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2,100 | Sample-efficient Deep Reinforcement Learning for Dialog Control | cs.AI | Representing a dialog policy as a recurrent neural network (RNN) is
attractive because it handles partial observability, infers a latent
representation of state, and can be optimized with supervised learning (SL) or
reinforcement learning (RL). For RL, a policy gradient approach is natural, but
is sample inefficient. I... | computer science |
2,101 | Theory-guided Data Science: A New Paradigm for Scientific Discovery from
Data | cs.LG | Data science models, although successful in a number of commercial domains,
have had limited applicability in scientific problems involving complex
physical phenomena. Theory-guided data science (TGDS) is an emerging paradigm
that aims to leverage the wealth of scientific knowledge for improving the
effectiveness of da... | computer science |
2,102 | Objective Improvement in Information-Geometric Optimization | cs.LG | Information-Geometric Optimization (IGO) is a unified framework of stochastic
algorithms for optimization problems. Given a family of probability
distributions, IGO turns the original optimization problem into a new
maximization problem on the parameter space of the probability distributions.
IGO updates the parameter ... | computer science |
2,103 | Labeled Directed Acyclic Graphs: a generalization of context-specific
independence in directed graphical models | stat.ML | We introduce a novel class of labeled directed acyclic graph (LDAG) models
for finite sets of discrete variables. LDAGs generalize earlier proposals for
allowing local structures in the conditional probability distribution of a
node, such that unrestricted label sets determine which edges can be deleted
from the underl... | computer science |
2,104 | Bayesian Optimization With Censored Response Data | cs.AI | Bayesian optimization (BO) aims to minimize a given blackbox function using a
model that is updated whenever new evidence about the function becomes
available. Here, we address the problem of BO under partially right-censored
response data, where in some evaluations we only obtain a lower bound on the
function value. T... | computer science |
2,105 | GPatt: Fast Multidimensional Pattern Extrapolation with Gaussian
Processes | stat.ML | Gaussian processes are typically used for smoothing and interpolation on
small datasets. We introduce a new Bayesian nonparametric framework -- GPatt --
enabling automatic pattern extrapolation with Gaussian processes on large
multidimensional datasets. GPatt unifies and extends highly expressive kernels
and fast exact... | computer science |
2,106 | Towards Using Unlabeled Data in a Sparse-coding Framework for Human
Activity Recognition | cs.LG | We propose a sparse-coding framework for activity recognition in ubiquitous
and mobile computing that alleviates two fundamental problems of current
supervised learning approaches. (i) It automatically derives a compact, sparse
and meaningful feature representation of sensor data that does not rely on
prior expert know... | computer science |
2,107 | Optimizing the CVaR via Sampling | stat.ML | Conditional Value at Risk (CVaR) is a prominent risk measure that is being
used extensively in various domains. We develop a new formula for the gradient
of the CVaR in the form of a conditional expectation. Based on this formula, we
propose a novel sampling-based estimator for the CVaR gradient, in the spirit
of the l... | computer science |
2,108 | Counting Markov Blanket Structures | stat.ML | Learning Markov blanket (MB) structures has proven useful in performing
feature selection, learning Bayesian networks (BNs), and discovering causal
relationships. We present a formula for efficiently determining the number of
MB structures given a target variable and a set of other variables. As
expected, the number of... | computer science |
2,109 | Practical Kernel-Based Reinforcement Learning | cs.LG | Kernel-based reinforcement learning (KBRL) stands out among reinforcement
learning algorithms for its strong theoretical guarantees. By casting the
learning problem as a local kernel approximation, KBRL provides a way of
computing a decision policy which is statistically consistent and converges to
a unique solution. U... | computer science |
2,110 | Gamma Processes, Stick-Breaking, and Variational Inference | stat.ML | While most Bayesian nonparametric models in machine learning have focused on
the Dirichlet process, the beta process, or their variants, the gamma process
has recently emerged as a useful nonparametric prior in its own right. Current
inference schemes for models involving the gamma process are restricted to
MCMC-based ... | computer science |
2,111 | Generalized Product of Experts for Automatic and Principled Fusion of
Gaussian Process Predictions | cs.LG | In this work, we propose a generalized product of experts (gPoE) framework
for combining the predictions of multiple probabilistic models. We identify
four desirable properties that are important for scalability, expressiveness
and robustness, when learning and inferring with a combination of multiple
models. Through a... | computer science |
2,112 | Influence Functions for Machine Learning: Nonparametric Estimators for
Entropies, Divergences and Mutual Informations | stat.ML | We propose and analyze estimators for statistical functionals of one or more
distributions under nonparametric assumptions. Our estimators are based on the
theory of influence functions, which appear in the semiparametric statistics
literature. We show that estimators based either on data-splitting or a
leave-one-out t... | computer science |
2,113 | Distinguishing cause from effect using observational data: methods and
benchmarks | cs.LG | The discovery of causal relationships from purely observational data is a
fundamental problem in science. The most elementary form of such a causal
discovery problem is to decide whether X causes Y or, alternatively, Y causes
X, given joint observations of two variables X, Y. An example is to decide
whether altitude ca... | computer science |
2,114 | From dependency to causality: a machine learning approach | cs.LG | The relationship between statistical dependency and causality lies at the
heart of all statistical approaches to causal inference. Recent results in the
ChaLearn cause-effect pair challenge have shown that causal directionality can
be inferred with good accuracy also in Markov indistinguishable configurations
thanks to... | computer science |
2,115 | Projective simulation with generalization | cs.AI | The ability to generalize is an important feature of any intelligent agent.
