This work studies reinforcement learning in the Sim-to-Real setting, in which an agent is first trained on a number of simulators before being deployed in the real world, with the aim of decreasing the real-world sample complexity requirement. Using a dynamic model known as a rich observation Markov decision process (R…
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48 results for “real-world”
Study real-world noisy labels from human annotations for better understanding.
problem Understanding and modeling real-world label noise in machine learning.
method Developed two new benchmark datasets (CIFAR-10N, CIFAR-100N) with human-annotated real-world noisy labels.
result Real-world noisy labels exhibit instance-dependent patterns, not class-dependent as previously assumed.
New model detects crying in real-world settings with improved accuracy.
problem Generalization of cry detection models to real-world environments.
method Evaluated machine learning approaches on a novel dataset of real-world infant crying.
result Improved F1 score of 0.613 for crying event recognition in real-world settings.
Paper tackles AI driving competition challenges with mixed simulation and real-world data.
problem AI algorithms perform poorly in real-world environments compared to simulated ones and vice versa.
method Employed imitation learning on a mixed dataset to train algorithms equally well in all environments.
result Trained algorithms performed well in both simulated and real-world environments.
Reinforcement learning (RL) has proven its worth in a series of artificial domains, and is beginning to show some successes in real-world scenarios. However, much of the research advances in RL are often hard to leverage in real-world systems due to a series of assumptions that are rarely satisfied in practice. We pres…
Myriad offers a testbed for integrating machine learning and trajectory optimization.
problem Real-world trajectory optimization challenges.
method JAX-based testbed with machine learning and trajectory optimization integration.
result End-to-end learning and planning with neural ODEs.
Study reveals how spectral bias affects learnability on real-world data.
problem Understanding how well complex datasets can be learned using kernel methods.
method Use eigenvalues and eigenfunctions from idealized data to reveal spectral bias on real-world data.
result Bound learnability on real-world data using symmetries of realistic kernels.
The paper critiques ε-fairness, showing it can lead to unfair outcomes and proposes a utility-based approach.
problem The limitations of probabilistic fairness metrics in real-world contexts.
method Utility-based approach to measure fairness, addressing the issue of unavailable data on false negatives.
result A utility-based approach uncovers necessary actions to achieve true fairness, contrasting with traditional probability-based evaluations.
Deep reinforcement learning provides a promising approach for vision-based control of real-world robots. However, the generalization of such models depends critically on the quantity and variety of data available for training. This data can be difficult to obtain for some types of robotic systems, such as fragile, smal…
Robust CD method for real-world time series with power-law distributions.
problem Challenges in causal discovery due to noise sensitivity.
method Power-law spectral feature extraction for robust CD.
result Consistently outperforms state-of-the-art alternatives on real-world datasets.
The paper reveals a spinning top geometry in real-world games.
problem Understanding the structure of real-world games.
method Developed a spinning top geometric model and used Nash clustering.
result Real-world games exhibit a spinning top structure with transitive and non-transitive dimensions.
Real-TabPFN: Improving Tabular Foundation Models via Continued Pre-training With Real-World Datacs.LG
Improved tabular models learn better from real-world data.
problem Tabular models perform poorly on real-world datasets when trained only on synthetic data.
method Continued pre-training on a curated set of real-world datasets.
result Real-TabPFN achieves superior predictive accuracy on 29 datasets.
This work shows how to use simulators to learn efficient exploration in real-world RL.
problem Sample complexity of real-world reinforcement learning.
method Coupling exploratory policies learned in simulators with practical approaches.
result Polynomial sample complexity in real world, exponential improvement over direct sim2real transfer.
This work tackles real-world robotic reinforcement learning challenges.
problem Limited success of reinforcement learning in real-world robotics.
method Proposes a system for autonomous real-world learning without instrumentation.
result Demonstrates a complete system that learns without human intervention.
Study finds real-world datasets contain natural experiments that can improve model performance.
problem Detecting natural experiments in real-world datasets for causal inference.
method Synthetic graph simulation and feature selection based on causal links.
result Real-world datasets contain natural experiments that can be exploited for improved model performance.
Mobile network that millions of people use every day is one of the most complex systems in the world. Optimization of mobile network to meet exploding customer demand and reduce capital/operation expenditures poses great challenges. Despite recent progress, application of deep reinforcement learning (DRL) to complex re…
Retail company uses Prophet algorithm for accurate sales forecasting.
problem Accurate sales forecasting in the retail industry.
method Facebook's Prophet algorithm and backtesting strategy.
result Framework demonstrates real-world use case capabilities.
