A new RL method handles uncertainty and constraints in real-time optimization.
problem Real-time optimization under process uncertainty and constraints.
method Chance-constrained reinforcement learning to handle probabilistic state constraints.
result Satisfies process constraints with high probability in real-time.
Paper optimizes industrial refrigeration using adaptive exploration.
problem Challenges in optimizing real-time industrial processes with unknown characteristics and safety constraints.
method Adaptive and explorative real-time optimization framework with Gaussian process uncertainty quantification.
result Approach increases energy efficiency of refrigeration process, approximating complete information solutions.
Deep model predicts robot trajectories in real time.
problem Real-time robot trajectory prediction with low latency.
method Deep conditional generative model trained with SGDVB.
result More accurate long-term predictions with lower latency.
ARTEO algorithm optimizes safety-critical systems with uncertainty.
problem Decision-making under uncertainty with safety constraints in real-time optimization.
method ARTEO algorithm uses multi-armed bandits as a mathematical programming problem subject to safety constraints, learning unknown characteristics through exploration and incorporating uncertainty quantification.
result ARTEO achieves less cumulative regret with accurate and safe decisions.
MLDemon monitors ML systems post-deployment, improving reliability with real-time performance estimates and expert labels.
problem Ensuring reliability of machine learning systems post-deployment, especially when user inputs differ from training data.
method Integrates unlabeled and on-demand labeled data to monitor ML model performance in real-time, deciding when to acquire expert labels.
result Outperforms existing approaches in temporal datasets with diverse distribution drifts, providing theoretical optimality for distribution drifts.
New algorithm for maximizing submodular functions in real-time data changes.
problem Maximizing submodular functions under dynamic constraints.
method Randomized algorithm with O(k2) amortized update time. result 4-approximate solution to submodular maximization problem.
Investigates safe decision-making in interactive environments.
problem Learning the best safe decision in real-time systems.
method Reduces to a constrained linear bandits problem, proposes adaptive experimental design-based algorithm.
result First results on best-arm identification in linear bandits with safety constraints.
This work addresses real-time constraints in Monte Carlo simulations.
problem Real-time constraints in Monte Carlo simulations, especially in SMC algorithms.
method Proposes an anytime framework using a continuous-time Markov jump process to manage real-time computing budgets.
result Eliminates length bias in the final state's distribution of MCMC algorithms under real-time constraints.
Optimizes RTB bidding without exploration, improving performance under various budgets.
problem Lack of clear evaluation and generalization issues in RTB systems.
method Maximum entropy principle and conditional independence structures to train a model that generalizes to unseen budget conditions.
result Significantly improved performance under various budget settings compared to baselines.
Model learns and plans in real-time under constraints for robotic systems.
problem Challenges in applying reinforcement learning to robotic systems, especially handling continuous state and action spaces, time/resource budget, and hard constraints.
method Combines Gaussian Process regression and Receding Horizon Control. Uses sparse spectrum Gaussian Processes for incremental model updates from sensory data.
result Demonstrates benefits of online learning on autonomous racing tasks and reusability of learned dynamics.
Research explores unsupervised methods for detecting vessel behavior changes in real-time data streams.
problem Detecting shifts in vessel behavior for maritime traffic monitoring.
method Investigates unsupervised and semi-supervised change detection methods.
result Identifies shifts in vessel behavior for unusual events detection.
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.
GraPhyR uses GNNs to optimize power grid reconfiguration in real-time.
problem Optimizing power grid reconfiguration for reliability and efficiency.
method Physics-informed Graph Neural Network (GNN) framework.
result GraPhyR learns to optimize DyR tasks efficiently.
LAD-BNet improves real-time energy forecasting on edge devices.
problem Real-time energy forecasting on edge devices for smart grid optimization and intelligent buildings.
method Hybrid neural architecture combining temporal lag exploitation and TCN with dilated convolutions.
result 14.49% MAPE at 1-hour horizon with 18ms inference time on Edge TPU, 8-12x faster than CPU.
A real-time adaptive background subtraction framework using a rejection cascade of Gaussians.
problem Real-time background subtraction with pixel-wise modeling trade-offs.
method Decompose Gaussian Mixture Model into an adaptive cascade of Gaussians (CoG).
result 4-5x speed-up and 17% accuracy improvement over baseline.