Not only because it may allow the agent to cope with large amounts of data, but
also because in some environments, an agent with no generalization capabilities
cannot learn. In this work we outline several criteria for generalization, and
prese... | computer science |
2,116 | Fast Sampling for Bayesian Max-Margin Models | stat.ML | Bayesian max-margin models have shown superiority in various practical
applications, such as text categorization, collaborative prediction, social
network link prediction and crowdsourcing, and they conjoin the flexibility of
Bayesian modeling and predictive strengths of max-margin learning. However,
Monte Carlo sampli... | computer science |
2,117 | Incentivizing Exploration In Reinforcement Learning With Deep Predictive
Models | cs.AI | Achieving efficient and scalable exploration in complex domains poses a major
challenge in reinforcement learning. While Bayesian and PAC-MDP approaches to
the exploration problem offer strong formal guarantees, they are often
impractical in higher dimensions due to their reliance on enumerating the
state-action space.... | computer science |
2,118 | The Max $K$-Armed Bandit: A PAC Lower Bound and tighter Algorithms | stat.ML | We consider the Max $K$-Armed Bandit problem, where a learning agent is faced
with several sources (arms) of items (rewards), and interested in finding the
best item overall. At each time step the agent chooses an arm, and obtains a
random real valued reward. The rewards of each arm are assumed to be i.i.d.,
with an un... | computer science |
2,119 | (Blue) Taxi Destination and Trip Time Prediction from Partial
Trajectories | stat.ML | Real-time estimation of destination and travel time for taxis is of great
importance for existing electronic dispatch systems. We present an approach
based on trip matching and ensemble learning, in which we leverage the patterns
observed in a dataset of roughly 1.7 million taxi journeys to predict the
corresponding fi... | computer science |
2,120 | Clamping Improves TRW and Mean Field Approximations | cs.LG | We examine the effect of clamping variables for approximate inference in
undirected graphical models with pairwise relationships and discrete variables.
For any number of variable labels, we demonstrate that clamping and summing
approximate sub-partition functions can lead only to a decrease in the
partition function e... | computer science |
2,121 | Local Rademacher Complexity Bounds based on Covering Numbers | cs.AI | This paper provides a general result on controlling local Rademacher
complexities, which captures in an elegant form to relate the complexities with
constraint on the expected norm to the corresponding ones with constraint on
the empirical norm. This result is convenient to apply in real applications and
could yield re... | computer science |
2,122 | Thoughts on Massively Scalable Gaussian Processes | cs.LG | We introduce a framework and early results for massively scalable Gaussian
processes (MSGP), significantly extending the KISS-GP approach of Wilson and
Nickisch (2015). The MSGP framework enables the use of Gaussian processes (GPs)
on billions of datapoints, without requiring distributed inference, or severe
assumption... | computer science |
2,123 | Censoring Representations with an Adversary | cs.LG | In practice, there are often explicit constraints on what representations or
decisions are acceptable in an application of machine learning. For example it
may be a legal requirement that a decision must not favour a particular group.
