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.
A new framework uses directed information to efficiently select context chunks.
Context-aware recommender systems (CARSs) apply sensing and analysis of user context in order to provide personalized services. Adding context to a recommendation model is challenging, since the addition of context may increases both the dimensionality and sparsity of the model. Recent research has shown that modeling …
CCM improves context for Meta-RL by contrastive learning.
Thompson Sampling tackles noisy context in stochastic bandits.
In the sentence classification task, context formed from sentences adjacent to the sentence being classified can provide important information for classification. This context is, however, often ignored. Where methods do make use of context, only small amounts are considered, making it difficult to scale. We present a …
This work introduces RISE to explain LLMs more reliably by distinguishing essential context.
This paper analyzes the relationship between public disclosure, private information and stock liquidity in Tunisian context using a sample of 41 listed firms in the Tunis Stock Exchange in 2007. First, we find no evidence that there is a relation between public and private information. Second, Tunisian investors do not…
Benchmark assesses forecasting models' ability to use textual context.
This paper improves context-aware recommender systems by selecting and incorporating relevant low-dimensional contextual information.
The informational context is regularly questioned in a transitional economic regime like the one implemented in China or Vietnam. This article investigates this issue and the predictive power of fundamental analysis in such context and more precisely in a Chinese context with an analysis of 3 different industries (medi…
LLMs generate answers under incomplete context, and their uncertainty should scale with missing information.
Proposes a new model for more accurate demand forecasting considering dynamic contextual information.
We present a Bayesian nonparametric framework for multilevel clustering which utilizes group-level context information to simultaneously discover low-dimensional structures of the group contents and partitions groups into clusters. Using the Dirichlet process as the building block, our model constructs a product base-m…
FGN improves Chinese NER by integrating glyph information and interactive context.
We extend manifold capacity to nonlinear neural representations with contextual information.
LLMs compress financial texts, but distort decision-making.
In this work we address the problem of argument search. The purpose of argument search is the distillation of pro and contra arguments for requested topics from large text corpora. In previous works, the usual approach is to use a standard search engine to extract text parts which are relevant to the given topic and su…
Pre-training on different modalities improves Transformer performance in offline reinforcement learning.
The paper explores how to find relevant vertices in one graph using another graph's attributes and structure.
New estimator uses clustering to improve off-policy evaluation accuracy.
NOTMAD estimates context-specific Bayesian networks without breaking datasets.
Contextualizing financial news improves stock price predictions.
New DR-IC estimator reduces bias and variance in OPE.
We consider a reinforcement learning (RL) setting in which the agent interacts with a sequence of episodic MDPs. At the start of each episode the agent has access to some side-information or context that determines the dynamics of the MDP for that episode. Our setting is motivated by applications in healthcare where ba…
Recent studies have introduced end-to-end TTS, which integrates the production of context and acoustic features in statistical parametric speech synthesis. As a result, a single neural network replaced laborious feature engineering with automated feature learning. However, little is known about what types of context in…
In this paper we propose the multi-objective contextual bandit problem with similarity information. This problem extends the classical contextual bandit problem with similarity information by introducing multiple and possibly conflicting objectives. Since the best arm in each objective can be different given the contex…
In many cases, feature selection is often more complicated than identifying a single subset of input variables that would together explain the output. There may be interactions that depend on contextual information, i.e., variables that reveal to be relevant only in some specific circumstances. In this setting, the con…
Trains word embeddings from music and text data to link music contexts.
CoDA adapts dynamics models to new physical systems by conditioning on context.
New method for contextual bandits with corrupted context.
We consider a system where agents enter in an online fashion and are evaluated based on their attributes or context vectors. There can be practical situations where this context is partially observed, and the unobserved part comes after some delay. We assume that an agent, once left, cannot re-enter the system. Therefo…
Given a sparse rating matrix and an auxiliary matrix of users or items, how can we accurately predict missing ratings considering different data contexts of entities? Many previous studies proved that utilizing the additional information with rating data is helpful to improve the performance. However, existing methods …
IDS improves reinforcement learning with contextual information.
New algorithm reduces regret with diverse contexts in bandits.
New method finds local anomalies in time series by considering context information.
Proposes ContSup to boost local learning by supplying context between isolated modules.
Digital histology images are amenable to the application of convolutional neural network (CNN) for analysis due to the sheer size of pixel data present in them. CNNs are generally used for representation learning from small image patches (e.g. 224x224) extracted from digital histology images due to computational and me…
We present an approach to learning multi-sense word embeddings relying both on monolingual and bilingual information. Our model consists of an encoder, which uses monolingual and bilingual context (i.e. a parallel sentence) to choose a sense for a given word, and a decoder which predicts context words based on the chos…
A new learning scheme improves model efficiency and performance.
Optimally explores dynamical systems with varying properties using context inference.
We propose an approach to self-supervised representation learning based on maximizing mutual information between features extracted from multiple views of a shared context. For example, one could produce multiple views of a local spatio-temporal context by observing it from different locations (e.g., camera positions w…
Proposes a context-aware approach to deep autoencoder novelty detection.
LLMs learn to recommend models and hyperparameters from dataset metadata.
With the rising number of interconnected devices and sensors, modeling distributed sensor networks is of increasing interest. Recurrent neural networks (RNN) are considered particularly well suited for modeling sensory and streaming data. When predicting future behavior, incorporating information from neighboring senso…
Contextual information helps identify the best arm more efficiently.
Local differential privacy (LDP) is a strong notion of privacy for individual users that often comes at the expense of a significant drop in utility. The classical definition of LDP assumes that all elements in the data domain are equally sensitive. However, in many applications, some symbols are more sensitive than ot…
The prevalence of social media has made information sharing possible across the globe. The downside, unfortunately, is the wide spread of misinformation. Methods applied in most previous rumor classifiers give an equal weight, or attention, to words in the microblog, and do not take the context beyond microblog content…
Decodes neural activity to detect context effects in natural settings.