New method combines population and completion tasks in knowledge graphs.
problem Insufficient external resources hinder statistical inference in knowledge graphs.
method Probabilistic factorisation method that uses path structure for both population and completion.
result Balanced exploitation-exploration helps incremental population and improves prediction of missing information.
Develops local population-risk certificates for model updates
problem Model updates in machine learning
method Certify population-risk increments around a model
result Certified upper endpoint yields a risk-controlled update rule
Develops certificates for local population-risk increments using cross-fitted ridge calibration.
problem Certifying local population-risk increments in statistical models.
method Cross-fitted ridge calibration for linear feature classes, separating Taylor fluctuations and remainders.
result Certifies measurable updates from the same sample with penalties dependent on empirical geometry.
A new algorithm estimates aggregate marginals from noisy data in an online manner.
problem Estimating aggregate marginals of a Markov chain from noisy aggregate observations.
method Sliding window Sinkhorn belief propagation (SW-SBP) algorithm.
result Demonstrated improved performance on inferring population flow.
Paper improves privacy-preserving measurement of advertising incrementality.
problem Privacy degradation in randomized lift tests for advertising measurement.
method Formulates a robust causal decision problem under signal losses, projecting clean worlds onto incrementality.
result Sharp decision frontier shows valid certification or rejection outside the frontier.
Growth rate of real GDP per capita is represented as a sum of two components -- a monotonically decreasing economic trend and fluctuations related to a specific age population change. The economic trend is modeled by an inverse function of real GDP per capita with a numerator potentially constant for the largest develo…
New algorithm estimates barycenters of distributions using Frank-Wolfe.
problem Estimating the average of arbitrary probability distributions.
method Frank-Wolfe optimization for Sinkhorn divergence, incrementally populating support.
result Converges in both discrete and continuous distributions, with proven rates.
Populations of species in ecosystems are often constrained by availability of resources within their environment. In effect this means that a growth of one population, needs to be balanced by comparable reduction in populations of others. In neutral models of biodiversity all populations are assumed to change increment…
DFI maps covariates to latent representations for feature importance.
problem Feature importance when predictors are statistically dependent.
method Disentangled Feature Importance (DFI) using entropic optimal transport.
result DFI yields stable, interpretable, uncertainty-quantified attributions of shared predictive signal.
The growth rate of real GDP per capita in the biggest OECD countries is represented as a sum of two components - a steadily decreasing trend and fluctuations related to the change in some specific age population. The long term trend in the growth rate is modelled by an inverse function of real GDP per capita with a con…
Study on stock price formation on trees with multi-population and non-rational agents.
problem Equilibrium price formation for risky stock with multi-population and non-rational agents.
method Combining mean-field game theory with binomial tree framework, proving existence of unique equilibrium, deriving explicit formula for transition probabilities.
result Existence of unique mean-field market-clearing equilibrium with explicit analytic formula for stock price transition probabilities.
Real GDP growth rate in developed countries is found to be a sum of two terms. The first term is the reciprocal value of the duration of the period of mean income growth with work experience, Tcr. The current value of Tcr in the USA is 40 years. The second term is inherently related to population and defined by the rel…
Proposes a resampling method to compare uplift models with uncertainty.
problem Uncertainty in estimating uplift curves when full population data is unavailable.
method Two-step sampling procedure and resampling-based approach.
result Validates the proposed method through simulations and real data applications.
Incremental variational inference speeds up LDA processing.
problem Efficiently processing large document collections in LDA.
method Inspired by incremental EM, introduces incremental variational inference for LDA.
result Incremental LDA converges faster and monotonically improves variational bound.
A new algorithm for faster incremental kernel PCA and Nyström approximation.
problem Efficiency in handling streaming data and large datasets.
method Incremental kernel PCA and Nyström approximation using rank one updates.
result More computationally and memory efficient than existing methods.
The paper extends cluster validity indices for incremental analysis.
problem Providing incremental alternatives for cluster validation.
method Extending iCVI family to include 6 incremental indices and examining their behavior under under- and over-partitioning.
result Over-partitioning is more challenging to detect than under-partitioning.
New approach AR1 improves performance in class-incremental learning.
problem Training deep models sequentially on a single incremental task without forgetting.
method Combining architectural and regularization strategies, AR1 is specifically designed for incremental task scenarios.
result AR1 outperformed existing regularization strategies on CORe50 and iCIFAR-100.