Alternatively it can be that that representation of data must not have
identifying in... | computer science |
2,124 | Gaussian Process Planning with Lipschitz Continuous Reward Functions:
Towards Unifying Bayesian Optimization, Active Learning, and Beyond | stat.ML | This paper presents a novel nonmyopic adaptive Gaussian process planning
(GPP) framework endowed with a general class of Lipschitz continuous reward
functions that can unify some active learning/sensing and Bayesian optimization
criteria and offer practitioners some flexibility to specify their desired
choices for defi... | computer science |
2,125 | Feature Representation for ICU Mortality | cs.AI | Good predictors of ICU Mortality have the potential to identify high-risk
patients earlier, improve ICU resource allocation, or create more accurate
population-level risk models. Machine learning practitioners typically make
choices about how to represent features in a particular model, but these
choices are seldom eva... | computer science |
2,126 | Probabilistic Programming with Gaussian Process Memoization | cs.LG | Gaussian Processes (GPs) are widely used tools in statistics, machine
learning, robotics, computer vision, and scientific computation. However,
despite their popularity, they can be difficult to apply; all but the simplest
classification or regression applications require specification and inference
over complex covari... | computer science |
2,127 | Bayes-Optimal Effort Allocation in Crowdsourcing: Bounds and Index
Policies | cs.LG | We consider effort allocation in crowdsourcing, where we wish to assign
labeling tasks to imperfect homogeneous crowd workers to maximize overall
accuracy in a continuous-time Bayesian setting, subject to budget and time
constraints. The Bayes-optimal policy for this problem is the solution to a
partially observable Ma... | computer science |
2,128 | Top-N Recommender System via Matrix Completion | cs.IR | Top-N recommender systems have been investigated widely both in industry and
academia. However, the recommendation quality is far from satisfactory. In this
paper, we propose a simple yet promising algorithm. We fill the user-item
matrix based on a low-rank assumption and simultaneously keep the original
information. T... | computer science |
2,129 | Q($λ$) with Off-Policy Corrections | cs.AI | We propose and analyze an alternate approach to off-policy multi-step
temporal difference learning, in which off-policy returns are corrected with
the current Q-function in terms of rewards, rather than with the target policy
in terms of transition probabilities. We prove that such approximate
corrections are sufficien... | computer science |
2,130 | Interactive Storytelling over Document Collections | cs.AI | Storytelling algorithms aim to 'connect the dots' between disparate documents
by linking starting and ending documents through a series of intermediate
documents. Existing storytelling algorithms are based on notions of coherence
and connectivity, and thus the primary way by which users can steer the story
construction... | computer science |
2,131 | Meta-learning within Projective Simulation | cs.AI | Learning models of artificial intelligence can nowadays perform very well on
a large variety of tasks. However, in practice different task environments are
best handled by different learning models, rather than a single, universal,
approach. Most non-trivial models thus require the adjustment of several to
many learnin... | computer science |
2,132 | Investigating practical linear temporal difference learning | cs.LG | Off-policy reinforcement learning has many applications including: learning
from demonstration, learning multiple goal seeking policies in parallel, and
representing predictive knowledge. Recently there has been an proliferation of
new policy-evaluation algorithms that fill a longstanding algorithmic void in
reinforcem... | computer science |
2,133 | On Learning High Dimensional Structured Single Index Models | stat.ML | Single Index Models (SIMs) are simple yet flexible semi-parametric models for
machine learning, where the response variable is modeled as a monotonic
function of a linear combination of features. Estimation in this context
requires learning both the feature weights and the nonlinear function that
relates features to ob... | computer science |
2,134 | Multi-fidelity Gaussian Process Bandit Optimisation | stat.ML | In many scientific and engineering applications, we are tasked with the
optimisation of an expensive to evaluate black box function $f$. Traditional
settings for this problem assume just the availability of this single function.
