method is
relate... | computer science |
2,527 | The Human Kernel | cs.LG | Bayesian nonparametric models, such as Gaussian processes, provide a
compelling framework for automatic statistical modelling: these models have a
high degree of flexibility, and automatically calibrated complexity. However,
automating human expertise remains elusive; for example, Gaussian processes
with standard kerne... | computer science |
2,528 | Sample Complexity of Episodic Fixed-Horizon Reinforcement Learning | stat.ML | Recently, there has been significant progress in understanding reinforcement
learning in discounted infinite-horizon Markov decision processes (MDPs) by
deriving tight sample complexity bounds. However, in many real-world
applications, an interactive learning agent operates for a fixed or bounded
period of time, for ex... | computer science |
2,529 | Train and Test Tightness of LP Relaxations in Structured Prediction | stat.ML | Structured prediction is used in areas such as computer vision and natural
language processing to predict structured outputs such as segmentations or
parse trees. In these settings, prediction is performed by MAP inference or,
equivalently, by solving an integer linear program. Because of the complex
scoring functions ... | computer science |
2,530 | Deep Kernel Learning | cs.LG | We introduce scalable deep kernels, which combine the structural properties
of deep learning architectures with the non-parametric flexibility of kernel
methods. Specifically, we transform the inputs of a spectral mixture base
kernel with a deep architecture, using local kernel interpolation, inducing
points, and struc... | computer science |
2,531 | Dynamic Sum Product Networks for Tractable Inference on Sequence Data
(Extended Version) | cs.LG | Sum-Product Networks (SPN) have recently emerged as a new class of tractable
probabilistic graphical models. Unlike Bayesian networks and Markov networks
where inference may be exponential in the size of the network, inference in
SPNs is in time linear in the size of the network. Since SPNs represent
distributions over... | computer science |
2,532 | Bayesian Optimization with Dimension Scheduling: Application to
Biological Systems | stat.ML | Bayesian Optimization (BO) is a data-efficient method for global black-box
optimization of an expensive-to-evaluate fitness function. BO typically assumes
that computation cost of BO is cheap, but experiments are time consuming or
costly. In practice, this allows us to optimize ten or fewer critical
parameters in up to... | computer science |
2,533 | Near-Optimal Active Learning of Multi-Output Gaussian Processes | stat.ML | This paper addresses the problem of active learning of a multi-output
Gaussian process (MOGP) model representing multiple types of coexisting
correlated environmental phenomena. In contrast to existing works, our active
learning problem involves selecting not just the most informative sampling
locations to be observed ... | computer science |
2,534 | Noisy Submodular Maximization via Adaptive Sampling with Applications to
Crowdsourced Image Collection Summarization | cs.AI | We address the problem of maximizing an unknown submodular function that can
only be accessed via noisy evaluations. Our work is motivated by the task of
summarizing content, e.g., image collections, by leveraging users' feedback in
form of clicks or ratings. For summarization tasks with the goal of maximizing
coverage... | computer science |
2,535 | Unifying Decision Trees Split Criteria Using Tsallis Entropy | stat.ML | The construction of efficient and effective decision trees remains a key
topic in machine learning because of their simplicity and flexibility. A lot of
heuristic algorithms have been proposed to construct near-optimal decision
trees. ID3, C4.5 and CART are classical decision tree algorithms and the split
criteria they... | computer science |
2,536 | A Novel Regularized Principal Graph Learning Framework on Explicit Graph
Representation | cs.AI | Many scientific datasets are of high dimension, and the analysis usually
requires visual manipulation by retaining the most important structures of
data. Principal curve is a widely used approach for this purpose. However, many
existing methods work only for data with structures that are not
self-intersected, which is ... | computer science |
2,537 | The Max $K$-Armed Bandit: PAC Lower Bounds and Efficient Algorithms | stat.ML | We consider the Max $K$-Armed Bandit problem, where a learning agent is faced
with several stochastic arms, each a source of i.i.d. rewards of unknown
distribution. At each time step the agent chooses an arm, and observes the
