arXiv research

A new method for multi-task learning by allocating parameters.

Lapse improves parameter servers by dynamically allocating parameters, achieving near-linear scaling.

Investigates optimal parameter allocation in Transformers for efficiency and expressivity.

Proposes a method to allocate time budgets in mixed criticality systems.

New model allocates features sublinearly, improving model fit and performance.

POCA optimizes hyperparameters with adaptive allocation for faster convergence.

We characterize the class of exchangeable feature allocations assigning probability $V_{n,k}\prod_{l=1}^{k}W_{m_{l}}U_{n-m_{l}}$ to a feature allocation of $n$ individuals, displaying $k$ features with counts $(m_{1},\ldots,m_{k})$ for these features. Each element of this class is parametrized by a countable matrix $V$…

Optimal resource allocation in censored semi-bandits with unknown thresholds.

Optimizes resource allocation for distributed parameter estimation in sensor networks.

New method improves portfolio allocation using local Gaussian correlation.

Although modern portfolio theory has been in existence for over 60 years, fund managers often struggle to get its models to produce reliable portfolio allocations without strongly constraining the decision vector by tight bands of strategic allocation targets. The two main root causes to this problem are inadequate par…

StatLoRA uses statistical inference to allocate ranks in LoRA fine-tuning, improving performance.

Paper calculates the exact error of LDA models.

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

Scheduling and power allocation improve federated learning efficiency in NOMA networks.

Designing neural network architectures is a task that lies somewhere between science and art. For a given task, some architectures are eventually preferred over others, based on a mix of intuition, experience, experimentation and luck. For many tasks, the final word is attributed to the loss function, while for some ot…

Unified framework for optimizing portfolios with distributions over weights, returns, and parameters.

NPAS trains neural networks with a fixed parameter budget, improving performance and compactness.

Robo-advisors use MPC to create dynamic investment strategies.

Study optimal adaptive allocation for multi-armed bandits with Markovian rewards.

The paper proposes a machine learning approach for state-dependent asset allocation.

Bayesian approach improves AdaLoRA's performance and efficiency.

Hyper-parameters play a major role in the learning and inference process of latent Dirichlet allocation (LDA). In order to begin the LDA latent variables learning process, these hyper-parameters values need to be pre-determined. We propose an extension for LDA that we call 'Latent Dirichlet allocation Gibbs Newton' (LD…

Improved resource allocation method reduces procurement costs.

A new DNN structure reduces complexity for wireless tasks.

Paper proposes ClipSMT algorithm for better ATE estimation.

Paper proposes OPF policy for fair resource allocation with sublinear regret.

Estimates expected information gain using density approximations and dimension reduction.

We consider the classical problem of sequential resource allocation where a decision maker must repeatedly divide a budget between several resources, each with diminishing returns. This can be recast as a specific stochastic optimization problem where the objective is to maximize the cumulative reward, or equivalently …

Paper introduces Market-adaptive Ratio for better portfolio management.

Framework for online resource allocation using social welfare functions.

Dynamic risk factor model improves portfolio performance in high dimensions.

We investigate a class of feature allocation models that generalize the Indian buffet process and are parameterized by Gibbs-type random measures. Two existing classes are contained as special cases: the original two-parameter Indian buffet process, corresponding to the Dirichlet process, and the stable (or three-param…

Paper proposes a CNN-based method for estimating intra frame bits and quality.

RDL-Net improves speech enhancement with fewer parameters and better performance.

The study identifies core and satellite segments in the cryptocurrency market.

It is challenging to develop stochastic gradient based scalable inference for deep discrete latent variable models (LVMs), due to the difficulties in not only computing the gradients, but also adapting the step sizes to different latent factors and hidden layers. For the Poisson gamma belief network (PGBN), a recently …

This paper optimizes portfolio selection by penalizing tracking error, improving Sharpe ratio.

We theoretically and empirically study portfolio optimization under transaction costs and establish a link between turnover penalization and covariance shrinkage with the penalization governed by transaction costs. We show how the ex ante incorporation of transaction costs shifts optimal portfolios towards regularized …

New algorithm learns optimal resource allocation in wireless systems without models.

lCARE improves EVaR model for time-varying tail risk by localizing parameters.

Neural networks enjoy widespread use, but many aspects of their training, representation, and operation are poorly understood. In particular, our view into the training process is limited, with a single scalar loss being the most common viewport into this high-dimensional, dynamic process. We propose a new window into …

We extend the analysis of investment strategies derived from penalized quantile regression models, introducing alternative approaches to improve state\textendash of\textendash art asset allocation rules. First, we use a post\textendash penalization procedure to deal with overshrinking and concentration issues. Second, …

The paper introduces isotropy as a regularizer to enhance portfolio stability.

We study the problem of multimodal generative modelling of images based on generative adversarial networks (GANs). Despite the success of existing methods, they often ignore the underlying structure of vision data or its multimodal generation characteristics. To address this problem, we introduce the Dirichlet prior fo…

End-to-end neural network optimizes portfolios by directly learning allocations from features.

Spatial statisticians and quantitative investors use the same mathematical object: a Schur complement, damped by one parameter.

Meta-learning performance is affected by how task diversity is allocated, not just overall variability.