Statistical models of economic distributions lead to Boltzmann distributions rather than a Pareto power law. This result is supported by two facts: 1. the distributions of income, car sales, marriages or jobs are a matter of chances and luck and not of reason! 2. Data for property, automobile sales, marriages and job m…
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48 results for “job market”
Inspired by the unsupervised learning or self-organization in the machine learning context, here we attempt to draw `learning curve' for the collective behavior of job-seeking `zero-intelligence' labors in successive job-hunting processes. Our labor market is supposed to be opened especially for university graduates in…
Comparative Companies' Stock Valuation through Financial Metrics and its Social Implicationsq-fin.PM
Dolby has the best financial health, but competition for patents could create jobs.
problem Comparing stock valuation of companies using financial metrics.
method Analysis of financial statements over three years.
result Dolby has stable profit margins and generates billions in revenue.
We consider the problem faced by a service platform that needs to match limited supply with demand but also to learn the attributes of new users in order to match them better in the future. We introduce a benchmark model with heterogeneous "workers" (demand) and a limited supply of "jobs" that arrive over time. Job typ…
A framework predicts employment status for students considering unconscious biases.
problem Unconscious biases hinder college students' job hunting.
method Developed a framework MAYA using GAN, LSTM, and bias regularization.
result Framework effectively predicts employment status with bias consideration.
JPLink uses machine learning to match jobs with RIASEC labels.
problem Matching jobs with RIASEC labels requires significant manual effort.
method JPLink uses text content and O*NET knowledge to assign RIASEC labels to jobs.
result JPLink outperforms conventional baselines in matching jobs with RIASEC labels.
Paper introduces a new job recommendation method using candidate job selection progression.
problem Traditional job recommendation methods are either filter-based or feature-based, limiting serendipitous and cold-start recommendations.
method Uses machine learning to analyze candidate job selection progression and derive latent competencies.
result Achieved best click-through rate in a real-world job recommender system.
Study schedules jobs with unknown holding costs to minimize expected cumulative cost.
problem Minimizing expected cumulative holding costs of jobs with unknown parameters.
method Learning-based rule scheduling with a preemption phase.
result Achieves near-optimal performance guarantees with nearly matching regret bounds.
Educational site for AI training at INSA Toulouse.
problem Adapting AI training to job market needs.
method Basic math, statistical learning, self-training tutorials, game.
result Students trained in self-training and practical AI.
We introduce a probabilistic model of labor markets for university graduates, in particular, in Japan. To make a model of the market efficiently, we take into account several hypotheses. Namely, each company fixes the (business year independent) number of opening positions for newcomers. The ability of gathering newcom…
Study detects illegal discrimination by employers using correspondence experiments.
problem Detecting illegal discrimination by individual employers based on protected characteristics.
method Correspondence experiments, bounding higher moments of causal effects, decision rules for investigation.
result 85% of jobs contacting both white and black applicants are likely to discriminate.
Study on scheduling jobs with unknown types, achieving sublinear excess cost.
problem Optimizing job scheduling with unknown job types and varying durations.
method Design of algorithms for non-preemptive and preemptive scenarios, proving lower bounds.
result Preemptive algorithms can significantly outperform non-preemptive ones when job types have distinct durations.
Model predicts real-time job applicant numbers for regional economic analysis.
problem Real-time economic analysis using alternative data.
method Mixed-Frequency Aggregate Learning (MF-AGL) model.
result Model accurately predicts regional labor market conditions and economic status changes.
Sequence-to-sequence models predict resource usage for co-scheduled jobs in data centers.
problem Challenges in co-scheduling jobs due to resource interference and inefficiencies.
method Sequence-to-sequence models based on recurrent neural networks for workload interference prediction.
result Models accurately forecast resource usage trends from job profiles, improving scheduling decisions.
New job recommendation system improves job seekers' welfare through field experiments.
problem Current job recommendation systems focus on clicks and applications, not job seekers' welfare.
method Developed a job-search model with two dimensions: utility and success probability. Conducted field experiments to validate model predictions.
result Welfare-optimal job recommendation algorithms outperform existing approaches and perform close to the benchmark.
Griffin automatically discovers job slowdown causes in cloud platforms without labeled data.
problem Detecting and resolving job slowdowns in cloud-based platforms is labor-intensive and error-prone.
method Griffin uses regression to predict job runtime and interpretable model features to rank potential causes.
result Griffin discovers slowdown causes consistent with expert validation in a fraction of the time.
