ReliefE ranks features faster and better in high-dimensional data.
problem Feature ranking in high-dimensional spaces.
method Adapting Relief algorithms to manifold embeddings.
result ReliefE outperforms traditional Relief algorithms in feature ranking.
Estimates funding impact from an algorithmic relief rule, finding little effect on hospital activities.
problem Evaluating the impact of algorithmic policy decisions.
method Developed a treatment-effect estimator using algorithmic decisions as instruments.
result Funding from an algorithmic relief rule had little effect on COVID-19-related hospital activities.
This paper reviews Relief-based feature selection algorithms.
problem Complex biomedical data with high feature dimensions.
method Relief-based algorithms (RBAs) for efficient feature selection.
result RBAs strike a balance between computational efficiency and sensitivity to complex patterns.
Model analyzes mortgage relief during financial hardship.
problem Understanding and optimizing mortgage relief during financial distress.
method Agent-based model of households and servicers.
result Model replicates real-world mortgage studies and provides fine-grained insights.
Basel III introduces new capital charges for CVA. These charges, and the Basel 2.5 default capital charge can be mitigated by CDS. Therefore, to price in the capital relief that CDS contracts provide, we introduce a CDS pricing model with three legs: premium; default protection; and capital relief. If markets are compl…
A scheme for UAVs to borrow spectrum from terrestrial networks for disaster relief.
problem Spectrum shortage in UAV networks for critical missions.
method Hierarchical spectrum sharing model with relaying UAVs and reinforcement learning.
result Autonomous spectrum management improves QoS and prolongs UAV lifetime.
BELIEF method efficiently selects features from Big Data sets.
problem Feature selection for large datasets in Big Data.
method Distributed feature weighting algorithm using redundancy elimination.
result BELIEF method ranks millions of features in parallel efficiently.
In this paper we introduce a new feature selection algorithm to remove the irrelevant or redundant features in the data sets. In this algorithm the importance of a feature is based on its fitting to the Catastrophe model. Akaike information crite- rion value is used for ranking the features in the data set. The propose…
Machine learning improves ASD diagnosis accuracy.
problem Early detection and treatment of ASD.
method Machine learning, specifically SMO-SVM and Relief Attributes.
result SMO-SVM classifier outperforms other algorithms in ASD detection.
IMMIGRATE selects features with interaction terms using margin-based weights.
problem Unclear differentiation of feature interactions from marginal effects.
method Includes and trains weights for interaction terms, applies large margin principle, considers robustness and local/global information.
result Achieves state-of-the-art results on several tasks.
This paper examines the economic benefits of monthly gratuity options.
problem Evaluating the economic advantages of monthly gratuity options over traditional ones.
method Quantitative analysis comparing tax relief benefits to savings or loan repayment.
result Monthly gratuity options provide economic benefits through tax relief.
RGRR allocates between QQQ and DIA based on relative states, improving Sharpe and CAGR.
problem Optimizing ETF allocation between QQQ and DIA for better risk-adjusted returns.
method Screened relative and macro states, globally screened interactions, fixed position mapping, walk-forward validation.
result RGRR improves Sharpe and CAGR compared to 100% QQQ and 50/50 QQQ-DIA allocations.
Proposes semi-supervised feature ranking for handling high-dimensional, unlabeled data.
problem Handling high-dimensional, unlabeled data in machine learning.
method Tree ensembles and Relief family algorithms for semi-supervised feature ranking.
result Semi-supervised feature ranking outperforms supervised methods across most datasets.
A framework for cost of belief revision in uncertain agents.
problem Cost of revising beliefs in uncertain agents.
method Axiomatic framework for transport-based belief costs, postulates P0 and P1.
result Cost metric is conformally reweighted by Fisher information, leading to a cost floor diverging at certainty.
Improved agnostic boosting with better sample efficiency.
problem Agnostic boosting's poor sample efficiency compared to Empirical Risk Minimization.
method Leverages sample reuse across rounds, guarantees better generalization.
result Substantially more sample-efficient agnostic boosting algorithm.
Between 2003 and 2015 the prices of apartments in Hong Kong (adjusted for inflation) increased by a factor of 3.8. This is much higher than in the United States prior to the so-called subprime crisis of 2007. The analysis of this speculative episode confirms the mechanism and regularities already highlighted by the pre…
The cost of belief changes with precision and is a hyperbolic geometry.
problem The cost of belief changes with precision and is a hyperbolic geometry.
method The cost of belief changes with precision and is a hyperbolic geometry.
result The cost of belief changes with precision and is a hyperbolic geometry.
