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4,800 | A Behavior Analysis-Based Game Bot Detection Approach Considering
Various Play Styles | cs.LG | An approach for game bot detection in MMORPGs is proposed based on the
analysis of game playing behavior. Since MMORPGs are large scale games, users
can play in various ways. This variety in playing behavior makes it hard to
detect game bots based on play behaviors. In order to cope with this problem,
the proposed appr... | computer science |
4,801 | Double Relief with progressive weighting function | cs.LG | Feature weighting algorithms try to solve a problem of great importance
nowadays in machine learning: The search of a relevance measure for the
features of a given domain. This relevance is primarily used for feature
selection as feature weighting can be seen as a generalization of it, but it is
also useful to better u... | computer science |
4,802 | Graph Kernels exploiting Weisfeiler-Lehman Graph Isomorphism Test
Extensions | cs.LG | In this paper we present a novel graph kernel framework inspired the by the
Weisfeiler-Lehman (WL) isomorphism tests. Any WL test comprises a relabelling
phase of the nodes based on test-specific information extracted from the graph,
for example the set of neighbours of a node. We defined a novel relabelling and
derive... | computer science |
4,803 | Attend, Adapt and Transfer: Attentive Deep Architecture for Adaptive
Transfer from multiple sources in the same domain | cs.AI | Transferring knowledge from prior source tasks in solving a new target task
can be useful in several learning applications. The application of transfer
poses two serious challenges which have not been adequately addressed. First,
the agent should be able to avoid negative transfer, which happens when the
transfer hampe... | computer science |
4,804 | The Inductive Constraint Programming Loop | cs.AI | Constraint programming is used for a variety of real-world optimisation
problems, such as planning, scheduling and resource allocation problems. At the
same time, one continuously gathers vast amounts of data about these problems.
Current constraint programming software does not exploit such data to update
schedules, r... | computer science |
4,805 | Evaluating Real-time Anomaly Detection Algorithms - the Numenta Anomaly
Benchmark | cs.AI | Much of the world's data is streaming, time-series data, where anomalies give
significant information in critical situations; examples abound in domains such
as finance, IT, security, medical, and energy. Yet detecting anomalies in
streaming data is a difficult task, requiring detectors to process data in
real-time, no... | computer science |
4,806 | Asymptotic Logical Uncertainty and The Benford Test | cs.LG | We give an algorithm A which assigns probabilities to logical sentences. For
any simple infinite sequence of sentences whose truth-values appear
indistinguishable from a biased coin that outputs "true" with probability p, we
have that the sequence of probabilities that A assigns to these sentences
converges to p. | computer science |
4,807 | Bad Universal Priors and Notions of Optimality | cs.AI | A big open question of algorithmic information theory is the choice of the
universal Turing machine (UTM). For Kolmogorov complexity and Solomonoff
induction we have invariance theorems: the choice of the UTM changes bounds
only by a constant. For the universally intelligent agent AIXI (Hutter, 2005)
no invariance theo... | computer science |
4,808 | High Performance Latent Variable Models | cs.LG | Latent variable models have accumulated a considerable amount of interest
from the industry and academia for their versatility in a wide range of
applications. A large amount of effort has been made to develop systems that is
able to extend the systems to a large scale, in the hope to make use of them on
industry scale... | computer science |
4,809 | Time-Sensitive Bayesian Information Aggregation for Crowdsourcing
Systems | cs.AI | Crowdsourcing systems commonly face the problem of aggregating multiple
judgments provided by potentially unreliable workers. In addition, several
aspects of the design of efficient crowdsourcing processes, such as defining
worker's bonuses, fair prices and time limits of the tasks, involve knowledge
of the likely dura... | computer science |
4,810 | Characterizing Concept Drift | cs.LG | Most machine learning models are static, but the world is dynamic, and
increasing online deployment of learned models gives increasing urgency to the
development of efficient and effective mechanisms to address learning in the
context of non-stationary distributions, or as it is commonly called concept
drift. However, ... | computer science |
4,811 | Unitary-Group Invariant Kernels and Features from Transformed Unlabeled
Data | cs.LG | The study of representations invariant to common transformations of the data
is important to learning. Most techniques have focused on local approximate
invariance implemented within expensive optimization frameworks lacking
explicit theoretical guarantees. In this paper, we study kernels that are
invariant to the unit... | computer science |
4,812 | Better Computer Go Player with Neural Network and Long-term Prediction | cs.LG | Competing with top human players in the ancient game of Go has been a
long-term goal of artificial intelligence. Go's high branching factor makes
traditional search techniques ineffective, even on leading-edge hardware, and
Go's evaluation function could change drastically with one stone change. Recent
works [Maddison ... | computer science |
4,813 | Learning Simple Algorithms from Examples | cs.AI | We present an approach for learning simple algorithms such as copying,
multi-digit addition and single digit multiplication directly from examples.
