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Found 25 papers, 8 papers with code
Cascading Blackout Severity Prediction with Statistically-Augmented Graph Neural Networks
Higher variability in grid conditions, resulting from growing renewable penetration and increased incidence of extreme weather events, has increased the difficulty of screening for scenarios that may lead to catastrophic cascading failures.
Climate Change Impact on Agricultural Land Suitability: An Interpretable Machine Learning-Based Eurasia Case Study
This study represents a pioneering effort in utilizing machine learning methods to assess the impact of climate change on agricultural land suitability under various carbon emissions scenarios.
CMIP X-MOS: Improving Climate Models with Extreme Model Output Statistics
Climate models are essential for assessing the impact of greenhouse gas emissions on our changing climate and the resulting increase in the frequency and severity of natural disasters.
Long-term drought prediction using deep neural networks based on geospatial weather data
The problem of high-quality drought forecasting up to a year in advance is critical for agriculture planning and insurance.
GP CC-OPF: Gaussian Process based optimization tool for Chance-Constrained Optimal Power Flow
The developed tool presents a novel data-driven approach based on the GP regression model for solving the CC-OPF problem with a trade-off between complexity and accuracy.
Long-term hail risk assessment with deep neural networks
But there are no machine learning models for data-driven forecasting of changes in hail frequency for a given area.
Ranking-Based Physics-Informed Line Failure Detection in Power Grids
Climate change increases the number of extreme weather events (wind and snowstorms, heavy rains, wildfires) that compromise power system reliability and lead to multiple equipment failures.
Data-Driven Chance Constrained AC-OPF using Hybrid Sparse Gaussian Processes
The alternating current (AC) chance-constrained optimal power flow (CC-OPF) problem addresses the economic efficiency of electricity generation and delivery under generation uncertainty.
Data-Driven Stochastic AC-OPF using Gaussian Processes
Unfortunately, the most accessible renewable power sources, such as wind and solar, are highly fluctuating and thus bring a lot of uncertainty to power grid operations and challenge existing optimization and control policies.
Learning over No-Preferred and Preferred Sequence of Items for Robust Recommendation (Extended Abstract)
This paper is an extended version of [Burashnikova et al., 2021, arXiv: 2012. 06910], where we proposed a theoretically supported sequential strategy for training a large-scale Recommender System (RS) over implicit feedback, mainly in the form of clicks.
Self-Training: A Survey
Semi-supervised algorithms aim to learn prediction functions from a small set of labeled observations and a large set of unlabeled observations.
Recommender systems: when memory matters
In this paper, we study the effect of long memory in the learnability of a sequential recommender system including users' implicit feedback.
Learning over no-Preferred and Preferred Sequence of items for Robust Recommendation
In this paper, we propose a theoretically founded sequential strategy for training large-scale Recommender Systems (RS) over implicit feedback, mainly in the form of clicks.
Tractable Minor-free Generalization of Planar Zero-field Ising Models
We present a new family of zero-field Ising models over $N$ binary variables/spins obtained by consecutive "gluing" of planar and $O(1)$-sized components and subsets of at most three vertices into a tree.
A New Family of Tractable Ising Models
To illustrate the utility of the new family of tractable graphical models, we first build an $O(N^{3/2})$ algorithm for inference and sampling of the K5-minor-free zero-field Ising models - an extension of the planar zero-field Ising models - which is neither genus- nor treewidth-bounded.
Data Structures and Algorithms Statistical Mechanics Data Analysis, Statistics and Probability Computation
Sequential Learning over Implicit Feedback for Robust Large-Scale Recommender Systems
This is to prevent from an abnormal number of clicks over some targeted items, mainly due to bots; or very few user interactions.
Learning a Generator Model from Terminal Bus Data
In this work we investigate approaches to reconstruct generator models from measurements available at the generator terminal bus using machine learning (ML) techniques.
Inference and Sampling of $K_{33}$-free Ising Models
We call an Ising model tractable when it is possible to compute its partition function value (statistical inference) in polynomial time.
Gauges, Loops, and Polynomials for Partition Functions of Graphical Models
We show that the Gauge Function has a natural polynomial representation in terms of gauges/variables associated with edges of the multi-graph.
Belief Propagation Min-Sum Algorithm for Generalized Min-Cost Network Flow
Belief Propagation algorithms are instruments used broadly to solve graphical model optimization and statistical inference problems.
Efficient Rank Minimization to Tighten Semidefinite Programming for Unconstrained Binary Quadratic Optimization
We propose a method for low-rank semidefinite programming in application to the semidefinite relaxation of unconstrained binary quadratic problems.
Optimization and Control
Representation Learning and Pairwise Ranking for Implicit Feedback in Recommendation Systems
The learning objective is based on three scenarios of ranking losses that control the ability of the model to maintain the ordering over the items induced from the users' preferences, as well as, the capacity of the dot-product defined in the learned embedded space to produce the ordering.
Aggressive Sampling for Multi-class to Binary Reduction with Applications to Text Classification
We address the problem of multi-class classification in the case where the number of classes is very large.
Rademacher Complexity Bounds for a Penalized Multiclass Semi-Supervised Algorithm
We propose Rademacher complexity bounds for multiclass classifiers trained with a two-step semi-supervised model.
Tight Risk Bounds for Multi-Class Margin Classifiers
We consider a problem of risk estimation for large-margin multi-class classifiers.