reward of the obtained sample. Each sample is considered here as a separate
item with the rewa... | computer science |
2,538 | On Clustering Time Series Using Euclidean Distance and Pearson
Correlation | cs.LG | For time series comparisons, it has often been observed that z-score
normalized Euclidean distances far outperform the unnormalized variant. In this
paper we show that a z-score normalized, squared Euclidean Distance is, in
fact, equal to a distance based on Pearson Correlation. This has profound
impact on many distanc... | computer science |
2,539 | Engineering Safety in Machine Learning | stat.ML | Machine learning algorithms are increasingly influencing our decisions and
interacting with us in all parts of our daily lives. Therefore, just like for
power plants, highways, and myriad other engineered sociotechnical systems, we
must consider the safety of systems involving machine learning. In this paper,
we first ... | computer science |
2,540 | Greedy Deep Dictionary Learning | cs.LG | In this work we propose a new deep learning tool called deep dictionary
learning. Multi-level dictionaries are learnt in a greedy fashion, one layer at
a time. This requires solving a simple (shallow) dictionary learning problem,
the solution to this is well known. We apply the proposed technique on some
benchmark deep... | computer science |
2,541 | Adaptive Skills, Adaptive Partitions (ASAP) | cs.LG | We introduce the Adaptive Skills, Adaptive Partitions (ASAP) framework that
(1) learns skills (i.e., temporally extended actions or options) as well as (2)
where to apply them. We believe that both (1) and (2) are necessary for a truly
general skill learning framework, which is a key building block needed to scale
up t... | computer science |
2,542 | A Minimalistic Approach to Sum-Product Network Learning for Real
Applications | cs.AI | Sum-Product Networks (SPNs) are a class of expressive yet tractable
hierarchical graphical models. LearnSPN is a structure learning algorithm for
SPNs that uses hierarchical co-clustering to simultaneously identifying similar
entities and similar features. The original LearnSPN algorithm assumes that all
the variables ... | computer science |
2,543 | Deep Exploration via Bootstrapped DQN | cs.LG | Efficient exploration in complex environments remains a major challenge for
reinforcement learning. We propose bootstrapped DQN, a simple algorithm that
explores in a computationally and statistically efficient manner through use of
randomized value functions. Unlike dithering strategies such as epsilon-greedy
explorat... | computer science |
2,544 | "Why Should I Trust You?": Explaining the Predictions of Any Classifier | cs.LG | Despite widespread adoption, machine learning models remain mostly black
boxes. Understanding the reasons behind predictions is, however, quite
important in assessing trust, which is fundamental if one plans to take action
based on a prediction, or when choosing whether to deploy a new model. Such
understanding also pr... | computer science |
2,545 | A Subsequence Interleaving Model for Sequential Pattern Mining | stat.ML | Recent sequential pattern mining methods have used the minimum description
length (MDL) principle to define an encoding scheme which describes an
algorithm for mining the most compressing patterns in a database. We present a
novel subsequence interleaving model based on a probabilistic model of the
sequence database, w... | computer science |
2,546 | Auxiliary Deep Generative Models | stat.ML | Deep generative models parameterized by neural networks have recently
achieved state-of-the-art performance in unsupervised and semi-supervised
learning. We extend deep generative models with auxiliary variables which
improves the variational approximation. The auxiliary variables leave the
generative model unchanged b... | computer science |
2,547 | Probably Approximately Correct Greedy Maximization | cs.AI | Submodular function maximization finds application in a variety of real-world
decision-making problems. However, most existing methods, based on greedy
maximization, assume it is computationally feasible to evaluate F, the function
being maximized. Unfortunately, in many realistic settings F is too expensive
to evaluat... | computer science |
2,548 | Thompson Sampling is Asymptotically Optimal in General Environments | cs.LG | We discuss a variant of Thompson sampling for nonparametric reinforcement
learning in a countable classes of general stochastic environments. These
environments can be non-Markov, non-ergodic, and partially observable. We show
that Thompson sampling learns the environment class in the sense that (1)
asymptotically its ... | computer science |
