A scheme incentivizes agents to provide high-quality data early in federated learning.
problem Incentivizing high-quality data in federated learning.
method Characterization of influence on loss function, payment structure design, efficient influence approximation.
result The scheme ensures truthful data reporting as a game-theoretic equilibrium and is robust to mixtures of truthful and heuristic data reports.
A new score measures data reliability without ground truth.
problem Assessing reliability of datasets without access to ground truth.
method Define ground-truth-based orderings and propose Gram determinant score.
result Gram determinant score effectively captures data quality across diverse observation processes.
COBRA addresses strategic behavior in online platforms by ensuring truthful reporting without monetary incentives.
problem Ensuring truthful reporting from strategic agents in online platforms.
method Proposes COBRA, an algorithm for contextual bandits involving strategic agents that disincentivizes strategic behavior.
result COBRA achieves sub-linear regret guarantee and incentive compatibility without monetary incentives.
Paper proposes incentives for federated learning to ensure truthful contributions.
problem Ensuring truthful contributions from decentralized users in federated learning.
method Introduces a scoring rule based framework to incentivize truthful reporting of local hypotheses at a Bayesian Nash Equilibrium.
result Proposed solution verified using MNIST and CIFAR-10 datasets, showing decreasing scores for low-quality hypotheses.
New truthful calibration errors improve model ranking in multiclass prediction.
problem Non-truthful calibration errors can mislead model comparisons.
method Introduced perfectly truthful calibration errors for multiclass predictions.
result Truthful calibration errors preserve decision-theoretic dominance and stabilize model rankings.
Optimal securities lending mechanism incentivizes truthfulness and privacy.
problem Maximizing resource usage in securities lending while ensuring truthful reporting and privacy.
method Bayesian optimal algorithm adapted for differential privacy, combined with market equilibrium dynamics.
result An algorithm that is simultaneously private, approximately optimal, and approximately dominant-strategy truthful.
A technique called 'prior laundering' uses legacy reconstructions to create uncertainty in Bayesian inverse problems.
problem Uncertainty in Bayesian inverse problems when data is uninformative.
method Using an archive of legacy reconstructions to create uncertainty in the posterior distribution, averaging the legacy posterior over measurements.
result The uncertainty reported in the posterior is inherited from the legacy reconstructions, not from the data itself.
This paper tackles sample elicitation for learning systems, introducing a method to incentivize truthful samples.
problem Eliciting credible training samples for complex distributions from humans is challenging.
method Introduces a deep learning aided method to incentivize truthful samples from self-interested and rational agents.
result Achieves approximate incentive compatibility in eliciting truthful samples via accurate estimation of f f f -divergence function. A new mechanism reduces expert belief regret in online forecasting.
problem Minimizing expert belief regret in strategic forecasting.
method Developed a no-regret mechanism for non-myopic experts using online I-ELF.
result Achieved i l d e O ( T N ) ilde{O}(\sqrt{T N}) i l d e O ( T N ) regret for full-information setting. We consider the problem of fitting a linear model to data held by individuals who are concerned about their privacy. Incentivizing most players to truthfully report their data to the analyst constrains our design to mechanisms that provide a privacy guarantee to the participants; we use differential privacy to model in…
New algorithm reduces regret in strategic prediction problem.
problem Designing an IC algorithm with sublinear regret for strategic experts.
method Developed a new algorithm WSU-UX and proved a worst-case regret bound.
result WSU-UX suffers a Ω ( T 2 / 3 ) Ω(T^{2/3}) Ω ( T 2/3 ) lower bound on regret. New model for recovering unverifiable signals from observers in decentralized networks.
problem Verifying the level of service provided by untrusted parties in decentralized networks.
method Formal model of signal elicitation, source identifiability condition, and peer prediction techniques.
result Existence of a strictly truthful mechanism for unverifiable signal recovery.
CheXpert++ improves CheXpert's accuracy and usability for medical radiology reports.
problem Infeasibility of obtaining ground truth labels for medical data.
method BERT-based approximation of CheXpert, addressing speed, differentiability, and probabilistic output.
result Achieves 99.81% parity with CheXpert, significantly faster, differentiable, and probabilistic.
Conformal Alignment ensures trustworthy outputs from foundation models.
problem Ensuring outputs from foundation models align with human values in high-stakes tasks.
method A framework that trains an alignment predictor using reference data to select trustworthy outputs.
result Conformal Alignment accurately identifies trustworthy outputs via lightweight training over moderate reference data.
ARA combines aggregated RAPPOR and Tf-Idf estimation for centralized DP analysis.
problem Gap between local and central DP approaches in terms of data storage, analysis speed, and amount of data.
method Collects RAPPOR reports from multiple clients, pushes them to a Tf-Idf estimation model, and analyzes them for centralized DP.
result Successfully and efficiently analyzed major truth values from multiple clients.
