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https://feeds.library.caltech.edu/people/Abu-Mostafa-Y-S/combined.rss
A Caltech Library Repository Feedhttp://www.rssboard.org/rss-specificationpython-feedgenenTue, 16 Apr 2024 14:51:43 +0000A Differentiation Test for Absolute Convergence
https://resolver.caltech.edu/CaltechAUTHORS:20190710-151636497
Authors: {'items': [{'id': 'Abu-Mostafa-Y-S', 'name': {'family': 'Abu-Mostafa', 'given': 'Yaser S.'}}]}
Year: 1984
DOI: 10.2307/2689682
In this note, we describe a new test which provides a necessary and sufficient condition for absolute convergence of infinite series. The test is based solely on differentiation and is very easy to apply. It also provides a pictorial illustration for absolute convergence and divergence.https://authors.library.caltech.eduhttps://authors.library.caltech.edu/records/zqbc3-95g71Recognitive Aspects of Moment Invariants
https://resolver.caltech.edu/CaltechAUTHORS:20190702-142428282
Authors: {'items': [{'id': 'Abu-Mostafa-Y-S', 'name': {'family': 'Abu-Mostafa', 'given': 'Yaser S.'}}, {'id': 'Psaltis-D', 'name': {'family': 'Psaltis', 'given': 'Demetri'}}]}
Year: 1984
DOI: 10.1109/tpami.1984.4767594
Moment invariants are evaluated as a feature space for pattern recognition in terms of discrimination power and noise tolerance. The notion of complex moments is introduced as a simple and straightforward way to derive moment invariants. Through this relation, properties of complex moments are used to characterize moment invariants. Aspects of information loss, suppression, and redundancy encountered in moment invariants are investigated and significant results are derived. The behavior of moment invariants in the presence of additive noise is also described.https://authors.library.caltech.eduhttps://authors.library.caltech.edu/records/3hgbt-rz712Image Normalization by Complex Moments
https://resolver.caltech.edu/CaltechAUTHORS:20190702-135608926
Authors: {'items': [{'id': 'Abu-Mostafa-Y-S', 'name': {'family': 'Abu-Mostafa', 'given': 'Yaser S.'}}, {'id': 'Psaltis-D', 'name': {'family': 'Psaltis', 'given': 'Demetri'}}]}
Year: 1985
DOI: 10.1109/tpami.1985.4767617
The role of moments in image normalization and invariant pattern recognition is addressed. The classical idea of the principal axes is analyzed and extended to a more general definition. The relationship between moment-based normalization, moment invariants, and circular harmonics is established. Invariance properties of moments, as opposed to their recognition properties, are identified using a new class of normalization procedures. The application of moment-based normalization in pattern recognition is demonstrated by experiment.https://authors.library.caltech.eduhttps://authors.library.caltech.edu/records/ggy16-dv650Two Theorems on Time Bounded Kolmogrov-Chaitin Complexity
https://resolver.caltech.edu/CaltechCSTR:1985.5205-tr-85
Authors: {'items': [{'id': 'Schweizer-D', 'name': {'family': 'Schweizer', 'given': 'David'}}, {'id': 'Abu-Mostafa-Y-S', 'name': {'family': 'Abu-Mostafa', 'given': 'Yaser'}}]}
Year: 1985
DOI: 10.7907/130cy-kxs54
An obvious extension of the KolmogorovChaitin notion of complexity is to require that the program which generates a string terminate within a prespecified time bound. We show that given a computable bound on the amount of time allowed for the production of a string from the program which generates it, there exist strings of arbitrarily low KolmogorovChaitin complexity which appear maximally random. That is, given a notion of fast, we show that there are strings which are generated by extremely short programs, but which are not generated by any fast programs shorter than the strings themselves. We show by enumeration that if we consider generating strings from programs some constant number of bits shorter than the strings themselves then these apparently random strings are significant (i.e are a proper fraction of all strings of a given length).https://authors.library.caltech.eduhttps://authors.library.caltech.edu/records/130cy-kxs54Information capacity of the Hopfield model
https://resolver.caltech.edu/CaltechAUTHORS:ABUieeetit85