2,549 | Easy Monotonic Policy Iteration | cs.LG | A key problem in reinforcement learning for control with general function
approximators (such as deep neural networks and other nonlinear functions) is
that, for many algorithms employed in practice, updates to the policy or
$Q$-function may fail to improve performance---or worse, actually cause the
policy performance ... | computer science |
2,550 | Automatic Differentiation Variational Inference | stat.ML | Probabilistic modeling is iterative. A scientist posits a simple model, fits
it to her data, refines it according to her analysis, and repeats. However,
fitting complex models to large data is a bottleneck in this process. Deriving
algorithms for new models can be both mathematically and computationally
challenging, wh... | computer science |
2,551 | Active Algorithms For Preference Learning Problems with Multiple
Populations | stat.ML | In this paper we model the problem of learning preferences of a population as
an active learning problem. We propose an algorithm can adaptively choose pairs
of items to show to users coming from a heterogeneous population, and use the
obtained reward to decide which pair of items to show next. We provide
computational... | computer science |
2,552 | Exploratory Gradient Boosting for Reinforcement Learning in Complex
Domains | cs.AI | High-dimensional observations and complex real-world dynamics present major
challenges in reinforcement learning for both function approximation and
exploration. We address both of these challenges with two complementary
techniques: First, we develop a gradient-boosting style, non-parametric
function approximator for l... | computer science |
2,553 | Learning Network of Multivariate Hawkes Processes: A Time Series
Approach | cs.LG | Learning the influence structure of multiple time series data is of great
interest to many disciplines. This paper studies the problem of recovering the
causal structure in network of multivariate linear Hawkes processes. In such
processes, the occurrence of an event in one process affects the probability of
occurrence... | computer science |
2,554 | One-Shot Generalization in Deep Generative Models | stat.ML | Humans have an impressive ability to reason about new concepts and
experiences from just a single example. In particular, humans have an ability
for one-shot generalization: an ability to encounter a new concept, understand
its structure, and then be able to generate compelling alternative variations
of the concept. We... | computer science |
2,555 | Debugging Machine Learning Tasks | cs.LG | Unlike traditional programs (such as operating systems or word processors)
which have large amounts of code, machine learning tasks use programs with
relatively small amounts of code (written in machine learning libraries), but
voluminous amounts of data. Just like developers of traditional programs debug
errors in the... | computer science |
2,556 | Towards Practical Bayesian Parameter and State Estimation | cs.AI | Joint state and parameter estimation is a core problem for dynamic Bayesian
networks. Although modern probabilistic inference toolkits make it relatively
easy to specify large and practically relevant probabilistic models, the silver
bullet---an efficient and general online inference algorithm for such
problems---remai... | computer science |
2,557 | Graph Clustering Bandits for Recommendation | stat.ML | We investigate an efficient context-dependent clustering technique for
recommender systems based on exploration-exploitation strategies through
multi-armed bandits over multiple users. Our algorithm dynamically groups users
based on their observed behavioral similarity during a sequence of logged
activities. In doing s... | computer science |
2,558 | A note on adjusting $R^2$ for using with cross-validation | cs.LG | We show how to adjust the coefficient of determination ($R^2$) when used for
measuring predictive accuracy via leave-one-out cross-validation. | computer science |
2,559 | Energy Disaggregation for Real-Time Building Flexibility Detection | stat.ML | Energy is a limited resource which has to be managed wisely, taking into
account both supply-demand matching and capacity constraints in the
distribution grid. One aspect of the smart energy management at the building
level is given by the problem of real-time detection of flexible demand
available. In this paper we pr... | computer science |
2,560 | A constrained L1 minimization approach for estimating multiple Sparse
Gaussian or Nonparanormal Graphical Models | cs.LG | Identifying context-specific entity networks from aggregated data is an
important task, arising often in bioinformatics and neuroimaging.
