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Found 18 papers, 3 papers with code
Online Learning Approach for Survival Analysis
We introduce an online mathematical framework for survival analysis, allowing real time adaptation to dynamic environments and censored data.
Adaptive Probabilistic Forecasting of Electricity (Net-)Load
The proliferation of local generation, demand response, and electrification of heat and transport are changing the fundamental drivers of electricity load and increasing the complexity of load modelling and forecasting.
Optimistically Tempered Online Learning
Optimistic Online Learning algorithms have been developed to exploit expert advices, assumed optimistically to be always useful.
Learning from time-dependent streaming data with online stochastic algorithms
This paper addresses stochastic optimization in a streaming setting with time-dependent and biased gradient estimates.
Non-Asymptotic Analysis of Stochastic Approximation Algorithms for Streaming Data
We provide non-asymptotic convergence rates of various gradient-based algorithms; this includes the famous Stochastic Gradient (SG) descent (a. k. a.
Viking: Variational Bayesian Variance Tracking
We introduce an augmented model in which the variances are represented as auxiliary gaussian latent variables in a tracking mode.
Hidden regular variation for point processes and the single/multiple large point heuristic
We consider regular variation for marked point processes with independent heavy-tailed marks and prove a single large point heuristic: the limit measure is concentrated on the cone of point measures with one single point.
Point Processes
Probability
Stochastic Online Convex Optimization. Application to probabilistic time series forecasting
We introduce a general framework of stochastic online convex optimization to obtain fast-rate stochastic regret bounds.
AdaVol: An Adaptive Recursive Volatility Prediction Method
An investigation of the convergence properties of the QML procedure in a general conditionally heteroscedastic time series model is conducted, and the classical batch optimization routines extended to the framework of streaming and large-scale problems.
Kalman Recursions Aggregated Online
In this article, we aim at improving the prediction of expert aggregation by using the underlying properties of the models that provide expert predictions.
Stochastic Online Optimization using Kalman Recursion
Second, for generalized linear regressions, we provide a martingale analysis of the excess risk in the local phase, improving existing ones in bounded stochastic optimization.
Consistent Regression using Data-Dependent Coverings
The proof of the consistency is based on a control of the error of the empirical estimation of conditional expectations which is interesting on its own.
Sparse regular variation
Regular variation provides a convenient theoretical framework to study large events.
Logarithmic Regret for parameter-free Online Logistic Regression
We consider online optimization procedures in the context of logistic regression, focusing on the Extended Kalman Filter (EKF).
Rule Induction Partitioning Estimator
RIPE is a novel deterministic and easily understandable prediction algorithm developed for continuous and discrete ordered data.
Efficient online algorithms for fast-rate regret bounds under sparsity
setting, we establish new risk bounds that are adaptive to the sparsity of the problem and to the regularity of the risk (ranging from a rate 1 / $\sqrt T$ for general convex risk to 1 /T for strongly convex risk).
A Strongly Quasiconvex PAC-Bayesian Bound
We propose a new PAC-Bayesian bound and a way of constructing a hypothesis space, so that the bound is convex in the posterior distribution and also convex in a trade-off parameter between empirical performance of the posterior distribution and its complexity.
Optimal learning with Bernstein Online Aggregation
The exponential weights include an accuracy term and a second order term that is a proxy of the quadratic variation as in Hazan and Kale (2010).
