135 research outputs found
A Modern Introduction to Online Learning
In this monograph, I introduce the basic concepts of Online Learning through
a modern view of Online Convex Optimization. Here, online learning refers to
the framework of regret minimization under worst-case assumptions. I present
first-order and second-order algorithms for online learning with convex losses,
in Euclidean and non-Euclidean settings. All the algorithms are clearly
presented as instantiation of Online Mirror Descent or
Follow-The-Regularized-Leader and their variants. Particular attention is given
to the issue of tuning the parameters of the algorithms and learning in
unbounded domains, through adaptive and parameter-free online learning
algorithms. Non-convex losses are dealt through convex surrogate losses and
through randomization. The bandit setting is also briefly discussed, touching
on the problem of adversarial and stochastic multi-armed bandits. These notes
do not require prior knowledge of convex analysis and all the required
mathematical tools are rigorously explained. Moreover, all the proofs have been
carefully chosen to be as simple and as short as possible.Comment: Fixed more typos, added more history bits, added local norms bounds
for OMD and FTR
The perceptron algorithm versus winnow: linear versus logarithmic mistake bounds when few input variables are relevant
AbstractWe give an adversary strategy that forces the Perceptron algorithm to make Ω(kN) mistakes in learning monotone disjunctions over N variables with at most k literals. In contrast, Littlestone's algorithm Winnow makes at most O(k log N) mistakes for the same problem. Both algorithms use thresholded linear functions as their hypotheses. However, Winnow does multiplicative updates to its weight vector instead of the additive updates of the Perceptron algorithm. In general, we call an algorithm additive if its weight vector is always a sum of a fixed initial weight vector and some linear combination of already seen instances. Thus, the Perceptron algorithm is an example of an additive algorithm. We show that an adversary can force any additive algorithm to make (N + k −1)2 mistakes in learning a monotone disjunction of at most k literals. Simple experiments show that for k ⪡ N, Winnow clearly outperforms the Perceptron algorithm also on nonadversarial random data
Mistake Bounds for Binary Matrix Completion
We study the problem of completing a binary matrix in an online learning setting.On each trial we predict a matrix entry and then receive the true entry. We propose a Matrix Exponentiated Gradient algorithm [1] to solve this problem. We provide a mistake bound for the algorithm, which scales with the margin complexity [2, 3] of the underlying matrix. The bound suggests an interpretation where each row of the matrix is a prediction task over a finite set of objects, the columns. Using this we show that the algorithm makes a number of mistakes which is comparable up to a logarithmic factor to the number of mistakes made by the Kernel Perceptron with an optimal kernel in hindsight. We discuss applications of the algorithm to predicting as well as the best biclustering and to the problem of predicting the labeling of a graph without knowing the graph in advance
Online Matrix Completion with Side Information
We give an online algorithm and prove novel mistake and regret bounds for
online binary matrix completion with side information. The mistake bounds we
prove are of the form . The term is
analogous to the usual margin term in SVM (perceptron) bounds. More
specifically, if we assume that there is some factorization of the underlying
matrix into where the rows of are interpreted
as "classifiers" in and the rows of as "instances" in
, then is the maximum (normalized) margin over all
factorizations consistent with the observed matrix. The
quasi-dimension term measures the quality of side information. In the
presence of vacuous side information, . However, if the side
information is predictive of the underlying factorization of the matrix, then
in an ideal case, where is the number of distinct row
factors and is the number of distinct column factors. We additionally
provide a generalization of our algorithm to the inductive setting. In this
setting, we provide an example where the side information is not directly
specified in advance. For this example, the quasi-dimension is now bounded
by
A modern introduction to online learning
In this monograph, I introduce the basic concepts of Online Learning through a modern view of Online Convex Optimization. Here, online learning refers to the framework of regret minimization under worst-case assumptions. I
present first-order and second-order algorithms for online learning with convex losses, in Euclidean and non-Euclidean settings. All the algorithms are clearly presented as instantiation of Online Mirror Descent or Follow-The RegularizedLeader and their variants. Particular attention is given to the issue of tuning the parameters of the algorithms and learning in unbounded domains, through adaptive and parameter-free online learning algorithms. Non-convex losses are dealt through convex surrogate losses and through randomization. The bandit setting is also briefly discussed, touching on the problem of adversarial and stochastic multi-armed bandits. These notes do not require prior knowledge of convex analysis and all the required mathematical tools are rigorously explained. Moreover, all the proofs have been
carefully chosen to be as simple and as short as possible. I want to thank all the people that checked the proofs and reasonings in these notes. In particular, the students in my class that mercilessly pointed out my mistakes, Nicolo Campolongo that found all the typos in my formulas, and Jake Abernethy for the brainstorming on how to make the Tsallis proof even simpler.https://arxiv.org/pdf/1912.13213.pdfFirst author draf
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