The study examines how individual ability and project benefit influence cooperation in public goods games.
problem Maximizing cooperation in public goods games with varying individual contributions and benefits.
method A generalized public goods game model incorporating individual ability and project benefit.
result The upper limit of individual benefit promotes cooperation, while the upper limit of individual contribution inhibits it.
A new method sorts projects using Quicksort and Bradley-Terry model for uncertain long-term benefits.
problem Selecting projects with uncertain long-term benefits.
method Combining Quicksort and Bradley-Terry model for ranking projects based on uncertain long-term benefits.
result Proposed methods outperform existing aggregation methods and can be combined with sampling techniques.
The article first describes characteristics of major infrastructure projects. Second, it documents a much neglected topic in economics: that ex ante estimates of costs and benefits are often very different from actual ex post costs and benefits. For large infrastructure projects the consequence is cost overruns, benefi…
Report assesses Edinburgh tram project's risk management.
problem Projects often exceed costs and timelines, underperforming intended benefits.
method Describes and evaluates risk assessment and management for the Edinburgh tram.
result Careful consideration of cost, schedule, and benefit risks is crucial for megaprojects.
This paper explores how theories of the planning fallacy and the outside view may be used to conduct quality control and due diligence in project management. First, a much-neglected issue in project management is identified, namely that the front-end estimates of costs and benefits--used in the business cases, cost-ben…
This article presents results from the first statistically significant study of cost escalation in transportation infrastructure projects. Based on a sample of 258 transportation infrastructure projects worth US$90 billion and representing different project types, geographical regions, and historical periods, it is fou…
The cost-benefit analysis formulates the holy trinity of objectives of project management - cost, schedule, and benefits. As our previous research has shown, ICT projects deviate from their initial cost estimate by more than 10% in 8 out of 10 cases. Academic research has argued that Optimism Bias and Black Swan Blindn…
The paper proposes a two-stage approach for high-dimensional data prediction and feature selection.
problem Predictive inference and feature selection for high-dimensional data with limited samples.
method Two-stage approach: first, build a predictive model; second, select a minimal feature subset.
result The projective approach provides an excellent balance between sparsity and predictive accuracy.
20 questions to improve AI research transparency, replicability, ethics, and effectiveness.
problem Lack of transparency, replicability, ethical concerns, and effectiveness in AI research.
method Presenting 20 questions to guide project planning and post-hoc evaluation.
result Facilitating a discussion to develop an international consensus framework.
This paper argues, first, that a major problem in the planning of large infrastructure projects is the high level of misinformation about costs and benefits that decision makers face in deciding whether to build, and the high risks such misinformation generates. Second, it explores the causes of misinformation and risk…
New algorithm LSTD(λ)-RP uses random projections and eligibility traces for efficient reinforcement learning.
problem Policy evaluation in high-dimensional feature spaces with linear function approximation.
method Proposes LSTD(λ)-RP algorithm combining random projections and eligibility traces. result Demonstrates improved performance and better error bounds compared to prior methods.
The aim of this note is to take benefit of the foam nature of the Khovanov-Kuperberg algebras to compute the Grothendieck groups of their categories of finitely generated projective modules. The computation relies on the Hattori-Stallings trace and some geometrical properties of foams in a solid torus.
Most learning methods with rank or sparsity constraints use convex relaxations, which lead to optimization with the nuclear norm or the ℓ1-norm. However, several important learning applications cannot benefit from this approach as they feature these convex norms as constraints in addition to the non-convex rank a…
Neural model parses non-projective dependency trees efficiently.
problem Parsing non-projective dependency trees.
method Probabilistic parsing model using neural representations and Kirchhoff's Matrix-Tree Theorem.
result State-of-the-art parsing performance on nine datasets.
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…
A new method approximates the Sliced-Wasserstein distance without random projections.
problem Efficiently approximating the Sliced-Wasserstein distance for machine learning applications.
method Utilizing the concentration of measure phenomenon to develop a deterministic approximation.
result The approximation error goes to zero as the dimension increases, under a weak dependence condition.
Data balancing reduces variance in machine learning models.
problem Reduction of variance in machine learning models.
method Non-asymptotic statistical bound and eigenvalue decay of Markov operators.
result Data balancing across modalities and sources reduces variance.
Optimal projections enhance Naive Bayes classification.
problem Improving Naive Bayes classification accuracy.
method Projection pursuit to find optimal linear projections.
result The approach substantially outperforms other models.