Our framework consists of a set of interfaces, accessed by a controller.
Typical interfaces are 1-D tapes or 2-D grids that hold the input and output
data. For the controll... | computer science |
4,814 | Interpretable Two-level Boolean Rule Learning for Classification | cs.LG | This paper proposes algorithms for learning two-level Boolean rules in
Conjunctive Normal Form (CNF, i.e. AND-of-ORs) or Disjunctive Normal Form (DNF,
i.e. OR-of-ANDs) as a type of human-interpretable classification model, aiming
for a favorable trade-off between the classification accuracy and the
simplicity of the ru... | computer science |
4,815 | Strategic Dialogue Management via Deep Reinforcement Learning | cs.AI | Artificially intelligent agents equipped with strategic skills that can
negotiate during their interactions with other natural or artificial agents are
still underdeveloped. This paper describes a successful application of Deep
Reinforcement Learning (DRL) for training intelligent agents with strategic
conversational s... | computer science |
4,816 | Incremental Truncated LSTD | cs.LG | Balancing between computational efficiency and sample efficiency is an
important goal in reinforcement learning. Temporal difference (TD) learning
algorithms stochastically update the value function, with a linear time
complexity in the number of features, whereas least-squares temporal difference
(LSTD) algorithms are... | computer science |
4,817 | Shaping Proto-Value Functions via Rewards | cs.AI | In this paper, we combine task-dependent reward shaping and task-independent
proto-value functions to obtain reward dependent proto-value functions (RPVFs).
In constructing the RPVFs we are making use of the immediate rewards which are
available during the sampling phase but are not used in the PVF construction.
We sho... | computer science |
4,818 | On the convergence of cycle detection for navigational reinforcement
learning | cs.LG | We consider a reinforcement learning framework where agents have to navigate
from start states to goal states. We prove convergence of a cycle-detection
learning algorithm on a class of tasks that we call reducible. Reducible tasks
have an acyclic solution. We also syntactically characterize the form of the
final polic... | computer science |
4,819 | Learning Using 1-Local Membership Queries | cs.LG | Classic machine learning algorithms learn from labelled examples. For
example, to design a machine translation system, a typical training set will
consist of English sentences and their translation. There is a stronger model,
in which the algorithm can also query for labels of new examples it creates.
E.g, in the trans... | computer science |
4,820 | Taxonomy grounded aggregation of classifiers with different label sets | cs.AI | We describe the problem of aggregating the label predictions of diverse
classifiers using a class taxonomy. Such a taxonomy may not have been available
or referenced when the individual classifiers were designed and trained, yet
mapping the output labels into the taxonomy is desirable to integrate the
effort spent in t... | computer science |
4,821 | Large Scale Distributed Semi-Supervised Learning Using Streaming
Approximation | cs.LG | Traditional graph-based semi-supervised learning (SSL) approaches, even
though widely applied, are not suited for massive data and large label
scenarios since they scale linearly with the number of edges $|E|$ and distinct
labels $m$. To deal with the large label size problem, recent works propose
sketch-based methods ... | computer science |
4,822 | How to Discount Deep Reinforcement Learning: Towards New Dynamic
Strategies | cs.LG | Using deep neural nets as function approximator for reinforcement learning
tasks have recently been shown to be very powerful for solving problems
approaching real-world complexity. Using these results as a benchmark, we
discuss the role that the discount factor may play in the quality of the
learning process of a deep... | computer science |
4,823 | Learning Discrete Bayesian Networks from Continuous Data | cs.AI | Real data often contains a mixture of discrete and continuous variables, but
many Bayesian network structure learning and inference algorithms assume all
random variables are discrete. Continuous variables are often discretized, but
the choice of discretization policy has significant impact on the accuracy,
speed, and ... | computer science |
4,824 | Convolutional Monte Carlo Rollouts in Go | cs.LG | In this work, we present a MCTS-based Go-playing program which uses
convolutional networks in all parts. Our method performs MCTS in batches,
explores the Monte Carlo search tree using Thompson sampling and a
convolutional network, and evaluates convnet-based rollouts on the GPU. We
achieve strong win rates against ope... | computer science |
4,825 | True Online Temporal-Difference Learning | cs.AI | The temporal-difference methods TD($\lambda$) and Sarsa($\lambda$) form a