However, in many cases, cheap approximations to $f$ may be obtainable. For
example, the exp... | computer science |
2,135 | On the Theory and Practice of Privacy-Preserving Bayesian Data Analysis | cs.LG | Bayesian inference has great promise for the privacy-preserving analysis of
sensitive data, as posterior sampling automatically preserves differential
privacy, an algorithmic notion of data privacy, under certain conditions
(Dimitrakakis et al., 2014; Wang et al., 2015). While this one posterior sample
(OPS) approach e... | computer science |
2,136 | Monotone Retargeting for Unsupervised Rank Aggregation with Object
Features | stat.ML | Learning the true ordering between objects by aggregating a set of expert
opinion rank order lists is an important and ubiquitous problem in many
applications ranging from social choice theory to natural language processing
and search aggregation. We study the problem of unsupervised rank aggregation
where no ground tr... | computer science |
2,137 | A Critical Examination of RESCAL for Completion of Knowledge Bases with
Transitive Relations | stat.ML | Link prediction in large knowledge graphs has received a lot of attention
recently because of its importance for inferring missing relations and for
completing and improving noisily extracted knowledge graphs. Over the years a
number of machine learning researchers have presented various models for
predicting the prese... | computer science |
2,138 | A PAC RL Algorithm for Episodic POMDPs | cs.LG | Many interesting real world domains involve reinforcement learning (RL) in
partially observable environments. Efficient learning in such domains is
important, but existing sample complexity bounds for partially observable RL
are at least exponential in the episode length. We give, to our knowledge, the
first partially ... | computer science |
2,139 | Unsupervised Discovery of El Nino Using Causal Feature Learning on
Microlevel Climate Data | stat.ML | We show that the climate phenomena of El Nino and La Nina arise naturally as
states of macro-variables when our recent causal feature learning framework
(Chalupka 2015, Chalupka 2016) is applied to micro-level measures of zonal wind
(ZW) and sea surface temperatures (SST) taken over the equatorial band of the
Pacific O... | computer science |
2,140 | Adaptive Learning Rate via Covariance Matrix Based Preconditioning for
Deep Neural Networks | cs.LG | Adaptive learning rate algorithms such as RMSProp are widely used for
training deep neural networks. RMSProp offers efficient training since it uses
first order gradients to approximate Hessian-based preconditioning. However,
since the first order gradients include noise caused by stochastic
optimization, the approxima... | computer science |
2,141 | VIME: Variational Information Maximizing Exploration | cs.LG | Scalable and effective exploration remains a key challenge in reinforcement
learning (RL). While there are methods with optimality guarantees in the
setting of discrete state and action spaces, these methods cannot be applied in
high-dimensional deep RL scenarios. As such, most contemporary RL relies on
simple heuristi... | computer science |
2,142 | Safe Exploration in Finite Markov Decision Processes with Gaussian
Processes | cs.LG | In classical reinforcement learning, when exploring an environment, agents
accept arbitrary short term loss for long term gain. This is infeasible for
safety critical applications, such as robotics, where even a single unsafe
action may cause system failure. In this paper, we address the problem of
safely exploring fin... | computer science |
2,143 | Bootstrapping with Models: Confidence Intervals for Off-Policy
Evaluation | cs.AI | For an autonomous agent, executing a poor policy may be costly or even
dangerous. For such agents, it is desirable to determine confidence interval
lower bounds on the performance of any given policy without executing said
policy. Current methods for exact high confidence off-policy evaluation that
use importance sampl... | computer science |
2,144 | Ancestral Causal Inference | cs.LG | Constraint-based causal discovery from limited data is a notoriously
difficult challenge due to the many borderline independence test decisions.
Several approaches to improve the reliability of the predictions by exploiting
redundancy in the independence information have been proposed recently. Though
promising, existi... | computer science |
2,145 | LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection | cs.AI | Mechanical devices such as engines, vehicles, aircrafts, etc., are typically
instrumented with numerous sensors to capture the behavior and health of the
machine. However, there are often external factors or variables which are not
captured by sensors leading to time-series which are inherently unpredictable.
For insta... | computer science |
2,146 | Bootstrap Model Aggregation for Distributed Statistical Learning | stat.ML | In distributed, or privacy-preserving learning, we are often given a set of
probabilistic models estimated from different local repositories, and asked to
combine them into a single model that gives efficient statistical estimation. A
simple method is to linearly average the parameters of the local models, which,
howev... | computer science |
2,147 | Efficient Hyperparameter Optimization of Deep Learning Algorithms Using
Deterministic RBF Surrogates | cs.AI | Automatically searching for optimal hyperparameter configurations is of
crucial importance for applying deep learning algorithms in practice. Recently,
Bayesian optimization has been proposed for optimizing hyperparameters of
various machine learning algorithms. Those methods adopt probabilistic
surrogate models like G... | computer science |
2,148 | Relational Similarity Machines | stat.ML | This paper proposes Relational Similarity Machines (RSM): a fast, accurate,
and flexible relational learning framework for supervised and semi-supervised
learning tasks. Despite the importance of relational learning, most existing
methods are hard to adapt to different settings, due to issues with efficiency,
scalabili... | computer science |
2,149 | Dynamic Collaborative Filtering with Compound Poisson Factorization | cs.LG | Model-based collaborative filtering analyzes user-item interactions to infer
