arXiv research

Study improves S&P 500 volatility forecasting through regime-switching methods.

This paper analyzes how periodic and soft target updates stabilize linear Q-learning.

Annealed Entropic Allocation improves ranking and selection by mitigating hard switching and improving finite-budget discrimination.

FedSGM tackles constrained federated learning with unified framework.

Proposes a new sampling policy for ranking and selection problems.

Estimates hybrid dynamical systems with polynomial expansions and Markovian switching.

The paper proposes a principle for dynamically adjusting the granularity of reinforcement learning abstractions.

SoftAD improves classification accuracy with less fine-tuning and fewer computational costs.

GDM models time series with smoother transitions and interpretable states.

New algorithm reduces switching costs in multinomial logit bandit problems.

New polynomial invariants derived from birack and switch structures.

In this paper, we study optimal switching problems under ambiguity. To characterize the optimal switching under ambiguity in the finite horizon, we use multidimensional reflected backward stochastic differential equations (multidimensional RBSDEs) and show that a value function of the optimal switching under ambiguity …

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.

This work extends identifiability analysis to sequential latent variable models, focusing on Switching Dynamical Systems.

The problem of optimal switching between nonlinear autonomous subsystems is investigated in this study where the objective is not only bringing the states to close to the desired point, but also adjusting the switching pattern, in the sense of penalizing switching occurrences and assigning different preferences to util…

Soft cells fill space without gaps, derived from minimal surfaces and deformed using edge bending.

Code-switching, the alternation of languages within a conversation or utterance, is a common communicative phenomenon that occurs in multilingual communities across the world. This survey reviews computational approaches for code-switched Speech and Natural Language Processing. We motivate why processing code-switched …

New algorithms improve sampling from complex distributions.

Squirrel switches between optimizers for better performance.

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…

Study approximates financial market with discrete-time models.

New method improves stability of soft FQI for offline RL.

This paper studies deep learning methodologies for portfolio optimization in the US equities market. We present a novel residual switching network that can automatically sense changes in market regimes and switch between momentum and reversal predictors accordingly. The residual switching network architecture combines …

Optimizes control of hybrid systems with multiple switching processes.

Study tackles balancing policy switching costs in offline RL.

New algorithm learns switching dynamics from multiple neural signals.

Paper tackles utility maximization with job-switching and retirement constraints.

New RL algorithm reduces policy switching cost to loglog(T) with similar regret.

Generalizes soft noncommutative schemes to flag varieties.

Paper proposes a new loss function for conditional models using soft targets.

Paper presents an efficient algorithm for linear MDP with low switching cost.

This paper studies the impact of limited switches on resource-constrained dynamic pricing with demand learning. We focus on the classical price-based blind network revenue management problem and extend our results to the bandits with knapsacks problem. In both settings, a decision maker faces stochastic and distributio…

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.

Study strategic competition in commodity markets using impulse-switching controls.

Improves deep neural networks using soft labels through alternating minimization.

One type of switch simplifies operations on lattice knots.

Optimal switching regret for all segmentations in online convex optimisation.

The paper develops a multi-unit soft sensing model for virtual flow meters that improves few-shot learning.

As a metric to measure the performance of an online method, dynamic regret with switching cost has drawn much attention for online decision making problems. Although the sublinear regret has been provided in many previous researches, we still have little knowledge about the relation between the dynamic regret and the s…

This paper tackles near-optimal adversarial RL with switching costs, providing algorithms and matching lower bounds.

This paper addresses parameter estimation for wave equations with Markovian switching.

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…

Algorithm for bandits with switching costs achieves optimal regret bounds.