This article presents results from the first statistically significant study of traffic forecasts in transportation infrastructure projects. The sample used is the largest of its kind, covering 210 projects in 14 nations worth US$59 billion. The study shows with very high statistical significance that forecasters gener…
Discussing AI's difficulty and physics' simplicity, suggesting AI benefits from physics principles.
problem AI difficulty compared to physics simplicity.
method Drawing on physical intuition and theoretical physics to improve AI.
result AI and physics principles are strongly coupled through sparsity.
Our project improves function approximation by seeking nonlinear analogues.
problem High-dimensional models are difficult to quantify due to sparse or expensive observations.
method Active Manifolds approach, seeking nonlinear projections to approximate the function.
result Improved estimation accuracy and accessible visualization.
Because of the prominent position of urban rail in reducing urban transport-related problems, such as congestion and air pollution, insights into the costs of possible new urban rail projects is very relevant for those involved with cost estimations, policy makers, cost-benefit analysts, and other target groups. Knowle…
The paper introduces DP algorithms using random projections and sign random projections for improved privacy in machine learning.
problem Improving differential privacy in machine learning applications.
method Developed algorithms based on random projections and sign random projections, focusing on individual differential privacy (iDP) and standard differential privacy (DP).
result DP-SignOPORP and iDP-SignRP achieve superior performance in differential privacy, especially for small epsilon values.
Climate-contingent finance helps adapt to uncertain climate risks.
problem Uncertainty in future climate scenarios makes proactive adaptation less feasible.
method Underwrite climate adaptation projects with repayment based on future climate scenarios.
result Optimal financing reduces over- and under-preparation risks.
MimicGAN improves robustness of image projections under corruption.
problem Robust projection onto image manifolds is challenging due to corruptions.
method Proposes corruption mimicking to robustly project images.
result Significantly more robust than PGD under various corruptions.
Method estimates joint probability density from samples using low-rank decomposition and random projections.
problem Estimating joint probability density from limited samples.
method Low-rank tensor decomposition, dictionaries, and Radon transforms.
result Algorithm outperforms previous methods in estimating synthetic probability densities.
Study evaluates UK CDC schemes, finding intergenerational cross-subsidies in flat-accrual schemes and dynamic-accrual schemes can reduce but not eliminate them.
problem Intergenerational cross-subsidies in UK CDC schemes, particularly in flat-accrual schemes.
method Comparison of flat-accrual and dynamic-accrual CDC schemes, analysis of performance and level of cross-subsidies.
result Dynamic-accrual schemes can reduce but not eliminate intergenerational cross-subsidies, while flat-accrual schemes often have significant cross-subsidies.
Paper projects GP basis functions using tensor networks to reduce complexity.
problem Efficiently approximating Gaussian process regression with a large number of basis functions.
method Develops a method using tensor networks to approximate GP regression with an exponential number of basis functions without exponential computational complexity.
result Shows efficient GP regression on an 18-dimensional benchmark data set.
Paper presents a fast and adaptive filter for SI suppression in full-duplex transceivers.
problem Self-interference suppression in full-duplex transceivers with nonlinearity.
method Adaptive projected subgradient method (APSM) in a reproducing kernel Hilbert space (RKHS).
result The proposed method achieves favorable digital SIC performance compared to benchmarks.
This paper advances theory on the process of collaboration between entities and its implications on the quality of services, information, and/or products (SIPs) that the collaborating entities provide to each other. It investigates the scenario of outsourced IS projects (such as custom software development) where the e…
New approach combines likelihood and adversarial losses for better precipitation predictions.
problem Spatially inconsistent precipitation projections from likelihood-based models.
method Fuses likelihood-based and adversarial losses for generative models.
result Improves spatial consistency in precipitation downscaling.
Research examines GMIB and reset options in variable annuities.
problem Understanding the value and rationality of GMIB and reset options.
method Exploration of various parameters affecting GMIB value and calculation of critical future interest rates for reset option rationality.
result Insight into how future market performance and interest rates influence policyholder and insurer actions.