latent factors that represent user preferences and item characteristics in
order to predict future interactions. Most collaborative filtering algorithms
assume that these latent factors are static, although it has been shown that
user preferen... | computer science |
2,150 | Incremental Minimax Optimization based Fuzzy Clustering for Large
Multi-view Data | cs.AI | Incremental clustering approaches have been proposed for handling large data
when given data set is too large to be stored. The key idea of these approaches
is to find representatives to represent each cluster in each data chunk and
final data analysis is carried out based on those identified representatives
from all t... | computer science |
2,151 | High Dimensional Human Guided Machine Learning | cs.AI | Have you ever looked at a machine learning classification model and thought,
I could have made that? Well, that is what we test in this project, comparing
XGBoost trained on human engineered features to training directly on data. The
human engineered features do not outperform XGBoost trained di- rectly on the
data, bu... | computer science |
2,152 | Column Networks for Collective Classification | cs.LG | Relational learning deals with data that are characterized by relational
structures. An important task is collective classification, which is to jointly
classify networked objects. While it holds a great promise to produce a better
accuracy than non-collective classifiers, collective classification is
computational cha... | computer science |
2,153 | Deep unsupervised learning through spatial contrasting | cs.LG | Convolutional networks have marked their place over the last few years as the
best performing model for various visual tasks. They are, however, most suited
for supervised learning from large amounts of labeled data. Previous attempts
have been made to use unlabeled data to improve model performance by applying
unsuper... | computer science |
2,154 | Deep Amortized Inference for Probabilistic Programs | cs.AI | Probabilistic programming languages (PPLs) are a powerful modeling tool, able
to represent any computable probability distribution. Unfortunately,
probabilistic program inference is often intractable, and existing PPLs mostly
rely on expensive, approximate sampling-based methods. To alleviate this
problem, one could tr... | computer science |
2,155 | Safety Verification of Deep Neural Networks | cs.AI | Deep neural networks have achieved impressive experimental results in image
classification, but can surprisingly be unstable with respect to adversarial
perturbations, that is, minimal changes to the input image that cause the
network to misclassify it. With potential applications including perception
modules and end-t... | computer science |
2,156 | Learning Cost-Effective Treatment Regimes using Markov Decision
Processes | cs.AI | Decision makers, such as doctors and judges, make crucial decisions such as
recommending treatments to patients, and granting bails to defendants on a
daily basis. Such decisions typically involve weighting the potential benefits
of taking an action against the costs involved. In this work, we aim to
automate this task... | computer science |
2,157 | Learning Scalable Deep Kernels with Recurrent Structure | cs.LG | Many applications in speech, robotics, finance, and biology deal with
sequential data, where ordering matters and recurrent structures are common.
However, this structure cannot be easily captured by standard kernel functions.
To model such structure, we propose expressive closed-form kernel functions for
Gaussian proc... | computer science |
2,158 | Estimating Causal Direction and Confounding of Two Discrete Variables | stat.ML | We propose a method to classify the causal relationship between two discrete
variables given only the joint distribution of the variables, acknowledging
that the method is subject to an inherent baseline error. We assume that the
causal system is acyclicity, but we do allow for hidden common causes. Our
algorithm presu... | computer science |
2,159 | Combining policy gradient and Q-learning | cs.LG | Policy gradient is an efficient technique for improving a policy in a
reinforcement learning setting. However, vanilla online variants are on-policy
only and not able to take advantage of off-policy data. In this paper we
describe a new technique that combines policy gradient with off-policy
Q-learning, drawing experie... | computer science |
2,160 | Averaged-DQN: Variance Reduction and Stabilization for Deep
Reinforcement Learning | cs.AI | Instability and variability of Deep Reinforcement Learning (DRL) algorithms
tend to adversely affect their performance. Averaged-DQN is a simple extension
to the DQN algorithm, based on averaging previously learned Q-values estimates,
which leads to a more stable training procedure and improved performance by
reducing ... | computer science |
2,161 | Reinforcement-based Simultaneous Algorithm and its Hyperparameters
Selection | cs.LG | Many algorithms for data analysis exist, especially for classification
problems. To solve a data analysis problem, a proper algorithm should be
chosen, and also its hyperparameters should be selected. In this paper, we
present a new method for the simultaneous selection of an algorithm and its
hyperparameters. In order... | computer science |
2,162 | Reinforcement Learning in Rich-Observation MDPs using Spectral Methods | cs.AI | Designing effective exploration-exploitation algorithms in Markov decision
processes (MDPs) with large state-action spaces is the main challenge in
reinforcement learning (RL). In fact, the learning performance degrades with
the number of states and actions in the MDP. However, MDPs often exhibit a
low-dimensional late... | computer science |
2,163 | Nothing Else Matters: Model-Agnostic Explanations By Identifying
Prediction Invariance | stat.ML | At the core of interpretable machine learning is the question of whether
humans are able to make accurate predictions about a model's behavior. Assumed
in this question are three properties of the interpretable output: coverage,
precision, and effort. Coverage refers to how often humans think they can
