Method finds influential training samples for GBDT models efficiently.
problem Finding influential training samples for GBDT models.
method Leave-one-out retraining, extending to non-parametric GBDT ensembles, and approximations.
result Efficiently finds influential training samples for GBDT models.
Influence sketching identifies influential samples in large datasets.
problem Finding important samples in large-scale regression models.
method Developed a scalable version of Cook's distance using random projections.
result Deleting high-influence samples reduces predictive accuracy significantly.
Develops a framework to test excessive influence of small data subsets.
problem Identifying when small data subsets significantly impact model conclusions.
method Formalizes the concept of most influential sets, deriving influence formulas and extreme value distributions.
result Allows rigorous hypothesis testing for excessive influence, resolving contested findings.
Proposes second-order influence functions for identifying influential groups in test-time predictions.
problem Identifying influential groups in test-time predictions for black-box models.
method Second-order approximations of the effect of removing a group of training samples on model predictions.
result Improves the correlation between computed influence values and ground truth values for linear models.
MISS identifies influential subsets in ML models.
problem Capturing collective influence of training samples.
method Analyzed and compared influence-based greedy heuristics and adaptive versions.
result Adaptive heuristics can better capture sample interactions.
For any matrix A in R^(m x n) of rank ρ, we present a probability distribution over the entries of A (the element-wise leverage scores of equation (2)) that reveals the most influential entries in the matrix. From a theoretical perspective, we prove that sampling at most s = O ((m + n) ρ^2 ln (m + n)) entries of the ma…
Improved real-time visualizations of conversation turns using dynamic attention weights.
problem Uniform attention weights in sequential analysis tasks prevent meaningful visualization.
method Developed a method to track changes in turn importance over time.
result More informative real-time visuals confirmed by human reviewers.
Algorithm detects influential observations in high-dimensional data.
problem Challenges in identifying influential observations in high-dimensional datasets.
method Three-step algorithm based on expectiles and asymmetric correlations.
result Higher detection power than competing methods.
New model detects influential users in hidden social networks.
problem Detecting influential users in networks with unknown structure.
method Developed a multi-task low rank linear influence model.
result Successfully identifies influential users for specific topics.
Identifies influential users in collaborative filtering systems.
problem Impact of influential users on collaborative filtering systems.
method Formalized influence, identified influential users, analyzed impact across domains.
result Identified and characterized influential users and analyzed their impact.
A model predicts influential nodes in complex networks by considering indirect interactions.
problem Identifying influential nodes in complex networks using indirect interactions.
method Proposes MOGen, a multi-order generative model that considers all indirect influences up to a maximum distance.
result MOGen consistently outperforms network models and path-based approaches in predicting influential nodes.
The study identifies influential factors in VAE representations.
problem Identifying and supervising influential factors in VAE representations without manual intervention.
method Mutual information as a necessary indicator to discover influential factors; design an algorithm for calculating mutual information.
result Mutual information influences the lower bound of VAE's reconstruction error and downstream classification task.
This study uses complex networks to analyze influential spreaders and their effects on different market sectors.
problem Existing methods failed to distinguish between positive and negative influences of market sectors.
method LIEST (Local Influential Effects for Specific Target) method using complex network analysis.
result LIEST effectively distinguishes positive and negative influences of market sectors during different periods.
P&C combines multiple perturbed graphs to improve influential spreader detection.
problem Ineffective algorithms are unstable to small network perturbations.
method Creates multiple perturbed graphs, applies scoring function to each, and combines results.
result P&C significantly improves influential spreader detection without extra cost.
Proposes an algorithm for data cleansing without domain knowledge.
problem Improving machine learning model accuracy without extensive domain knowledge.
method Infers influential instances by retracing SGD steps and incorporating intermediate models.
result Accurately infers influential instances for models trained with SGD.
A method for finding most influential sets reduces a complex problem to a sequence of simpler top-k problems.
problem Identifying most influential subsets in complex models.
method Reduces the problem to a sequence of top-k problems using Dinkelbach's method. result The method returns a globally optimal set for the univariate ratio objective, including partial linear models.
