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Found 10 papers, 1 papers with code
Online Feature Updates Improve Online (Generalized) Label Shift Adaptation
This paper addresses the prevalent issue of label shift in an online setting with missing labels, where data distributions change over time and obtaining timely labels is challenging.
Near Optimal Heteroscedastic Regression with Symbiotic Learning
Our work shows that we can estimate $\mathbf{w}^{*}$ in squared norm up to an error of $\tilde{O}\left(\|\mathbf{f}^{*}\|^2 \cdot \left(\frac{1}{n} + \left(\frac{d}{n}\right)^2\right)\right)$ and prove a matching lower bound (upto log factors).
Optimal Dynamic Regret in LQR Control
We consider the problem of nonstochastic control with a sequence of quadratic losses, i. e., LQR control.
Second Order Path Variationals in Non-Stationary Online Learning
We consider the problem of universal dynamic regret minimization under exp-concave and smooth losses.
Optimal Dynamic Regret in Proper Online Learning with Strongly Convex Losses and Beyond
We study the framework of universal dynamic regret minimization with strongly convex losses.
Dynamic Regret for Strongly Adaptive Methods and Optimality of Online KRR
When the loss functions are strongly convex or exp-concave, we demonstrate that Strongly Adaptive (SA) algorithms can be viewed as a principled way of controlling dynamic regret in terms of path variation $V_T$ of the comparator sequence.
Optimal Dynamic Regret in Exp-Concave Online Learning
We consider the problem of the Zinkevich (2003)-style dynamic regret minimization in online learning with exp-concave losses.
An Optimal Reduction of TV-Denoising to Adaptive Online Learning
We consider the problem of estimating a function from $n$ noisy samples whose discrete Total Variation (TV) is bounded by $C_n$.
Adaptive Online Estimation of Piecewise Polynomial Trends
We consider the framework of non-stationary stochastic optimization [Besbes et al, 2015] with squared error losses and noisy gradient feedback where the dynamic regret of an online learner against a time varying comparator sequence is studied.
Online Forecasting of Total-Variation-bounded Sequences
We design an $O(n\log n)$-time algorithm that achieves a cumulative square error of $\tilde{O}(n^{1/3}C_n^{2/3}\sigma^{4/3} + C_n^2)$ with high probability. We also prove a lower bound that matches the upper bound in all parameters (up to a $\log(n)$ factor).