predict the model... | computer science |
2,164 | A Deep Learning Approach for Joint Video Frame and Reward Prediction in
Atari Games | cs.AI | Reinforcement learning is concerned with identifying reward-maximizing
behaviour policies in environments that are initially unknown. State-of-the-art
reinforcement learning approaches, such as deep Q-networks, are model-free and
learn to act effectively across a wide range of environments such as Atari
games, but requ... | computer science |
2,165 | Limbo: A Fast and Flexible Library for Bayesian Optimization | cs.LG | Limbo is an open-source C++11 library for Bayesian optimization which is
designed to be both highly flexible and very fast. It can be used to optimize
functions for which the gradient is unknown, evaluations are expensive, and
runtime cost matters (e.g., on embedded systems or robots). Benchmarks on
standard functions ... | computer science |
2,166 | Feature Importance Measure for Non-linear Learning Algorithms | cs.AI | Complex problems may require sophisticated, non-linear learning methods such
as kernel machines or deep neural networks to achieve state of the art
prediction accuracies. However, high prediction accuracies are not the only
objective to consider when solving problems using machine learning. Instead,
particular scientif... | computer science |
2,167 | Local Discriminant Hyperalignment for multi-subject fMRI data alignment | stat.ML | Multivariate Pattern (MVP) classification can map different cognitive states
to the brain tasks. One of the main challenges in MVP analysis is validating
the generated results across subjects. However, analyzing multi-subject fMRI
data requires accurate functional alignments between neuronal activities of
different sub... | computer science |
2,168 | Accelerated Gradient Temporal Difference Learning | cs.AI | The family of temporal difference (TD) methods span a spectrum from
computationally frugal linear methods like TD({\lambda}) to data efficient
least squares methods. Least square methods make the best use of available data
directly computing the TD solution and thus do not require tuning a typically
highly sensitive le... | computer science |
2,169 | Reinforcement Learning Algorithm Selection | stat.ML | This paper formalises the problem of online algorithm selection in the
context of Reinforcement Learning. The setup is as follows: given an episodic
task and a finite number of off-policy RL algorithms, a meta-algorithm has to
decide which RL algorithm is in control during the next episode so as to
maximize the expecte... | computer science |
2,170 | Cluster-based Kriging Approximation Algorithms for Complexity Reduction | cs.LG | Kriging or Gaussian Process Regression is applied in many fields as a
non-linear regression model as well as a surrogate model in the field of
evolutionary computation. However, the computational and space complexity of
Kriging, that is cubic and quadratic in the number of data points respectively,
becomes a major bott... | computer science |
2,171 | Knowledge Graph Completion via Complex Tensor Factorization | cs.AI | In statistical relational learning, knowledge graph completion deals with
automatically understanding the structure of large knowledge graphs---labeled
directed graphs---and predicting missing relationships---labeled edges.
State-of-the-art embedding models propose different trade-offs between modeling
expressiveness, ... | computer science |
2,172 | Optimal Experiment Design for Causal Discovery from Fixed Number of
Experiments | cs.LG | We study the problem of causal structure learning over a set of random
variables when the experimenter is allowed to perform at most $M$ experiments
in a non-adaptive manner. We consider the optimal learning strategy in terms of
minimizing the portions of the structure that remains unknown given the limited
number of e... | computer science |
2,173 | Towards A Rigorous Science of Interpretable Machine Learning | stat.ML | As machine learning systems become ubiquitous, there has been a surge of
interest in interpretable machine learning: systems that provide explanation
for their outputs. These explanations are often used to qualitatively assess
other criteria such as safety or non-discrimination. However, despite the
interest in interpr... | computer science |
2,174 | OptNet: Differentiable Optimization as a Layer in Neural Networks | cs.LG | This paper presents OptNet, a network architecture that integrates
optimization problems (here, specifically in the form of quadratic programs) as
individual layers in larger end-to-end trainable deep networks. These layers
encode constraints and complex dependencies between the hidden states that
traditional convoluti... | computer science |
2,175 | Adaptive Matching for Expert Systems with Uncertain Task Types | cs.AI | Online two-sided matching markets such as Q&A forums (e.g. StackOverflow,
Quora) and online labour platforms (e.g. Upwork) critically rely on the ability
to propose adequate matches based on imperfect knowledge of the two parties to
be matched. This prompts the following question: Which matching recommendation
algorith... | computer science |
2,176 | On the Limits of Learning Representations with Label-Based Supervision | cs.LG | Advances in neural network based classifiers have transformed automatic
feature learning from a pipe dream of stronger AI to a routine and expected
property of practical systems. Since the emergence of AlexNet every winning
submission of the ImageNet challenge has employed end-to-end representation
learning, and due to... | computer science |
2,177 | Deep Robust Kalman Filter | cs.AI | A Robust Markov Decision Process (RMDP) is a sequential decision making model
that accounts for uncertainty in the parameters of dynamic systems. This
uncertainty introduces difficulties in learning an optimal policy, especially
for environments with large state spaces. We propose two algorithms, RTD-DQN
and Deep-RoK, ... | computer science |
2,178 | Prediction and Control with Temporal Segment Models | cs.LG | We introduce a method for learning the dynamics of complex nonlinear systems
based on deep generative models over temporal segments of states and actions.
