139 research outputs found
Further Optimal Regret Bounds for Thompson Sampling
Thompson Sampling is one of the oldest heuristics for multi-armed bandit
problems. It is a randomized algorithm based on Bayesian ideas, and has
recently generated significant interest after several studies demonstrated it
to have better empirical performance compared to the state of the art methods.
In this paper, we provide a novel regret analysis for Thompson Sampling that
simultaneously proves both the optimal problem-dependent bound of
and the
first near-optimal problem-independent bound of on the
expected regret of this algorithm. Our near-optimal problem-independent bound
solves a COLT 2012 open problem of Chapelle and Li. The optimal
problem-dependent regret bound for this problem was first proven recently by
Kaufmann et al. [ALT 2012]. Our novel martingale-based analysis techniques are
conceptually simple, easily extend to distributions other than the Beta
distribution, and also extend to the more general contextual bandits setting
[Manuscript, Agrawal and Goyal, 2012].Comment: arXiv admin note: substantial text overlap with arXiv:1111.179
Discretizing Continuous Action Space for On-Policy Optimization
In this work, we show that discretizing action space for continuous control
is a simple yet powerful technique for on-policy optimization. The explosion in
the number of discrete actions can be efficiently addressed by a policy with
factorized distribution across action dimensions. We show that the discrete
policy achieves significant performance gains with state-of-the-art on-policy
optimization algorithms (PPO, TRPO, ACKTR) especially on high-dimensional tasks
with complex dynamics. Additionally, we show that an ordinal parameterization
of the discrete distribution can introduce the inductive bias that encodes the
natural ordering between discrete actions. This ordinal architecture further
significantly improves the performance of PPO/TRPO.Comment: Accepted at AAAI Conference on Artificial Intelligence (2020) in New
York, NY, USA. An open source implementation can be found at
https://github.com/robintyh1/onpolicybaseline
A Framework for High-Accuracy Privacy-Preserving Mining
To preserve client privacy in the data mining process, a variety of
techniques based on random perturbation of data records have been proposed
recently. In this paper, we present a generalized matrix-theoretic model of
random perturbation, which facilitates a systematic approach to the design of
perturbation mechanisms for privacy-preserving mining. Specifically, we
demonstrate that (a) the prior techniques differ only in their settings for the
model parameters, and (b) through appropriate choice of parameter settings, we
can derive new perturbation techniques that provide highly accurate mining
results even under strict privacy guarantees. We also propose a novel
perturbation mechanism wherein the model parameters are themselves
characterized as random variables, and demonstrate that this feature provides
significant improvements in privacy at a very marginal cost in accuracy.
While our model is valid for random-perturbation-based privacy-preserving
mining in general, we specifically evaluate its utility here with regard to
frequent-itemset mining on a variety of real datasets. The experimental results
indicate that our mechanisms incur substantially lower identity and support
errors as compared to the prior techniques
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