The study models commuting distances based on income and finds a power-law distribution.
problem Understanding the relation between income and commuting distances.
method Data from Denmark, UK, and US; power-law distribution; alternative job search model.
result The commuting distance distribution decays as 1/r^3 and is independent of job quality.
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.
Genie optimizes search marketplaces by estimating policy impacts without risky experiments.
problem Optimizing search marketplaces with frequent policy changes and limited randomized experiments.
method Genie uses an open box simulation engine and click calibration model to estimate KPI impacts.
result Genie outperforms existing approaches in optimizing Bing Ads Marketplace.
This paper tackles JSSP with uncertain task durations using DRL.
problem Job Shop Scheduling Problem with uncertain task durations.
method Integrates Graph Neural Networks (GNNs) and Deep Reinforcement Learning (DRL) to generate robust schedules.
result Advances DRL applications to JSSPs, enhancing generalization and scalability.
NeuRewriter learns to choose and rewrite heuristics in combinatorial problems.
problem Time-consuming tuning of heuristics in combinatorial optimization.
method NeuRewriter uses reinforcement learning to learn a policy for picking heuristics and rewriting solutions.
result NeuRewriter outperforms existing methods in various combinatorial tasks.
Proposes a framework for fairness in two-sided marketplaces.
problem Achieving fairness in two-sided marketplaces.
method Developed an end-to-end framework for fairness constraints from both sides of the marketplace, including dynamic aspects.
result Efficacy of the proposed framework demonstrated through simulations.
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.
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.
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 improves job scheduling by accurately predicting runtime classes.
problem Improving scheduling performance by separating short jobs from long jobs.
method Uses a CART classifier trained on Gaussian mixture representations of job runtimes.
result Overall accuracy of 90% for separating short jobs from long jobs.
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 cμ rule scheduling with a preemption phase. result Achieves near-optimal performance guarantees with nearly matching regret bounds.
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.
We consider the role of unobservables, such as differences in search frictions, reservation wages, and productivities for the explanation of wage differentials between migrants and natives. We disentangle these by estimating an empirical general equilibrium search model with on-the-job search due to Bontemps, Robin, an…
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.
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.
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.
Deep learning models improve talent search at LinkedIn.
problem Match candidates to hiring needs using complex feature interactions.
method Deep and representation learning models, including neural network models and learning to rank approaches.
result Improved offline and online evaluation results for talent search systems.
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 …
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.
Auptimizer simplifies hyperparameter tuning for machine learning models.
problem Difficulty and time-consuming hyperparameter tuning for machine learning models.
method General HPO framework that distributes computing resources and integrates various HPO techniques.
result Simplified model tuning and bookkeeping for data scientists.
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.
Improved job recommendations using temporal learning and sequence modeling.
problem Enhancing job recommendation accuracy through complex user-item activity patterns.
method Combining time-based ranking with hybrid matrix factorization and RNN for sequence modeling.
result RNN-based model achieved 5th place in RecSys Challenge 2016.
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.
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.
Predicts promising hyperparameters early to speed up machine learning.
problem Finding optimal hyperparameters is computationally expensive.
method Predict model performance without completing training, using early data.
result Improves performance of random search approach.
Meta-learning improves hyperparameter tuning for XGBoost.
problem Improving hyperparameter tuning for XGBoost models.
method Proposed MeSH algorithm using meta-regressors to guide hyperparameter selection.
result MeSH often finds superior hyperparameter configurations compared to SH and random search.
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.
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…
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.
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 \(T\).
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.
Paper proposes a method to accurately match soft skills in job ads.
problem Matching soft skills in job ads leads to false positives.
method Phrase-matching approach with context-based binary classification.
result LSTM tagging-based input representation achieved highest recall of 83.92%.
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.
Platform learns user types while matching limited supply to demand.
problem Matching limited supply to demand while learning user types.
method Multi-armed bandit framework with capacity constraints.
result Optimal policy characterized in the limit of many jobs per worker.
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.
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…
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.
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.
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.