Distributions over permutations arise in applications ranging from multi-object tracking to ranking of instances. The difficulty of dealing with these distributions is caused by the size of their domain, which is factorial in the number of considered entities (n!). It makes the direct definition of a multinomial dist…
This research develops a new framework to measure AI investment returns considering both gains and risks.
problem Traditional ROI calculations fail to account for AI's dual impact on risk reduction and new exposures.
method Integrates ISO 42001 and regulatory exposure into a comprehensive financial framework using risk quantification methods.
result Accurate AI investment evaluation requires modeling both productivity gains and risk exposures.
Project extends emotion recognition database and trains neural networks for categorical and dimensional emotions.
problem Understanding and recognizing emotions for human-computer interaction, safety, and medical research.
method Training CNN + RNN models with emotion category and valence/arousal representations, comparing three model types.
result Categorical emotion recognition performance improves with combined model training.
Optimized algorithms for online learning with linear constraints improve performance and provide worst-case analysis.
problem Improving online learning algorithms for constrained optimization problems.
method Developed an optimized variant of an online Frank-Wolfe algorithm and used semidefinite programming for numerical analysis.
result No pure online Frank-Wolfe algorithm can have a better regret guarantee than O(T^3/4) without additional assumptions.
Sparse JL with higher sparsity improves feature hashing accuracy.
problem Efficiently reducing high-dimensional feature vectors to lower dimensions.
method Sparse Johnson-Lindenstrauss transform with varying sparsity levels.
result Sparse JL with sparsity greater than 1 provides better norm preservation.
Graph Prolongation Convolutional Networks improve model performance in microtubule bending simulations.
problem Improving prediction accuracy in coarse-grained mechanochemical simulations of microtubule bending.
method Defines a novel ensemble Graph Convolutional Network model using optimized linear projection operators to map between graph scales.
result Graph Prolongation-Convolutional Network outperforms other GCN ensemble models in predicting microtubule bending potential energy.
Dynamic treatment effects estimated over time using covariate balancing.
problem Estimating treatment effects in panel data with dynamic treatments.
method Dynamic covariate balancing with potential local projections.
result Established inferential guarantees for the proposed method.
Efficient ANN search for sparse embeddings in ads targeting.
problem Efficiently searching near neighbors in sparse data for applications like ads targeting.
method Graph-based ANN algorithms (HNSW, chi-square two-tower model, Sign Cauchy Projections).
result Sparse embeddings and ANN algorithms improve efficiency in EBR applications.
Random projection improves deep learning performance on high-dimensional data.
problem Training deep neural networks on high-dimensional data is infeasible.
method Prepending the network with an input layer initialized with random projection matrices.
result Neural networks with RP layers achieve competitive or improved performance on high-dimensional datasets.
Bayesian approach to portfolio selection reduces pessimism in frequent trading.
problem Tackling the challenge of estimating drift in Merton's portfolio selection model.
method Bayesian distributionally robust control with nonlinear Wasserstein projections.
result Reduced pessimism and improved performance in frequent rebalancing compared to existing methods.
Algorithm COOL coordinates online learners to improve user preference learning.
problem Learning user preferences in a multi-task setting with sequential data.
method COOL algorithm coordinates task-specific online learners via weighted projections onto a convex set.
result COOL algorithm achieves better user preference learning with reduced computation/communication costs.
Accelerated optimization methods improve robustness and privacy in estimation.
problem Improving robustness and privacy in estimation methods.
method Accelerated gradient methods based on Frank-Wolfe and projected gradient descent, with tailored learning rates and Nesterov's momentum.
result Reduction in iteration complexity, leading to stronger statistical guarantees.
Two novel methods improve network embedding for completely-imbalanced labels.
problem Improving network embedding for networks with completely-imbalanced labels.
method Two novel semi-supervised network embedding methods: RSDNE and RECT.
result Experimental results show the superiority of the proposed methods.
Neural networks learn adaptive kernels that improve approximation and representation.
problem Improving neural network performance in approximating and representing functions from data.
method Dynamic reproducing kernel Hilbert space (RKHS) approach.
result Gradient flow in neural networks learns an adaptive RKHS representation and performs global least-squares projection.
HiPPO framework optimizes memory compression for sequential data.
problem Incremental representation of cumulative history in sequential data.
method Optimal polynomial projections for online function approximation.
result HiPPO-LegS achieves state-of-the-art accuracy on MNIST.
A hybrid framework reduces ML complexity on edge devices.
problem Limited memory and energy on edge devices.
method Compressed data collection and tailored deep learning network.
result Significant reduction in computational complexity and memory.