143 research outputs found
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
We introduce an extremely computation-efficient CNN architecture named
ShuffleNet, which is designed specially for mobile devices with very limited
computing power (e.g., 10-150 MFLOPs). The new architecture utilizes two new
operations, pointwise group convolution and channel shuffle, to greatly reduce
computation cost while maintaining accuracy. Experiments on ImageNet
classification and MS COCO object detection demonstrate the superior
performance of ShuffleNet over other structures, e.g. lower top-1 error
(absolute 7.8%) than recent MobileNet on ImageNet classification task, under
the computation budget of 40 MFLOPs. On an ARM-based mobile device, ShuffleNet
achieves ~13x actual speedup over AlexNet while maintaining comparable
accuracy
Improved High-Probability Regret for Adversarial Bandits with Time-Varying Feedback Graphs
We study high-probability regret bounds for adversarial -armed bandits
with time-varying feedback graphs over rounds. For general strongly
observable graphs, we develop an algorithm that achieves the optimal regret
with high probability, where is the independence number of the
feedback graph at round . Compared to the best existing result [Neu, 2015]
which only considers graphs with self-loops for all nodes, our result not only
holds more generally, but importantly also removes any
dependence that can be prohibitively large for applications such as contextual
bandits. Furthermore, we also develop the first algorithm that achieves the
optimal high-probability regret bound for weakly observable graphs, which even
improves the best expected regret bound of [Alon et al., 2015] by removing the
term with a refined analysis. Our algorithms are based
on the online mirror descent framework, but importantly with an innovative
combination of several techniques. Notably, while earlier works use optimistic
biased loss estimators for achieving high-probability bounds, we find it
important to use a pessimistic one for nodes without self-loop in a strongly
observable graph
Autobidders with Budget and ROI Constraints: Efficiency, Regret, and Pacing Dynamics
We study a game between autobidding algorithms that compete in an online
advertising platform. Each autobidder is tasked with maximizing its
advertiser's total value over multiple rounds of a repeated auction, subject to
budget and/or return-on-investment constraints. We propose a gradient-based
learning algorithm that is guaranteed to satisfy all constraints and achieves
vanishing individual regret. Our algorithm uses only bandit feedback and can be
used with the first- or second-price auction, as well as with any
"intermediate" auction format. Our main result is that when these autobidders
play against each other, the resulting expected liquid welfare over all rounds
is at least half of the expected optimal liquid welfare achieved by any
allocation. This holds whether or not the bidding dynamics converges to an
equilibrium and regardless of the correlation structure between advertiser
valuations
No-Regret Learning in Two-Echelon Supply Chain with Unknown Demand Distribution
Supply chain management (SCM) has been recognized as an important discipline
with applications to many industries, where the two-echelon stochastic
inventory model, involving one downstream retailer and one upstream supplier,
plays a fundamental role for developing firms' SCM strategies. In this work, we
aim at designing online learning algorithms for this problem with an unknown
demand distribution, which brings distinct features as compared to classic
online optimization problems. Specifically, we consider the two-echelon supply
chain model introduced in [Cachon and Zipkin, 1999] under two different
settings: the centralized setting, where a planner decides both agents'
strategy simultaneously, and the decentralized setting, where two agents decide
their strategy independently and selfishly. We design algorithms that achieve
favorable guarantees for both regret and convergence to the optimal inventory
decision in both settings, and additionally for individual regret in the
decentralized setting. Our algorithms are based on Online Gradient Descent and
Online Newton Step, together with several new ingredients specifically designed
for our problem. We also implement our algorithms and show their empirical
effectiveness
Differentially Private Diffusion Auction: The Single-unit Case
Diffusion auction refers to an emerging paradigm of online marketplace where
an auctioneer utilises a social network to attract potential buyers. Diffusion
auction poses significant privacy risks. From the auction outcome, it is
possible to infer hidden, and potentially sensitive, preferences of buyers. To
mitigate such risks, we initiate the study of differential privacy (DP) in
diffusion auction mechanisms. DP is a well-established notion of privacy that
protects a system against inference attacks. Achieving DP in diffusion auctions
is non-trivial as the well-designed auction rules are required to incentivise
the buyers to truthfully report their neighbourhood. We study the single-unit
case and design two differentially private diffusion mechanisms (DPDMs):
recursive DPDM and layered DPDM. We prove that these mechanisms guarantee
differential privacy, incentive compatibility and individual rationality for
both valuations and neighbourhood. We then empirically compare their
performance on real and synthetic datasets
- β¦