Proposes a bandit framework for dynamic user incentives.
problem Designing personalized incentives for users with evolving preferences.
method Combines greedy matching, UCB, and Markov chain theory.
result Algorithm provides theoretical regret bounds and practical examples.
Predict bike flow at station-level with multi-graph CNNs.
problem Fine-grained bike flow prediction for station-level management.
method Multi-graph convolutional neural network model.
result Reduces 25.1% and 17.0% prediction error in New York City and Chicago.
This study analyzes how weather impacts bike sharing usage in Washington D.C.
problem Understanding how weather affects bike sharing usage patterns.
method Gathered bike usage and weather data, used k-means clustering algorithm to identify clusters.
result Weather significantly impacts bike usage, with temperature and precipitation being the most influential factors.
Paper improves bike-sharing demand prediction by adapting to changing patterns.
problem Improving bike-sharing demand prediction under temporal domain shifts.
method Gen-ROTDA, a robust optimal transport-guided residual domain adaptation framework.
result Gen-ROTDA achieves the lowest MAE and is the best OT-family method on average.
A new model predicts bike-sharing station demands using graph neural networks.
problem Predicting hourly bike-sharing station demands in large networks.
method Graph Convolutional Neural Network (GCNN-DDGF) with various adjacency matrices.
result GCNNrec-DDGF model outperforms other models in predicting bike-sharing demands.
Dockless bike sharing systems need effective bike flow prediction models.
problem Imbalanced and dynamic use of bikes leads to mandatory rebalancing operations.
method Divide urban area into regions, model spatio-temporal bike flows, extract traffic patterns, and predict bike flows.
result Interpretable bike flow prediction model provides valuable insights into bike flow analysis.
Study improves cross-modal bike-share and transit demand prediction.
problem Cross-modal ripple effects in urban transportation demand.
method Transfer learning and stacked LSTM models for cross-modal demand prediction.
result Transfer learning models outperform unimodal models in cross-modal demand prediction.
Proposes AtCoR for predicting bike station usage, improving station network reconfiguration.
problem Challenges in predicting new bike stations due to lack of historical data.
method AtCoR algorithm that predicts both existing and new bike stations using station-centered heatmaps and historical correlations.
result AtCoR outperforms existing models in predicting bike station usage.
The paper optimizes sensor selection for network time series data.
problem Optimizing sensor selection for network time series data with minimal error.
method Data-driven strategies to turn off sensors or select a sampling set of nodes.
result Proposes and compares various data-driven strategies for sensor selection.
Proposes a new model for more accurate demand forecasting considering dynamic contextual information.
problem Traditional methods fail to capture spatio-temporal and dynamic contextual dependencies in demand forecasting.
method Integrates temporal, relational, spatial, and dynamic contextual dependencies using a Context Integrated Graph Neural Network (CIGNN).
result CIGNN outperforms state-of-the-art baselines in multi-step ahead demand forecasting.
Platform learns user policies to avoid abandonment.
problem Personalized policies risk user abandonment.
method Thresholded learning model for personalized policies.
result Optimal policies and feedback impact results.
IntraLayer connects digital finance platforms efficiently.
problem Inefficient interconnectivity in digital finance.
method Core infrastructure and adaptive fiscal policy.
result Optimised transactional efficiency for various agents.
ApolloRL offers a platform for RL research in autonomous driving.
problem Improving reinforcement learning for autonomous driving.
method Open platform with training, simulation, and evaluation components.
result Baseline agents perform well in the ApolloRL environment.
Privacy-preserving deep learning for medical data across distributed platforms.
problem Data leakage in medical platforms.
method Separates hidden layers; first layer local, others centralized for training.
result Improved learning performance with all data used during training.
Proposes a model to incentivize exploration in web platforms with payments.
problem Learning and social welfare goals of web platforms with myopic users.
method Contextual bandit model with payments to incentivize exploration.
result Achieves sublinear regret while maximizing cumulative social welfare.
Federated learning platform for drug discovery without sharing data.
problem Lack of secure collaboration in drug discovery.
method Industry-scale federated learning platform using cryptographic aggregation.
result Generated new scientific discoveries in drug discovery.
