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.
Beta-SOD detects and corrects noisy object re-identification using cosine similarity and Beta mixtures.
Deep learning algorithms can fare poorly when the training dataset suffers from heavy class-imbalance but the testing criterion requires good generalization on less frequent classes. We design two novel methods to improve performance in such scenarios. First, we propose a theoretically-principled label-distribution-awa…
Stylized facts of empirical assets log-returns include the existence of (semi) heavy tailed distributions and a non-linear spectrum of Hurst exponents . Empirical data considered are daily prices of 10 large indices from 01/01/1990 to 12/31/2004. We propose a stylized model of price dynamics which is…
New method calibrates probabilistic regression models without restrictive assumptions.
New research shows shrinkage methods re-scale portfolio efficient frontiers under distributional misspecification.
This paper improves robust cluster enumeration for RES data.
New method calibrates photometric redshift PDFs more accurately.
A new method aligns source and target distributions by tuning their weights.
MixFT re-partitions data into sub-domains for better TSFM fine-tuning.
New GoF test improves change point detection in multivariate time series.
FedReLa: A novel data-level approach for imbalanced federated learning
Model for dynamic pricing across multiple RE groups to maximize revenue.
The paper improves SMC algorithm for multi-modal distributions by proving variance bounds.
With latent variables, stochastic recurrent models have achieved state-of-the-art performance in modeling sound-wave sequence. However, opposite results are also observed in other domains, where standard recurrent networks often outperform stochastic models. To better understand this discrepancy, we re-examine the role…
A new algorithm improves stochastic linear bandit performance using residual bootstrap.
DisCor corrects reinforcement learning issues by re-weighting collected data.
A new PLL method uses class activation values to improve robustness.
This paper addresses detection of a reverse engineering (RE) attack targeting a deep neural network (DNN) image classifier; by querying, RE's aim is to discover the classifier's decision rule. RE can enable test-time evasion attacks, which require knowledge of the classifier. Recently, we proposed a quite effective app…
We study conformal -subgeometry of submanifolds in a semi-Riemannian -manifold, focusing on conformal -manifolds and their Poincaré-Einstein metrics . Our approach is based on the spectral theory of Dirac operator in the ambient -manifold, and associated spinor valued meromorp…
Federated Learning is a distributed learning paradigm with two key challenges that differentiate it from traditional distributed optimization: (1) significant variability in terms of the systems characteristics on each device in the network (systems heterogeneity), and (2) non-identically distributed data across the ne…
Estimates calibration error under label shift without labels.
Gaussian process model for vector-valued function has been shown to be useful for multi-output prediction. The existing method for this model is to re-formulate the matrix-variate Gaussian distribution as a multivariate normal distribution. Although it is effective in many cases, re-formulation is not always workable a…
Research optimizes a small RES utility's portfolio by dynamically trading in German electricity markets.
We compare the isoperimetric profiles of $S^2 \times \re^3$ and of $S^3 \times \re^2$ with that of a round 5-sphere (of appropriate radius). Then we use this comparison to obtain lower bounds for the Yamabe constants of $S^2 \times \re^3$ and $S^3 \times \re^2$. Explicitly we show that $Y(S^3 \times \re^2, [g_0^3 +dx^2…
Re-initializing neural networks improves generalization but not as much as other techniques.
Model predicts wound and episode-level readmission risk and time to re-admit.
Person Re-identification (re-id) faces two major challenges: the lack of cross-view paired training data and learning discriminative identity-sensitive and view-invariant features in the presence of large pose variations. In this work, we address both problems by proposing a novel deep person image generation model for…
Estimates causal contributions of multiple causes on outcome changes.
Unsupervised Domain Adaptation aims to learn a model on a source domain with labeled data in order to perform well on unlabeled data of a target domain. Current approaches focus on learning \textit{Domain Invariant Representations}. It relies on the assumption that such representations are well-suited for learning the …
Gaussian processes (GPs) are a powerful tool for probabilistic inference over functions. They have been applied to both regression and non-linear dimensionality reduction, and offer desirable properties such as uncertainty estimates, robustness to over-fitting, and principled ways for tuning hyper-parameters. However t…
Efficiently combines autoregressive and set-based models for joint distributions.
Knowledge graph reasoning is a critical task in natural language processing. The task becomes more challenging on temporal knowledge graphs, where each fact is associated with a timestamp. Most existing methods focus on reasoning at past timestamps and they are not able to predict facts happening in the future. This pa…
EBR improves NMT by re-ranking samples drawn from MLE-trained models.
High-dimensional settings, where the data dimension () far exceeds the number of observations (), are common in many statistical and machine learning applications. Methods based on -relaxation, such as Lasso, are very popular for sparse recovery in these settings. Restricted Eigenvalue (RE) condition is a…
We implement a method for re-ranking top-10 results of a state-of-the-art question answering (QA) system. The goal of our re-ranking approach is to improve the answer selection given the user question and the top-10 candidates. We focus on improving deployed QA systems that do not allow re-training or re-training comes…
Paper proves Reshetikhin-Turaev link invariants appear in higher order terms of re-normalized link invariants for plumbed links.
We introduce a new approach for designing computationally efficient learning algorithms that are tolerant to noise, and demonstrate its effectiveness by designing algorithms with improved noise tolerance guarantees for learning linear separators. We consider both the malicious noise model and the adversarial label nois…
It is natural to ask: what kinds of matrices satisfy the Restricted Eigenvalue (RE) condition? In this paper, we associate the RE condition (Bickel-Ritov-Tsybakov 09) with the complexity of a subset of the sphere in , where is the dimensionality of the data, and show that a class of random matrices with indep…
Develops RES metrics for stable rare-event forecasting evaluation.
The paper analyzes how re-weighting helps in reducing variance in high-dimensional kernel methods under covariate shifts.
By the Fox's re-embedding theorem, any compact submanifold of the 3-sphere can be re-embedded in the 3-sphere so that it is unknotted. It is unknown whether the Fox's re-embedding can be replaced with twistings. In this paper, we will show that any closed 2-manifold embedded in the 3-sphere can be unknotted by twisting…
Efficiently bootstraps massive distributed data without over-resampling.
Examines climate financing for renewable energy projects using structured funds.
RE enhances DL by learning model behavior, enabling iterative self-improvement.
Re-speaking is a mechanism for obtaining high quality subtitles for use in live broadcast and other public events. Because it relies on humans performing the actual re-speaking, the task of estimating the quality of the results is non-trivial. Most organisations rely on humans to perform the actual quality assessment, …
New theory shows perishable goods markets are more stable and efficient.
We study and generalize in various ways the model of rational expectation (RE) bubbles introduced by Blanchard and Watson in the economic literature. First, bubbles are argued to be the equivalent of Goldstone modes of the fundamental rational pricing equation, associated with the symmetry-breaking introduced by non-va…
Optimizing full likelihoods adapts loss scales and shapes for robust modeling.