Momentum-based Gradient Methods in Multi-Objective Recommendation

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Collaborative learning algorithms, such as distributed SGD (or D-SGD), are prone to faulty machines that may deviate from their prescribed algorithm because of software or hardware bugs, poisoned data or malicious behaviors. While many solutions have been ...

In this paper we fully describe the trajectory of gradient flow over diagonal linear networks in the limit of vanishing initialisation. We show that the limiting flow successively jumps from a saddle of the training loss to another until reaching the minim ...

The objective of this paper is to investigate a new numerical method for the approximation of the self-diffusion matrix of a tagged particle process defined on a grid. While standard numerical methods make use of long-time averages of empirical means of de ...

The increasing prevalence of personal devices motivates the design of algorithms that can leverage their computing power, together with the data they generate, in order to build privacy-preserving and effective machine learning models. However, traditional ...

Renewable energy resources have an increasing share of the electrical power, and the concept of distributed generation and storage units is about to completely change the basic architecture and control algorithms of the electric power grids. In this tutori ...

We analyze the influence of adversarial training on the loss landscape of machine learning models. To this end, we first provide analytical studies of the properties of adversarial loss functions under different adversarial budgets. We then demonstrate tha ...

We study generalization properties of distributed algorithms in the setting of nonparametric regression over a reproducing kernel Hilbert space (RKHS). We first investigate distributed stochastic gradient methods (SGM), with mini-batches and multi-passes o ...

We propose two novel conditional gradient-based methods for solving structured stochastic convex optimization problems with a large number of linear constraints. Instances of this template naturally arise from SDP-relaxations of combinatorial problems, whi ...

The diffusion strategy for distributed learning from streaming data employs local stochastic gradient updates along with exchange of iterates over neighborhoods. In this work we establish that agents cluster around a network centroid in the mean-fourth sen ...

Deep learning has been immensely successful at a variety of tasks, ranging from classification to artificial intelligence. Learning corresponds to fitting training data, which is implemented by descending a very high-dimensional loss function. Understandin ...

Machine learning promises to accelerate materials discovery by allowing computational efficient property predictions from a small number of reference calculations. As a result, the literature has spent a considerable effort in designing representations tha ...

Traffic congestion is a significant issue in all urban areas with concentration of activities for various city topologies and distribution of population and land use around the world. Developing realistic models that are able to replicate congestion spread ...

In modern-data analysis applications, the abundance of data makes extracting meaningful information from it challenging, in terms of computation, storage, and interpretability. In this setting, exploiting sparsity in data has been essential to the developm ...

Next generation radio telescopes, like the Square Kilometre Array, will acquire an unprecedented amount of data for radio astronomy. The development of fast, parallelisable or distributed algorithms for handling such large-scale data sets is of prime impor ...

Real-time optimization (RTO) methods use measurements to offset the effect of uncertainty and drive the plant to optimality. RTO schemes differ in the way measurements are incorporated in the optimization framework. Explicit RTO schemes solve a static opti ...

Recent advances in process synthesis, design, operations, and control have created an increasing demand for efficient numerical algorithms for optimizing a dynamic system coupled with discrete decisions; these problems are termed mixed-integer dynamic opti ...

Momentum-based Gradient Methods in Multi-Objective Recommendation

Chat with Graph Search

Robust Collaborative Learning with Linear Gradient Overhead

Saddle-to-Saddle Dynamics in Diagonal Linear Networks

Tensor approximation of the self-diffusion matrix of tagged particle processes

Democratizing Machine Learning

Data-Driven Control and Its Applications to Microgrids

On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them

Optimal Convergence for Distributed Learning with Stochastic Gradient Methods and Spectral Algorithms

Conditional gradient methods for stochastically constrained convex minimization

Polynomial Escape-Time From Saddle Points In Distributed Non-Convex Optimization

Jamming transition as a paradigm to understand the loss landscape of deep neural networks

Chemical machine learning with kernels: The impact of loss functions

Dynamic Clustering and Control for Large Scale Urban Networks

Learning with Structured Sparsity: From Discrete to Convex and Back

A randomised primal-dual algorithm for distributed radio-interferometric imaging

Use of Transient Measurements for the Optimization of Steady-State Performance via Modifier Adaptation

Global mixed-integer dynamic optimization