Combines machine learning and optimization for real-time decision-making.
problem Optimizing decisions in contextually constrained problems.
method Generative model combining interior point methods and adversarial learning.
result Generative model produces optimal decisions with in-sample and out-of-sample guarantees.
SkyGP improves Gaussian process scalability for real-time learning.
problem Scalability issues with exact Gaussian processes for streaming data.
method Streaming kernel-induced progressively generated Gaussian process experts (SkyGP).
result SkyGP maintains performance guarantees while improving scalability.
CoCoPIE shows AI can run on regular devices without special hardware.
problem Real-time AI on mobile devices without special hardware.
method Compression-compiler co-design using CoCoPIE framework.
result Real-time AI applications on off-the-shelf mobile devices.
New method allows real-time audio synthesis using non-causal convolutions.
problem Real-time audio synthesis limitations due to offline model constraints.
method Post-training reconfiguration of non-causal models for real-time buffer-based processing.
result Non-causal streaming models can be transformed from offline-trained models without quality loss.
In online advertising, display ads are increasingly being placed based on real-time auctions where the advertiser who wins gets to serve the ad. This is called real-time bidding (RTB). In RTB, auctions have very tight time constraints on the order of 100ms. Therefore mechanisms for bidding intelligently such as clickth…
A novel one-class classifier fusion method for robust anomaly detection.
problem Fundamental challenges in ensemble-based anomaly detection.
method Locally adaptive learning with dynamic ℓp-norm constraints and interior-point optimization.
result Significantly improved computational efficiency and superior performance across diverse anomaly types.
WamOL uses PINNs to efficiently calibrate IVS from sparse data.
problem Calibrating time-dependent IVS from sparse market data.
method Physics-Informed Neural Networks (PINNs) with adaptive reweighting.
result WamOL outperforms in calibrating intraday IVS from uneven data.
Anticipation-RNN generates music with user-defined constraints.
problem Interactive music generation with user-defined constraints.
method Introduces Anticipation-RNN, a novel architecture that allows user-defined positional constraints.
result Demonstrates efficient generation of melodies satisfying user-defined constraints.
A new algorithm detects changes in high-dimensional data efficiently under sampling constraints.
problem Real-time monitoring of high-dimensional streaming data with limited sampling.
method Incorporates multi-armed bandit approaches into sequential change-point detection.
result Proposes TSSRP algorithm for efficient detection of changes.
Study improves self-driving safety in dynamic environments.
problem Safe self-driving in non-stationary urban settings.
method Neurosymbolic Meta-Reinforcement Lookahead Learning (NUMERLA).
result Self-driving agents can adapt safely in real-time.
Proposes an efficient algorithm for mHealth that makes real-time physical activity suggestions.
problem Training complex models efficiently in real-time mHealth applications.
method Streamlined empirical Bayes procedure for fitting linear mixed effects models.
result Improves accuracy and speed over state-of-the-art approaches in mHealth applications.
xYOLO speeds up object detection on low-end hardware for humanoid soccer robots.
problem Real-time object detection on resource-constrained devices like Raspberry Pi.
method Adaptation of YOLO CNN model to achieve faster inference speed.
result Achieved 9.66 FPS on Raspberry Pi 3 B, 70x faster than Tiny-YOLO.
AES learns feasible domains in unbounded spaces with bounded query budget.
problem Learning feasible domains in unbounded input spaces with limited query budget.
method Active Expansion Sampling (AES) progressively expands knowledge of the input space, switching between learning decision boundaries and searching for new feasible domains.
result AES has a misclassification loss guarantee within the explored region, independent of iterations or labeled samples.
Optimizes memory and computation in wearable systems.
problem Minimizing resource usage in real-time classification for wearable systems.
method Hierarchical SVM classifier structure and memory optimization techniques.
result Up to 56% reduction in memory storage for activity recognition.
Paper proposes unsupervised learning for optimizing deep neural networks in time-sensitive applications.
problem Inaccurate optimization solutions from supervised learning for real-time applications.
method Proposes unsupervised deep learning to learn latent function with optimization constraints as supervision.
result DNN can optimize variable problems in ultra-reliable communications without supervision labels.
BAT benchmark for autobidding tasks in RTB auctions.
problem Lack of comprehensive datasets and benchmarks for autobidding.
method Developed a benchmark for two auction formats, implemented robust baselines.
result Provides a framework for developing and refining autobidding algorithms.