Computationally, this task can be formulated as jointly estimating multiple
different, but related, sparse Undirected Graphical Models (UGM) from
aggregated samples across several co... | computer science |
2,561 | Learning Representations for Counterfactual Inference | stat.ML | Observational studies are rising in importance due to the widespread
accumulation of data in fields such as healthcare, education, employment and
ecology. We consider the task of answering counterfactual questions such as,
"Would this patient have lower blood sugar had she received a different
medication?". We propose ... | computer science |
2,562 | Online Optimization Methods for the Quantification Problem | stat.ML | The estimation of class prevalence, i.e., the fraction of a population that
belongs to a certain class, is a very useful tool in data analytics and
learning, and finds applications in many domains such as sentiment analysis,
epidemiology, etc. For example, in sentiment analysis, the objective is often
not to estimate w... | computer science |
2,563 | Generalized Linear Models for Aggregated Data | stat.ML | Databases in domains such as healthcare are routinely released to the public
in aggregated form. Unfortunately, naive modeling with aggregated data may
significantly diminish the accuracy of inferences at the individual level. This
paper addresses the scenario where features are provided at the individual
level, but th... | computer science |
2,564 | Off-policy evaluation for slate recommendation | cs.LG | This paper studies the evaluation of policies that recommend an ordered set
of items (e.g., a ranking) based on some context---a common scenario in web
search, ads, and recommendation. We build on techniques from combinatorial
bandits to introduce a new practical estimator that uses logged data to
estimate a policy's p... | computer science |
2,565 | Learning Convolutional Neural Networks for Graphs | cs.LG | Numerous important problems can be framed as learning from graph data. We
propose a framework for learning convolutional neural networks for arbitrary
graphs. These graphs may be undirected, directed, and with both discrete and
continuous node and edge attributes. Analogous to image-based convolutional
networks that op... | computer science |
2,566 | Variational hybridization and transformation for large inaccurate
noisy-or networks | cs.LG | Variational inference provides approximations to the computationally
intractable posterior distribution in Bayesian networks. A prominent medical
application of noisy-or Bayesian network is to infer potential diseases given
observed symptoms. Previous studies focus on approximating a handful of
complicated pathological... | computer science |
2,567 | Adversarial Delays in Online Strongly-Convex Optimization | cs.LG | We consider the problem of strongly-convex online optimization in presence of
adversarial delays; in a T-iteration online game, the feedback of the player's
query at time t is arbitrarily delayed by an adversary for d_t rounds and
delivered before the game ends, at iteration t+d_t-1. Specifically for
\algo{online-gradi... | computer science |
2,568 | Fast Bayesian Optimization of Machine Learning Hyperparameters on Large
Datasets | cs.LG | Bayesian optimization has become a successful tool for hyperparameter
optimization of machine learning algorithms, such as support vector machines or
deep neural networks. Despite its success, for large datasets, training and
validating a single configuration often takes hours, days, or even weeks, which
limits the ach... | computer science |
2,569 | Alternating Optimisation and Quadrature for Robust Control | cs.LG | Bayesian optimisation has been successfully applied to a variety of
reinforcement learning problems. However, the traditional approach for learning
optimal policies in simulators does not utilise the opportunity to improve
learning by adjusting certain environment variables: state features that are
unobservable and ran... | computer science |
2,570 | Data Programming: Creating Large Training Sets, Quickly | stat.ML | Large labeled training sets are the critical building blocks of supervised
learning methods and are key enablers of deep learning techniques. For some
applications, creating labeled training sets is the most time-consuming and
expensive part of applying machine learning. We therefore propose a paradigm
for the programm... | computer science |
2,571 | Toward a general, scaleable framework for Bayesian teaching with
applications to topic models | cs.LG | Machines, not humans, are the world's dominant knowledge accumulators but
humans remain the dominant decision makers. Interpreting and disseminating the
knowledge accumulated by machines requires expertise, time, and is prone to
failure. The problem of how best to convey accumulated knowledge from computers
to humans i... | computer science |
2,572 | Kronecker Determinantal Point Processes | cs.LG | Determinantal Point Processes (DPPs) are probabilistic models over all
subsets a ground set of $N$ items. They have recently gained prominence in
several applications that rely on "diverse" subsets. However, their
applicability to large problems is still limited due to the $\mathcal O(N^3)$
complexity of core tasks suc... | computer science |
2,573 | Quantifying the probable approximation error of probabilistic inference
programs | cs.AI | This paper introduces a new technique for quantifying the approximation error
of a broad class of probabilistic inference programs, including ones based on
both variational and Monte Carlo approaches. The key idea is to derive a
subjective bound on the symmetrized KL divergence between the distribution
achieved by an a... | computer science |
2,574 | Learning to Optimize | cs.LG | Algorithm design is a laborious process and often requires many iterations of
ideation and validation. In this paper, we explore automating algorithm design
and present a method to learn an optimization algorithm, which we believe to be
