52 research outputs found
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Models for Iterative Multiattribute Procurement Auctions
Multiattribute auctions extend traditional auction settings to allow negotiation over nonprice attributes such as weight, color, and terms of delivery, in addition to price and promise to improve market efficiency in markets with configurable goods.
This paper provides an iterative auction design for an important special case of the multiattribute allocation problem with special (preferential independent) additive structure on the buyer value and seller costs. Auction Additive&Discrete provides a refined design for a price-based auction in which the price feedback decomposes to an additive part with a price for each attribute and an aggregate part that appears as a price discount for each supplier. In addition, this design also has excellent information revelation properties that are validated through computational experiments. The auction terminates with an outcome of a modified Vickrey-Clarke-Groves mechanism. This paper also develops Auction NonLinear&Discrete for the more general nonlinear case-a particularly simple design that solves the general multiattribute allocation problem, but requires that the auctioneer maintains prices on bundles of attribute levels.Engineering and Applied Science
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Auctions, Bidding and Exchange Design
The different auction types are outlined using a classification framework along six dimensions. The economic properties that are desired in the design of auction mechanisms and the complexities that arise in their implementation are discussed. Some of the most interesting designs from the literature are analyzed in detail to establish known results and to identify the emerging research directions.Engineering and Applied Science
A computational study of the Kemeny rule for preference aggregation
Abstract We consider from a computational perspective the problem of how to aggregate the ranking preferences of a number of alternatives by a number of different voters into a single consensus ranking, following the majority voting rule. Social welfare functions for aggregating preferences in this way have been widely studied since the time of Condorcet (1785). One drawback of majority voting procedures when three or more alternatives are being ranked is the presence of cycles in the majority preference relation. The Kemeny order is a social welfare function which has been designed to tackle the presence of such cycles. However computing a Kemeny order is known to be NP-hard. We develop a greedy heuristic and an exact branch and bound procedure for computing Kemeny orders. We present results of a computational study on these procedures
QoS-Aware Middleware for Web Services Composition
The paradigmatic shift from a Web of manual interactions to a Web of programmatic interactions driven by Web services is creating unprecedented opportunities for the formation of online Business-to-Business (B2B) collaborations. In particular, the creation of value-added services by composition of existing ones is gaining a significant momentum. Since many available Web services provide overlapping or identical functionality, albeit with different Quality of Service (QoS), a choice needs to be made to determine which services are to participate in a given composite service. This paper presents a middleware platform which addresses the issue of selecting Web services for the purpose of their composition in a way that maximizes user satisfaction expressed as utility functions over QoS attributes, while satisfying the constraints set by the user and by the structure of the composite service. Two selection approaches are described and compared: one based on local (task-level) selection of services and the other based on global allocation of tasks to services using integer programming
A Time Series is Worth 64 Words: Long-term Forecasting with Transformers
We propose an efficient design of Transformer-based models for multivariate
time series forecasting and self-supervised representation learning. It is
based on two key components: (i) segmentation of time series into
subseries-level patches which are served as input tokens to Transformer; (ii)
channel-independence where each channel contains a single univariate time
series that shares the same embedding and Transformer weights across all the
series. Patching design naturally has three-fold benefit: local semantic
information is retained in the embedding; computation and memory usage of the
attention maps are quadratically reduced given the same look-back window; and
the model can attend longer history. Our channel-independent patch time series
Transformer (PatchTST) can improve the long-term forecasting accuracy
significantly when compared with that of SOTA Transformer-based models. We also
apply our model to self-supervised pre-training tasks and attain excellent
fine-tuning performance, which outperforms supervised training on large
datasets. Transferring of masked pre-trained representation on one dataset to
others also produces SOTA forecasting accuracy. Code is available at:
https://github.com/yuqinie98/PatchTST
TSMixer: Lightweight MLP-Mixer Model for Multivariate Time Series Forecasting
Transformers have gained popularity in time series forecasting for their
ability to capture long-sequence interactions. However, their high memory and
computing requirements pose a critical bottleneck for long-term forecasting. To
address this, we propose TSMixer, a lightweight neural architecture exclusively
composed of multi-layer perceptron (MLP) modules. TSMixer is designed for
multivariate forecasting and representation learning on patched time series,
providing an efficient alternative to Transformers. Our model draws inspiration
from the success of MLP-Mixer models in computer vision. We demonstrate the
challenges involved in adapting Vision MLP-Mixer for time series and introduce
empirically validated components to enhance accuracy. This includes a novel
design paradigm of attaching online reconciliation heads to the MLP-Mixer
backbone, for explicitly modeling the time-series properties such as hierarchy
and channel-correlations. We also propose a Hybrid channel modeling approach to
effectively handle noisy channel interactions and generalization across diverse
datasets, a common challenge in existing patch channel-mixing methods.