Unlike dynamics models that operate over individual discrete timesteps, we
learn the distribution over future state trajectories conditioned on past
state, past acti... | computer science |
2,179 | Modeling Relational Data with Graph Convolutional Networks | stat.ML | Knowledge graphs enable a wide variety of applications, including question
answering and information retrieval. Despite the great effort invested in their
creation and maintenance, even the largest (e.g., Yago, DBPedia or Wikidata)
remain incomplete. We introduce Relational Graph Convolutional Networks
(R-GCNs) and app... | computer science |
2,180 | Unifying PAC and Regret: Uniform PAC Bounds for Episodic Reinforcement
Learning | cs.LG | Statistical performance bounds for reinforcement learning (RL) algorithms can
be critical for high-stakes applications like healthcare. This paper introduces
a new framework for theoretically measuring the performance of such algorithms
called Uniform-PAC, which is a strengthening of the classical Probably
Approximatel... | computer science |
2,181 | Fast Stochastic Variance Reduced Gradient Method with Momentum
Acceleration for Machine Learning | cs.LG | Recently, research on accelerated stochastic gradient descent methods (e.g.,
SVRG) has made exciting progress (e.g., linear convergence for strongly convex
problems). However, the best-known methods (e.g., Katyusha) requires at least
two auxiliary variables and two momentum parameters. In this paper, we propose
a fast ... | computer science |
2,182 | Adaptive Simulation-based Training of AI Decision-makers using Bayesian
Optimization | cs.LG | This work studies how an AI-controlled dog-fighting agent with tunable
decision-making parameters can learn to optimize performance against an
intelligent adversary, as measured by a stochastic objective function evaluated
on simulated combat engagements. Gaussian process Bayesian optimization (GPBO)
techniques are dev... | computer science |
2,183 | Probabilistic Search for Structured Data via Probabilistic Programming
and Nonparametric Bayes | cs.AI | Databases are widespread, yet extracting relevant data can be difficult.
Without substantial domain knowledge, multivariate search queries often return
sparse or uninformative results. This paper introduces an approach for
searching structured data based on probabilistic programming and nonparametric
Bayes. Users speci... | computer science |
2,184 | Recurrent Environment Simulators | cs.AI | Models that can simulate how environments change in response to actions can
be used by agents to plan and act efficiently. We improve on previous
environment simulators from high-dimensional pixel observations by introducing
recurrent neural networks that are able to make temporally and spatially
coherent predictions f... | computer science |
2,185 | Larger is Better: The Effect of Learning Rates Enjoyed by Stochastic
Optimization with Progressive Variance Reduction | cs.LG | In this paper, we propose a simple variant of the original stochastic
variance reduction gradient (SVRG), where hereafter we refer to as the variance
reduced stochastic gradient descent (VR-SGD). Different from the choices of the
snapshot point and starting point in SVRG and its proximal variant, Prox-SVRG,
the two vec... | computer science |
2,186 | Learning to Acquire Information | cs.AI | We consider the problem of diagnosis where a set of simple observations are
used to infer a potentially complex hidden hypothesis. Finding the optimal
subset of observations is intractable in general, thus we focus on the problem
of active diagnosis, where the agent selects the next most-informative
observation based o... | computer science |
2,187 | From Language to Programs: Bridging Reinforcement Learning and Maximum
Marginal Likelihood | cs.AI | Our goal is to learn a semantic parser that maps natural language utterances
into executable programs when only indirect supervision is available: examples
are labeled with the correct execution result, but not the program itself.