core part of modern reinforcement learning. Their appeal comes from their good
performance, low computational cost, and their simple interpretation, given by
their forward view. Recently, new versions of these methods were introduced,
called true... | computer science |
4,826 | Policy Gradient Methods for Off-policy Control | cs.AI | Off-policy learning refers to the problem of learning the value function of a
way of behaving, or policy, while following a different policy. Gradient-based
off-policy learning algorithms, such as GTD and TDC/GQ, converge even when
using function approximation and incremental updates. However, they have been
developed ... | computer science |
4,827 | From One Point to A Manifold: Knowledge Graph Embedding For Precise Link
Prediction | cs.AI | Knowledge graph embedding aims at offering a numerical knowledge
representation paradigm by transforming the entities and relations into
continuous vector space. However, existing methods could not characterize the
knowledge graph in a fine degree to make a precise prediction. There are two
reasons: being an ill-posed ... | computer science |
4,828 | Increasing the Action Gap: New Operators for Reinforcement Learning | cs.AI | This paper introduces new optimality-preserving operators on Q-functions. We
first describe an operator for tabular representations, the consistent Bellman
operator, which incorporates a notion of local policy consistency. We show that
this local consistency leads to an increase in the action gap at each state;
increas... | computer science |
4,829 | Unsupervised Feature Construction for Improving Data Representation and
Semantics | cs.AI | Feature-based format is the main data representation format used by machine
learning algorithms. When the features do not properly describe the initial
data, performance starts to degrade. Some algorithms address this problem by
internally changing the representation space, but the newly-constructed
features are rarely... | computer science |
4,830 | Regularized Orthogonal Tensor Decompositions for Multi-Relational
Learning | cs.LG | Multi-relational learning has received lots of attention from researchers in
various research communities. Most existing methods either suffer from
superlinear per-iteration cost, or are sensitive to the given ranks. To address
both issues, we propose a scalable core tensor trace norm Regularized
Orthogonal Iteration D... | computer science |
4,831 | A Unified Approach for Learning the Parameters of Sum-Product Networks | cs.LG | We present a unified approach for learning the parameters of Sum-Product
networks (SPNs). We prove that any complete and decomposable SPN is equivalent
to a mixture of trees where each tree corresponds to a product of univariate
distributions. Based on the mixture model perspective, we characterize the
objective functi... | computer science |
4,832 | Angrier Birds: Bayesian reinforcement learning | cs.AI | We train a reinforcement learner to play a simplified version of the game
Angry Birds. The learner is provided with a game state in a manner similar to
the output that could be produced by computer vision algorithms. We improve on
the efficiency of regular {\epsilon}-greedy Q-Learning with linear function
approximation... | computer science |
4,833 | Ensemble Methods of Classification for Power Systems Security Assessment | cs.AI | One of the most promising approaches for complex technical systems analysis
employs ensemble methods of classification. Ensemble methods enable to build a
reliable decision rules for feature space classification in the presence of
many possible states of the system. In this paper, novel techniques based on
decision tre... | computer science |
4,834 | On the Latent Variable Interpretation in Sum-Product Networks | cs.AI | One of the central themes in Sum-Product networks (SPNs) is the
interpretation of sum nodes as marginalized latent variables (LVs). This
interpretation yields an increased syntactic or semantic structure, allows the
application of the EM algorithm and to efficiently perform MPE inference. In
literature, the LV interpre... | computer science |
4,835 | Expected Similarity Estimation for Large-Scale Batch and Streaming
Anomaly Detection | cs.LG | We present a novel algorithm for anomaly detection on very large datasets and
data streams. The method, named EXPected Similarity Estimation (EXPoSE), is
kernel-based and able to efficiently compute the similarity between new data
points and the distribution of regular data. The estimator is formulated as an
inner prod... | computer science |
4,836 | Learning to Communicate to Solve Riddles with Deep Distributed Recurrent
Q-Networks | cs.AI | We propose deep distributed recurrent Q-networks (DDRQN), which enable teams
of agents to learn to solve communication-based coordination tasks. In these
tasks, the agents are not given any pre-designed communication protocol.