CompactNet optimizes CNN models for resource-limited platforms.
problem Challenges in implementing CNN models on resource-limited platforms.
method Guided by a simulator, CompactNet progressively trims a pre-trained network to achieve target speedup while maintaining accuracy.
result Achieves up to 1.8x kernel computation speedup on embedded platforms.
Robotics: Rolling robots on a moving platform can be controlled.
problem Controlling the motion of rolling robots atop a moving platform.
method Developed a mathematical model and demonstrated simulations.
result Platform acceleration can control robot's heading and motion.
Method generates resource-optimized ML models for different platforms.
problem Deployment of deep learning models on edge platforms requires model reduction and retraining.
method Conditional Neural Architecture Search using Generative Adversarial Networks (GAN)
result Successfully generates resource-optimized ML models for different platforms.
PAMS is a Python-based platform for simulating artificial markets.
problem Simulating complex market behaviors for research and education.
method Developed as a Python-based simulator with deep learning integration.
result Demonstrated effectiveness through agent price prediction studies.
Unity offers a flexible platform for AI research with rich simulations.
problem Limited configurability and complexity in existing simulation environments.
method Proposed a taxonomy of simulation platforms and demonstrated Unity's suitability.
result Unity enables rich, complex simulations for AI research.
Collectives can manipulate learning platforms by coordinated data submission, requiring strategic assessments and algorithms.
problem Collectives can influence learning platforms by altering data, posing risks and requiring strategic planning.
method Developed a theoretical and algorithmic framework to understand and mitigate collective manipulation of learning platforms.
result Demonstrated the need for strategic assessments and implementable coordination algorithms to prevent collective manipulation.
AI platforms disrupt investment by personalizing deal sourcing and insights.
problem Lack of scalable, personalized, and privacy-compliant deal sourcing and insights solutions.
method Development of in-house AI platforms that interact directly with funds and learn from interactions.
result AI platforms provide smarter, personalized use cases for funds, offering a competitive advantage.
Horizon is Facebook's open RL platform for large, slow feedback datasets.
problem Training RL models with large, slow feedback datasets.
method End-to-end RL platform with workflows, data preprocessing, distributed training, etc.
result RL models trained with Horizon significantly outperform supervised learning systems.
Simulates DeLend Platform behavior to optimize operational parameters.
problem Optimizing the DeLend Platform's operational parameters.
method Agent-based simulations to connect and test different agent sets.
result Estimates how key variables respond to different policies.
Interactive platform for knot invariant computation and identification.
problem Efficient computation and identification of knot invariants.
method Unified web platform using Feynman ribbon diagrams and tensor networks.
result First platform combining construction, evaluation, computation, and identification.
A method for fast, accurate cross-temporal forecasts using machine learning.
problem Inconsistent forecasts across different levels of platform data.
method Non-linear hierarchical forecast reconciliation using machine learning.
result Automated direct production of reconciled forecasts for high-frequency decision making.
A new platform predicts object movements without needing specific context.
problem Predicting the next position of movable objects.
method λ-Architecture for batch and stream analytics, combining for improved accuracy.
result Combining λ-Architecture parts improves overall accuracy and performance.
Transfer learning improves understanding of users on new Web platforms.
problem Lack of knowledge about novel phenomena on new Web platforms due to data sparsity.
method TraNet, a transfer learning-based approach, adapts knowledge from one domain to another.
result TraNet outperforms other approaches in transferring knowledge about users across different Web platforms.
Dynamic assortment problem on two-sided platform with unknown parameters
problem Optimizing assortment display in an online platform with incomplete information and heterogeneous customers
method Data-driven algorithm that learns choice parameters while optimizing revenue
result Worst-case regret grows polylogarithmically over time
For the last two decades, most financial markets have undergone an evolution toward electronification. The market for corporate bonds is one of the last major financial markets to follow this unavoidable path. Traditionally quote-driven i.e., dealer-driven) rather than order-driven, the market for corporate bonds is st…
Secure social recommendation framework using secret sharing.
problem Privacy concerns and reluctance to share social data in recommender systems.
method Secret Sharing based Matrix Multiplication (SSMM) protocol for secure data sharing and collaborative recommendation.
result SeSoRec framework improves recommendation performance and is secure.