Consequently, we must search the space of programs for those that output the
correct resu... | computer science |
2,188 | Parseval Networks: Improving Robustness to Adversarial Examples | stat.ML | We introduce Parseval networks, a form of deep neural networks in which the
Lipschitz constant of linear, convolutional and aggregation layers is
constrained to be smaller than 1. Parseval networks are empirically and
theoretically motivated by an analysis of the robustness of the predictions
made by deep neural networ... | computer science |
2,189 | Machine Learning with World Knowledge: The Position and Survey | cs.AI | Machine learning has become pervasive in multiple domains, impacting a wide
variety of applications, such as knowledge discovery and data mining, natural
language processing, information retrieval, computer vision, social and health
informatics, ubiquitous computing, etc. Two essential problems of machine
learning are ... | computer science |
2,190 | Deep Episodic Value Iteration for Model-based Meta-Reinforcement
Learning | stat.ML | We present a new deep meta reinforcement learner, which we call Deep Episodic
Value Iteration (DEVI). DEVI uses a deep neural network to learn a similarity
metric for a non-parametric model-based reinforcement learning algorithm. Our
model is trained end-to-end via back-propagation. Despite being trained using
the mode... | computer science |
2,191 | Demystifying Relational Latent Representations | cs.AI | Latent features learned by deep learning approaches have proven to be a
powerful tool for machine learning. They serve as a data abstraction that makes
learning easier by capturing regularities in data explicitly. Their benefits
motivated their adaptation to relational learning context. In our previous
work, we introdu... | computer science |
2,192 | Estimating Accuracy from Unlabeled Data: A Probabilistic Logic Approach | cs.LG | We propose an efficient method to estimate the accuracy of classifiers using
only unlabeled data. We consider a setting with multiple classification
problems where the target classes may be tied together through logical
constraints. For example, a set of classes may be mutually exclusive, meaning
that a data instance c... | computer science |
2,193 | AIDE: An algorithm for measuring the accuracy of probabilistic inference
algorithms | stat.ML | Approximate probabilistic inference algorithms are central to many fields.
Examples include sequential Monte Carlo inference in robotics, variational
inference in machine learning, and Markov chain Monte Carlo inference in
statistics. A key problem faced by practitioners is measuring the accuracy of
an approximate infe... | computer science |
2,194 | A unified view of entropy-regularized Markov decision processes | cs.LG | We propose a general framework for entropy-regularized average-reward
reinforcement learning in Markov decision processes (MDPs). Our approach is
based on extending the linear-programming formulation of policy optimization in
MDPs to accommodate convex regularization functions. Our key result is showing
that using the ... | computer science |
2,195 | A Unified Approach to Interpreting Model Predictions | cs.AI | Understanding why a model makes a certain prediction can be as crucial as the
prediction's accuracy in many applications. However, the highest accuracy for
large modern datasets is often achieved by complex models that even experts
struggle to interpret, such as ensemble or deep learning models, creating a
tension betw... | computer science |
2,196 | Reinforcement Learning with a Corrupted Reward Channel | cs.AI | No real-world reward function is perfect. Sensory errors and software bugs
may result in RL agents observing higher (or lower) rewards than they should.
For example, a reinforcement learning agent may prefer states where a sensory
error gives it the maximum reward, but where the true reward is actually small.
We formal... | computer science |
2,197 | MMD GAN: Towards Deeper Understanding of Moment Matching Network | cs.LG | Generative moment matching network (GMMN) is a deep generative model that
differs from Generative Adversarial Network (GAN) by replacing the
discriminator in GAN with a two-sample test based on kernel maximum mean
discrepancy (MMD). Although some theoretical guarantees of MMD have been
studied, the empirical performanc... | computer science |
2,198 | Beyond Parity: Fairness Objectives for Collaborative Filtering | cs.IR | We study fairness in collaborative-filtering recommender systems, which are
sensitive to discrimination that exists in historical data. Biased data can
lead collaborative-filtering methods to make unfair predictions for users from
minority groups. We identify the insufficiency of existing fairness metrics and
propose f... | computer science |
2,199 | Modeling The Intensity Function Of Point Process Via Recurrent Neural
Networks | cs.LG | Event sequence, asynchronously generated with random timestamp, is ubiquitous
among applications. The precise and arbitrary timestamp can carry important
clues about the underlying dynamics, and has lent the event data fundamentally
different from the time-series whereby series is indexed with fixed and equal
time inte... | computer science |
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