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.
New method simplifies causal inference with tiered background knowledge.
Paper presents new algorithms for causal discovery with latent variables and overlapping datasets.
New algorithm improves causal discovery in biomedical data.
Cluster-DAGs improve causal discovery with prior knowledge.
Paper tackles robust knowledge transfer in parallel RL tasks.
Model analyzes RFQ markets using stochastic control to optimize dealer performance and inventory.
New framework uses background knowledge to speed up causal discovery.
Tiered latent representations and latent spaces for molecular graphs provide a simple but effective way to explicitly represent and utilize groups (e.g., functional groups), which consist of the atom (node) tier, the group tier and the molecule (graph) tier. They can be learned using the tiered graph autoencoder archit…
Paper characterizes and represents pairwise causal background knowledge for improved causal inference.
Proposes a method to identify causal relationships using background knowledge.
Refines neural network predictions using background knowledge for improved accuracy.
One of the key advantages of Inductive Logic Programming systems is the ability of the domain experts to provide background knowledge as modes that allow for efficient search through the space of hypotheses. However, there is an inherent assumption that this expert should also be an ILP expert to provide effective mode…
The paper improves Gaussian processes by adding sum constraints, enhancing prediction accuracy.
Assessing the quality of discovered results is an important open problem in data mining. Such assessment is particularly vital when mining itemsets, since commonly many of the discovered patterns can be easily explained by background knowledge. The simplest approach to screen uninteresting patterns is to compare the ob…
New algorithm improves interpretability in sequence classification.
Federated Learning (FL) enables learning a shared model across many clients without violating the privacy requirements. One of the key attributes in FL is the heterogeneity that exists in both resource and data due to the differences in computation and communication capacity, as well as the quantity and content of data…
b-LOAD extends local causal discovery with prior knowledge, improving causal effect estimation.
Paper proposes a novel RRL framework that learns from images and incorporates expert knowledge.
Enhances machine learning with background knowledge through feature generation.
Knowledge graphs are used to represent relational information in terms of triples. To enable learning about domains, embedding models, such as tensor factorization models, can be used to make predictions of new triples. Often there is background taxonomic information (in terms of subclasses and subproperties) that shou…
Most work in machine reading focuses on question answering problems where the answer is directly expressed in the text to read. However, many real-world question answering problems require the reading of text not because it contains the literal answer, but because it contains a recipe to derive an answer together with …
Proposes ConiVAT for better cluster assessment and clustering with background knowledge.
We explore the effect of introducing prior information into the intermediate level of neural networks for a learning task on which all the state-of-the-art machine learning algorithms tested failed to learn. We motivate our work from the hypothesis that humans learn such intermediate concepts from other individuals via…
This paper integrates LLMs into SCD to improve causal inference accuracy.
A new reinforcement learning framework separates users into risk-tolerant and risk-averse groups for better performance.
ASCEND discovers causal relationships in multi-omics data by leveraging known hierarchical structure.
The paper improves neural network predictions by integrating process knowledge.
TIER uses extended strain data to improve gravitational wave detection sensitivity.
Adversarial examples are inputs to machine learning models designed to cause the model to make a mistake. They are useful for understanding the shortcomings of machine learning models, interpreting their results, and for regularisation. In NLP, however, most example generation strategies produce input text by using kno…
Motivated by the phenomenon that companies introduce new products to keep abreast with customers' rapidly changing tastes, we consider a novel online learning setting where a profit-maximizing seller needs to learn customers' preferences through offering recommendations, which may contain existing products and new prod…
New method finds ideal circle patterns on spheres.
Given a huge set of applicants, how should a firm allocate sequential resume screenings, phone interviews, and in-person site visits? In a tiered interview process, later stages (e.g., in-person visits) are more informative, but also more expensive than earlier stages (e.g., resume screenings). Using accepted hiring mo…
Semantic Image Interpretation is the task of extracting a structured semantic description from images. This requires the detection of visual relationships: triples (subject,relation,object) describing a semantic relation between a subject and an object. A pure supervised approach to visual relationship detection requir…
Study infinite combinatorial Ricci flow on spherical surfaces.
Paper introduces combinatorial Ricci flows on infinite disk triangulations.
Although we have tons of machine learning tools to analyze data, most of them require users have some programming backgrounds. Here we introduce a SaaS application which allows users analyze their data without any coding and even without any knowledge of machine learning. Users can upload, train, predict and download t…
Proposes CRA framework for certifying fair predictive models.
The recent advances in computer-assisted learning systems and the availability of open educational resources today promise a pathway to providing cost-efficient, high-quality education to large masses of learners. One of the most ambitious use cases of computer-assisted learning is to build a lifelong learning recommen…
An explorative data analysis system should be aware of what the user already knows and what the user wants to know of the data: otherwise the system cannot provide the user with the most informative and useful views of the data. We propose a principled way to do exploratory data analysis, where the user's background kn…
The paper proposes Tier Balancing for dynamic fairness in decision-making.
The paper audits trading filters, finding a high save-to-miss ratio.
Achieving machine intelligence requires a smooth integration of perception and reasoning, yet models developed to date tend to specialize in one or the other; sophisticated manipulation of symbols acquired from rich perceptual spaces has so far proved elusive. Consider a visual arithmetic task, where the goal is to car…
Entity linking is the task of mapping potentially ambiguous terms in text to their constituent entities in a knowledge base like Wikipedia. This is useful for organizing content, extracting structured data from textual documents, and in machine learning relevance applications like semantic search, knowledge graph const…
Cryptonite tests NLP models with cryptic crossword clues.
The aim of this paper is to provide a gentle introduction to Chabauty topology, while very little background knowledge is assumed. As an example, we provide pictures for the Chabauty space of C*. Note that the description of this space is not new; however the pictures are novel.
Cross-lingual Text Classification (CLC) consists of automatically classifying, according to a common set C of classes, documents each written in one of a set of languages L, and doing so more accurately than when naively classifying each document via its corresponding language-specific classifier. In order to obtain an…
Defines explainability as reasoning under background knowledge.
We present a family of novel methods for embedding knowledge graphs into real-valued tensors. These tensor-based embeddings capture the ordered relations that are typical in the knowledge graphs represented by semantic web languages like RDF. Unlike many previous models, our methods can easily use prior background know…