Algorithmic insurance tackles financial risks from AI errors, proving CVaR-optimal thresholds reduce tail risk.
problem High-stakes AI errors lead to heterogeneous losses, challenging traditional insurance assumptions.
method Analyzed binary classification performance to tail risk exposure, using CVaR to quantify extreme losses.
result CVaR-optimal thresholds reduce tail risk up to 13-fold compared to accuracy maximization.
New insights into how to inspect and learn from multi-stage processes and AI reasoning.
problem Understanding how to attribute outcomes to early stages in multi-stage operations and AI reasoning.
method Information-theoretic analysis and mathematical proofs of four key results.
result Uniform checkpoint spacing is minimax-optimal for inspection design under homogeneous signal attenuation.
This paper proposes a continuous timing strategy for growth vs. defensive style allocation.
problem Dynamic allocation of growth and defensive ETF baskets using macro-market timing signals.
method Continuous smooth score combining multiple factors, mapped to G/D weights, smoothed with EWMA.
result Continuous style timing strategy outperforms static benchmarks in risk-adjusted returns.
New method reduces deep learning complexity on IoT devices.
problem High computational complexity limits deep learning on IoT devices.
method Local quantization region for low-bit data representation.
result Models retain accuracy with reduced computational complexity.
System builds Somali ASR for UN humanitarian efforts.
problem Developing ASR for under-resourced Somali language.
method Acoustic model training with annotated speech, neural architectures, language model data augmentation, acoustic data perturbation.
result Best system achieved 53.75% word error rate.
Paper presents a model for identifying informative COVID-19 tweets.
problem Identifying informative COVID-19 tweets on Twitter.
method Leveraged transformers (RoBERTa, XLNet, BERTweet) trained in Semi-Supervised Learning (SSL) setting.
result Achieved F1 score of 0.9011 on test set, ranking 7th on leaderboard.
Unified RMOT framework for non-modelable risk factors reduces audit bounds.
problem Infinite audit bounds for exotic derivatives pricing with sparse market data.
method Rough Martingale Optimal Transport (RMOT) with rough volatility regularization.
result Finite, explicit, and asymptotically tight extrapolation bounds for non-modelable risk factors.
Proposes efficient multi-fidelity Bayesian optimization for deep neural network hyperparameter tuning.
problem Time-consuming validation error evaluation for hyperparameter tuning in deep neural networks.
method Introduces trace-aware knowledge-gradient acquisition function and a provably convergent optimization method.
result Outperforms state-of-the-art alternatives for hyperparameter tuning of deep neural networks.
CNN-DTW system improves keyword spotting in under-resourced languages.
problem Keyword spotting in nearly zero-resource languages.
method Multilingual bottleneck features, CNN-DTW, DTW template matching, convolutional neural network.
result Multilingual BNFs improve CNN-DTW by 10.9%.
Develops a two-layer model to design mortgage assistance products.
problem Designing effective mortgage assistance products to improve household resilience.
method Two-layer approach: simulation and optimization.
result Shows how the approach can design and evaluate mortgage assistance products.
The paper uses CNNs on Sentinel-2 imagery to assess landslide risks.
problem Landslide risk assessment and prediction.
method Image augmentation, 3-D CNNs, satellite imagery.
result CNNs achieve significantly better accuracy than baseline.
New method estimates active subspaces for jump-discontinuous functions.
problem Estimating active subspaces for discontinuous functions like ABMs.
method Extending active subspaces to discontinuous functions, using Gaussian process.
result Identifies important parameters in ABM simulations of refugee movement.
Deep learning model extracts location references from tweets during emergencies.
problem Challenges in extracting reliable location information from tweets during crises.
method Convolutional Neural Network (CNN) based model.
result Achieved high accuracy in extracting location references from tweets.
Improved SPSA-FSR method for feature selection and ranking in machine learning.
problem Feature selection and ranking in machine learning.
method Improved Simultaneous Perturbation Stochastic Approximation (SPSA) method with Barzilai and Borwein (BB) method for non-monotone iteration gains.
result Dramatically reduces the number of iterations required for convergence without impacting solution quality.
Improved ASR-free keyword spotting in under-resourced languages.
problem Dynamic time warping for keyword spotting in languages with limited resources.
method Multilingual bottleneck extractor and correspondence autoencoder integration.
result More than 11% absolute improvement in ROC AUC over MFCCs.