Authors: {'items': [{'id': 'Abu-Mostafa-Y-S', 'name': {'family': 'Abu-Mostafa', 'given': 'Yaser S.'}}, {'id': 'St-Jacques-J-M', 'name': {'family': 'St. Jacques', 'given': 'Jeannine-Marie'}}]}
Year: 1985
DOI: 10.1109/TIT.1985.1057069
The information capacity of general forms of memory is formalized. The number of bits of information that can be stored in the Hopfield model of associative memory is estimated. It is found that the asymptotic information capacity of a Hopfield network of N neurons is of the order N^3b. The number of arbitrary state vectors that can be made stable in a Hopfield network of N neurons is proved to be bounded above by N.https://authors.library.caltech.eduhttps://authors.library.caltech.edu/records/qs9be-eqg96The complexity of information extraction
https://resolver.caltech.edu/CaltechAUTHORS:ABUieeetit86
Authors: {'items': [{'id': 'Abu-Mostafa-Y-S', 'name': {'family': 'Abu-Mostafa', 'given': 'Yaser S.'}}]}
Year: 1986
How difficult are decision problems based on natural data, such as pattern recognition? To answer this question, decision problems are characterized by introducing four measures defined on a Boolean function f of N variables: the implementation cost C(f), the randomness R(f), the deterministic entropy H(f), and the complexity K(f). The highlights and main results are roughly as follows, 1) C(f) approx R(f) H(f) approx K(f), all measured in bits. 2) Decision problems based on natural data are partially random (in the Kolmogorov sense) and have low entropy with respect to their dimensionality, and the relations between the four measures translate to lower and upper bounds on the cost of solving these problems. 3) Allowing small errors in the implementation of f saves a lot in the low entropy case but saves nothing in the high-entropy case. If f is partially structured, the implementation cost is reduced substantially.https://authors.library.caltech.eduhttps://authors.library.caltech.edu/records/2zsw2-m3b78On the Time-Bandwidth Proof in VLSI Complexity
https://resolver.caltech.edu/CaltechAUTHORS:ABUieeetc87
Authors: {'items': [{'id': 'Abu-Mostafa-Y-S', 'name': {'family': 'Abu-Mostafa', 'given': 'Yaser S.'}}]}
Year: 1987
DOI: 10.1109/TC.1987.1676888
A subtle fallacy in the original proof [1] that the computation time T is lowerbounded by a factor inversely proportional to the minimum bisection width of a VLSI chip is pointed out. A corrected version of the proof using the idea of conditionally self-delimiting messages is given.https://authors.library.caltech.eduhttps://authors.library.caltech.edu/records/p2p4x-mbj36Optical Neural Computers
https://resolver.caltech.edu/CaltechAUTHORS:20190710-142228887
Authors: {'items': [{'id': 'Abu-Mostafa-Y-S', 'name': {'family': 'Abu-Mostafa', 'given': 'Yaser S.'}}, {'id': 'Psaltis-D', 'name': {'family': 'Psaltis', 'given': 'Demetri'}}]}
Year: 1987
Can computers be built to solve problems, such as recognizing patterns, that entail memorizing all possible solutions? The key may be to arrange optical elements in the same way as neurons are arranged in the brain.https://authors.library.caltech.eduhttps://authors.library.caltech.edu/records/twbxx-4z132Connectivity Versus Entropy
https://resolver.caltech.edu/CaltechAUTHORS:20160107-155110636
Authors: {'items': [{'id': 'Abu-Mostafa-Y-S', 'name': {'family': 'Abu-Mostafa', 'given': 'Yaser S.'}}]}
Year: 1988
How does the connectivity of a neural network (number of synapses per
neuron) relate to the complexity of the problems it can handle (measured by
the entropy)? Switching theory would suggest no relation at all, since all Boolean
functions can be implemented using a circuit with very low connectivity (e.g.,
using two-input NAND gates). However, for a network that learns a problem
from examples using a local learning rule, we prove that the entropy of the
problem becomes a lower bound for the connectivity of the network.https://authors.library.caltech.eduhttps://authors.library.caltech.edu/records/ca7c6-x5f57The capacity of multilevel threshold functions
https://resolver.caltech.edu/CaltechAUTHORS:OLAieeetpami88
Authors: {'items': [{'id': 'Olafsson-S', 'name': {'family': 'Olafsson', 'given': 'Sverrir'}}, {'id': 'Abu-Mostafa-Y-S', 'name': {'family': 'Abu-Mostafa', 'given': 'Yaser S.'}}]}
Year: 1988