Therefore, in order to successfully communicate, they must first automatically
develop and ag... | computer science |
4,837 | Decoy Bandits Dueling on a Poset | cs.LG | We adress the problem of dueling bandits defined on partially ordered sets,
or posets. In this setting, arms may not be comparable, and there may be
several (incomparable) optimal arms. We propose an algorithm, UnchainedBandits,
that efficiently finds the set of optimal arms of any poset even when pairs of
comparable a... | computer science |
4,838 | Iterative Hierarchical Optimization for Misspecified Problems (IHOMP) | cs.LG | For complex, high-dimensional Markov Decision Processes (MDPs), it may be
necessary to represent the policy with function approximation. A problem is
misspecified whenever, the representation cannot express any policy with
acceptable performance. We introduce IHOMP : an approach for solving
misspecified problems. IHOMP... | computer science |
4,839 | Unsupervised Domain Adaptation Using Approximate Label Matching | cs.LG | Domain adaptation addresses the problem created when training data is
generated by a so-called source distribution, but test data is generated by a
significantly different target distribution. In this work, we present
approximate label matching (ALM), a new unsupervised domain adaptation
technique that creates and leve... | computer science |
4,840 | Determining the best attributes for surveillance video keywords
generation | cs.LG | Automatic video keyword generation is one of the key ingredients in reducing
the burden of security officers in analyzing surveillance videos. Keywords or
attributes are generally chosen manually based on expert knowledge of
surveillance. Most existing works primarily aim at either supervised learning
approaches relyin... | computer science |
4,841 | Modeling cumulative biological phenomena with Suppes-Bayes Causal
Networks | cs.AI | Several diseases related to cell proliferation are characterized by the
accumulation of somatic DNA changes, with respect to wildtype conditions.
Cancer and HIV are two common examples of such diseases, where the mutational
load in the cancerous/viral population increases over time. In these cases,
selective pressures ... | computer science |
4,842 | Large-Scale Detection of Non-Technical Losses in Imbalanced Data Sets | cs.LG | Non-technical losses (NTL) such as electricity theft cause significant harm
to our economies, as in some countries they may range up to 40% of the total
electricity distributed. Detecting NTLs requires costly on-site inspections.
Accurate prediction of NTLs for customers using machine learning is therefore
crucial. To ... | computer science |
4,843 | Probabilistic Relational Model Benchmark Generation | cs.LG | The validation of any database mining methodology goes through an evaluation
process where benchmarks availability is essential. In this paper, we aim to
randomly generate relational database benchmarks that allow to check
probabilistic dependencies among the attributes. We are particularly interested
in Probabilistic ... | computer science |
4,844 | Learning Shared Representations in Multi-task Reinforcement Learning | cs.AI | We investigate a paradigm in multi-task reinforcement learning (MT-RL) in
which an agent is placed in an environment and needs to learn to perform a
series of tasks, within this space. Since the environment does not change,
there is potentially a lot of common ground amongst tasks and learning to solve
them individuall... | computer science |
4,845 | Negative Learning Rates and P-Learning | cs.AI | We present a method of training a differentiable function approximator for a
regression task using negative examples. We effect this training using negative
learning rates. We also show how this method can be used to perform direct
policy learning in a reinforcement learning setting. | computer science |
4,846 | Common-Description Learning: A Framework for Learning Algorithms and
Generating Subproblems from Few Examples | cs.AI | Current learning algorithms face many difficulties in learning simple
patterns and using them to learn more complex ones. They also require more
examples than humans do to learn the same pattern, assuming no prior knowledge.
In this paper, a new learning framework is introduced that is called
common-description learnin... | computer science |
4,847 | Online Learning of Commission Avoidant Portfolio Ensembles | cs.AI | We present a novel online ensemble learning strategy for portfolio selection.
