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.
The paper introduces a method to measure the benefits of incidental supervision signals.
Paper tackles supervision bottleneck in machine learning.
Big Data bring new opportunities to modern society and challenges to data scientists. On one hand, Big Data hold great promises for discovering subtle population patterns and heterogeneities that are not possible with small-scale data. On the other hand, the massive sample size and high dimensionality of Big Data intro…
We define a metric filtration of the Gordian graph by an infinite family of 1-dense subgraphs. The n-th subgraph of this family is generated by all knots whose fundamental groups surject to a symmetric group with parameter at least n, where all meridians are mapped to transpositions. Incidentally, we verify the Meridio…
Uniform covers with a finite-dimensional nerve are rare (i.e., do not form a cofinal family) in many separable metric spaces of interest. To get hold on uniform homotopy properties of these spaces, a reasonably behaved notion of an infinite-dimensional metric polyhedron is needed; a specific list of desired properties …
Classifies 7D manifolds with specific geometric properties.
We consider the Hele-Shaw flow that arises from injection of two-dimensional fluid into a point of a curved surface. The resulting fluid domains have and are more or less determined implicitly by a mean value property for harmonic functions. We improve on the results of Hedenmalm and Shimorin \cite{HS} and obtain essen…
In this paper we investigate the asymptotics of forward-start options and the forward implied volatility smile in the Heston model as the maturity approaches zero. We prove that the forward smile for out-of-the-money options explodes and compute a closed-form high-order expansion detailing the rate of the explosion. Fu…
New interpretation of OT regularization as adversarial ground cost.
We introduce Parseval networks, a form of deep neural networks in which the Lipschitz constant of linear, convolutional and aggregation layers is constrained to be smaller than 1. Parseval networks are empirically and theoretically motivated by an analysis of the robustness of the predictions made by deep neural networ…
We present a theoretically grounded approach to train deep neural networks, including recurrent networks, subject to class-dependent label noise. We propose two procedures for loss correction that are agnostic to both application domain and network architecture. They simply amount to at most a matrix inversion and mult…
New pricing theory solves St. Petersburg paradox.
We study a problem of the geometric quantization for the quaternion projective space. First we explain a Kaehler structure on the punctured cotangent bundle of the quaternion projective space, whose Kaehler form coincides with the natural symplectic form on the cotangent bundle and show that the canonical line bundle o…
Randomly guessing weights helps analyze RL benchmarks objectively.
Researchers find optimal configurations of complex knots and links.
Paper stabilizes bandit learning with regularization, improving inference under adaptive sampling.
This paper studies the infinitesimal structure of Carnot manifolds. By a Carnot manifold we mean a manifold together with a subbundle filtration of its tangent bundle which is compatible with the Lie bracket of vector fields. We introduce a notion of differential, called Carnot differential, for Carnot manifolds maps (…
Deep neural networks map brain lesions to deficits for better brain function understanding.
A privacy-preserving synthetic data generation framework that distinguishes between true and phantom data disclosures.
New defence against data-poisoning attacks in neural networks.
The paper tackles robust statistical methods using Wasserstein DRO formulations.
New DL algorithm detects critical chest X-ray findings without manual annotations.
Paper develops inference methods for low-rank tensors without debiasing.
SupSiam and SupBYOL improve supervised representation learning with ANCL.
S4 learns new self-supervision automatically, improving accuracy with less human effort.
Improves label propagation for weakly supervised learning.
Paper proposes an algorithm to recover full supervision from weakly labeled data.
Self-supervision provides effective representations for downstream tasks without requiring labels. However, existing approaches lag behind fully supervised training and are often not thought beneficial beyond obviating or reducing the need for annotations. We find that self-supervision can benefit robustness in a varie…
In this study, importance of user inputs is studied in the context of personalizing human activity recognition models using incremental learning. Inertial sensor data from three body positions are used, and the classification is based on Learn++ ensemble method. Three different approaches to update models are compared:…
Recent advances in semi-supervised learning have shown tremendous potential in overcoming a major barrier to the success of modern machine learning algorithms: access to vast amounts of human-labeled training data. Previous algorithms based on consistency regularization can harness the abundance of unlabeled data to pr…
The scarcity of data annotated at the desired level of granularity is a recurring issue in many applications. Significant amounts of effort have been devoted to developing weakly supervised methods tailored to each individual setting, which are often carefully designed to take advantage of the particular properties of …
Traditionally, there are three species of classification: unsupervised, supervised, and semi-supervised. Supervised and semi-supervised classification differ by whether or not weight is given to unlabelled observations in the classification procedure. In unsupervised classification, or clustering, all observations are …
Proposes a constrained labeling method for weakly supervised learning.
Self-supervised and supervised methods learn similar intermediate visual representations but diverge in final layers.
Improved VAE learns disentangled representations with less supervision.
Paper develops methods for semi-supervised Fréchet regression.
Generative Adversarial Networks (GAN) have shown promising results on a wide variety of complex tasks. Recent experiments show adversarial training provides useful gradients to the generator that helps attain better performance. In this paper, we intend to theoretically analyze whether supervised learning with adversar…
Paper resolves the debate on process vs. outcome supervision in reinforcement learning.
Optimal and safe semi-supervised learning estimator for high-dimensional data.
SPO optimizes LLMs by eliminating group-based baselines and variance issues.
Survey of self-supervised learning methods in computer vision, NLP, and graph learning.
Improved self-supervised learning for document images.
Weak supervision challenges black-box models, suggesting fusion of modeling cultures.
Proposes a framework for semi-supervised continual learning from sequentially arriving data.
Unified framework for semi-supervised learning reduces annotation needs.
Self-supervision improves GCNs' generalizability and robustness.
We present a technique to improve the transferability of deep representations learned on small labeled datasets by introducing self-supervised tasks as auxiliary loss functions. While recent approaches for self-supervised learning have shown the benefits of training on large unlabeled datasets, we find improvements in …
For semi-supervised techniques to be applied safely in practice we at least want methods to outperform their supervised counterparts. We study this question for classification using the well-known quadratic surrogate loss function. Using a projection of the supervised estimate onto a set of constraints imposed by the u…