DOI: 10.1109/34.3890
Lower and upper bounds for the capacity of multilevel threshold elements are estimated, using two essentially different enumeration techniques. It is demonstrated that the exact number of multilevel threshold functions depends strongly on the relative topology of the input set. The results correct a previously published estimate and indicate that adding threshold levels enhances the capacity more than adding variables.https://authors.library.caltech.eduhttps://authors.library.caltech.edu/records/86mkv-00m71The Vapnik-Chervonenkis Dimension: Information versus Complexity in Learning
https://resolver.caltech.edu/CaltechAUTHORS:ABUnc89
Authors: {'items': [{'id': 'Abu-Mostafa-Y-S', 'name': {'family': 'Abu-Mostafa', 'given': 'Yaser S.'}}]}
Year: 1989
DOI: 10.1162/neco.1989.1.3.312
When feasible, learning is a very attractive alternative to explicit programming. This is particularly true in areas where the problems do not lend themselves to systematic programming, such as pattern recognition in natural environments. The feasibility of learning an unknown function from examples depends on two questions:
1. Do the examples convey enough information to determine the function?
2. Is there a speedy way of constructing the function from the examples?
These questions contrast the roles of information and complexity in learning. While the two roles share some ground, they are conceptually and technically different. In the common language of learning, the information question is that of generalization and the complexity question is that of scaling. The work of Vapnik and Chervonenkis (1971) provides the key tools for dealing with the information issue. In this review, we develop the main ideas of this framework and discuss how complexity fits in.https://authors.library.caltech.eduhttps://authors.library.caltech.edu/records/bz3m3-qjx13On the K-Winners-Take-All Network
https://resolver.caltech.edu/CaltechAUTHORS:20160107-160213913
Authors: {'items': [{'id': 'Majani-E', 'name': {'family': 'Majani', 'given': 'E.'}}, {'id': 'Erlanson-R', 'name': {'family': 'Erlanson', 'given': 'R.'}}, {'id': 'Abu-Mostafa-Y-S', 'name': {'family': 'Abu-Mostafa', 'given': 'Y.'}}]}
Year: 1989
We present and rigorously analyze a generalization of the Winner-Take-All Network: the K-Winners-Take-All Network. This network
identifies the K largest of a set of N real numbers. The
network model used is the continuous Hopfield model.https://authors.library.caltech.eduhttps://authors.library.caltech.edu/records/6wj5t-rrn63Information theory, complexity and neural networks
https://resolver.caltech.edu/CaltechAUTHORS:ABUieeecm89
Authors: {'items': [{'id': 'Abu-Mostafa-Y-S', 'name': {'family': 'Abu-Mostafa', 'given': 'Yaser S.'}}]}
Year: 1989
DOI: 10.1109/35.41397
Some of the main results in the mathematical evaluation of neural networks as information processing systems are discussed. The basic operation of feedback and feed-forward neural networks is described. Their memory capacity and computing power are considered. The concept of learning by example as it applies to neural networks is examined.https://authors.library.caltech.eduhttps://authors.library.caltech.edu/records/me6t9-pfb15A Method for the Associative Storage of Analog Vectors
https://resolver.caltech.edu/CaltechAUTHORS:20160107-161548455
Authors: {'items': [{'id': 'Atiya-A-F', 'name': {'family': 'Atiya', 'given': 'Amir'}}, {'id': 'Abu-Mostafa-Y-S', 'name': {'family': 'Abu-Mostafa', 'given': 'Yaser'}}]}
Year: 1990
A method for storing analog vectors in Hopfield's continuous feedback
model is proposed. By analog vectors we mean vectors whose
components are real-valued. The vectors to be stored are set as
equilibria of the network. The network model consists of one layer
of visible neurons and one layer of hidden neurons. We propose
a learning algorithm, which results in adjusting the positions of
the equilibria, as well as guaranteeing their stability. Simulation
results confirm the effectiveness of the method.https://authors.library.caltech.eduhttps://authors.library.caltech.edu/records/htz96-3nv21Analog Neural Networks as Decoders
https://resolver.caltech.edu/CaltechAUTHORS:20160119-162724779