Paper ranks influential Tor Darknet onion domains using content features.
problem Measuring influence of criminal onion domains in Tor Darknet.
method Content-based features from multiple sources, Learning-to-Rank approach.
result Listwise approach outperforms other methods with NDCG of 0.95 for top-10.
Paper develops a method to highlight influential transitions for off-policy evaluation.
problem Validating off-policy evaluation in high-stakes settings.
method Highlight influential transitions, develop influence functions for Q-evaluation.
result Method can identify limitations and make evaluation more robust.
Study improves sample complexity for finding influential seed nodes in networks.
problem Determining the most influential set of nodes in a network with limited simulations.
method Upper bounds on reachability variance and data-adaptive simulation method.
result Significantly improved upper bound on sample complexity for IM.
MARGIN uses graph signal analysis to identify influential nodes in neural networks.
problem Improving interpretability of deep neural networks.
method MARGIN exploits graph signal analysis to identify influential nodes in a graph.
result MARGIN outperforms existing approaches in various interpretability challenges.
Improves many-shot learning by optimizing and generating influential examples.
problem Limited performance in many-shot in-context learning (ICL).
method Iterative optimization and generation of influential examples.
result Significant improvements across various tasks using BRIDGE.
Establishes statistical and computational bounds for influence diagnostics.
problem Identifying influential datapoints or subsets in machine learning models.
method Finite-sample statistical bounds and computational complexity for influence functions and approximate maximum influence perturbations.
result Established statistical and computational guarantees for influence diagnostics.
Study shows influence functions are poor for neural networks but useful for identifying influential examples.
problem Influence functions misalign with leave-one-out retraining in neural networks.
method Decomposed the discrepancy into five terms and studied their contributions across different architectures and datasets.
result Influence functions are a good approximation to the proximal Bregman response function (PBRF), useful for identifying influential examples.
Study examines influence diagnostics in high-dimensional M-estimation.
problem Understanding influence diagnostics in high-dimensional settings.
method Characterized the distribution of leave-one-out influences in high-dimensional Gaussian M-estimation.
result The distribution of influences converges to a limiting measure in high-dimensional settings.
Enhances VAEs without additional tuning for better sample quality.
problem Understanding and improving VAE model performance.
method Rigorously analyzed VAE objective, developed a simple enhancement.
result Produces competitive samples and stable FID scores.
Framework detects influential actors in disinformation networks.
problem Identifying and countering hostile influence operations on social media.
method Combines NLP, ML, graph analytics, and causal inference.
result 96% precision, 79% recall, 96% PR-curve area for IO detection.
The study identifies influential bioinformatics algorithms for scalable computing.
problem Data deluge in bioinformatics and need for scalable computing solutions.
method Identifying and analyzing influential data mining and machine learning algorithms.
result Guiding scalable computing experts to focus on specific bioinformatics algorithms.
Methodology calculates car insurance premiums for partial damage losses.
problem Estimating premiums for partial damage losses in automobile insurance.
method Used generalized linear models to analyze claim frequency and severity.
result Identified key variables influencing claim frequency and severity.
Kernel methods can learn hierarchical polynomials efficiently.
problem Learning hierarchical structure from data.
method Iteratively reweighting kernel machines using derivatives.
result Efficient learning of hierarchical polynomials.
New algorithms for privately learning decision lists and halfspaces.
problem Private learning of decision lists and halfspaces.
method Differentially private algorithms for PAC and online models.
result Private algorithms match or surpass non-private guarantees.
New algorithm learns Markov decision processes from test data.
problem Learning Markov decision processes from limited test data.
method L*-based active learning with sampling.
result Sampling-based algorithm outperforms passive learning.
Identifies influential neurons in deep networks for better explanations.
problem Explaining complex behaviors of deep neural networks.
method Identifies neurons with high influence using an influence measure and provides interpretations.
result Identifies influential concepts that generalize across instances and isolates individual features used by the network.
New method learns influential action sequences without privileged final states.
problem Learning meaningful action sequences in large action spaces.
method Model-free approach that considers the full trajectory.
result Successfully applied to large action spaces.