New algorithm explains neural network predictions in real-time.
problem Interpreting deep neural network predictions.
method Streaming algorithm for combinatorial maximization with weakly submodular objective.
result First theoretical guarantee for this class of functions.
Paper designs distributed CNN execution for IoT units.
problem Memory and processing constraints in IoT units.
method Optimization problem to minimize latency, considering memory and processing constraints.
result Design supports multiple sources and CNNs in a single IoT system.
This paper proposes Relational Similarity Machines (RSM): a fast, accurate, and flexible relational learning framework for supervised and semi-supervised learning tasks. Despite the importance of relational learning, most existing methods are hard to adapt to different settings, due to issues with efficiency, scalabili…
This review explores probabilistic forecasting methods in evolving energy markets.
problem Volatility and uncertainty in renewable energy markets require probabilistic forecasting for risk assessment.
method Traces evolution from Bayesian and distribution-based approaches to conformal prediction.
result Probabilistic forecasting offers a more comprehensive approach to risk assessment and market participation.
The paper proposes a new method to forecast winning prices in real-time bidding.
problem Accurately forecasting winning prices in real-time bidding with limited data.
method The paper introduces a heteroscedastic fully parametric censored regression approach and a mixture density censored network.
result The proposed method significantly improves winning price forecasting compared to existing methods.
New method uses neural nets to control systems safely with disturbances.
problem Designing safe control laws for systems with disturbances.
method Imitation learning to train neural network controllers that satisfy CBF constraints.
result Demonstrated on a unicycle model with external disturbances.
Piano Genie lets non-musicians improvise on a piano.
problem Improvising on the piano for non-musicians.
method Trained recurrent neural network autoencoders with discrete bottlenecks to learn mappings between user inputs and piano pieces.
result Piano Genie allows users to improvise on the piano with musically meaningful constraints.
A deep learning approach for efficient power control in wireless video transmissions.
problem Optimizing power control for real-time wireless video transmissions with quality constraints.
method Proposes a learning-based approach using a deep neural network to solve the non-convex power control problem.
result The deep neural network can quickly provide optimal power levels for given channel conditions.
Energy is a limited resource which has to be managed wisely, taking into account both supply-demand matching and capacity constraints in the distribution grid. One aspect of the smart energy management at the building level is given by the problem of real-time detection of flexible demand available. In this paper we pr…
Efficiently estimates quantile regression under memory constraints.
problem Inference in quantile regression with limited memory.
method Divide-and-conquer with iterative refinement.
result Asymptotic normality and efficiency of the estimator.
New RL framework improves real-time control performance.
problem Real-time RL systems assume static states, leading to suboptimal outcomes.
method Introduces a new real-time RL framework where states and actions evolve simultaneously.
result RTAC algorithm outperforms existing state-of-the-art algorithms in real-time and non-real-time settings.
AdaKoop efficiently models nonlinear dynamics from nonstationary data streams.
problem Capturing nonlinear dynamics in nonstationary data streams with computational efficiency.
method Koopman operator theory and probabilistic framework for streaming data.
result AdaKoop outperforms state-of-the-art methods in real-time forecasting accuracy and efficiency.
ExDAG solves DAG learning problems with low structural Hamming distance.
problem Learning DAGs with low structural Hamming distance under identifiability assumptions.
method Mixed-integer quadratic programming (MIQP) with branch-and-bound-and-cut algorithm and lazy constraints.
result ExDAG guarantees global convergence and provides a real-time quality assessment.
A new DSP bidding strategy maximizes revenue under budget constraints.
problem Optimizing ad selection and bid price in RTB auctions.
method Formalized as a constrained optimization problem, proposed augmented MMKP solution.
result Our strategy outperforms state-of-the-art methods in real applications.
Proposes a simple algorithm to generate data similar to real series.
problem Generating data similar to real series with constraints.
method Random permutation of Monte Carlo generated numbers, accepted if objective function is minimized.
result Demonstrated by generating S\&P 500 log-returns.
New deep learning method for real-time regression analysis.
problem Real-time regression analysis for time series data.
method Novel deep learning algorithms for real-time regression analysis.
result Demonstrated real-time regression analysis for time series data.
HOLMES improves real-time model serving for ICU patients, balancing accuracy and speed.
problem Real-time model serving is crucial in ICU due to urgency and cost.
method Online model ensemble serving framework for healthcare applications.
result HOLMES achieves high accuracy and sub-second latency for model ensemble serving.