Authors: {'items': [{'id': 'Erlanson-R', 'name': {'family': 'Erlanson', 'given': 'Ruth'}}, {'id': 'Abu-Mostafa-Y-S', 'name': {'family': 'Abu-Mostafa', 'given': 'Yaser'}}]}
Year: 1991
Analog neural networks with feedback can be used to implement l(Winner-Take-All (KWTA) networks. In turn, KWTA networks can be
used as decoders of a class of nonlinear error-correcting codes. By interconnecting
such KWTA networks, we can construct decoders capable
of decoding more powerful codes. We consider several families of interconnected
KWTA networks, analyze their performance in terms of coding
theory metrics, and consider the feasibility of embedding such networks in
VLSI technologies.https://authors.library.caltech.eduhttps://authors.library.caltech.edu/records/fzqcs-ntm16A Method for Learning from Hints
https://resolver.caltech.edu/CaltechAUTHORS:20160128-163222557
Authors: {'items': [{'id': 'Abu-Mostafa-Y-S', 'name': {'family': 'Abu-Mostafa', 'given': 'Yaser S.'}}]}
Year: 1993
We address the problem of learning an unknown function by
putting together several pieces of information (hints) that we know about the function. We introduce a method that generalizes learning from examples to learning from hints. A canonical representation of hints is defined and illustrated for new types of hints. All the hints are represented to the learning process by examples, and
examples of the function are treated on equal footing with the rest of the hints. During learning, examples from different hints are selected for processing according to a given schedule. We present two types of schedules; fixed schedules that specify the relative emphasis of each hint, and adaptive schedules that are based on how well each hint has been learned so far. Our learning method is compatible with any descent technique that we may choose to use.https://authors.library.caltech.eduhttps://authors.library.caltech.edu/records/mwmvb-va834An analog feedback associative memory
https://resolver.caltech.edu/CaltechAUTHORS:ATIieeetnn93
Authors: {'items': [{'id': 'Atiya-A-F', 'name': {'family': 'Atiya', 'given': 'Amir'}}, {'id': 'Abu-Mostafa-Y-S', 'name': {'family': 'Abu-Mostafa', 'given': 'Yaser S.'}}]}
Year: 1993
DOI: 10.1109/72.182701
A method for the storage of analog vectors, i.e., vectors whose components are real-valued, is developed for the Hopfield continuous-time network. An important requirement is that each memory vector has to be an asymptotically stable (i.e. attractive) equilibrium of the network. Some of the limitations imposed by the continuous Hopfield model on the set of vectors that can be stored are pointed out. These limitations can be relieved by choosing a network containing visible as well as hidden units. An architecture consisting of several hidden layers and a visible layer, connected in a circular fashion, is considered. It is proved that the two-layer case is guaranteed to store any number of given analog vectors provided their number does not exceed 1 + the number of neurons in the hidden layer. A learning algorithm that correctly adjusts the locations of the equilibria and guarantees their asymptotic stability is developed. Simulation results confirm the effectiveness of the approach.https://authors.library.caltech.eduhttps://authors.library.caltech.edu/records/e7r25-pdp23Hints and the VC Dimension
https://resolver.caltech.edu/CaltechAUTHORS:ABUnc93
Authors: {'items': [{'id': 'Abu-Mostafa-Y-S', 'name': {'family': 'Abu-Mostafa', 'given': 'Yaser S.'}}]}
Year: 1993
DOI: 10.1162/neco.1993.5.2.278
Learning from hints is a generalization of learning from examples that allows for a variety of information about the unknown function to be used in the learning process. In this paper, we use the VC dimension, an established tool for analyzing learning from examples, to analyze learning from hints. In particular, we show how the VC dimension is affected by the introduction of a hint. We also derive a new quantity that defines a VC dimension for the hint itself. This quantity is used to estimate the number of examples needed to "absorb" the hint. We carry out the analysis for two types of hints, invariances and catalysts. We also describe how the same method can be applied to other types of hints.https://authors.library.caltech.eduhttps://authors.library.caltech.edu/records/ynexf-s1764An algorithm for learning from hints