Crowdsourced data helps detect incidents faster, balancing accuracy and practicality.
problem Detecting incidents from crowdsourced data is challenging due to noise and uncertainty.
method CROME (Crowdsourced Multi-objective Event Detection) uses CNN and Pareto optimization.
result The approach outperforms existing methods in incident detection and practicality.
Eye Movement analysis with Hidden Markov Models (EMHMM) is a method for modeling eye fixation sequences using hidden Markov models (HMMs). In this report, we run a simulation study to investigate the estimation error for learning HMMs with variational Bayesian inference, with respect to the number of sequences and the …
Study allocates resources to strategic agents while balancing cost and incentives.
problem Dynamic allocation of reusable resources to strategic agents with private valuations under long-term cost constraints.
method Incentive-aware framework combining epoch-based lazy updates and randomized exploration rounds.
result Achieves i l d e O ( T ) ilde{\mathcal{O}}(\sqrt{T}) i l d e O ( T ) social welfare regret, satisfies all cost constraints, and ensures incentive alignment. If you want to tell people the truth, make them laugh, otherwise they'll kill you. (source unclear) Machine learning and deep learning are the technologies of the day for developing intelligent automatic systems. However, a key hurdle for progress in the field is the literature itself: we often encounter papers that re…
Normalizing flows model atomic solids without needing ground-truth samples.
problem Modeling atomic solids without ground-truth samples.
method Normalizing flows to transform a base distribution into the target solid.
result Excellent agreement between model estimates and literature values for Helmholtz free energy.
New bounds on majority voting's accuracy for multi-class classification problems.
problem Determining the accuracy of majority voting for multi-class classification.
method Analyzing the majority voting function under different voter conditions and distributions.
result The error rate of majority voting exponentially decays or grows with the number of voters under certain conditions.
Paper presents a machine learning method to improve significance tests for misspecified linear models.
problem Misspecification of linear assumptions in social science models leads to inaccurate significance levels.
method Apply machine learning to fit ground truth function, calculate linear approximation, and adjust the estimator.
result The method significantly outperforms linear regression for non-linear ground truth functions.
ALONE uses neural networks to accelerate cine MRI without needing ground truth.
problem Accelerated MRI reconstruction without using ground truth.
method Unsupervised learning of shallow CNNs to approximate MRI patches.
result ALONE outperforms TV and DIC methods in cine MRI reconstruction.
Much work has been done in understanding human creativity and defining measures to evaluate creativity. This is necessary mainly for the reason of having an objective and automatic way of quantifying creative artifacts. In this work, we propose a regression-based learning framework which takes into account quantitative…
Deep learning improves cECG denoising for better cardiac health monitoring.
problem Motion artifacts limit the clinical use of cECG for long-term monitoring.
method End-to-end deep learning architecture trained on motion-corrupted cECG and reference ECG.
result MSE of 0.167 and Cross Correlation of 0.476 for signal denoising.
Universally valid ground truth is almost impossible to obtain or would come at a very high cost. For supervised learning without universally valid ground truth, a recommended approach is applying crowdsourcing: Gathering a large data set annotated by multiple individuals of varying possibly expertise levels and inferri…
Paper introduces metrics to evaluate missing data imputation without ground truth.
problem Handling missing data in time series without ground truth.
method Introduces Wasserstein distance (WD) and Jensen-Shannon divergence (JSD) as metrics to evaluate imputation quality.
result WD and JSD are effective metrics for assessing missing data imputation quality.
RealCause provides a realistic benchmark for causal inference.
problem Lack of a reliable benchmark for comparing causal effect estimators.
method Flexible generative models to create a benchmark that is both ground-truth and realistic.
result Evaluation of over 1500 causal estimators provides evidence for choosing hyperparameters using predictive metrics.
Novel algorithm detects causal macrovariables from high-dimensional data.
problem Leveraging high-dimensional observational datasets for coarse-grained causal models.
method Inspired by information bottlenecks, novel algorithm detects macrovariables and investigates causal relationships through additive noise models.
result Algorithm robustly detects and infers causal relationships in both synthetic and real climate datasets.
Recently developed deep-learning-based denoisers often outperform state-of-the-art conventional denoisers such as the BM3D. They are typically trained to minimize the mean squared error (MSE) between the output image of a deep neural network (DNN) and a ground truth image. Thus, it is important for deep-learning-based …
Detects project management anti-patterns using code and issue data.
problem Detecting project management anti-patterns requires expert judgment and is expensive.
method Convert descriptions to detectable metrics, quantify deviations, and optimize patterns.
result Automatic calibration enhances pattern detection and severity assessment.