EILearn learns incrementally using previous classifier knowledge.
problem Incremental learning with previous data.
method Retains and uses previous classifier knowledge, monitors performance, eliminates poorly performing classifiers.
result Outperforms existing incremental learning approaches.
Paper proposes faster incremental subclass discriminant analysis.
problem Efficiently classify subclasses in incremental data.
method Exact and approximate linear and kernelized solutions.
result Superior training time and accuracy compared to existing methods.
Depth helps neural networks learn simpler solutions incrementally.
problem Understanding why deep neural networks generalize well despite complex architectures.
method Formal definition of incremental learning dynamics, theoretical analysis of depth and initialization effects, experiments with various models.
result Incremental learning dynamics can arise in deeper models, but not in shallow ones, under specific conditions.
Paper tackles few-shot class-incremental learning with a neural gas network.
problem Incrementally learn new classes from very few labelled samples without forgetting old classes.
method Proposes TOPIC framework using a neural gas network to preserve class topology and adapt to new samples.
result Significantly outperforms other methods on CIFAR100, miniImageNet, and CUB200 datasets.
Study of electronic corn futures trading shows discrete price changes and non-Gaussian distributions.
problem Discrepancy between theoretical continuous price models and actual intra-day trading data.
method Analysis of discrete price increments, volume, and profit strategies using statistical distributions and probability theory.
result Kumaraswamy distribution better fits waiting times than Weibull, and price jumps resemble branching reactions.
iCaRL learns new classes incrementally from data streams.
problem Incremental learning of new concepts over time from a stream of data.
method iCaRL: learns strong classifiers and data representation simultaneously, allowing progressive addition of new classes.
result iCaRL can learn many classes incrementally over a long period of time, unlike other methods.
Paper tackles private incremental regression in streaming data.
problem Private incremental regression in streaming data.
method Generic transformation of private batch ERM to incremental ERM, and two mechanisms for private incremental regression.
result Improved empirical risk for certain regression problems.
Deep Incremental Boosting speeds up Deep Learning training with improved generalization.
problem Reducing training time and improving generalization in Deep Learning.
method Inspired by AdaBoost and Transfer Learning, Deep Incremental Boosting adapts Ensemble methods to Deep Learning.
result Reduces training time and improves generalization on Deep Learning datasets.
Paper introduces a new method for learning robot dynamics.
problem Learning robot dynamics with limited prior information.
method Incremental semiparametric inverse dynamics learning combining parametric and nonparametric models.
result Validated the approach on a humanoid robot arm.
A new model learns preferences incrementally without personal data.
problem Incremental session-based recommendation without personal data.
method Memory Augmented Neural model (MAN) that combines a neural recommender with a nonparametric memory.
result MAN consistently outperforms existing methods in incremental session-based recommendation.
Paper proposes CNE-net to tackle incremental learning in (T)ACSA tasks.
problem Catastrophic forgetting in multi-task incremental learning for (T)ACSA.
method Category Name Embedding network (CNE-net) with shared encoder and decoder.
result State-of-the-art performance on (T)ACSA benchmark datasets.
New method for clustering large multi-view data.
problem Handling large multi-view data efficiently.
method Incremental minimax optimization based fuzzy clustering (IminimaxFCM).
result IminimaxFCM outperforms related methods in clustering accuracy.
GraphSAIL updates GNN-based recommender models incrementally to reduce computation time and improve frequent updates.
problem Incremental updates in GNN-based recommender systems are computationally expensive and prone to forgetting.
method GraphSAIL uses a graph structure preservation strategy to update GNN models incrementally, preserving long-term preferences and properties.
result GraphSAIL reduces computation time and improves frequent updates compared to other incremental learning techniques.
Proposes a self-learning algorithm using reinforcement learning to adapt to data changes.
problem Overcoming concept drift in statistical models over time.
method Reinforcement learning combined with genetic algorithms and Q-learning.
result Algorithm can adapt to new data patterns and self-calibrate.
A new method for fast incremental/decremental analysis without recursion.
problem Efficiently updating support-vector models with new data.
method Ridge Support Vector Models with Weight-Error Curves (WECs) for recursion-free computation.
result All new Lagrangian multipliers can be computed simultaneously, relaxing previous constraints.
