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AI Blogs
Density Estimation on Low-Dimensional Manifolds: An Inflation-Deflation Approach Authors: Christian Horvat, Jean-Pascal Pfister; Volume 24, Issue 61, Pages 1-37, 2023. Abstract Normalizing flows (NFs) are density estimators that use neural networks. However, NFs have a limitation: the density’s support must…
We present a novel approach called the Independent Component Laplace Process (ICLP) mechanism for releasing differentially private functional summaries. Unlike previous methods that assume finite-dimensional data trajectories, our approach treats the functional summaries as infinite-dimensional objects and perturbs them with…
Infinite-dimensional optimization and Bayesian nonparametric learning of stochastic differential equations Authors: Arnab Ganguly, Riten Mitra, Jinpu Zhou; 24(159):1−39, 2023. Abstract This paper focuses on two main topics. The first part of the paper presents general results for infinite-dimensional optimization problems…
The modern financial electronic exchanges are dynamic and fast-moving marketplaces where billions of dollars are exchanged daily. However, these exchanges are also susceptible to manipulation and fraudulent activities. Traditionally, identifying such activities has been a task assigned to humans. However,…
Tractability and Non-Approximability of SHAP-Score-Based Explanations Marcelo Arenas, Pablo Barcelo, Leopoldo Bertossi, Mikael Monet; 24(63):1−58, 2023. Abstract Shapley values-based scores are commonly used to provide explanations for classification results in machine learning models. One such score is the Shap-score, which…
In this paper, we present a novel approach to visual model-based RL methods. These methods often encode image observations into low-dimensional representations, but they do not effectively eliminate redundant information. As a result, they are vulnerable to spurious variations, such…
Multivariate Soft Rank via Entropy-Regularized Optimal Transport: Sample Efficiency and Generative Modeling Shoaib Bin Masud, Matthew Werenski, James M. Murphy, Shuchin Aeron; 24(160):1−65, 2023. Abstract This paper introduces the concept of multivariate soft rank, which addresses some limitations of existing…
Previous research utilized backward error analysis to identify ordinary differential equations (ODEs) that approximate the gradient descent trajectory. These studies revealed that finite step sizes implicitly regulate solutions by penalizing the two-norm of the loss gradients in the ODEs. In…
Wide-minima Density Hypothesis and the Explore-Exploit Learning Rate Schedule Nikhil Iyer, V. Thejas, Nipun Kwatra, Ramachandran Ramjee, Muthian Sivathanu; 24(65):1−37, 2023. Abstract This paper presents detailed experiments that support the argument that wide minima generalize better than narrow minima. Additionally,…
Predicting stock prices over a long-term period is crucial for various financial purposes such as forecasting volatility, pricing derivatives, hedging, and risk quantification. It is also important for institutional investors to have a liquidity horizon of several days to exit…
