A new correlation coefficient between categorical, ordinal and interval variables with Pearson characteristics
Automated Algorithm Selection: Survey and Perspectives
It has long been observed that for practically any computational problem that has been intensely studied, different instances are best solved using different algorithms. This is particularly pronounced for computationally hard problems, where in most cases, no single algorithm defines the state of the art; instead, there is a set of algorithms with complementary strengths. This performance complementarity can be exploited in various ways, one of which is based on the idea of selecting, from a set of given algorithms, for each problem instance to be solved the one expected to perform best. The task of automatically selecting an algorithm from a given set is known as the per-instance algorithm selection problem and has been intensely studied over the past 15 years, leading to major improvements in the state of the art in solving a growing number of discrete combinatorial problems, including propositional satisfiability and AI planning. Per-instance algorithm selection also shows much promise for boosting performance in solving continuous and mixed discrete/continuous optimisation problems. This survey provides an overview of research in automated algorithm selection, ranging from early and seminal works to recent and promising application areas. Different from earlier work, it covers applications to discrete and continuous problems, and discusses algorithm selection in context with conceptually related approaches, such as algorithm configuration, scheduling or portfolio selection. Since informative and cheaply computable problem instance features provide the basis for effective per-instance algorithm selection systems, we also provide an overview of such features for discrete and continuous problems. Finally, we provide perspectives on future work in the area and discuss a number of open research challenges.
Deep Reinforcement Learning for Time Optimal Velocity Control using Prior Knowledge
While autonomous navigation has recently gained great interest in the field of reinforcement learning, only a few works in this field have focused on the time optimal velocity control problem, i.e. controlling a vehicle such that it travels at the maximal stable speed. Achieving maximal stable speed is important in many situations, such as emergency vehicles traveling at high speeds to their destinations, and regular vehicles executing emergency maneuvers to avoid imminent collisions. Traditionally, time optimal velocity control is solved by numerical computations that are based on optimal control and vehicle dynamics. In this paper, we show that a deep reinforcement learning method for the time optimal velocity control problem outperforms a numerically derived solution. We propose a method for using the numerical solution to further improve the performance of the reinforcement learner, especially at early stages of learning. This result may contribute to the optimal control of robots in applications where some analytical model is available.
Kalman filter demystified: from intuition to probabilistic graphical model to real case in financial markets
In this paper, we revisit the Kalman filter theory. After giving the intuition on a simplified financial markets example, we revisit the maths underlying it. We then show that Kalman filter can be presented in a very different fashion using graphical models. This enables us to establish the connection between Kalman filter and Hidden Markov Models. We then look at their application in financial markets and provide various intuitions in terms of their applicability for complex systems such as financial markets. Although this paper has been written more like a self contained work connecting Kalman filter to Hidden Markov Models and hence revisiting well known and establish results, it contains new results and brings additional contributions to the field. First, leveraging on the link between Kalman filter and HMM, it gives new algorithms for inference for extended Kalman filters. Second, it presents an alternative to the traditional estimation of parameters using EM algorithm thanks to the usage of CMA-ES optimization. Third, it examines the application of Kalman filter and its Hidden Markov models version to financial markets, providing various dynamics assumptions and tests. We conclude by connecting Kalman filter approach to trend following technical analysis system and showing their superior performances for trend following detection.
Multi-step Time Series Forecasting Using Ridge Polynomial Neural Network with Error-Output Feedbacks
Time series forecasting gets much attention due to its impact on many practical applications. Higher-order neural network with recurrent feedback is a powerful technique which used successfully for forecasting. It maintains fast learning and the ability to learn the dynamics of the series over time. For that, in this paper, we propose a novel model which is called Ridge Polynomial Neural Network with Error-Output Feedbacks (RPNN-EOFs) that combines the properties of higher order and error-output feedbacks. The well-known Mackey-Glass time series is used to test the forecasting capability of RPNN-EOFS. Simulation results showed that the proposed RPNN-EOFs provides better understanding for the Mackey-Glass time series with root mean square error equal to 0.00416. This result is smaller than other models in the literature. Therefore, we can conclude that the RPNN-EOFs can be applied successfully for time series forecasting.
Prepare for the Expected Worst: Algorithms for Reconfigurable Resources Under Uncertainty
In this paper we study how to optimally balance cheap inflexible resources with more expensive, reconfigurable resources despite uncertainty in the input problem. Specifically, we introduce the MinEMax model to study ‘build versus rent’ problems. In our model different scenarios appear independently. Before knowing which scenarios appear, we may build rigid resources that cannot be changed for different scenarios. Once we know which scenarios appear, we are allowed to rent reconfigurable but expensive resources to use across scenarios. While the objective in our model is difficult to compute, we show it is well-estimated by a surrogate objective which is representable by an LP. In this surrogate objective we pay for each scenario only to the extent that it exceeds a certain threshold. Using this objective we design algorithms that approximately-optimally balance inflexible and reconfigurable resources for several NP-hard covering problems. For example, we study minimum spanning and Steiner trees, minimum cuts and facility location variants. Up to constants our approximation guarantees match those of previous algorithms for the previously-studied demand-robust and stochastic two-stage models. Lastly, we demonstrate that our problem is sufficiently general to smoothly interpolate between previous demand-robust and stochastic two-stage problems.