Additionally, a simple gated attention mechanism is introduced in the backbone
to prioritize important features. By incorporating these lightweight
components, we significantly enhance the learning capability of simple MLP
structures, outperforming complex Transformer models with minimal computing
usage. Moreover, TSMixer's modular design enables compatibility with both
supervised and masked self-supervised learning methods, making it a promising
building block for time-series Foundation Models. TSMixer outperforms
state-of-the-art MLP and Transformer models in forecasting by a considerable
margin of 8-60%. It also outperforms the latest strong benchmarks of
Patch-Transformer models (by 1-2%) with a significant reduction in memory and
runtime (2-3X).Comment: Accepted in the Proceedings of the 29th ACM SIGKDD Conference on
Knowledge Discovery and Data Mining (KDD 23), Research Track. Delayed release
in arXiv to comply with the conference policies on the double-blind review
process. This paper has been submitted to the KDD peer-review process on Feb
02, 202
Federated Learning's Blessing: FedAvg has Linear Speedup
Federated learning (FL) learns a model jointly from a set of participating
devices without sharing each other's privately held data. The characteristics
of non-iid data across the network, low device participation, and the mandate
that data remain private bring challenges in understanding the convergence of
FL algorithms, particularly in regards to how convergence scales with the
number of participating devices. In this paper, we focus on Federated Averaging
(FedAvg)--the most widely used and effective FL algorithm in use today--and
provide a comprehensive study of its convergence rate. Although FedAvg has
recently been studied by an emerging line of literature, it remains open as to
how FedAvg's convergence scales with the number of participating devices in the
FL setting--a crucial question whose answer would shed light on the performance
of FedAvg in large FL systems. We fill this gap by establishing convergence
guarantees for FedAvg under three classes of problems: strongly convex smooth,
convex smooth, and overparameterized strongly convex smooth problems. We show
that FedAvg enjoys linear speedup in each case, although with different
convergence rates. For each class, we also characterize the corresponding
convergence rates for the Nesterov accelerated FedAvg algorithm in the FL
setting: to the best of our knowledge, these are the first linear speedup
guarantees for FedAvg when Nesterov acceleration is used. To accelerate FedAvg,
we also design a new momentum-based FL algorithm that further improves the
convergence rate in overparameterized linear regression problems. Empirical
studies of the algorithms in various settings have supported our theoretical
results
Variational inference formulation for a model-free simulation of a dynamical system with unknown parameters by a recurrent neural network
We propose a recurrent neural network for a "model-free" simulation of a
dynamical system with unknown parameters without prior knowledge. The deep
learning model aims to jointly learn the nonlinear time marching operator and
the effects of the unknown parameters from a time series dataset. We assume
that the time series data set consists of an ensemble of trajectories for a
range of the parameters. The learning task is formulated as a statistical
inference problem by considering the unknown parameters as random variables. A
latent variable is introduced to model the effects of the unknown parameters,
and a variational inference method is employed to simultaneously train
probabilistic models for the time marching operator and an approximate
posterior distribution for the latent variable. Unlike the classical
variational inference, where a factorized distribution is used to approximate
the posterior, we employ a feedforward neural network supplemented by an
encoder recurrent neural network to develop a more flexible probabilistic
model. The approximate posterior distribution makes an inference on a
trajectory to identify the effects of the unknown parameters. The time marching
operator is approximated by a recurrent neural network, which takes a latent
state sampled from the approximate posterior distribution as one of the input
variables, to compute the time evolution of the probability distribution
conditioned on the latent variable. In the numerical experiments, it is shown
that the proposed variational inference model makes a more accurate simulation
compared to the standard recurrent neural networks. It is found that the
proposed deep learning model is capable of correctly identifying the dimensions
of the random parameters and learning a representation of complex time series
data
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