A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
DoubleAdapt improves stock trend forecasting by adapting models to evolving data.
DPTA improves CIL by adapting PTMs with dual prototypes.
Proposes a method to adapt to new classes in a domain shift.
Sparse Meta Networks adapt deep neural networks incrementally for fast learning.
Continuous appearance shifts such as changes in weather and lighting conditions can impact the performance of deployed machine learning models. While unsupervised domain adaptation aims to address this challenge, current approaches do not utilise the continuity of the occurring shifts. In particular, many robotics appl…
This paper introduces Deep Incremental Boosting, a new technique derived from AdaBoost, specifically adapted to work with Deep Learning methods, that reduces the required training time and improves generalisation. We draw inspiration from Transfer of Learning approaches to reduce the start-up time to training each incr…
iCVI-ARTMAP accelerates clustering with adaptive resonance theory and validity indices.
AdaAnn optimizes annealing for efficient probability density approximation.
BI-MAML learns multiple tasks without forgetting old ones.
Paper tackles few-shot class-incremental learning with a neural gas network.
This paper tackles federated incremental learning with dynamic memory allocation for improved model performance in non-IID data.
CILF learns adaptive embeddings for class-incremental learning with novel class detection and model update.
Validation is one of the most important aspects of clustering, but most approaches have been batch methods. Recently, interest has grown in providing incremental alternatives. This paper extends the incremental cluster validity index (iCVI) family to include incremental versions of Calinski-Harabasz (iCH), I index and …
Real-time personalization for HAR models learns from new users without prior data.
In the context of supervised statistical learning, it is typically assumed that the training set comes from the same distribution that draws the test samples. When this is not the case, the behavior of the learned model is unpredictable and becomes dependent upon the degree of similarity between the distribution of the…
AANets balance stability and plasticity in CIL.
Develops an efficient method for real-time data analysis and visualization.
Incremental machine learning models predict COVID-19 cases more efficiently than traditional methods.
Recognising human activities from streaming videos poses unique challenges to learning algorithms: predictive models need to be scalable, incrementally trainable, and must remain bounded in size even when the data stream is arbitrarily long. Furthermore, as parameter tuning is problematic in a streaming setting, suitab…
Meta-learning approach improves object detection on new classes.
New algorithm improves on EM for streaming data, outperforming existing methods.
Efficiently processes dynamic inputs in AI writing assistants with incremental computation.
Incremental learning from non-stationary data poses special challenges to the field of machine learning. Although new algorithms have been developed for this, assessment of results and comparison of behaviors are still open problems, mainly because evaluation metrics, adapted from more traditional tasks, can be ineffec…
Optimal Volt/VAR control rules are designed using deep neural networks.
Nonnegative matrix factorization (NMF) has attracted much attention in the last decade as a dimension reduction method in many applications. Due to the explosion in the size of data, naturally the samples are collected and stored distributively in local computational nodes. Thus, there is a growing need to develop algo…
Study large deviations in random walks on Lie groups.
DIVA clusters dynamic data without needing cluster count, outperforming baselines.
Meta-learning approach prevents forgetting across tasks.
We develop importance sampling based efficient simulation techniques for three commonly encountered rare event probabilities associated with random walks having i.i.d. regularly varying increments; namely, 1) the large deviation probabilities, 2) the level crossing probabilities, and 3) the level crossing probabilities…
In recent years, dynamically growing data and incrementally growing number of classes pose new challenges to large-scale data classification research. Most traditional methods struggle to balance the precision and computational burden when data and its number of classes increased. However, some methods are with weak pr…
Predictions and predictive knowledge have seen recent success in improving not only robot control but also other applications ranging from industrial process control to rehabilitation. A property that makes these predictive approaches well suited for robotics is that they can be learned online and incrementally through…
Foundation models improve volatility forecasting in finance.
In this work we introduce a new optimisation method called SAGA in the spirit of SAG, SDCA, MISO and SVRG, a set of recently proposed incremental gradient algorithms with fast linear convergence rates. SAGA improves on the theory behind SAG and SVRG, with better theoretical convergence rates, and has support for compos…
TKIL improves class-balanced performance in incremental learning.
An incremental/online state dynamic learning method is proposed for identification of the nonlinear Gaussian state space models. The method embeds the stochastic variational sparse Gaussian process as the probabilistic state dynamic model inside a particle filter framework. Model updating is done at measurement sample …
Method estimates causal effects from incremental data, overcoming missing data challenges.
Gradient-based meta-learning has proven to be highly effective at learning model initializations, representations, and update rules that allow fast adaptation from a few samples. The core idea behind these approaches is to use fast adaptation and generalization -- two second-order metrics -- as training signals on a me…
Estimates outcomes under hypothetical scenarios using a flexible framework.
Adaptive XGBoost improves accuracy on evolving data streams by updating the ensemble dynamically.
In class-incremental learning, a model learns continuously from a sequential data stream in which new classes occur. Existing methods often rely on static architectures that are manually crafted. These methods can be prone to capacity saturation because a neural network's ability to generalize to new concepts is limite…
DriftMoE adapts to concept drifts in data streams efficiently.
UIClust efficiently clusters data streams with concept drift detection.
In this paper, we propose an adaptive stopping rule for kernel-based gradient descent (KGD) algorithms. We introduce the empirical effective dimension to quantify the increments of iterations in KGD and derive an implementable early stopping strategy. We analyze the performance of the adaptive stopping rule in the fram…
Many current approaches to the design of intrusion detection systems apply feature selection in a static, non-adaptive fashion. These methods often neglect the dynamic nature of network data which requires to use adaptive feature selection techniques. In this paper, we present a simple technique based on incremental le…
JADAI optimizes design and inference for parameter estimation.
Paper proposes adaptive parameter selection for KGD algorithms.
Efficiently preserves old class knowledge in memory-limited settings.
Here we propose using the successor representation (SR) to accelerate learning in a constructive knowledge system based on general value functions (GVFs). In real-world settings like robotics for unstructured and dynamic environments, it is infeasible to model all meaningful aspects of a system and its environment by h…