the first method that can automatically discover a better algorithm. We
approach th... | computer science |
2,575 | Active Long Term Memory Networks | cs.LG | Continual Learning in artificial neural networks suffers from interference
and forgetting when different tasks are learned sequentially. This paper
introduces the Active Long Term Memory Networks (A-LTM), a model of sequential
multi-task deep learning that is able to maintain previously learned
association between sens... | computer science |
2,576 | Safe and Efficient Off-Policy Reinforcement Learning | cs.LG | In this work, we take a fresh look at some old and new algorithms for
off-policy, return-based reinforcement learning. Expressing these in a common
form, we derive a novel algorithm, Retrace($\lambda$), with three desired
properties: (1) it has low variance; (2) it safely uses samples collected from
any behaviour polic... | computer science |
2,577 | Scan Order in Gibbs Sampling: Models in Which it Matters and Bounds on
How Much | cs.LG | Gibbs sampling is a Markov Chain Monte Carlo sampling technique that
iteratively samples variables from their conditional distributions. There are
two common scan orders for the variables: random scan and systematic scan. Due
to the benefits of locality in hardware, systematic scan is commonly used, even
though most st... | computer science |
2,578 | Robust Probabilistic Modeling with Bayesian Data Reweighting | stat.ML | Probabilistic models analyze data by relying on a set of assumptions. Data
that exhibit deviations from these assumptions can undermine inference and
prediction quality. Robust models offer protection against mismatch between a
model's assumptions and reality. We propose a way to systematically detect and
mitigate mism... | computer science |
2,579 | Estimating individual treatment effect: generalization bounds and
algorithms | stat.ML | There is intense interest in applying machine learning to problems of causal
inference in fields such as healthcare, economics and education. In particular,
individual-level causal inference has important applications such as precision
medicine. We give a new theoretical analysis and family of algorithms for
predicting... | computer science |
2,580 | The Parallel Knowledge Gradient Method for Batch Bayesian Optimization | stat.ML | In many applications of black-box optimization, one can evaluate multiple
points simultaneously, e.g. when evaluating the performances of several
different neural network architectures in a parallel computing environment. In
this paper, we develop a novel batch Bayesian optimization algorithm --- the
parallel knowledge... | computer science |
2,581 | Unsupervised Risk Estimation Using Only Conditional Independence
Structure | cs.LG | We show how to estimate a model's test error from unlabeled data, on
distributions very different from the training distribution, while assuming
only that certain conditional independencies are preserved between train and
test. We do not need to assume that the optimal predictor is the same between
train and test, or t... | computer science |
2,582 | On the Expressive Power of Deep Neural Networks | stat.ML | We propose a new approach to the problem of neural network expressivity,
which seeks to characterize how structural properties of a neural network
family affect the functions it is able to compute. Our approach is based on an
interrelated set of measures of expressivity, unified by the novel notion of
trajectory length... | computer science |
2,583 | An Efficient Large-scale Semi-supervised Multi-label Classifier Capable
of Handling Missing labels | cs.LG | Multi-label classification has received considerable interest in recent
years. Multi-label classifiers have to address many problems including:
handling large-scale datasets with many instances and a large set of labels,
compensating missing label assignments in the training set, considering
correlations between labels... | computer science |
2,584 | Complex Embeddings for Simple Link Prediction | cs.AI | In statistical relational learning, the link prediction problem is key to
automatically understand the structure of large knowledge bases. As in previous
studies, we propose to solve this problem through latent factorization.
However, here we make use of complex valued embeddings. The composition of
complex embeddings ... | computer science |
2,585 | A Learning Algorithm for Relational Logistic Regression: Preliminary
Results | cs.AI | Relational logistic regression (RLR) is a representation of conditional
probability in terms of weighted formulae for modelling multi-relational data.
In this paper, we develop a learning algorithm for RLR models. Learning an RLR
model from data consists of two steps: 1- learning the set of formulae to be
used in the m... | computer science |
2,586 | A Local Density-Based Approach for Local Outlier Detection | cs.AI | This paper presents a simple but effective density-based outlier detection
approach with the local kernel density estimation (KDE). A Relative
Density-based Outlier Score (RDOS) is introduced to measure the local
outlierness of objects, in which the density distribution at the location of an
object is estimated with a ... | computer science |
2,587 | Non-linear Label Ranking for Large-scale Prediction of Long-Term User
Interests | cs.AI | We consider the problem of personalization of online services from the
viewpoint of ad targeting, where we seek to find the best ad categories to be
shown to each user, resulting in improved user experience and increased
advertisers' revenue. We propose to address this problem as a task of ranking
the ad categories dep... | computer science |
2,588 | Missing Data Estimation in High-Dimensional Datasets: A Swarm
Intelligence-Deep Neural Network Approach | cs.AI | In this paper, we examine the problem of missing data in high-dimensional
datasets by taking into consideration the Missing Completely at Random and
Missing at Random mechanisms, as well as theArbitrary missing pattern.