The new strategy controls and exploits any set of commission-oblivious
portfolio selection algorithms. The strategy handles transaction costs using a
novel commission avoidance mechanism. We prove a logarithmic regret bound for
our strategy w... | computer science |
4,848 | Learning from the memory of Atari 2600 | cs.LG | We train a number of neural networks to play games Bowling, Breakout and
Seaquest using information stored in the memory of a video game console Atari
2600. We consider four models of neural networks which differ in size and
architecture: two networks which use only information contained in the RAM and
two mixed networ... | computer science |
4,849 | Causal Discovery for Manufacturing Domains | cs.LG | Yield and quality improvement is of paramount importance to any manufacturing
company. One of the ways of improving yield is through discovery of the root
causal factors affecting yield. We propose the use of data-driven interpretable
causal models to identify key factors affecting yield. We focus on factors that
are m... | computer science |
4,850 | Towards Automation of Knowledge Understanding: An Approach for
Probabilistic Generative Classifiers | cs.LG | After data selection, pre-processing, transformation, and feature extraction,
knowledge extraction is not the final step in a data mining process. It is then
necessary to understand this knowledge in order to apply it efficiently and
effectively. Up to now, there is a lack of appropriate techniques that support
this si... | computer science |
4,851 | Backprop KF: Learning Discriminative Deterministic State Estimators | cs.LG | Generative state estimators based on probabilistic filters and smoothers are
one of the most popular classes of state estimators for robots and autonomous
vehicles. However, generative models have limited capacity to handle rich
sensory observations, such as camera images, since they must model the entire
distribution ... | computer science |
4,852 | Near-optimal Bayesian Active Learning with Correlated and Noisy Tests | cs.LG | We consider the Bayesian active learning and experimental design problem,
where the goal is to learn the value of some unknown target variable through a
sequence of informative, noisy tests. In contrast to prior work, we focus on
the challenging, yet practically relevant setting where test outcomes can be
conditionally... | computer science |
4,853 | Learning Purposeful Behaviour in the Absence of Rewards | cs.LG | Artificial intelligence is commonly defined as the ability to achieve goals
in the world. In the reinforcement learning framework, goals are encoded as
reward functions that guide agent behaviour, and the sum of observed rewards
provide a notion of progress. However, some domains have no such reward signal,
or have a r... | computer science |
4,854 | Learning Multiagent Communication with Backpropagation | cs.LG | Many tasks in AI require the collaboration of multiple agents. Typically, the
communication protocol between agents is manually specified and not altered
during training. In this paper we explore a simple neural model, called
CommNet, that uses continuous communication for fully cooperative tasks. The
model consists of... | computer science |
4,855 | Adaptive Neural Compilation | cs.AI | This paper proposes an adaptive neural-compilation framework to address the
problem of efficient program learning. Traditional code optimisation strategies
used in compilers are based on applying pre-specified set of transformations
that make the code faster to execute without changing its semantics. In
contrast, our w... | computer science |
4,856 | Model-Free Imitation Learning with Policy Optimization | cs.LG | In imitation learning, an agent learns how to behave in an environment with
an unknown cost function by mimicking expert demonstrations. Existing imitation
learning algorithms typically involve solving a sequence of planning or
reinforcement learning problems. Such algorithms are therefore not directly
applicable to la... | computer science |
4,857 | Towards a Job Title Classification System | cs.LG | Document classification for text, images and other applicable entities has
long been a focus of research in academia and also finds application in many
industrial settings. Amidst a plethora of approaches to solve such problems,
machine-learning techniques have found success in a variety of scenarios. In
this paper we ... | computer science |
4,858 | OpenAI Gym | cs.LG | OpenAI Gym is a toolkit for reinforcement learning research. It includes a
growing collection of benchmark problems that expose a common interface, and a
website where people can share their results and compare the performance of
algorithms. This whitepaper discusses the components of OpenAI Gym and the
design decision... | computer science |
4,859 | e-Commerce product classification: our participation at cDiscount 2015
challenge | cs.LG | This report describes our participation in the cDiscount 2015 challenge where
the goal was to classify product items in a predefined taxonomy of products.