A game-theoretic framework identifies influential hyperparameters for neural networks.
problem Understanding which hyperparameters are most important for neural network performance.
method Employing Shapley Effects for global sensitivity analysis and Pareto front sets for identifying effective configurations.
result Reveals which hyperparameters are most influential for different objectives in neural networks.
ALEVS uses statistical leverage scores for more efficient active learning.
problem Efficiently selecting queries for better classifier accuracy with fewer labels.
method Sampling data instances with high statistical leverage scores.
result Sampling high leverage points leads to effective active learning.
This work simplifies Bayesian inference for neural networks by identifying influential parameter directions.
problem High computational complexity in Bayesian inference for neural networks due to high-dimensional parameter space.
method Constructing an active subspace of influential parameter directions to reduce dimensionality.
result Effective and scalable Bayesian inference achieved via reduced active subspace.
Proposes a new sensitivity measure for optimization problems.
problem Optimization of high-dimensional functions with expensive computer codes.
method Introduces a new influence measure based on the Hilbert-Schmidt Independence Criterion.
result The new measure significantly reduces the number of function evaluations.
Kurdistan Region is a tourist hub. This research analyzes other Non-Oil Sectors that have huge attractions of Foreign Direct Investments into the Kurdistan Region from 2005 to 2013. Comparative analysis was carried out between Iraq and the Region, and among influential Sectors of the Economy. T-test and ANOVA are stati…
CorePPR combines PageRank and CoreRank for scalable GNNs.
problem Expensive message passing in GNNs for large graphs.
method Learnable convex combination of approximate PPR and CoreRank, dynamic node selection.
result CorePPR outperforms PPRGo on large graphs.
Study uses VC correlation to uncover directional financial relationships.
problem Understanding causal relationships between financial variables.
method Volatility constrained correlation (VC correlation) method.
result Operating income is most influential, while market capitalization and revenue are most susceptible.
New method reveals how training data influence diffusion model outputs.
problem Difficulty in assessing training data impact on diffusion model outputs.
method Use of ensembles trained on carefully engineered splits of training data to identify influential training examples.
result Demonstrated the viability of ensembles as generative models and validity of assessing influence.
Bayesian approach scores influential training examples for model predictions.
problem Enhance interpretability and safety of machine learning models.
method Formulate TDA as a Bayesian information-theoretic problem, scoring subsets by information loss.
result Method aligns with classical influence scores while promoting diversity for subsets.
Unified framework interprets SSL models, revealing biases.
problem Interpreting black-box SSL models for transparency.
method KREPES framework using Representer Landmarks and Nyström approximation.
result Direct audit of SSL latent space without supervision.
Improved VAE model enhances uncertainty estimation for out-of-distribution samples.
problem VAEs assign higher likelihood to out-of-distribution inputs.
method INCPVAE integrates noise contrastive prior into VAEs for reliable uncertainty estimation.
result INCPVAE outperforms standard VAEs in uncertainty estimation for OOD inputs.
Identification of the influential clinical symptoms and laboratory features that help in the diagnosis of dengue fever in early phase of the illness would aid in designing effective public health management and virological surveillance strategies. Keeping this as our main objective we develop in this paper, a new compu…
Study on weather forecasting errors for solar energy forecasting.
problem Uncertainty in weather forecasting for solar PV generation.
method Comparison of forecasted and observed weather data, statistical metrics, and sensitivity test.
result Identified influential weather variables improving solar PV generation forecasting.
Proposes HSIC-Lasso for selective inference in non-linear data.
problem Detecting influential features in non-linear and high-dimensional data.
method Model-free HSIC-Lasso based on truncated Gaussians and polyhedral lemma.
result Tight control of type-I error even for small sample sizes.
A new method improves robustness and efficiency of Bayesian LOO-CV.
problem Computational expense and unreliability of classical LOO-CV in high-dimensional Bayesian models.
method Proposes a mixture estimator to compute Bayesian LOO-CV criteria with finite asymptotic variance.
result Improved robustness and efficiency in high-dimensional problems.