https://resolver.caltech.edu/CaltechAUTHORS:ABUijcnn93
Authors: {'items': [{'id': 'Abu-Mostafa-Y-S', 'name': {'family': 'Abu-Mostafa', 'given': 'Y. S.'}}]}
Year: 1993
DOI: 10.1109/IJCNN.1993.716969
To take advantage of prior knowledge (hints) about the function one wants to learn, we introduce a method that generalizes learning from examples to learning from hints. A canonical representation of hints is defined and illustrated. All hints are represented to the learning process by examples, and examples of the function are treated on equal footing with the rest of the hints. During learning, examples from different hints are selected for processing according to a given schedule. We present two types of schedules; fixed schedules that specify the relative emphasis of each hint, and adaptive schedules that are based on how well each hint has been learned so far. Our learning method is compatible with any descent technique.https://authors.library.caltech.eduhttps://authors.library.caltech.edu/records/c6enm-z1321Financial Applications of Learning from Hints
https://resolver.caltech.edu/CaltechAUTHORS:20150305-151907939
Authors: {'items': [{'id': 'Abu-Mostafa-Y-S', 'name': {'family': 'Abu-Mostafa', 'given': 'Yaser S.'}}]}
Year: 1995
The basic paradigm for learning in neural networks is 'learning from examples' where a training set of input-output examples is used to teach the network the target function. Learning from hints is a generalization
of learning from examples where additional information
about the target function can be incorporated in the same learning process. Such information can come from common sense rules or special expertise. In financial market applications where the training data is very noisy, the use of such hints can have a decisive advantage. We demonstrate the use of hints in foreign-exchange trading of the U.S. Dollar versus the British Pound, the German
Mark, the Japanese Yen, and the Swiss Franc, over a period of 32 months. We explain the general method of learning from hints and how it can be applied to other markets. The learning model for this method is not restricted to neural networks.https://authors.library.caltech.eduhttps://authors.library.caltech.edu/records/x02vv-1w353Hints
https://resolver.caltech.edu/CaltechAUTHORS:ABUnc95
Authors: {'items': [{'id': 'Abu-Mostafa-Y-S', 'name': {'family': 'Abu-Mostafa', 'given': 'Yaser S.'}}]}
Year: 1995
DOI: 10.1162/neco.1995.7.4.639
The systematic use of hints in the learning-from-examples paradigm is the subject of this review. Hints are the properties of the target function that are known to us independently of the training examples. The use of hints is tantamount to combining rules and data in learning, and is compatible with different learning models, optimization techniques, and regularization techniques. The hints are represented to the learning process by virtual examples, and the training examples of the target function are treated on equal footing with the rest of the hints. A balance is achieved between the information provided by the different hints through the choice of objective functions and learning schedules. The Adaptive Minimization algorithm achieves this balance by relating the performance on each hint to the overall performance. The application of hints in forecasting the very noisy foreign-exchange markets is illustrated. On the theoretical side, the information value of hints is contrasted to the complexity value and related to the VC dimension.https://authors.library.caltech.eduhttps://authors.library.caltech.edu/records/0b77f-pma41Introduction to financial forecasting
https://resolver.caltech.edu/CaltechAUTHORS:20190628-091500494
Authors: {'items': [{'id': 'Abu-Mostafa-Y-S', 'name': {'family': 'Abu-Mostafa', 'given': 'Yaser S.'}}, {'id': 'Atiya-A-F', 'name': {'family': 'Atiya', 'given': 'Amir F.'}}]}
Year: 1996
DOI: 10.1007/bf00126626