Employers actively look for talents having not only specific hard skills but also various soft skills. To analyze the soft skill demands on the job market, it is important to be able to detect soft skill phrases from job advertisements automatically. However, a naive matching of soft skill phrases can lead to false pos…
Predicts and classifies computational jobs for efficient resource allocation in cloud centers.
problem Efficiently scheduling and assigning resources to computational jobs in cloud centers.
method Applied LSTM neural network for job arrival prediction and BIRCH clustering for job classification.
result Improved accuracy in predicting and classifying computational jobs compared to existing methods.
We introduce a toy probabilistic model to analyze job-matching processes in recent Japanese labor markets for university graduates by means of statistical physics. We show that the aggregation probability of each company is rewritten by means of non-linear map under several conditions. Mathematical treatment of the map…
Pareto index induced from the scale of companiesphysics.soc-ph
Employing profits data of Japanese companies in 2002 and 2003, we confirm that Pareto's law and the Pareto index are derived from the law of detailed balance and Gibrat's law. The last two laws are observed beyond the region where Pareto's law holds. By classifying companies into job categories, we find that companies …
Separating the short jobs from the long is a known technique to improve scheduling performance. In this paper we describe a method we developed for accurately predicting the runtimes classes of the jobs to enable this separation. Our method uses the fact that the runtimes can be represented as a mixture of overlapping …
Algorithm solves job acceptance problem with random arrivals and values.
problem Decision-making under random job arrivals and values with limited acceptance.
method Proposes Non-Parametric Sequential Allocation (NPSA) algorithm.
result Expected reward converges to optimality as sample size increases.
Paper develops multilingual job classification for ISCO and KZiS.
problem Classifying job advertisements for accurate occupation coding.
method Transformer architecture for hierarchical multi-class classification.
result Hierarchical structure improves prediction accuracy by 1-2 percentage points.
TonY simplifies distributed ML job management.
problem Managing distributed ML jobs is complex and resource-intensive.
method TonY is an open-source orchestrator for distributed ML jobs.
result TonY simplifies distributed ML job management.
The paper proposes an online algorithm for network resource allocation with reduced costs.
problem Optimizing resource allocation and job transfers in a network of servers.
method Randomized online algorithm based on the exponentially weighted method.
result The algorithm achieves sub-linear regret, indicating improved efficiency over time.
We discuss the distribution of commuting distances and its relation to income. Using data from Denmark, the UK, and the US, we show that the commuting distance is (i) broadly distributed with a slow decaying tail that can be fitted by a power law with exponent and (ii) an average growing slowly as a power …
System automates discovery and classification of training videos for career progression.
problem Difficulties in planning and navigating career paths due to changing job requirements and emerging sectors.
method Extracted educational videos, built a machine learning classifier, and optimized probability thresholds.
result Significant improvements in model performance by incorporating video attributes.
New framework values ESOs with multiple exercises and job termination risk.
problem Valuing ESOs with complex exercise patterns and job termination risk.
method Fourier transform, finite differences, and maturity randomization methods.
result Analytic formulae for ESO costs under various conditions.
DL2 uses deep learning to optimize resource allocation in DL clusters.
problem Efficient resource scheduling for deep learning clusters is challenging.
method DL2 combines supervised learning and reinforcement learning to dynamically allocate resources.
result DL2 reduces average training completion time by 44.1% compared to fairness scheduler.
Decima uses machine learning to automatically generate efficient scheduling policies.
problem Scheduling data processing jobs on distributed clusters is complex and requires tuning for each workload.
method Decima employs reinforcement learning and neural networks to learn workload-specific scheduling policies without human intervention.
result Decima improves average job completion time by at least 21% compared to hand-tuned heuristics.
Graph neural networks-based Scheduler for Production planning problems using Reinforcement Learningcs.LG
GraSP-RL uses graph neural networks to improve job shop scheduling.
problem Capturing machine-unit-job sequence relationships and managing state space growth.
method Graph neural networks for feature extraction, reinforcement learning for decision-making, decentralized optimization.
result GraSP-RL outperforms existing methods in minimizing makespan for complex production environments.
The paper optimizes dynamic scheduling for ring architectures in deep learning training.
problem Optimizing deep learning training times with ring architectures.
method Formulated a non-convex, non-linear, NP-hard integer programming problem and developed a doubling heuristic.
result Dynamic scheduling can significantly reduce job completion times in ring architectures.