Our best submission yielded an accuracy score of 64.20\% in the private part of
the leaderboard and we were ranked 10th out of 175 participating teams. We
followed a... | computer science |
4,860 | Generative Adversarial Imitation Learning | cs.LG | Consider learning a policy from example expert behavior, without interaction
with the expert or access to reinforcement signal. One approach is to recover
the expert's cost function with inverse reinforcement learning, then extract a
policy from that cost function with reinforcement learning. This approach is
indirect ... | computer science |
4,861 | Strategic Attentive Writer for Learning Macro-Actions | cs.AI | We present a novel deep recurrent neural network architecture that learns to
build implicit plans in an end-to-end manner by purely interacting with an
environment in reinforcement learning setting. The network builds an internal
plan, which is continuously updated upon observation of the next input from the
environmen... | computer science |
4,862 | Concrete Problems in AI Safety | cs.AI | Rapid progress in machine learning and artificial intelligence (AI) has
brought increasing attention to the potential impacts of AI technologies on
society. In this paper we discuss one such potential impact: the problem of
accidents in machine learning systems, defined as unintended and harmful
behavior that may emerg... | computer science |
4,863 | Adaptive Training of Random Mapping for Data Quantization | cs.LG | Data quantization learns encoding results of data with certain requirements,
and provides a broad perspective of many real-world applications to data
handling. Nevertheless, the results of encoder is usually limited to
multivariate inputs with the random mapping, and side information of binary
codes are hardly to mostl... | computer science |
4,864 | Neighborhood Features Help Detecting Non-Technical Losses in Big Data
Sets | cs.LG | Electricity theft is a major problem around the world in both developed and
developing countries and may range up to 40% of the total electricity
distributed. More generally, electricity theft belongs to non-technical losses
(NTL), which are losses that occur during the distribution of electricity in
power grids. In th... | computer science |
4,865 | A New Hierarchical Redundancy Eliminated Tree Augmented Naive Bayes
Classifier for Coping with Gene Ontology-based Features | cs.LG | The Tree Augmented Naive Bayes classifier is a type of probabilistic
graphical model that can represent some feature dependencies. In this work, we
propose a Hierarchical Redundancy Eliminated Tree Augmented Naive Bayes
(HRE-TAN) algorithm, which considers removing the hierarchical redundancy
during the classifier lear... | computer science |
4,866 | Characterizing Driving Styles with Deep Learning | cs.AI | Characterizing driving styles of human drivers using vehicle sensor data,
e.g., GPS, is an interesting research problem and an important real-world
requirement from automotive industries. A good representation of driving
features can be highly valuable for autonomous driving, auto insurance, and
many other application ... | computer science |
4,867 | Possibilistic Networks: Parameters Learning from Imprecise Data and
Evaluation strategy | cs.AI | There has been an ever-increasing interest in multidisciplinary research on
representing and reasoning with imperfect data. Possibilistic networks present
one of the powerful frameworks of interest for representing uncertain and
imprecise information. This paper covers the problem of their parameters
learning from impr... | computer science |
4,868 | Online Learning of Event Definitions | cs.LG | Systems for symbolic event recognition infer occurrences of events in time
using a set of event definitions in the form of first-order rules. The Event
Calculus is a temporal logic that has been used as a basis in event recognition
applications, providing among others, direct connections to machine learning,
via Induct... | computer science |
4,869 | Can Active Learning Experience Be Transferred? | cs.LG | Active learning is an important machine learning problem in reducing the
human labeling effort. Current active learning strategies are designed from
human knowledge, and are applied on each dataset in an immutable manner. In
other words, experience about the usefulness of strategies cannot be updated
and transferred to... | computer science |
4,870 | Multi-Sensor Prognostics using an Unsupervised Health Index based on
LSTM Encoder-Decoder | cs.LG | Many approaches for estimation of Remaining Useful Life (RUL) of a machine,
using its operational sensor data, make assumptions about how a system degrades
or a fault evolves, e.g., exponential degradation. However, in many domains
degradation may not follow a pattern. We propose a Long Short Term Memory based
Encoder-... | computer science |
4,871 | KSR: A Semantic Representation of Knowledge Graph within a Novel
Unsupervised Paradigm | cs.LG | Knowledge representation is a long-history topic in AI, which is very
important. A variety of models have been proposed for knowledge graph
embedding, which projects symbolic entities and relations into continuous
vector space. However, most related methods merely focus on the data-fitting of
knowledge graph, and ignor... | computer science |
4,872 | An Integrated Classification Model for Financial Data Mining | cs.AI | Nowadays, financial data analysis is becoming increasingly important in the
business market. As companies collect more and more data from daily operations,
they expect to extract useful knowledge from existing collected data to help
make reasonable decisions for new customer requests, e.g. user credit category,
churn a... | computer science |
4,873 | Episodic Exploration for Deep Deterministic Policies: An Application to
StarCraft Micromanagement Tasks | cs.AI | We consider scenarios from the real-time strategy game StarCraft as new
benchmarks for reinforcement learning algorithms. We propose micromanagement
tasks, which present the problem of the short-term, low-level control of army
members during a battle. From a reinforcement learning point of view, these
scenarios are cha... | computer science |