This paper provides a brief introduction to forecasting in financial markets with emphasis on commodity futures and foreign exchange. We describe the basic approaches to forecasting, and discuss the noisy nature of financial data. Using neural networks as a learning paradigm, we describe different techniques for choosing the inputs, outputs, and error function. We also describe the learning from hints technique that augments the standard learning from examples method. We demonstrate the use of hints in foreign-exchange trading of the U.S. Dollar versus the British Pound, the German Mark, the Japanese Yen, and the Swiss Franc, over a period of 32 months. The paper does not assume a background in financial markets.https://authors.library.caltech.eduhttps://authors.library.caltech.edu/records/yny48-z8064Monotonicity: Theory and Implementation
https://resolver.caltech.edu/CaltechAUTHORS:20190710-141334973
Authors: {'items': [{'id': 'Sill-J', 'name': {'family': 'Sill', 'given': 'Joseph'}}, {'id': 'Abu-Mostafa-Y-S', 'name': {'family': 'Abu-Mostafa', 'given': 'Yaser'}}]}
Year: 1997
DOI: 10.1007/978-1-4612-2018-3_6
We present a systematic method for incorporating prior knowledge (hints) into the learning-from-examples paradigm. The hints are represented in a canonical form that is compatible with descent techniques for learning. We focus in particular on the monotonicity hint, which states that the function to be learned is monotonic in some or all of the input variables. The application of monotonicity hints is demonstrated on two real-world problems-a credit card application task, and a problem in medical diagnosis. We report experimental results which show that using monotonicity hints leads to a statistically significant improvement in performance on both problems. Monotonicity is also analyzed from a theoretical perspective. We consider the class M of monotonically increasing binary output functions. Necessary and sufficient conditions for monotonic separability of a dichotomy are proven. The capacity of M is shown to depend heavily on the input distribution.https://authors.library.caltech.eduhttps://authors.library.caltech.edu/records/5517z-zb676Monotonicity Hints
https://resolver.caltech.edu/CaltechAUTHORS:20160223-161511946
Authors: {'items': [{'id': 'Sill-J', 'name': {'family': 'Sill', 'given': 'Joseph'}}, {'id': 'Abu-Mostafa-Y-S', 'name': {'family': 'Abu-Mostafa', 'given': 'Yaser S.'}}]}
Year: 1997
A hint is any piece of side information about the target function to be learned. We consider the monotonicity hint, which states that the function to be learned is monotonic in some or all of the input variables. The application of monotonicity hints is demonstrated on two real-world problems- a credit card application task, and a problem in medical diagnosis. A measure of the monotonicity error
of a candidate function is defined and an objective function for the enforcement of monotonicity is derived from Bayesian principles. We report experimental results which show that using monotonicity hints leads to a statistically significant improvement in performance
on both problems.https://authors.library.caltech.eduhttps://authors.library.caltech.edu/records/g83x3-ng568Incorporating Contextual Information in White Blood Cell Identification
https://resolver.caltech.edu/CaltechAUTHORS:20160224-143921726
Authors: {'items': [{'id': 'Song-Xubo', 'name': {'family': 'Song', 'given': 'Xubo'}}, {'id': 'Abu-Mostafa-Y-S', 'name': {'family': 'Abu-Mostafa', 'given': 'Yaser'}}, {'id': 'Sill-J', 'name': {'family': 'Sill', 'given': 'Joseph'}}, {'id': 'Kasdan-H-L', 'name': {'family': 'Kasdan', 'given': 'Harvey'}}]}
Year: 1998
In this paper we propose a technique to incorporate contextual information into object classification. In the real world there are cases where the identity of an object is ambiguous due to the noise in the measurements
based on which the classification should be made. It is helpful to reduce the ambiguity by utilizing extra information referred to as context, which in our case is the identities of the accompanying objects. This
technique is applied to white blood cell classification. Comparisons are made against "no context" approach, which demonstrates the superior classification performance achieved by using context. In our particular
application, it significantly reduces false alarm rate and thus greatly reduces the cost due to expensive clinical tests.https://authors.library.caltech.eduhttps://authors.library.caltech.edu/records/jkktk-ekg96Validation of volatility models