Additionally, this paper employs a methodology based on Deep Learning and Swarm
Intelligence algorit... | computer science |
2,589 | Why is Posterior Sampling Better than Optimism for Reinforcement
Learning? | stat.ML | Computational results demonstrate that posterior sampling for reinforcement
learning (PSRL) dramatically outperforms algorithms driven by optimism, such as
UCRL2. We provide insight into the extent of this performance boost and the
phenomenon that drives it. We leverage this insight to establish an
$\tilde{O}(H\sqrt{SA... | computer science |
2,590 | A Greedy Approach to Adapting the Trace Parameter for Temporal
Difference Learning | cs.AI | One of the main obstacles to broad application of reinforcement learning
methods is the parameter sensitivity of our core learning algorithms. In many
large-scale applications, online computation and function approximation
represent key strategies in scaling up reinforcement learning algorithms. In
this setting, we hav... | computer science |
2,591 | How to Allocate Resources For Features Acquisition? | cs.AI | We study classification problems where features are corrupted by noise and
where the magnitude of the noise in each feature is influenced by the resources
allocated to its acquisition. This is the case, for example, when multiple
sensors share a common resource (power, bandwidth, attention, etc.). We develop
a method f... | computer science |
2,592 | Towards Representation Learning with Tractable Probabilistic Models | cs.LG | Probabilistic models learned as density estimators can be exploited in
representation learning beside being toolboxes used to answer inference queries
only. However, how to extract useful representations highly depends on the
particular model involved. We argue that tractable inference, i.e. inference
that can be compu... | computer science |
2,593 | On Context-Dependent Clustering of Bandits | cs.LG | We investigate a novel cluster-of-bandit algorithm CAB for collaborative
recommendation tasks that implements the underlying feedback sharing mechanism
by estimating the neighborhood of users in a context-dependent manner. CAB
makes sharp departures from the state of the art by incorporating collaborative
effects into ... | computer science |
2,594 | A Shallow High-Order Parametric Approach to Data Visualization and
Compression | cs.AI | Explicit high-order feature interactions efficiently capture essential
structural knowledge about the data of interest and have been used for
constructing generative models. We present a supervised discriminative
High-Order Parametric Embedding (HOPE) approach to data visualization and
compression. Compared to deep emb... | computer science |
2,595 | Probabilistic Data Analysis with Probabilistic Programming | cs.AI | Probabilistic techniques are central to data analysis, but different
approaches can be difficult to apply, combine, and compare. This paper
introduces composable generative population models (CGPMs), a computational
abstraction that extends directed graphical models and can be used to describe
and compose a broad class... | computer science |
2,596 | Probabilistic Knowledge Graph Construction: Compositional and
Incremental Approaches | stat.ML | Knowledge graph construction consists of two tasks: extracting information
from external resources (knowledge population) and inferring missing
information through a statistical analysis on the extracted information
(knowledge completion). In many cases, insufficient external resources in the
knowledge population hinde... | computer science |
2,597 | Multi-View Fuzzy Clustering with Minimax Optimization for Effective
Clustering of Data from Multiple Sources | cs.AI | Multi-view data clustering refers to categorizing a data set by making good
use of related information from multiple representations of the data. It
becomes important nowadays because more and more data can be collected in a
variety of ways, in different settings and from different sources, so each data
set can be repr... | computer science |
2,598 | Neural Coarse-Graining: Extracting slowly-varying latent degrees of
freedom with neural networks | cs.AI | We present a loss function for neural networks that encompasses an idea of
trivial versus non-trivial predictions, such that the network jointly
determines its own prediction goals and learns to satisfy them. This permits
the network to choose sub-sets of a problem which are most amenable to its
abilities to focus on s... | computer science |
2,599 | Q-Learning with Basic Emotions | cs.LG | Q-learning is a simple and powerful tool in solving dynamic problems where
environments are unknown. It uses a balance of exploration and exploitation to
find an optimal solution to the problem. In this paper, we propose using four
basic emotions: joy, sadness, fear, and anger to influence a Qlearning agent.
Simulation... | computer science |
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