521 research outputs found
Variance Reduction on Adaptive Stochastic Mirror Descent
We study the idea of variance reduction applied to adaptive stochastic mirror
descent algorithms in nonsmooth nonconvex finite-sum optimization problems. We
propose a simple yet generalized adaptive mirror descent algorithm with
variance reduction named SVRAMD and provide its convergence analysis in
different settings. We prove that variance reduction reduces the gradient
complexity of most adaptive mirror descent algorithms and boost their
convergence. In particular, our general theory implies variance reduction can
be applied to algorithms using time-varying step sizes and self-adaptive
algorithms such as AdaGrad and RMSProp. Moreover, our convergence rates recover
the best existing rates of non-adaptive algorithms. We check the validity of
our claims using experiments in deep learning.Comment: NeurIPS 2020 OPT worksho
Efficient Non-Learning Similar Subtrajectory Search
Similar subtrajectory search is a finer-grained operator that can better
capture the similarities between one query trajectory and a portion of a data
trajectory than the traditional similar trajectory search, which requires the
two checked trajectories are similar to each other in whole. Many real
applications (e.g., trajectory clustering and trajectory join) utilize similar
subtrajectory search as a basic operator. It is considered that the time
complexity is O(mn^2) for exact algorithms to solve the similar subtrajectory
search problem under most trajectory distance functions in the existing
studies, where m is the length of the query trajectory and n is the length of
the data trajectory. In this paper, to the best of our knowledge, we are the
first to propose an exact algorithm to solve the similar subtrajectory search
problem in O(mn) time for most of widely used trajectory distance functions
(e.g., WED, DTW, ERP, EDR and Frechet distance). Through extensive experiments
on three real datasets, we demonstrate the efficiency and effectiveness of our
proposed algorithms.Comment: VLDB 202
Online Ridesharing with Meeting Points [Technical Report]
Nowadays, ridesharing becomes a popular commuting mode. Dynamically arriving
riders post their origins and destinations, then the platform assigns drivers
to serve them. In ridesharing, different groups of riders can be served by one
driver if their trips can share common routes. Recently, many ridesharing
companies (e.g., Didi and Uber) further propose a new mode, namely "ridesharing
with meeting points". Specifically, with a short walking distance but less
payment, riders can be picked up and dropped off around their origins and
destinations, respectively. In addition, meeting points enables more flexible
routing for drivers, which can potentially improve the global profit of the
system. In this paper, we first formally define the Meeting-Point-based Online
Ridesharing Problem (MORP). We prove that MORP is NP-hard and there is no
polynomial-time deterministic algorithm with a constant competitive ratio for
it. We notice that a structure of vertex set, -skip cover, fits well to the
MORP. -skip cover tends to find the vertices (meeting points) that are
convenient for riders and drivers to come and go. With meeting points, MORP
tends to serve more riders with these convenient vertices. Based on the idea,
we introduce a convenience-based meeting point candidates selection algorithm.
We further propose a hierarchical meeting-point oriented graph (HMPO graph),
which ranks vertices for assignment effectiveness and constructs -skip cover
to accelerate the whole assignment process. Finally, we utilize the merits of
-skip cover points for ridesharing and propose a novel algorithm, namely
SMDB, to solve MORP. Extensive experiments on real and synthetic datasets
validate the effectiveness and efficiency of our algorithms.Comment: 18 page
Medical Dialogue Generation via Dual Flow Modeling
Medical dialogue systems (MDS) aim to provide patients with medical services,
such as diagnosis and prescription. Since most patients cannot precisely
describe their symptoms, dialogue understanding is challenging for MDS.
Previous studies mainly addressed this by extracting the mentioned medical
entities as critical dialogue history information. In this work, we argue that
it is also essential to capture the transitions of the medical entities and the
doctor's dialogue acts in each turn, as they help the understanding of how the
dialogue flows and enhance the prediction of the entities and dialogue acts to
be adopted in the following turn. Correspondingly, we propose a Dual Flow
enhanced Medical (DFMed) dialogue generation framework. It extracts the medical
entities and dialogue acts used in the dialogue history and models their
transitions with an entity-centric graph flow and a sequential act flow,
respectively. We employ two sequential models to encode them and devise an
interweaving component to enhance their interactions. Experiments on two
datasets demonstrate that our method exceeds baselines in both automatic and
manual evaluations.Comment: Accepted as Findings of ACL 202
- …