A Problem of Finite-Horizon Optimal Switching and Stochastic Control for Utility Maximizationmath.OC
Paper tackles utility maximization with job-switching and retirement constraints.
problem Maximizing utility with job-switching and retirement constraints.
method Dual-martingale approach and double obstacle problem theory.
result Characterization of optimal job-switching strategy and wealth boundaries.
Algorithm stabilizes queues in asymmetric systems with unknown service rates.
problem Stabilizing queues in multi-class multi-server systems with unknown service rates.
method Proposes UCB and Thompson Sampling algorithms to stabilize queues while learning service rates.
result Achieves system stability with an average queue length bound of \(O(\min\{N,K\}/ε)\) for large time horizon \(T\).
Minimizing the Weighted Number of Tardy Jobs: Data-Driven Heuristic for Single-Machine Schedulingcs.LG
A heuristic minimizes tardy jobs' total weight on single-machine scheduling.
problem Minimizing tardy jobs' total weight on single-machine scheduling.
method Data-driven heuristic combining machine learning and problem-specific characteristics.
result Significantly outperforms state-of-the-art in optimality gap and adaptability.
Deep RL learns effective job shop scheduling rules from raw features.
problem Designing effective priority dispatching rules for job shop scheduling is challenging.
method End-to-end deep reinforcement learning using Graph Neural Networks.
result Agent learns high-quality dispatching rules from raw features and generalizes well to unseen instances.
Paper presents algorithm for optimal job selection with dynamic scoring.
problem Optimal job assignment in a sequential selection process with dynamic scores.
method Developed using dynamic programming, with extensions for partial and no-information cases.
result Algorithm allows for optimal job assignment with limited information.
Machine learning biases in job recommendations can lead to unfair outcomes.
problem Biased recommendations from recommender systems in job matching.
method Addressing biases at various stages of recommender systems training and deployment.
result Techniques can reduce bias in job recommendations, ensuring fair outcomes.
We present our solution to the job recommendation task for RecSys Challenge 2016. The main contribution of our work is to combine temporal learning with sequence modeling to capture complex user-item activity patterns to improve job recommendations. First, we propose a time-based ranking model applied to historical obs…
Deep learning predicts employment changes and industry health.
problem Forecasting short-term employment changes and assessing long-term industry health.
method LSTNet, a multi-scale deep learning model, processes multivariate time series data.
result LSTNet outperforms baseline models in most sectors, especially stable ones.
The paper uses ML to predict oil rate post-HF, comparing it to engineers' predictions.
problem Predicting oil rate post-hydraulic fracturing.
method Data-driven model using ML techniques on fracturing job data.
result ML predictions outperform engineers' predictions.
Proposes a deep hybrid model for better recommendation systems.
problem Limited studies on hybrid recommender systems and the need for more advanced approaches.
method Integrates deep learning with ID embeddings and auxiliary features for improved recommendation.
result Improves recommendation results over deep learning models using ID embeddings.
Optimizes resource allocation in a network with random job requests.
problem Minimizing costs while satisfying job requests within a budget.
method Formalizes as a repeated game, proposes an online saddle-point algorithm.
result Upper bounds for regret and constraint violations are derived.
The paper analyzes team formation on online platforms, tackling the exploration vs. exploitation dilemma.
problem Matching workers with tasks on online platforms, especially complex ones.
method Analyzed two settings: strongest member vs. weakest member, using regret bounds and optimal algorithms.
result Established fundamental regret bounds and designed near-optimal algorithms for team matchings.
Proposes a new IPW-based ranking metric for two-sided markets.
problem Addressing bias in implicit user feedback in two-sided markets.
method Extends IPW estimator to two-sided markets, addressing position bias.
result Proposed estimator is unbiased for ground-truth ranking metric.
Optimal transport strategy reduces gender bias in job recommendation systems.
problem Mitigating gender biases in AI-driven job recommendation systems.
method Model agnostic optimal transport strategy applied to multi-class neural networks.
result Reduced undesirable algorithmic biases in job recommendation tasks.
In this paper, we implement and test two types of market-based models for European-type options, based on the tangent Levy models proposed recently by R. Carmona and S. Nadtochiy. As a result, we obtain a method for generating Monte Carlo samples of future paths of implied volatility surfaces. These paths and the surfa…
Employee stock options (ESOs) are American-style call options that can be terminated early due to employment shock. This paper studies an ESO valuation framework that accounts for job termination risk and jumps in the company stock price. Under general Lévy stock price dynamics, we show that a higher job termination ri…