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 proposes efficient weight updates for edge nodes with minimal communication.
A new update rule for deep reinforcement learning reduces learning variance and variance in reference signals.
Paper proves Jeffrey's update rule minimizes relative entropy.
AMUSE uses reinforcement learning to predict optimal model updates.
We shed new insights on the two commonly used updates for the online -PCA problem, namely, Krasulina's and Oja's updates. We show that Krasulina's update corresponds to a projected gradient descent step on the Stiefel manifold of the orthonormal -frames, while Oja's update amounts to a gradient descent step using…
Efficiently updates posterior tree distributions over meta-trees.
Recently, the technique of local updates is a powerful tool in centralized settings to improve communication efficiency via periodical communication. For decentralized settings, it is still unclear how to efficiently combine local updates and decentralized communication. In this work, we propose an algorithm named as L…
The paper examines how updates to probabilistic models influence behavior based on evidence.
Paper improves policy updates in reinforcement learning to speed up learning.
In this paper, we study the randomized distributed coordinate descent algorithm with quantized updates. In the literature, the iteration complexity of the randomized distributed coordinate descent algorithm has been characterized under the assumption that machines can exchange updates with an infinite precision. We con…
Proposes a new method for nonlinear Bayesian updates using ensemble kernel regression.
This paper analyzes how periodic and soft target updates stabilize linear Q-learning.
In this letter, we generalize the convolutional NMF by taking the -divergence as the contrast function and present the correct multiplicative updates for its factors in closed form. The new updates unify the -NMF and the convolutional NMF. We state why almost all of the existing updates are inexact and approximat…
EnKF's update is shown to be similar to Matheron's method in Gaussian process regression.
Improved HGF networks avoid negative precision errors in volatility updates.
Federated learning is a distributed learning method to train a shared model by aggregating the locally-computed gradient updates. In federated learning, bandwidth and privacy are two main concerns of gradient updates transmission. This paper proposes an end-to-end encrypted neural network for gradient updates transmiss…
Algorithm estimates bounds of updated classifier coefficients efficiently.
Develops efficient method for updating models with small data changes.
Most of the recent successful applications of neural networks have been based on training with gradient descent updates. However, for some small networks, other mirror descent updates learn provably more efficiently when the target is sparse. We present a general framework for casting a mirror descent update as a gradi…
GANs excel at learning high dimensional distributions, but they can update generator parameters in directions that do not correspond to the steepest descent direction of the objective. Prominent examples of problematic update directions include those used in both Goodfellow's original GAN and the WGAN-GP. To formally d…
A new method improves few-shot image classification by updating top layers.
A new method for efficient neural network fine-tuning using queryable low-rank update atoms.
Study examines auditing fairness in evolving models, identifying strategic updates that preserve audit properties.
Drop-Muon updates only some layers, speeding up training.
Most of real-world graphs are dynamic, i.e., they change over time by a sequence of update operations. While the regression problem has been studied for static graphs and temporal graphs, it is not investigated for general dynamic graphs. In this paper, we study regression over dynamic graphs. First, we present the not…
This work analyzes how often to update the target network in Q-learning.
In this paper, we extend the -CNMF to two dimensions and derive exact multiplicative updates for its factors. The new updates generalize and correct the nonnegative matrix factor deconvolution previously proposed by Schmidt and Mørup. We show by simulation that the updates lead to a monotonically decreasing -dive…
In this paper, we present a novel approach for fine-tuning a decoder-side neural network in the context of image compression, such that the weight-updates are better compressible. At encoder side, we fine-tune a pre-trained artifact removal network on target data by using a compression objective applied on the weight-u…
Framework for safely updating machine learning models.
The (stochastic) gradient descent and the multiplicative update method are probably the most popular algorithms in machine learning. We introduce and study a new regularization which provides a unification of the additive and multiplicative updates. This regularization is derived from an hyperbolic analogue of the entr…
A new graph neural network (NBA-GNN) avoids revisiting nodes to improve accuracy.
FedLoRU improves FL efficiency by using low-rank updates.
Many policy gradient methods are variants of Actor-Critic (AC), where a value function (critic) is learned to facilitate updating the parameterized policy (actor). The update to the actor involves a log-likelihood update weighted by the action-values, with the addition of entropy regularization for soft variants. In th…
Paper analyzes ensemble Kalman updates for effective dimension and localization.
SUMER updates deployed models with new data, improving performance.
All-goals updating exploits the off-policy nature of Q-learning to update all possible goals an agent could have from each transition in the world, and was introduced into Reinforcement Learning (RL) by Kaelbling (1993). In prior work this was mostly explored in small-state RL problems that allowed tabular representati…
The thesis clarifies when local updates outperform centralized methods in heterogeneous data environments.
Develops local population-risk certificates for model updates
We describe -MLE, a fast and efficient local search algorithm for learning finite statistical mixtures of exponential families such as Gaussian mixture models. Mixture models are traditionally learned using the expectation-maximization (EM) soft clustering technique that monotonically increases the incomplete (expec…
The paper bounds generalization error for iterative learning with bounded updates.
Two algorithms for linear contextual bandits with rare updates achieve optimal regret and efficiency.
EigenGame improves eigendecomposition by offering unbiased updates for larger datasets.
Federated learning can be vulnerable to adversarial attacks, which this work addresses.
The paper updates SVD of evolving matrices using projection techniques.
We show that deep networks can be trained using Hebbian updates yielding similar performance to ordinary back-propagation on challenging image datasets. To overcome the unrealistic symmetry in connections between layers, implicit in back-propagation, the feedback weights are separate from the feedforward weights. The f…
This paper focuses on coordinate update methods, which are useful for solving problems involving large or high-dimensional datasets. They decompose a problem into simple subproblems, where each updates one, or a small block of, variables while fixing others. These methods can deal with linear and nonlinear mappings, sm…
A new method reduces communication costs in decentralized optimization.
We present an updated version of the VESICLE-CNN algorithm presented by Roncal et al. (2014). The original implementation makes use of a patch-based approach. This methodology is known to be slow due to repeated computations. We update this implementation to be fully convolutional through the use of dilated convolution…