A Structure-aware Online Learning Algorithm for Markov Decision Processes
To overcome the curse of dimensionality and curse of modeling in Dynamic Programming (DP) methods for solving classical Markov Decision Process (MDP) problems, Reinforcement Learning (RL) algorithms are popular. In this paper, we consider an infinite-horizon average reward MDP problem and prove the optimality of the threshold policy under certain conditions. Traditional RL techniques do not exploit the threshold nature of optimal policy while learning. In this paper, we propose a new RL algorithm which utilizes the known threshold structure of the optimal policy while learning by reducing the feasible policy space. We establish that the proposed algorithm converges to the optimal policy. It provides a significant improvement in convergence speed and computational and storage complexity over traditional RL algorithms. The proposed technique can be applied to a wide variety of optimization problems that include energy efficient data transmission and management of queues. We exhibit the improvement in convergence speed of the proposed algorithm over other RL algorithms through simulations.
The complexity of matrix multiplication: developments since 2014. Extended abstract of 2018 Oberwolfach Complexity meeting plenary lecture
This is an overview of recent developments regarding the complexity of matrix multiplication, with an emphasis on the uses of algebraic geometry and representation theory in complexity theory.
Towards Identifying and Managing Sources of Uncertainty in AI and Machine Learning Models – An Overview
Quantifying and managing uncertainties that occur when data-driven models such as those provided by AI and machine learning methods are applied is crucial. This whitepaper provides a brief motivation and first overview of the state of the art in identifying and quantifying sources of uncertainty for data-driven components as well as means for analyzing their impact.
Experience Replay for Continual Learning
Continual learning is the problem of learning new tasks or knowledge while protecting old knowledge and ideally generalizing from old experience to learn new tasks faster. Neural networks trained by stochastic gradient descent often degrade on old tasks when trained successively on new tasks with different data distributions. This phenomenon, referred to as catastrophic forgetting, is considered a major hurdle to learning with non-stationary data or sequences of new tasks, and prevents networks from continually accumulating knowledge and skills. We examine this issue in the context of reinforcement learning, in a setting where an agent is exposed to tasks in a sequence. Unlike most other work, we do not provide an explicit indication to the model of task boundaries, which is the most general circumstance for a learning agent exposed to continuous experience. While various methods to counteract catastrophic forgetting have recently been proposed, we explore a straightforward, general, and seemingly overlooked solution – that of using experience replay buffers for all past events – with a mixture of on- and off-policy learning, leveraging behavioral cloning. We show that this strategy can still learn new tasks quickly yet can substantially reduce catastrophic forgetting in both Atari and DMLab domains, even matching the performance of methods that require task identities. When buffer storage is constrained, we confirm that a simple mechanism for randomly discarding data allows a limited size buffer to perform almost as well as an unbounded one.
Few-Shot Generalization Across Dialogue Tasks
Machine-learning based dialogue managers are able to learn complex behaviors in order to complete a task, but it is not straightforward to extend their capabilities to new domains. We investigate different policies’ ability to handle uncooperative user behavior, and how well expertise in completing one task (such as restaurant reservations) can be reapplied when learning a new one (e.g. booking a hotel). We introduce the Recurrent Embedding Dialogue Policy (REDP), which embeds system actions and dialogue states in the same vector space. REDP contains a memory component and attention mechanism based on a modified Neural Turing Machine, and significantly outperforms a baseline LSTM classifier on this task. We also show that both our architecture and baseline solve the bAbI dialogue task, achieving 100% test accuracy.
• ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural Network• Beyond Pham’s algorithm for joint diagonalization• Partial Evaluation of Logic Programs in Vector Spaces• Neural Sign Language Translation based on Human Keypoint Estimation• Interplay between tilt, disorder, and Coulomb interaction in type-I Dirac fermions• An infinite family of locally X graphs based on incidence geometries• Trajectory-based Learning for Ball-in-Maze Games• Robust Dynamic Programming for Temporal Logic Control of Stochastic Systems• Robust Invariant Sets Computation for Switched Discrete-Time Polynomial Systems• CrowdCam: Dynamic Region Segmentation• GIRNet: Interleaved Multi-Task Recurrent State Sequence Models• Coordinate-based Texture Inpainting for Pose-Guided Image Generation• Isospectralization, or how to hear shape, style, and correspondence• Improved Calibration of Numerical Integration Error in Sigma-Point Filters• Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Data• Image Reconstruction with Predictive Filter Flow• Topological Bounds on the Dimension of Orthogonal Representations of Graphs• Fixed-length Bit-string Representation of Fingerprint by Normalized Local Structures• A randomized gradient-free attack on ReLU networks• Counting Complexity for Reasoning in Abstract Argumentation• A bilevel learning approach for optimal observation placement in variational data assimilation• One-Shot Instance Segmentation• Topological optimization via cost penalization• Identity Preserving Generative Adversarial Network for Cross-Domain Person Re-identification• Simple Local Polynomial Density Estimators• Optimal arrangements of classical and quantum states with limited purity• Hamiltonian cycles and paths in hypercubes with disjoint faulty edges• Network-induced multistability: Lossy coupling and exotic solitary states• Towards Decentralization of Social Media• Sequence Learning with RNNs for Medical Concept Normalization in User-Generated Texts• Multi-granularity Generator for Temporal Action Proposal• Taming correlations through entropy-efficient measure decompositions with applications to mean-field approximation• Uniform sets in a family with restricted intersections• Gender Differences in Participation and Reward on Stack Overflow• Core-fringe link prediction• Regional Enlarged Observability of Fractional Differential Equations with Riemann-Liouville Time Derivatives• Strike (with) a Pose: Neural Networks Are Easily Fooled by Strange Poses of Familiar Objects• Free fermions and $α$-determinantal processes• A Coupling for Triple Stochastic Integrals• Approximate Evaluation of Label-Constrained Reachability Queries• Automatic Liver Segmentation with Adversarial Loss and Convolutional Neural Network• Quantity over Quality: Dithered Quantization for Compressive Radar Systems• GOSPAL: An Efficient Strategy-Proof mechanism for Constrained Resource Allocation• Millimeter-Wave Downlink Positioning with a Single-Antenna Receiver• Convergence Analysis of the Relaxed Douglas-Rachford Algorithm• Attendance Maximization for Successful Social Event Planning• Exploring Hypergraph Representation on Face Anti-spoofing Beyond 2D Attacks• Approximate Schedules for Non-Migratory Parallel Jobs in Speed-Scaled Multiprocessor Systems• A Note on Specializations of Grothendieck Polynomials• The Dirichlet-Ferguson Diffusion on the Space of Probability Measures over a Closed Riemannian Manifold• Distribution Regression with Sample Selection, with an Application to Wage Decompositions in the UK• Escaping Plato’s Cave using Adversarial Training: 3D Shape From Unstructured 2D Image Collections• On the relation between topological entropy and restoration entropy• A Generative Appearance Model for End-to-end Video Object Segmentation• Local polynomial estimation of the intensity of a doubly stochastic Poisson process with bandwidth selection procedure• Large Scale Audio-Visual Video Analytics Platform for Forensic Investigations of Terroristic Attacks• Spatio-Spectral Radar Beampattern Design for Co-existence with Wireless Communication Systems• Basis Pursuit Denoise with Nonsmooth Constraints• Harmonic analysis on directed graphs and applications: from Fourier analysis to wavelets• Adaptive Stochastic Variance Reduction for Subsampled Newton Method with Cubic Regularization• WaveletNet: Logarithmic Scale Efficient Convolutional Neural Networks for Edge Devices• Cooperative Resolvability and Secrecy in the Cribbing Multiple-Access Channel• Accelerating Sensitivity Analysis in Microscopy Image Segmentation Workflows• Infinite Families of Asymmetric Graphs• Robust Face Detection via Learning Small Faces on Hard Images• A Game of Martingales• Racial categories in machine learning• Asymptotic Properties of Random Voronoi Cells with Arbitrary Underlying Density• Toward breaking the curse of dimensionality: an FPTAS for stochastic dynamic programs with multidimensional actions and scalar states• Limit theorems for random walks with absorption• Multi-level Multimodal Common Semantic Space for Image-Phrase Grounding• Shared Representational Geometry Across Neural Networks• Convergence of three-dimensional loop-erased random walk in the natural parametrization• Finding or counting all shellings of a simplicial complex• Theoretical Performance Limits of Massive MIMO with Uncorrelated Rician Fading Channels• Computing Vertex-Weighted Multi-Level Steiner Trees• On gradual-impulse control of continuous-time Markov decision processes with multiplicative cost• An Adversarial Approach for Explainable AI in Intrusion Detection Systems• Aliasing effects for random fields over spheres of arbitrary dimension• High-dimensional Log-Error-in-Variable Regression with Applications to Microbial Compositional Data Analysis• Neural probabilistic motor primitives for humanoid control• Variational Selection of Features for Molecular Kinetics• Partial Convolution based Padding• Low-temperature anomalies in structural glasses: impact of jamming criticality• CCNet: Criss-Cross Attention for Semantic Segmentation• Future-State Predicting LSTM for Early Surgery Type Recognition• Attributed Network Embedding for Incomplete Structure Information• SegET: Deep Neural Network with Rich Contextual Features for Cellular Structures Segmentation in Electron Tomography Image• CAPNet: Continuous Approximation Projection For 3D Point Cloud Reconstruction Using 2D Supervision• orthoDr: semiparametric dimension reduction via orthogonality constrained optimization• Spaces of algebraic measure trees and triangulations of the circle• Transformations on Double Occurrence Words Motivated by DNA Rearrangement• 3D human pose estimation in video with temporal convolutions and semi-supervised training
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