We analyze the question whether sliding window time averages applied to stationary increment processes converge to a limit in probability. The question centers on averages, correlations, and densities constructed via time averages of the increment x(t,T)=x(t+T)-x(t)and the assumption is that the increment is distribute…
Paper aims to improve AI's ability to learn new tasks incrementally.
problem Catastrophic Forgetting in AI systems learning multiple tasks simultaneously.
method Analyzed iCaRL method, identified knowledge distillation as key, proposed dynamic threshold moving algorithm.
result Demonstrated effectiveness of dynamic threshold moving algorithm on CIFAR100 and MNIST datasets.
Dex improves reinforcement learning by solving complex environments incrementally.
problem Training reinforcement learning agents for complex, ever-changing environments.
method Incremental learning approach, using optimal weights from simpler environments.
result Incremental learning yields superior performance across multiple Dex environments.
New algorithm improves on EM for streaming data, outperforming existing methods.
problem Processing high-volume, streaming data efficiently.
method Incremental stochastic Majorization-Minimization (MM) algorithm.
result The algorithm converges to a stationary point with vanishing gradient.
SaMbaTen efficiently maintains tensor decompositions for growing datasets.
problem Maintaining tensor decompositions for dynamic, growing datasets.
method Sampling-based batch incremental tensor decomposition algorithm.
result SaMbaTen achieves comparable accuracy to state-of-the-art techniques but is significantly faster and scalable.
Proposes MEDIC to improve incremental learning by preventing forgetting and intransigence.
problem Challenges of forgetting old knowledge and intransigence on new knowledge in incremental learning.
method Maximum Entropy Regularizer (MER) and DropOut Sampling (DOS) to penalize uncertain knowledge and reduce class imbalance.
result Proposed method 'MEDIC' outperforms state-of-the-art algorithms in accuracy, forgetting, and intransigence.
Stochastic Gradient Trees learn decision trees incrementally.
problem Learning decision trees using stochastic gradient information.
method Incremental learning setting, soft splits not used, new tree not constructed per update.
result Performs similarly to standard incremental classification trees, outperforms state of the art incremental regression trees, comparable to batch multi-instance learning methods.
Paper analyzes fast SAGA method for nonconvex optimization problems.
problem Optimizing nonconvex problems of the form minx∑ifi(x) method Incremental aggregated gradient method (SAGA) within an Incremental First-order Oracle framework
result SAGA converges to a stationary point faster than gradient descent and stochastic gradient descent, and at a linear rate to the global optimum for a specific class of nonconvex problems.
Incremental training method for deep neural networks.
problem Training deep neural networks efficiently and with incremental growth.
method Partitioning the network into sub-networks, gradually incorporating them, and using look-ahead initialization.
result Incremental approach reaches baseline accuracy and identifies smaller network partitions.
Paper evaluates incremental learning from non-stationary data.
problem Lack of effective evaluation metrics for non-stationary incremental learning.
method Synthetic datasets and testbed for algorithms evaluation.
result Proposed methodology effectively characterizes strengths and weaknesses of algorithms.
A new generative classification strategy outperforms existing methods in class-incremental learning.
problem Incrementally training deep neural networks to recognize new classes is challenging.
method Proposes learning the joint distribution p(x,y) and performing classification using Bayes' rule, implemented with variational autoencoders and importance sampling.
result Performs very well on continual learning benchmarks, outperforming existing baselines.
The statistical properties of the increments x(t+T) - x(t) of a financial time series depend on the time resolution T on which the increments are considered. A non-parametric approach is used to study the scale dependence of the empirical distribution of the price increments x(t+T) - x(t) of S&P Index futures, for time…
For classification of the high frequency trading quantities, waiting times, price increments within and between sessions are referred to as the a-, b-, and c-increments. Statistics of the a-b-c-increments are computed for the Time & Sales records posted by the Chicago Mercantile Exchange Group for the futures traded on…
Efficiently updates KRR for big streams with minimal redundant computation.
problem Redundant computation in incremental KRR for big data streams.
method Supports incremental/decremental processing for single and multiple samples, dividing data into batches.
result Significantly reduced computational time without sacrificing accuracy.
The condition for stationary increments, not scaling, detemines long time pair autocorrelations. An incorrect assumption of stationary increments generates spurious stylized facts, fat tails and a Hurst exponent H_s=1/2, when the increments are nonstationary, as they are in FX markets. The nonstationarity arises from s…
Approximate Incremental Value-at-Risk formulae provide an easy-to-use preliminary guideline for risk allocation. Both the cases of risk adding and risk pooling are examined and beta-based formulae achieved. Results highlight how much the conditions for adding new risky positions are stronger than those required for ris…