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.
Theory for soft-margin classifiers on object manifolds.
Margin maximization in the hard-margin sense, proposed as feature elimination criterion by the MFE-LO method, is combined here with data radius utilization to further aim to lower generalization error, as several published bounds and bound-related formulations pertaining to lowering misclassification risk (or error) pe…
Framework learns stochastic dynamics from endpoint and intermediate distributions using soft energy constraints.
Gradient descent finds halfspaces with low error for agnostic learning.
Support vector machine (SVM) has attracted great attentions for the last two decades due to its extensive applications, and thus numerous optimization models have been proposed. To distinguish all of them, in this paper, we introduce a new model equipped with an soft-margin loss (dubbed as -SVM) whic…
New algorithms estimate Q-functions under partial coverage and realizability, improving offline RL guarantees.
Binary classification is a common statistical learning problem in which a model is estimated on a set of covariates for some outcome indicating the membership of one of two classes. In the literature, there exists a distinction between hard and soft classification. In soft classification, the conditional class probabil…
MSLG generates soft labels to improve DNN performance on noisy datasets.
End-to-end text-to-speech (TTS) synthesis is a method that directly converts input text to output acoustic features using a single network. A recent advance of end-to-end TTS is due to a key technique called attention mechanisms, and all successful methods proposed so far have been based on soft attention mechanisms. H…
A framework learns dynamic soft labels to improve model generalization and accuracy.
The lack of interpretability often makes black-box models difficult to be applied to many practical domains. For this reason, the current work, from the black-box model input port, proposes to incorporate data-based prior information into the black-box soft-margin SVM model to enhance its interpretability. The concept …
Heterogeneous domain adaptation (HDA) aims to facilitate the learning task in a target domain by borrowing knowledge from a heterogeneous source domain. In this paper, we propose a Soft Transfer Network (STN), which jointly learns a domain-shared classifier and a domain-invariant subspace in an end-to-end manner, for a…
Soft modularization improves sample efficiency and performance in reinforcement learning.
Let X be a data matrix of rank ρ, whose rows represent n points in d-dimensional space. The linear support vector machine constructs a hyperplane separator that maximizes the 1-norm soft margin. We develop a new oblivious dimension reduction technique which is precomputed and can be applied to any input matrix X. We pr…
Tropical SVM tackles phylogenomics by classifying multi-locus data.
Cumulative entropy regularization introduces a regulatory signal to the reinforcement learning (RL) problem that encourages policies with high-entropy actions, which is equivalent to enforcing small deviations from a uniform reference marginal policy. This has been shown to improve exploration and robustness, and it ta…
Generative AI connects to Schrödinger bridge problems with soft constraints for stability.
Neural machine translation (NMT) systems have been shown to give undesirable translation when a small change is made in the source sentence. In this paper, we study the behaviour of NMT systems when multiple changes are made to the source sentence. In particular, we ask the following question "Is it possible for an NMT…
Recently, there has been much interest in finding globally optimal Bayesian network structures. These techniques were developed for generative scores and can not be directly extended to discriminative scores, as desired for classification. In this paper, we propose an exact method for finding network structures maximiz…
SoftKI combines SKI and variational methods for scalable GP regression.
CoCP optimizes prediction intervals by jointly learning center and radius, improving efficiency and coverage.
CriticSMC improves planning efficiency in constrained environments.
Automatically learns flexible symmetry constraints in neural networks using gradients.
This work establishes the equivalence between neural networks and support vector machines.
Employers actively look for talents having not only specific hard skills but also various soft skills. To analyze the soft skill demands on the job market, it is important to be able to detect soft skill phrases from job advertisements automatically. However, a naive matching of soft skill phrases can lead to false pos…
Soft labeling impacts OOD detection in neural networks.
Developed a new thresholding method that connects soft and hard thresholding.
Soft cells fill space without gaps, derived from minimal surfaces and deformed using edge bending.
ASBART accelerates Soft BART for faster Bayesian regression.
Maximum entropy deep reinforcement learning (RL) methods have been demonstrated on a range of challenging continuous tasks. However, existing methods either suffer from severe instability when training on large off-policy data or cannot scale to tasks with very high state and action dimensionality such as 3D humanoid l…
New method improves stability of soft FQI for offline RL.
LLMs add value in commodity portfolio construction when information set and implementation rules are held fixed.
Generalizes soft noncommutative schemes to flag varieties.
Paper proposes a new loss function for conditional models using soft targets.
Introduces Soft-SVM for binary classification bridging logistic and SVM.
Deep model learns from labeled and unlabeled data for industrial soft sensing.
This paper introduces TNTK to study infinite soft tree ensembles.
Improves deep neural networks using soft labels through alternating minimization.
The paper develops a multi-unit soft sensing model for virtual flow meters that improves few-shot learning.
In this article supervised learning problems are solved using soft rule ensembles. We first review the importance sampling learning ensembles (ISLE) approach that is useful for generating hard rules. The soft rules are then obtained with logistic regression from the corresponding hard rules. In order to deal with the p…
Recently, deep learning becomes the main focus of machine learning research and has greatly impacted many important fields. However, deep learning is criticized for lack of interpretability. As a successful unsupervised model in deep learning, the autoencoder embraces a wide spectrum of applications, yet it suffers fro…
Proposes a new method for deep ensembles that improves accuracy and calibration.
Bayesian neural networks update beliefs with soft evidence, improving accuracy and calibration.
A method for selecting pseudo-labeled data in semi-supervised learning using generalized Bayes and soft revision.
Soft Actor-Critic (SAC) is an off-policy actor-critic deep reinforcement learning (DRL) algorithm based on maximum entropy reinforcement learning. By combining off-policy updates with an actor-critic formulation, SAC achieves state-of-the-art performance on a range of continuous-action benchmark tasks, outperforming pr…
Floer homology is a good example of homological invariants living in the infinite dimension. We suggest a way to construct this kind of invariants using only soft essentially finite-dimensional tools; no hard analysis or PDE is involved. This work is partially inspired by the M. Gromov's survey ``Soft and hard symplect…
We investigate the relation of two fundamental tools in machine learning and signal processing, that is the support vector machine (SVM) for classification, and the Lasso technique used in regression. We show that the resulting optimization problems are equivalent, in the following sense. Given any instance of an $\ell…
Constructs noncommutative spaces for D-branes on complex algebraic spaces.