Adaptive display exposure improves e-commerce platform revenue.
problem Optimize ad display for better revenue and user experience.
method Constrained Markov Decision Process (psCMDP) and two-level reinforcement learning.
result Our approach increases platform revenue under constraints.
Benchmarking TPU, GPU, and CPU for deep learning models.
problem Improving performance in deep learning training.
method ParaDnn benchmark suite for FC, CNN, and RNN models on TPU, GPU, and CPU.
result TPU, GPU, and CPU have unique strengths for different types of models.
Optimizes impression allocation for e-commerce platforms using reinforcement learning.
problem Short-term and long-term returns are not optimized in current e-commerce platform allocation mechanisms.
method Formal lifecycle model of products, reinforcement learning framework, first principal component based permutation, novel experiences generation method.
result Significant improvement in platform and participant health with optimized impression allocation.
DFKI Cabin Simulator tests visual monitoring functions in vehicles.
problem Validating novel human-vehicle interfaces and driver assistance systems.
method Driving simulator with in-cabin mock-up and camera system.
result Validation of in-cabin monitoring functions for advanced driver assistance and automated driving.
Unified platform for statistical and machine learning in bioinformatics.
problem Workflow inefficiencies in using multiple tools for data analysis.
method Automated hyperparameter optimization, feature importance analysis, statistical tests.
result Accelerates biological discovery workflows with methodological soundness.
CodeReef enables sharing ML models across platforms efficiently.
problem Sharing and deploying ML models across different systems efficiently.
method Developed an open platform to share ML components, automate deployment, and benchmark models.
result Demonstrated efficient deployment and benchmarking of ML models across diverse platforms.
Study optimizes crowdfunding platform offerings based on customer behavior.
problem Maximizing crowdfunding platform revenue through optimal product assortment.
method Multinomial logit model and machine learning methods (multivariate regression, classification) for revenue prediction.
result Optimal assortments can significantly increase platform revenue.
Platform learns user preferences to avoid abandonment due to marketing fatigue.
problem Maximizing platform's cumulative payoff while avoiding user abandonment.
method Dynamic sequential choice model to balance exploration and exploitation.
result Proposed algorithm achieves optimal regret bound for online learning.
fintech-kMC simulates financial platforms for AI/ML model validation.
problem Validation of AI/ML models in real-world financial applications.
method Agent-based model with kinetic Monte Carlo engine.
result Generates realistic synthetic data for testing AI/ML models.
Study predicts P2P lending platform failures using machine learning.
problem Predicting failures of P2P lending platforms in China.
method Used machine learning models with filter and wrapper methods, forward selection, and backward elimination.
result Identified robust variables for predicting platform failures with high AUC and F1 scores.
OpenML is an online machine learning platform where researchers can easily share data, machine learning tasks and experiments as well as organize them online to work and collaborate more efficiently. In this paper, we present an R package to interface with the OpenML platform and illustrate its usage in combination wit…
Study personalizes user experience to maximize rewards with patience budget.
problem Maximizing rewards for a platform while respecting user patience.
method Proposes bandit algorithms for sequential choice with feedback models.
result Upper and lower bounds on regret of order O(N2/3) and Ω(N2/3). A monitoring procedure improves machine learning forecasts for digital platforms.
problem Maintaining accurate and stable forecasts for data streams at digital platforms.
method Developed a monitoring procedure to determine when to retrain machine learning algorithms.
result Monitor-based retraining produces accurate forecasts compared to benchmarks.
BIDIFAC integrates multi-platform, multi-cohort data for shared and unique patterns.
problem Integration of multi-platform, multi-cohort data for shared and unique patterns.
method BIDIFAC integrates bidimensionally linked matrices into four components: globally shared, row-shared, column-shared, and single-matrix structural components.
result BIDIFAC reveals shared and unique patterns of variability in multi-platform, multi-cohort data.
We introduce COCO, an open source platform for Comparing Continuous Optimizers in a black-box setting. COCO aims at automatizing the tedious and repetitive task of benchmarking numerical optimization algorithms to the greatest possible extent. The platform and the underlying methodology allow to benchmark in the same f…
ImJoy simplifies deep learning for biomedical research.
problem Computational barriers limit deep learning adoption in biomedical research.
method Open-source browser-based platform for deep learning.
result Facilitates widespread reuse of deep learning solutions.