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.
Study shows challenges in reinforcement learning math problems, proposing enhancements and a hardness measure.
Paper analyzes BIHT for noisy 1-bit CS, improving results with up to τ-fraction of incorrect measurements.
Study shows exponential sample complexity for stabilizing certain linear systems.
As the Securities and Exchange Commission(SEC) has implemented a new regulation on short-sellings, short-sellers are required to repurchase stocks once the clearing risk rises to a certain level. Avellaneda and Lipkin proposed a fully coupled SDE system to describe the mechanism which is referred as Hard-To-Borrow(HTB)…
Proves hardness of semi-discrete optimal transport and proposes regularization methods.
Financial markets are not random, but hard to predict due to hidden causes and strategic use.
Binary Iterative Hard Thresholding converges with optimal number of 1-bit measurements.
Meta-learning strategy improves few-shot classification performance.
New RF dissimilarity measures improve multi-view learning accuracy.
New findings on maximizing noise stability in partitions of Gaussian space.
Iterative hard thresholding (IHT) is a projected gradient descent algorithm, known to achieve state of the art performance for a wide range of structured estimation problems, such as sparse inference. In this work, we consider IHT as a solution to the problem of learning sparse discrete distributions. We study the hard…
An algorithmically hard phase was described in a range of inference problems: even if the signal can be reconstructed with a small error from an information theoretic point of view, known algorithms fail unless the noise-to-signal ratio is sufficiently small. This hard phase is typically understood as a metastable bran…
Recent convolutional neural networks (CNNs) have led to impressive performance but often suffer from poor calibration. They tend to be overconfident, with the model confidence not always reflecting the underlying true ambiguity and hardness. In this paper, we propose angular visual hardness (AVH), a score given by the …
Paper tackles 1-bit compressed sensing, presenting efficient algorithm for sparse signal estimation.
Sparse linear regression is hard to solve efficiently, even with k-sparse solutions.
This paper explains the math behind a generative adversarial network (GAN) model and why it is hard to be trained. Wasserstein GAN is intended to improve GANs' training by adopting a smooth metric for measuring the distance between two probability distributions.
Coresets are efficient representations of data sets such that models trained on the coreset are provably competitive with models trained on the original data set. As such, they have been successfully used to scale up clustering models such as K-Means and Gaussian mixture models to massive data sets. However, until now,…
We study the minimax optimal rates for estimating a range of Integral Probability Metrics (IPMs) between two unknown probability measures, based on independent samples from them. Curiously, we show that estimating the IPM itself between probability measures, is not significantly easier than estimating the probabili…
We consider stochastic gradient descent (SGD) for least-squares regression with potentially several passes over the data. While several passes have been widely reported to perform practically better in terms of predictive performance on unseen data, the existing theoretical analysis of SGD suggests that a single pass i…
Proposes new random models for fuzzy clustering similarity measures.
Graph neural networks struggle to distinguish certain graph structures.
New lower bounds show sparse recovery is hard even with multiple preconditioners.
Recovery of low-rank matrices from a small number of linear measurements is now well-known to be possible under various model assumptions on the measurements. Such results demonstrate robustness and are backed with provable theoretical guarantees. However, extensions to tensor recovery have only recently began to be st…
Study on computing and estimating calibration distance, showing hardness and efficiency.
New algorithms optimize a soft-robust criterion in reinforcement learning, reducing conservatism.
Trivial links are unique up to number of link components, but they can be hard to recognize from arbitrary diagrams. We define a new measure of the complexity of a link embedding, the crumple, and show how this may be used to measure progress toward a trivial embedding. In conjunction with a modified form of arc presen…
Developed a new thresholding method that connects soft and hard thresholding.
New algorithm resists contamination in high-dimensional regression with optimal performance.
We introduce a new family of minmax rank aggregation problems under two distance measures, the Kendall τ and the Spearman footrule. As the problems are NP-hard, we proceed to describe a number of constant-approximation algorithms for solving them. We conclude with illustrative applications of the aggregation methods on…
Study uncovers bias in image classification models using attribution maps.
Hard to approximate critical points for simple nonconvex functions.
New method improves structure learning on sparse graphs.
Random forest is widely exploited as an ensemble learning method. In many practical applications, however, there is still a significant challenge to learn from imbalanced data. To alleviate this limitation, we propose a deep dynamic boosted forest (DDBF), a novel ensemble algorithm that incorporates the notion of hard …
This paper is concerned with the hard thresholding operator which sets all but the largest absolute elements of a vector to zero. We establish a {\em tight} bound to quantitatively characterize the deviation of the thresholded solution from a given signal. Our theoretical result is universal in the sense that it ho…
In this paper, we consider the problem of compressed sensing where the goal is to recover almost all the sparse vectors using a small number of fixed linear measurements. For this problem, we propose a novel partial hard-thresholding operator that leads to a general family of iterative algorithms. While one extreme of …
We study probability measures induced by set functions with constraints. Such measures arise in a variety of real-world settings, where prior knowledge, resource limitations, or other pragmatic considerations impose constraints. We consider the task of rapidly sampling from such constrained measures, and develop fast M…
The paper optimizes stock portfolios with constraints based on performance attribution.
Paper proves hardness of learning various complex models under local pseudorandom generators.
This work connects hardness of approximation and learning.
Paper solves NP-hard sparse mixed linear regression problem with provable guarantees.
Study on hard Legendrian unknots using normal rulings.
Moving between 3-manifold triangulations is NP-hard
Hard instances, which require a long time for a specific algorithm to solve, help (1) analyze the algorithm for accelerating it and (2) build a good benchmark for evaluating the performance of algorithms. There exist several efforts for automatic generation of hard instances. For example, evolutionary algorithms have b…
Reliable measures of statistical dependence could be useful tools for learning independent features and performing tasks like source separation using Independent Component Analysis (ICA). Unfortunately, many of such measures, like the mutual information, are hard to estimate and optimize directly. We propose to learn i…
Deciding effective and timely preventive measures against complex social problems affecting relatively low income geographies is a difficult challenge. There is a strong need to adopt intelligent automation based solutions with low cost imprints to tackle these problems at larger scales. Starting with the hypothesis th…
Tackles the computational hardness of HPC detection, conjecturing equivalence to PC detection.
We develop mask iterative hard thresholding algorithms (mask IHT and mask DORE) for sparse image reconstruction of objects with known contour. The measurements follow a noisy underdetermined linear model common in the compressive sampling literature. Assuming that the contour of the object that we wish to reconstruct i…
Paper tackles optimal network compression for financial systems.