4,874 | Exploration Potential | cs.LG | We introduce exploration potential, a quantity that measures how much a
reinforcement learning agent has explored its environment class. In contrast to
information gain, exploration potential takes the problem's reward structure
into account. This leads to an exploration criterion that is both necessary and
sufficient ... | computer science |
4,875 | SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient | cs.LG | As a new way of training generative models, Generative Adversarial Nets (GAN)
that uses a discriminative model to guide the training of the generative model
has enjoyed considerable success in generating real-valued data. However, it
has limitations when the goal is for generating sequences of discrete tokens. A
major ... | computer science |
4,876 | Towards Deep Symbolic Reinforcement Learning | cs.AI | Deep reinforcement learning (DRL) brings the power of deep neural networks to
bear on the generic task of trial-and-error learning, and its effectiveness has
been convincingly demonstrated on tasks such as Atari video games and the game
of Go. However, contemporary DRL systems inherit a number of shortcomings from
the ... | computer science |
4,877 | Playing FPS Games with Deep Reinforcement Learning | cs.AI | Advances in deep reinforcement learning have allowed autonomous agents to
perform well on Atari games, often outperforming humans, using only raw pixels
to make their decisions. However, most of these games take place in 2D
environments that are fully observable to the agent. In this paper, we present
the first archite... | computer science |
4,878 | Outlier Detection from Network Data with Subnetwork Interpretation | cs.AI | Detecting a small number of outliers from a set of data observations is
always challenging. This problem is more difficult in the setting of multiple
network samples, where computing the anomalous degree of a network sample is
generally not sufficient. In fact, explaining why the network is exceptional,
expressed in th... | computer science |
4,879 | Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows
Prediction | cs.AI | Forecasting the flow of crowds is of great importance to traffic management
and public safety, yet a very challenging task affected by many complex
factors, such as inter-region traffic, events and weather. In this paper, we
propose a deep-learning-based approach, called ST-ResNet, to collectively
forecast the in-flow ... | computer science |
4,880 | Can Evolutionary Sampling Improve Bagged Ensembles? | cs.LG | Perturb and Combine (P&C) group of methods generate multiple versions of the
predictor by perturbing the training set or construction and then combining
them into a single predictor (Breiman, 1996b). The motive is to improve the
accuracy in unstable classification and regression methods. One of the most
well known meth... | computer science |
4,881 | $\ell_1$ Regularized Gradient Temporal-Difference Learning | cs.AI | In this paper, we study the Temporal Difference (TD) learning with linear
value function approximation. It is well known that most TD learning algorithms
are unstable with linear function approximation and off-policy learning. Recent
development of Gradient TD (GTD) algorithms has addressed this problem
successfully. H... | computer science |
4,882 | Deep Reinforcement Learning From Raw Pixels in Doom | cs.LG | Using current reinforcement learning methods, it has recently become possible
to learn to play unknown 3D games from raw pixels. In this work, we study the
challenges that arise in such complex environments, and summarize current
methods to approach these. We choose a task within the Doom game, that has not
been approa... | computer science |
4,883 | Extrapolation and learning equations | cs.LG | In classical machine learning, regression is treated as a black box process
of identifying a suitable function from a hypothesis set without attempting to
gain insight into the mechanism connecting inputs and outputs. In the natural
sciences, however, finding an interpretable function for a phenomenon is the
prime goal... | computer science |
4,884 | Bank Card Usage Prediction Exploiting Geolocation Information | cs.LG | We describe the solution of team ISMLL for the ECML-PKDD 2016 Discovery
Challenge on Bank Card Usage for both tasks. Our solution is based on three
pillars. Gradient boosted decision trees as a strong regression and
classification model, an intensive search for good hyperparameter
configurations and strong features tha... | computer science |
4,885 | Wind ramp event prediction with parallelized Gradient Boosted Regression
Trees | cs.LG | Accurate prediction of wind ramp events is critical for ensuring the
reliability and stability of the power systems with high penetration of wind
energy. This paper proposes a classification based approach for estimating the
future class of wind ramp event based on certain thresholds. A parallelized
gradient boosted re... | computer science |
4,886 | DPPred: An Effective Prediction Framework with Concise Discriminative
Patterns | cs.LG | In the literature, two series of models have been proposed to address
prediction problems including classification and regression. Simple models,
such as generalized linear models, have ordinary performance but strong
interpretability on a set of simple features. The other series, including
tree-based models, organize ... | computer science |
4,887 | Robust Spectral Inference for Joint Stochastic Matrix Factorization | cs.LG | Spectral inference provides fast algorithms and provable optimality for
latent topic analysis. But for real data these algorithms require additional
ad-hoc heuristics, and even then often produce unusable results. We explain
this poor performance by casting the problem of topic inference in the
framework of Joint Stoch... | computer science |
4,888 | TorchCraft: a Library for Machine Learning Research on Real-Time
Strategy Games | cs.LG | We present TorchCraft, a library that enables deep learning research on
Real-Time Strategy (RTS) games such as StarCraft: Brood War, by making it
easier to control these games from a machine learning framework, here Torch.