Paper proposes a method to recover accurate labels from partially valid data in multi-label learning.
problem Tackles noisy supervision in multi-label learning with partially valid labels.
method Develops a two-stage method that estimates label enrichment and ground-truth confidences.
result Demonstrates improved performance over state-of-the-art PML methods.
Ranking a set of objects involves establishing an order allowing for comparisons between any pair of objects in the set. Oftentimes, due to the unavailability of a ground truth of ranked orders, researchers resort to obtaining judgments from multiple annotators followed by inferring the ground truth based on the collec…
Deep learning for Venus images uses high-res hyperspectral data to simulate ground truth.
problem Lack of accurate ground truth data for training deep neural networks in remote sensing.
method Unmixing high-resolution hyperspectral images to simulate ground truth for training a CNN.
result The model can classify mid-resolution Venus images successfully.
Self-supervised methods learn from noisy data alone, useful for imaging problems.
problem Inferring signals from noisy and incomplete observations.
method Learning a solver from measurement data alone, without ground-truth references.
result Self-supervised methods can learn meaningful estimates from noisy data.
An unsupervised neural network learns event truths from social network data.
problem Estimating event truths from conflicting opinions in social networks.
method Autoencoder learns relationships, Bayesian network models agent reliability and social relationships, variational inference estimates hidden variables and parameters.
result The approach outperforms state-of-the-art methods on real datasets.
InfoSEM infers gene regulatory networks without GT labels, improving performance.
problem Inferring GRNs from gene expression data with high accuracy and avoiding biases.
method InfoSEM uses deep generative models with informative priors (textual gene embeddings).
result InfoSEM outperforms existing models by 38.5% across four datasets.
Synthetic learning improves neonatal brain MRI segmentation robustness.
problem Challenges in neonatal brain MRI segmentation due to image contrast and anatomical variations.
method Synthetic learning model trained on few T2-weighted volumes, then enhanced with motion artifacts and over-segmentation.
result Synthetic learning robust to image contrast and improves segmentation of both T1- and T2-weighted images.
Affinity propagation is an exemplar-based clustering algorithm that finds a set of data-points that best exemplify the data, and associates each datapoint with one exemplar. We extend affinity propagation in a principled way to solve the hierarchical clustering problem, which arises in a variety of domains including bi…
Generative model combines multi-dimensional annotations for more accurate ground truth estimation.
problem Inaccurate ground truth estimation from naive annotators' multi-dimensional annotations.
method Proposes a joint multi-dimensional model for global and time-series annotation fusion using Expectation-Maximization algorithm.
result More accurate ground truth estimates through joint modeling of multiple dimensions.
URC improves model accuracy without ground truth for new data.
problem Improving model accuracy with new, unseen data without ground truth.
method URC observes and corrects model predictions when training data is not representative of field data.
result URC corrects model bias without retraining or ground truth for some subpopulations.
CSP improves time-series forecasting without training, outperforming DeepNPTS in speed and accuracy.
problem Improving probabilistic time-series forecasting without training.
method Mixing empirical and residual draws around a seasonal naive forecast.
result CSP significantly outperforms DeepNPTS on CRPS, normalized mean quantile loss, and coverage metrics.
Estimates classifier errors without ground truth using algebraic geometry.
problem Lack of ground truth in real-world production systems.
method Non-parametric estimation using algebraic geometry to solve the self-assessment problem.
result Accuracy estimators are better than one part in a hundred.
The paper analyzes how noise affects distances in high-dimensional data and when they remain useful.
problem Noise corrupts distances in high-dimensional data, making them unreliable for identifying true nearest and farthest neighbors.
method The paper uses asymptotic probabilistic expressions to characterize noise effects and decomposes data into ground truth and noise components.
result Under certain conditions, empirical neighborhood relations remain truthful even when distance concentration occurs.
Proposes a new imputation method using autoencoders and feedback mechanisms.
problem Missing data undermines the performance of downstream data products.
method Multiple Imputation with Denoising Autoencoders and Metamorphic Truth/Imputation Feedback.
result Outperforms other methods in various missingness mechanisms and data patterns.
New method falsifies causal discovery results without ground truth.
problem Evaluation of causal discovery algorithms without ground truth data.
method Detects incompatibilities between causal graphs learned on different subsets of variables.
result Detection of incompatibilities can falsify wrongly inferred causal relations.
Proposes a method for explaining tabular data using copulas.
problem Lack of ground truth for explainability in complex datasets.
method Uses copulas to specify statistical properties and build intuition.
result Demonstrates improved explainability on logistic regression and correlation use cases.
Improved DLG extracts accurate labels from gradients, overcoming DLG's convergence issues.
problem Private training data leakage from shared gradients in distributed learning systems.
method Proposes iDLG, a simple approach to synthesize accurate labels from gradients.
result iDLG reliably extracts ground-truth labels from gradients, unlike DLG.