New algorithms improve federated learning accuracy and stability with real-world data.
problem Real-world data diversity and imbalance challenge federated learning.
method Developed new algorithms (FedVC, FedIR) to resample and reweight data.
result Significant improvements in accuracy and stability of federated learning.
This study examines whether tokenized assets improve liquidity and finds significant differences across categories.
problem Improving liquidity for real-world assets through tokenization.
method Examined tokenized real-world assets using Ethereum-based data, measuring liquidity through turnover, active addresses, and active-month indicator.
result Gold-backed tokens show more persistent on-chain activity than Treasury and private-credit-related products, but asset value alone does not reliably predict liquidity.
Study evaluates scalability and real-world impact of disentangled representations.
problem Scalability and real-world impact of disentangled representations.
method New high-resolution dataset and architectures for disentangled representation learning.
result Disentanglement predicts out-of-distribution task performance.
Exploring applications of deep reinforcement learning for real-world autonomous driving systemscs.LG
Deep Reinforcement Learning (DRL) has become increasingly powerful in recent years, with notable achievements such as Deepmind's AlphaGo. It has been successfully deployed in commercial vehicles like Mobileye's path planning system. However, a vast majority of work on DRL is focused on toy examples in controlled synthe…
GANs can learn hierarchical distributions in real-world images efficiently.
problem Understanding and efficiently learning complex, real-world distributions with GANs.
method Formally studying how GANs can learn hierarchically generated distributions close to real-life image distributions using SGDA.
result Training GANs via SGDA can efficiently learn distributions with a 'forward super-resolution' structure, both in sample and time complexities.
Unified framework models multiple financial and insurance term structures.
problem Modeling multiple term structures in various markets.
method Extended Heath-Jarrow-Morton (HJM) approach under real-world probability.
result Characterization of local martingale deflators and existence of affine realizations.
Optimizes control interventions in real-world networks using deep-learning and network science.
problem Optimizing control over socioeconomic networks subject to constraints.
method Integrates optimization tools from deep-learning with network science.
result Characterizes vulnerability of corporate networks to takeovers.
This paper surveys fairness notions in ML and recommends the most suitable one for real-world scenarios.
problem Ensuring ML systems do not discriminate against specific individuals or sub-populations.
method Identifying fairness-related characteristics of real-world scenarios and analyzing the behavior of fairness notions.
result A decision diagram to recommend the most suitable fairness notion for specific setups.
This paper proposes a paradigm shift in the valuation of long term annuities, away from classical no-arbitrage valuation towards valuation under the real world probability measure. Furthermore, we apply this valuation method to two examples of annuity products, one having annual payments linked to a mortality index and…
Extends trading framework to incorporate real-world constraints.
problem Trading strategies in multi-player non-cooperative games with constraints.
method Re-framed as quadratic programming problem, constraints readily incorporated.
result Two-trader equilibria calculated dynamically.
Study evaluates large language models' ability to understand probabilistic real-world distributions.
problem Understanding how LLMs grasp probabilistic knowledge of real-world distributions.
method Developed a benchmark to test LLMs' ability to learn and represent empirical distributions across various domains.
result LLMs perform poorly in understanding real-world statistics and do not naturally internalize these distributions.
In this paper, we propose a new metric to measure goodness-of-fit for classifiers, the Real World Cost function. This metric factors in information about a real world problem, such as financial impact, that other measures like accuracy or F1 do not. This metric is also more directly interpretable for users. To optimize…
The Boolean Satisfiability (SAT) problem is the canonical NP-complete problem and is fundamental to computer science, with a wide array of applications in planning, verification, and theorem proving. Developing and evaluating practical SAT solvers relies on extensive empirical testing on a set of real-world benchmark f…
This paper analyzes and improves active learning techniques for real-world projects.
problem Reducing labelling effort in machine learning models with real-world constraints.
method Systematic study of active learning issues, proposing techniques to address model convergence, annotation error, and dataset imbalance.
result Presentation of two techniques to speed up active learning: partial uncertainty sampling and larger query size.
Study evaluates methods for improving model robustness to various real-world distribution shifts.
problem Improving model robustness to real-world distribution shifts like geographic changes.
method Introduced new datasets and evaluated existing methods on four types of shifts (style, blurriness, location, camera operation).
result Data augmentations and larger models can improve robustness on real-world distribution shifts, contrary to prior claims.