DeepBark improves tree bark re-identification accuracy.
problem Challenging illuminations make tree bark hard to re-identify.
method Used a large dataset of 2,400 bark images to train DeepBark and SqueezeBark.
result DeepBark achieves 87.2% mAP in retrieving relevant bark images.
End-to-end deep learning detects emotions in real-life emergency calls.
problem Recognizing emotions in real-life emergency call center recordings.
method Used an end-to-end deep learning architecture trained on IEMOCAP and CEMO datasets.
result Obtained 45.6% Unweighted Accuracy Recall on CEMO with 4 classes, 76.9% on 2 classes (Anger, Neutral).
New metric m-coherence measures gradient alignment during training, revealing surprising memorization patterns.
problem Measuring and understanding the alignment of per-example gradients during training.
method Introducing m-coherence as a metric to study gradient alignment, showing its advantages over existing metrics. result Training with random labels leads to high m-coherence, indicating common patterns even when generalization is not possible. Bayesian model uses mobile data to assess business resilience after hurricanes.
problem Evaluating economic impact of extreme shocks on businesses.
method Bayesian structural time series model with mobile phone data.
result Estimates business resilience after hurricanes, revealing key characteristics.
This paper shows hedging algorithms improve performance in repeated matrix games.
problem Improving multi-agent learning algorithms in repeated matrix games.
method Develops and experiments with hedging algorithms combining a top-level and a set of basic algorithms.
result Well-selected hedging algorithms outperform previous MAL algorithms on repeated matrix games.
Examines algorithmic modeling across three cultures.
problem Tackles algorithmic modeling in different cultural contexts.
method Uses parametric regressions, interpretable algorithms, and complex algorithms.
result Extension of Leo Breiman's thesis to include cultural differences.
Meta-algorithm selection aims to choose the best algorithm selector for a given problem instance.
problem Selecting the best algorithm selector for a specific problem instance.
method Apply algorithm selection to the selection of other algorithms (meta-algorithm selection).
result Meta-algorithm selection can be beneficial in some cases but faces challenges in solving the meta-level problem.
Proposes CLRS benchmark to evaluate algorithmic reasoning.
problem Difficulty in transferring results across publications due to targeted algorithmic data.
method Develops a comprehensive benchmark covering various algorithmic tasks.
result Demonstrates performance of algorithmic reasoning baselines on the CLRS benchmark.
Combines multiple bandit algorithms to create a nearly optimal single algorithm.
problem Designing a single bandit algorithm that performs nearly as well as the best individual algorithm in a stochastic environment.
method Develops two general corralling algorithms that achieve favorable regret guarantees.
result The regret of the corralling algorithms is no worse than the best individual algorithm's performance.
New algorithms improve stochastic optimization and online learning efficiency.
problem Efficient optimization and online learning algorithms for stochastic problems.
method Accelerated randomized coordinate descent algorithms.
result Significantly less per-iteration complexity and better regret performance.
The exchange algorithm is studied for its convergence and asymptotic variance.
problem Theoretical limitations of the exchange algorithm in sampling from doubly-intractable distributions.
method Theoretical analysis of the exchange algorithm's convergence speed and asymptotic variance.
result The exchange algorithm converges at a geometric rate and satisfies a Central Limit Theorem.
New algorithms optimize algorithm parameters in online settings with reduced computational costs.
problem Optimizing algorithm parameters in online settings with volatile and discontinuous losses.
method Developed semi-bandit optimization algorithms that leverage extra information to reduce computational costs.
result Achieved regret bounds as good as full-information feedback with significantly less computational effort.
Bayesian networks (BN) are used in a big range of applications but they have one issue concerning parameter learning. In real application, training data are always incomplete or some nodes are hidden. To deal with this problem many learning parameter algorithms are suggested foreground EM, Gibbs sampling and RBE algori…
Parallel algorithm finds sparse solutions for nonconvex problems.
problem Nonconvex sparsity-regularized rank minimization.
method Parallel best-response algorithm with exact line search.
result Guaranteed convergence to a stationary point.
No algorithm outperforms uniform sampling in A/B testing.
problem Identifying the best arm in A/B testing with fixed budget.
method Introducing consistent and stable algorithms, deriving lower bounds, and proving optimality of uniform sampling.
result No algorithm performs better than uniform sampling in A/B testing.
Improves algorithm selection for thousands of candidates using dyadic features.
problem Selecting the best algorithm from a large set of candidates for specific problems.
method Proposes extreme algorithm selection (XAS) with dyadic feature representation.
result Improves significantly over current state of the art in various metrics.