1,822 research outputs found
Online Learning for Changing Environments using Coin Betting
A key challenge in online learning is that classical algorithms can be slow
to adapt to changing environments. Recent studies have proposed "meta"
algorithms that convert any online learning algorithm to one that is adaptive
to changing environments, where the adaptivity is analyzed in a quantity called
the strongly-adaptive regret. This paper describes a new meta algorithm that
has a strongly-adaptive regret bound that is a factor of
better than other algorithms with the same time complexity, where is the
time horizon. We also extend our algorithm to achieve a first-order (i.e.,
dependent on the observed losses) strongly-adaptive regret bound for the first
time, to our knowledge. At its heart is a new parameter-free algorithm for the
learning with expert advice (LEA) problem in which experts sometimes do not
output advice for consecutive time steps (i.e., \emph{sleeping} experts). This
algorithm is derived by a reduction from optimal algorithms for the so-called
coin betting problem. Empirical results show that our algorithm outperforms
state-of-the-art methods in both learning with expert advice and metric
learning scenarios.Comment: submitted to a journal. arXiv admin note: substantial text overlap
with arXiv:1610.0457
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
Scoring dynamics across professional team sports: tempo, balance and predictability
Despite growing interest in quantifying and modeling the scoring dynamics
within professional sports games, relative little is known about what patterns
or principles, if any, cut across different sports. Using a comprehensive data
set of scoring events in nearly a dozen consecutive seasons of college and
professional (American) football, professional hockey, and professional
basketball, we identify several common patterns in scoring dynamics. Across
these sports, scoring tempo---when scoring events occur---closely follows a
common Poisson process, with a sport-specific rate. Similarly, scoring
balance---how often a team wins an event---follows a common Bernoulli process,
with a parameter that effectively varies with the size of the lead. Combining
these processes within a generative model of gameplay, we find they both
reproduce the observed dynamics in all four sports and accurately predict game
outcomes. These results demonstrate common dynamical patterns underlying
within-game scoring dynamics across professional team sports, and suggest
specific mechanisms for driving them. We close with a brief discussion of the
implications of our results for several popular hypotheses about sports
dynamics.Comment: 18 pages, 8 figures, 4 tables, 2 appendice
Towards Fair Disentangled Online Learning for Changing Environments
In the problem of online learning for changing environments, data are
sequentially received one after another over time, and their distribution
assumptions may vary frequently. Although existing methods demonstrate the
effectiveness of their learning algorithms by providing a tight bound on either
dynamic regret or adaptive regret, most of them completely ignore learning with
model fairness, defined as the statistical parity across different
sub-population (e.g., race and gender). Another drawback is that when adapting
to a new environment, an online learner needs to update model parameters with a
global change, which is costly and inefficient. Inspired by the sparse
mechanism shift hypothesis, we claim that changing environments in online
learning can be attributed to partial changes in learned parameters that are
specific to environments and the rest remain invariant to changing
environments. To this end, in this paper, we propose a novel algorithm under
the assumption that data collected at each time can be disentangled with two
representations, an environment-invariant semantic factor and an
environment-specific variation factor. The semantic factor is further used for
fair prediction under a group fairness constraint. To evaluate the sequence of
model parameters generated by the learner, a novel regret is proposed in which
it takes a mixed form of dynamic and static regret metrics followed by a
fairness-aware long-term constraint. The detailed analysis provides theoretical
guarantees for loss regret and violation of cumulative fairness constraints.
Empirical evaluations on real-world datasets demonstrate our proposed method
sequentially outperforms baseline methods in model accuracy and fairness.Comment: Accepted by KDD 202
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