This white paper argues for using RTS games as a benchmark for AI research, and
describes the des... | computer science |
4,889 | Quantile Reinforcement Learning | cs.LG | In reinforcement learning, the standard criterion to evaluate policies in a
state is the expectation of (discounted) sum of rewards. However, this
criterion may not always be suitable, we consider an alternative criterion
based on the notion of quantiles. In the case of episodic reinforcement
learning problems, we prop... | computer science |
4,890 | Extracting Actionability from Machine Learning Models by Sub-optimal
Deterministic Planning | cs.AI | A main focus of machine learning research has been improving the
generalization accuracy and efficiency of prediction models. Many models such
as SVM, random forest, and deep neural nets have been proposed and achieved
great success. However, what emerges as missing in many applications is
actionability, i.e., the abil... | computer science |
4,891 | Learning Continuous Semantic Representations of Symbolic Expressions | cs.LG | Combining abstract, symbolic reasoning with continuous neural reasoning is a
grand challenge of representation learning. As a step in this direction, we
propose a new architecture, called neural equivalence networks, for the problem
of learning continuous semantic representations of algebraic and logical
expressions. T... | computer science |
4,892 | Playing SNES in the Retro Learning Environment | cs.LG | Mastering a video game requires skill, tactics and strategy. While these
attributes may be acquired naturally by human players, teaching them to a
computer program is a far more challenging task. In recent years, extensive
research was carried out in the field of reinforcement learning and numerous
algorithms were intr... | computer science |
4,893 | Sequence Tutor: Conservative Fine-Tuning of Sequence Generation Models
with KL-control | cs.LG | This paper proposes a general method for improving the structure and quality
of sequences generated by a recurrent neural network (RNN), while maintaining
information originally learned from data, as well as sample diversity. An RNN
is first pre-trained on data using maximum likelihood estimation (MLE), and the
probabi... | computer science |
4,894 | The Sum-Product Theorem: A Foundation for Learning Tractable Models | cs.LG | Inference in expressive probabilistic models is generally intractable, which
makes them difficult to learn and limits their applicability. Sum-product
networks are a class of deep models where, surprisingly, inference remains
tractable even when an arbitrary number of hidden layers are present. In this
paper, we genera... | computer science |
4,895 | A Connection between Generative Adversarial Networks, Inverse
Reinforcement Learning, and Energy-Based Models | cs.LG | Generative adversarial networks (GANs) are a recently proposed class of
generative models in which a generator is trained to optimize a cost function
that is being simultaneously learned by a discriminator. While the idea of
learning cost functions is relatively new to the field of generative modeling,
learning costs h... | computer science |
4,896 | #Exploration: A Study of Count-Based Exploration for Deep Reinforcement
Learning | cs.AI | Count-based exploration algorithms are known to perform near-optimally when
used in conjunction with tabular reinforcement learning (RL) methods for
solving small discrete Markov decision processes (MDPs). It is generally
thought that count-based methods cannot be applied in high-dimensional state
spaces, since most st... | computer science |
4,897 | Study on Feature Subspace of Archetypal Emotions for Speech Emotion
Recognition | cs.LG | Feature subspace selection is an important part in speech emotion
recognition. Most of the studies are devoted to finding a feature subspace for
representing all emotions. However, some studies have indicated that the
features associated with different emotions are not exactly the same. Hence,
traditional methods may f... | computer science |
4,898 | Analysis of a Design Pattern for Teaching with Features and Labels | cs.AI | We study the task of teaching a machine to classify objects using features
and labels. We introduce the Error-Driven-Featuring design pattern for teaching
using features and labels in which a teacher prefers to introduce features only
if they are needed. We analyze the potential risks and benefits of this
teaching patt... | computer science |
4,899 | Options Discovery with Budgeted Reinforcement Learning | cs.LG | We consider the problem of learning hierarchical policies for Reinforcement
Learning able to discover options, an option corresponding to a sub-policy over
a set of primitive actions. Different models have been proposed during the last
decade that usually rely on a predefined set of options. We specifically
address the... | computer science |
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