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.
Meta clustering categorizes learners for collaborative learning.
Meta-BO method clusters and learns from prior tasks to optimize heterogeneous functions.
Meta-learning neural networks for better clustering representations.
In order to learn quickly with few samples, meta-learning utilizes prior knowledge learned from previous tasks. However, a critical challenge in meta-learning is task uncertainty and heterogeneity, which can not be handled via globally sharing knowledge among tasks. In this paper, based on gradient-based meta-learning,…
MARCO-GE selects clustering algorithms using graph embeddings.
A new method generates meta-tasks using generative models for unsupervised learning.
PNN-smoothing improves -means clustering by merging subsets' clusterings.
Task-adaptive clustering improves few-shot learning with unlabeled data.
Meta-continual learning improves few-shot learning performance.
We consider the stochastic multi-armed bandit problem and the contextual bandit problem with historical observations and pre-clustered arms. The historical observations can contain any number of instances for each arm, and the pre-clustering information is a fixed clustering of arms provided as part of the input. We de…
Conditional meta-learning improves meta-learning performance in diverse task environments.
Clustering algorithms have regained momentum with recent popularity of data mining and knowledge discovery approaches. To obtain good clustering in reasonable amount of time, various meta-heuristic approaches and their hybridization, sometimes with K-Means technique, have been employed. A Kalman Filtering based heurist…
Clustering is one of the most fundamental and wide-spread techniques in exploratory data analysis. Yet, the basic approach to clustering has not really changed: a practitioner hand-picks a task-specific clustering loss to optimize and fit the given data to reveal the underlying cluster structure. Some types of losses--…
We propose a deep amortized clustering (DAC), a neural architecture which learns to cluster datasets efficiently using a few forward passes. DAC implicitly learns what makes a cluster, how to group data points into clusters, and how to count the number of clusters in datasets. DAC is meta-learned using labelled dataset…
New method learns shared structures in non-linear tasks.
GeneraLight improves traffic signal control models' generalization ability.
In this thesis, we study the problem of feature learning on heterogeneous knowledge graphs. These features can be used to perform tasks such as link prediction, classification and clustering on graphs. Knowledge graphs provide rich semantics encoded in the edge and node types. Meta-paths consist of these types and abst…
Meta-causal states group equivalent qualitative causal dynamics, useful for analyzing system changes.
ProtoTransfer learns from unlabeled data to classify unseen tasks.
A central goal of unsupervised learning is to acquire representations from unlabeled data or experience that can be used for more effective learning of downstream tasks from modest amounts of labeled data. Many prior unsupervised learning works aim to do so by developing proxy objectives based on reconstruction, disent…
FedCluster accelerates federated learning convergence by cycling device groups.
In recent years, the majority of works on deep-learning-based image colorization have focused on how to make a good use of the enormous datasets currently available. What about when the data at disposal are scarce? The main objective of this work is to prove that a network can be trained and can provide excellent color…
This work proposes an ensemble clustering method using transfer learning approach. We consider a clustering problem, in which in addition to data under consideration, "similar" labeled data are available. The datasets can be described with different features. The method is based on constructing meta-features which desc…
Paper tackles cross-granularity few-shot learning with meta-embedder.
Enhances few-shot image classification using unlabelled examples.
End-to-end meta-learned system for image compression.
Model interpretability is a requirement in many applications in which crucial decisions are made by users relying on a model's outputs. The recent movement for "algorithmic fairness" also stipulates explainability, and therefore interpretability of learning models. And yet the most successful contemporary Machine Learn…
The combination of multiple classifiers using ensemble methods is increasingly important for making progress in a variety of difficult prediction problems. We present a comparative analysis of several ensemble methods through two case studies in genomics, namely the prediction of genetic interactions and protein functi…
The excited states of polyatomic systems are rather complex, and often exhibit meta-stable dynamical behaviors. Static analysis of reaction pathway often fails to sufficiently characterize excited state motions due to their highly non-equilibrium nature. Here, we proposed a time series guided clustering algorithm to ge…
We discuss here the mean-field theory for a cellular automata model of meta-learning. The meta-learning is the process of combining outcomes of individual learning procedures in order to determine the final decision with higher accuracy than any single learning method. Our method is constructed from an ensemble of inte…
Contextualized ML learns context-dependent effects using deep learning.
(Unsupervised) Domain Adaptation (DA) seeks for classifying target instances when solely provided with source labeled and target unlabeled examples for training. Learning domain-invariant features helps to achieve this goal, whereas it underpins unlabeled samples drawn from a single or multiple explicit target domains …
CoHiRF extends clustering methods to handle high-dimensional data efficiently.
Adaptive-Step Graph Meta-Learner tackles few-shot graph classification with limited labeled data.
Recent metric-based meta-learning approaches, which learn a metric space that generalizes well over combinatorial number of different classification tasks sampled from a task distribution, have been shown to be effective for few-shot classification tasks of unseen classes. They are often trained with episodic training …
Events are happening in real-world and real-time, which can be planned and organized occasions involving multiple people and objects. Social media platforms publish a lot of text messages containing public events with comprehensive topics. However, mining social events is challenging due to the heterogeneous event elem…
A new meta-meta classification method tackles few-shot learning tasks.
Meta-learning adapts models for unseen tasks across AI, robotics, and NLP.
Gradient-based meta-RL fails with incorrect task distributions, leading to instability and poor performance.
A genetic algorithm with community detection improves feature selection accuracy.
Majority of the modern meta-learning methods for few-shot classification tasks operate in two phases: a meta-training phase where the meta-learner learns a generic representation by solving multiple few-shot tasks sampled from a large dataset and a testing phase, where the meta-learner leverages its learnt internal rep…
Meta-learning overfits both model and base learner; meta-augmentation helps.
In spite of the success of existing meta reinforcement learning methods, they still have difficulty in learning a meta policy effectively for RL problems with sparse reward. In this respect, we develop a novel meta reinforcement learning framework called Hyper-Meta RL(HMRL), for sparse reward RL problems. It is consist…
M3PO improves model-based meta-RL with theoretical guarantees.
Meta learning can adapt fast but is vulnerable to adversarial attacks.
Paper tackles offline meta-reinforcement learning with a new algorithm.
Bayesian MAML outperforms MAML in meta learning tasks with theoretical guarantees.
Modified Meta-TS for linear contextual bandits reduces regret.