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.
Paper introduces impact curves for evaluating binarized regression models with varying costs.
We study learning problems in which the conditional distribution of the output given the input varies as a function of additional task variables. In varying-coefficient models with Gaussian process priors, a Gaussian process generates the functional relationship between the task variables and the parameters of this con…
A method to approximate instance-dependent label noise using instance-confidence embedding.
This paper reviews FSL for open-world learning, addressing uncertainties and dynamic conditions.
New algorithm for non-stationary bandits with slow drifts.
Paper uses financial news for stock trend forecasting using deep multiple instance learning.
Algorithm generates adaptive confidence sets for instance segmentation with guaranteed coverage.
TOQ-Nets learn to recognize complex temporal events with varying objects and sequences.
New CTRL algorithm adapts to varying problem difficulty.
Weakly supervised instance labeling using only image-level labels, in lieu of expensive fine-grained pixel annotations, is crucial in several applications including medical image analysis. In contrast to conventional instance segmentation scenarios in computer vision, the problems that we consider are characterized by …
While tasks could come with varying the number of instances and classes in realistic settings, the existing meta-learning approaches for few-shot classification assume that the number of instances per task and class is fixed. Due to such restriction, they learn to equally utilize the meta-knowledge across all the tasks…
Estimates financial market impacts of COVID-19 using time-varying kernel density.
The study analyzes how deep neural networks treat instances with regular and irregular patterns.
FaStR improves scalability for time-aware RS with varying coefficients.
Collective classification models attempt to improve classification performance by taking into account the class labels of related instances. However, they tend not to learn patterns of interactions between classes and/or make the assumption that instances of the same class link to each other (assortativity assumption).…
Automates MIPs solution with semi-supervised graph neural networks.
Despite tremendous progress in outlier detection research in recent years, the majority of existing methods are designed only to detect unconditional outliers that correspond to unusual data patterns expressed in the joint space of all data attributes. Such methods are not applicable when we seek to detect conditional …
Decentralized learning for matching markets with time-varying preferences.
Defense against adversarial attacks by manipulating feature thickness.
New taxonomy and improved solvers for discrete energy minimization.
New research shows that binary classification can be done with noisy data, but only if there are clean samples available.
Develops ML tool for macroeconomic forecasting with clear interpretations.
New method for estimating spatial associations with discrete data, even under model misspecification.
Deep neural networks have yielded superior performance in many applications; however, the gradient computation in a deep model with millions of instances lead to a lengthy training process even with modern GPU/TPU hardware acceleration. In this paper, we propose AutoAssist, a simple framework to accelerate training of …
Mathematical Reinforcement Learning faces a 'Two-Hump' problem due to sparse rewards and a scarcity of intermediate 'hard-but-solvable' instances.
We establish a connection between trend filtering and system identification which results in a family of new identification methods for linear, time-varying (LTV) dynamical models based on convex optimization. We demonstrate how the design of the cost function promotes a model with either a continuous change in dynamic…
Dynamic neural network toolkits such as PyTorch, DyNet, and Chainer offer more flexibility for implementing models that cope with data of varying dimensions and structure, relative to toolkits that operate on statically declared computations (e.g., TensorFlow, CNTK, and Theano). However, existing toolkits - both static…
Efficiently infers gene regulatory networks from spatial data.
Optimizes decisions in time-varying distributions using online stochastic methods and Wasserstein distance.
In this paper, we propose the uncertain volatility models with stochastic bounds. Like the regular uncertain volatility models, we know only that the true model lies in a family of progressively measurable and bounded processes, but instead of using two deterministic bounds, the uncertain volatility fluctuates between …
In this paper, we are concerned with the problem of creating flattening maps of simply-connected open surfaces in . Using a natural principle of density diffusion in physics, we propose an effective algorithm for computing density-equalizing flattening maps with any prescribed density distribution. By var…
Study shows AMM liquidity providers lose more than they earn, with varying profitability across pairs.
Study improves model robustness in noisy datasets.
NeuralBeta uses deep learning to estimate beta, outperforming traditional methods.
Algorithm optimizes functions without parameters, converging to global minima.
Survey compares methods for generating artificial outliers.
Large-scale Hierarchical Classification (HC) involves datasets consisting of thousands of classes and millions of training instances with high-dimensional features posing several big data challenges. Feature selection that aims to select the subset of discriminant features is an effective strategy to deal with large-sc…
Adversarial self-play in two-player games has delivered impressive results when used with reinforcement learning algorithms that combine deep neural networks and tree search. Algorithms like AlphaZero and Expert Iteration learn tabula-rasa, producing highly informative training data on the fly. However, the self-play t…
In this paper, we study time-varying graphical models based on data measured over a temporal grid. Such models are motivated by the needs to describe and understand evolving interacting relationships among a set of random variables in many real applications, for instance the study of how stocks interact with each other…
Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-p…
Adaptive feature normalization improves model robustness to extraneous variables.
Study shows how varying levels of supervision and orthonormality constraints affect generalization errors in subspace fitting.
Method selects most useful network model for various tasks.
We introduce and study some deformations of complete finite-volume hyperbolic four-manifolds that may be interpreted as four-dimensional analogues of Thurston's hyperbolic Dehn filling. We construct in particular an analytic path of complete, finite-volume cone four-manifolds that interpolates between two hyperbo…
We propose SPARFA-Trace, a new machine learning-based framework for time-varying learning and content analytics for education applications. We develop a novel message passing-based, blind, approximate Kalman filter for sparse factor analysis (SPARFA), that jointly (i) traces learner concept knowledge over time, (ii) an…
NetFuse merges different DNN models with varying weights for faster inference.
Imbalanced classification has been a major challenge for machine learning because many standard classifiers mainly focus on balanced datasets and tend to have biased results towards the majority class. We modify entropy fuzzy support vector machine (EFSVM) and introduce instance-based entropy fuzzy support vector machi…
Unordered feature sets are a nonstandard data structure that traditional neural networks are incapable of addressing in a principled manner. Providing a concatenation of features in an arbitrary order may lead to the learning of spurious patterns or biases that do not actually exist. Another complication is introduced …