https://resolver.caltech.edu/CaltechAUTHORS:20170408-150548267
Authors: {'items': [{'id': 'Magdon-Ismail-M', 'name': {'family': 'Magdon-Ismail', 'given': 'Malik'}}, {'id': 'Abu-Mostafa-Y-S', 'name': {'family': 'Abu-Mostafa', 'given': 'Yaser S.'}}]}
Year: 1998
DOI: 10.1002/(SICI)1099-131X(1998090)17:5/6<349::AID-FOR701>3.0.CO;2-X
In forecasting a financial time series, the mean prediction can be validated by direct comparison with the value of the series. However, the volatility or variance can only be validated by indirect means such as the likelihood function. Systematic errors in volatility prediction have an 'economic value' since volatility is a tradable quantity (e.g. in options and other derivatives) in addition to being a risk measure. We analyse the fidelity of the likelihood function as a means of training (in sample) and validating (out of sample) a volatility model. We report several cases where the likelihood function leads to an erroneous model. We correct for this error by scaling the volatility prediction using a predetermined factor that depends on the number of data points.https://authors.library.caltech.eduhttps://authors.library.caltech.edu/records/9765t-xrn98Financial markets: very noisy information processing
https://resolver.caltech.edu/CaltechAUTHORS:MAGprocieee98
Authors: {'items': [{'id': 'Magdon-Ismail-M', 'name': {'family': 'Magdon-Ismail', 'given': 'Malik'}}, {'id': 'Nicholson-A', 'name': {'family': 'Nicholson', 'given': 'Alexander'}}, {'id': 'Abu-Mostafa-Y-S', 'name': {'family': 'Abu-Mostafa', 'given': 'Yaser S.'}}]}
Year: 1998
DOI: 10.1109/5.726786
We report new results about the impact of noise on information processing with application to financial markets. These results quantify the tradeoff between the amount of data and the noise level in the data. They also provide estimates for the performance of a learning system in terms of the noise level. We use these results to derive a method for detecting the change in market volatility from period to period. We successfully apply these results to the four major foreign exchange (FX) markets. The results hold for linear as well as nonlinear learning models and algorithms and for different noise models.https://authors.library.caltech.eduhttps://authors.library.caltech.edu/records/ght0h-qtr28No Free Lunch for Early Stopping
https://resolver.caltech.edu/CaltechAUTHORS:20111201-140641206
Authors: {'items': [{'id': 'Çataltepe-Zehra-Kök', 'name': {'family': 'Çataltepe', 'given': 'Zehra'}}, {'id': 'Abu-Mostafa-Y-S', 'name': {'family': 'Abu-Mostafa', 'given': 'Yaser S.'}}, {'id': 'Magdon-Ismail-M', 'name': {'family': 'Magdon-Ismail', 'given': 'Malik'}}]}
Year: 1999
DOI: 10.1162/089976699300016557
We show that with a uniform prior on models having the same training error, early stopping at some fixed training error above the training error minimum results in an increase in the expected generalization error.https://authors.library.caltech.eduhttps://authors.library.caltech.edu/records/dy91j-tnb64Image Recognition in Context: Application to Microscopic Urinalysis
https://resolver.caltech.edu/CaltechAUTHORS:20160229-163056107
Authors: {'items': [{'id': 'Song-Xubo', 'name': {'family': 'Song', 'given': 'Xubo'}}, {'id': 'Sill-J', 'name': {'family': 'Sill', 'given': 'Joseph'}}, {'id': 'Abu-Mostafa-Y-S', 'name': {'family': 'Abu-Mostafa', 'given': 'Yaser'}}, {'id': 'Kasdan-H', 'name': {'family': 'Kasdan', 'given': 'Harvey'}}]}
Year: 2000
We propose a new and efficient technique for incorporating contextual information into object classification. Most of the current techniques face the problem of exponential computation cost. In this paper, we propose a new general framework that incorporates partial context at a linear cost. This technique is applied to microscopic urinalysis image recognition, resulting in a significant improvement of recognition rate over the context free approach. This gain would have been impossible using conventional context incorporation techniques.https://authors.library.caltech.eduhttps://authors.library.caltech.edu/records/s3nvm-sh902Maximal codeword lengths in Huffman codes