Model-free deep reinforcement learning has been shown to exhibit good performance in domains ranging from video games to simulated robotic manipulation and locomotion. However, model-free methods are known to perform poorly when the interaction time with the environment is limited, as is the case for most real-world ro…
The paper provides guidelines for choosing between SBI methods in complex biological models.
problem Choosing appropriate SBI methods for real-world biological data.
method Comprehensive guidelines and application to agent-based models.
result Statistical SBI methods outperform neural SBI methods with sufficient computational resources.
Research on deep learning generalization in real-world applications.
problem Understanding and improving deep learning generalization in non-i.i.d. real-world data.
method Analyzing deep net generalization, identifying and addressing assumptions and problem settings failures.
result Proposes methods to address failures in deep net generalization for real-world applications.
Although reinforcement learning methods can achieve impressive results in simulation, the real world presents two major challenges: generating samples is exceedingly expensive, and unexpected perturbations or unseen situations cause proficient but specialized policies to fail at test time. Given that it is impractical …
Paper addresses challenges in benchmarking stream learning algorithms with real-world data.
problem Lack of publicly available non-stationary real-world datasets for evaluating stream algorithms.
method Proposes a new public data repository for benchmarking stream algorithms with real-world data.
result Mitigates problems related to dataset choice in experimental evaluation of stream classifiers and drift detectors.
Framework uses physics knowledge to improve spatiotemporal prediction with limited data.
problem Challenges in modeling physical systems with limited real-world data.
method Physics-aware meta-learning with auxiliary tasks, incorporating PDE-independent spatial and temporal modules.
result Framework outperforms in spatiotemporal prediction tasks with limited data.
To the best of our knowledge, this paper presents the first large-scale study that tests whether network categories (e.g., social networks vs. web graphs) are distinguishable from one another (using both categories of real-world networks and synthetic graphs). A classification accuracy of was achieved using a …
Federated learning is a new machine learning paradigm which allows data parties to build machine learning models collaboratively while keeping their data secure and private. While research efforts on federated learning have been growing tremendously in the past two years, most existing works still depend on pre-existin…
A new method learns from synthetic data without needing real-world examples.
problem Learning robust classifiers from limited real-world data.
method A novel setting and algorithm exploiting synthetic data independence.
result Robust classifiers trained on synthetic data generalize well to real-world domains.
A Benchmark Suite for Evaluating Neural Mutual Information Estimators on Unstructured Datasetsstat.ML
Study introduces a benchmark suite for evaluating neural MI estimators on real-world unstructured datasets.
problem Lack of comprehensive evaluation methods for neural MI estimators on real-world unstructured datasets.
method Developed a benchmark suite using same-class sampling and a binary symmetric channel trick.
result Showed accurate manipulation of true MI values of real-world datasets.
Framework for transitioning financial models from risk-neutral to real-world measure.
problem Transitioning financial models from risk-neutral to real-world measure to better reflect market dynamics and investor preferences.
method Leveraging probability theory, specifically Girsanov's theorem, to incorporate real-world dynamics into financial models.
result Validation of the robustness and practical relevance of the methodology through case studies involving financial forecasts and stress tests.
Study compares Interval Regression models and their performance.
problem Regression models struggle with interval target values.
method Comprehensive review and comparative analysis of existing Interval Regression models.
result No single model is universally optimal for all scenarios.
Learning to control robots directly based on images is a primary challenge in robotics. However, many existing reinforcement learning approaches require iteratively obtaining millions of robot samples to learn a policy, which can take significant time. In this paper, we focus on learning a realistic world model capturi…
Algorithm calibrates predictions for covariate shift using domain adaptation.
problem Uncertainty estimates overestimate certainty when real-world data differs from training data.
method Uses importance weighting and learns a feature map to equalize distributions.
result Outperforms existing approaches in calibrated prediction when covariate shift occurs.
Modern CATE models often fail to outperform a trivial zero-effect predictor, highlighting significant challenges.
problem Lack of robustness in CATE models when applied to real-world data.
method Large-scale benchmark study using diverse observational sampling strategies and novel statistics.
result 62% of CATE estimates have higher MSE than a trivial zero-effect predictor, indicating poor performance.
Enhances portfolio optimization under uncertainty using robust multi-objective methods.
problem Uncertainties in real-world portfolio optimization scenarios.
method Robust multi-objective optimization with benchmark comparisons.
result More reliable and adaptable portfolio strategies for market uncertainties.