https://resolver.caltech.edu/CaltechAUTHORS:20190710-133740050
Authors: {'items': [{'id': 'Abu-Mostafa-Y-S', 'name': {'family': 'Abu-Mostafa', 'given': 'Y. S.'}}, {'id': 'McEliece-R-J', 'name': {'family': 'McEliece', 'given': 'R. J.'}}]}
Year: 2000
DOI: 10.1016/S0898-1221(00)00119-X
In this paper, we consider the following question about Huffman coding, which is an important technique for compressing data from a discrete source. If p is the smallest source probability, how long, in terms of p, can the longest Huffman codeword be? We show that if p is in the range 0 < p ≤12, and if K is the unique index such that 1F_(K+3)< p ≤1F_(K+2), where F_K denotes the Kth Fibonacci number, then the longest Huffman codeword for a source whose least probability is p is at most K, and no better bound is possible. Asymptotically, this implies the surprising fact that for small values of p, a Huffman code's longest codeword can be as much as 44% larger than that of the corresponding Shannon code.https://authors.library.caltech.eduhttps://authors.library.caltech.edu/records/s8rx2-m1v33No Free Lunch for Early Stopping
https://resolver.caltech.edu/CaltechCSTR:1998.cs-tr-98-02
Authors: {'items': [{'id': 'Çataltepe-Zehra-Kök', 'name': {'family': 'Çataltepe', 'given': 'Zehra'}}, {'id': 'Abu-Mostafa-Y-S', 'name': {'family': 'Abu-Mostafa', 'given': 'Yaser S.'}}, {'id': 'Magdon-Ismail-M', 'name': {'family': 'Magdon-Ismail', 'given': 'Malik'}}]}
Year: 2001
DOI: 10.7907/Z9B8565P
We show that, with a uniform prior on hypothesis functions having the same training error, early stopping at some fixed training error above the training error minimum results in an increase in the expected generalization error. We also show that regularization methods are equivalent to early stopping with certain non-uniform prior on the early stopping solutions.https://authors.library.caltech.eduhttps://authors.library.caltech.edu/records/f2t2r-bk048Minimizing memory loss in learning a new environment
https://resolver.caltech.edu/CaltechAUTHORS:20190702-153115049
Authors: {'items': [{'id': 'Al-Mashouq-K', 'name': {'family': 'Al-Mashouq', 'given': 'Khalid'}}, {'id': 'Abu-Mostafa-Y-S', 'name': {'family': 'Abu-Mostafa', 'given': 'Yaser'}}, {'id': 'Al-Ghoneim-K', 'name': {'family': 'Al-Ghoneim', 'given': 'Khaled'}}]}
Year: 2001
DOI: 10.1016/s0925-2312(01)00400-3
Human and other living species can learn new concepts without losing the old ones. On the other hand, artificial neural networks tend to "forget" old concepts. In this paper, we present three methods to minimize the loss of the old information. These methods are analyzed and compared for the linear model. In particular, a method called network sampling is shown to be optimal under certain condition on the sampled data distribution. We also show how to apply these methods in the nonlinear models.https://authors.library.caltech.eduhttps://authors.library.caltech.edu/records/47ngp-06v03Introduction to the special issue on neural networks in financial engineering
https://resolver.caltech.edu/CaltechAUTHORS:ABUieeetnn01a
Authors: {'items': [{'id': 'Abu-Mostafa-Y-S', 'name': {'family': 'Abu-Mostafa', 'given': 'Yaser S.'}}, {'id': 'Atiya-A-F', 'name': {'family': 'Atiya', 'given': 'Amir F.'}}, {'id': 'Magdon-Ismail-M', 'name': {'family': 'Magdon-Ismail', 'given': 'Malik'}}, {'id': 'White-H', 'name': {'family': 'White', 'given': 'Halbert'}}]}
Year: 2001
DOI: 10.1109/TNN.2001.935079
There are several phases that an emerging field goes through before it reaches maturity, and computational finance is no exception. There is usually a trigger for the birth of the field. In our case, new techniques such as neural networks, significant progress in computing technology, and the need for results that rely on more realistic assumptions inspired new researchers to revisit the traditional problems of finance, problems that have often been tackled by introducing simplifying assumptions in the past. The result has been a wealth of new approaches to these time-honored problems, with significant improvements in many cases.https://authors.library.caltech.eduhttps://authors.library.caltech.edu/records/n558f-fsb09Financial model calibration using consistency hints
https://resolver.caltech.edu/CaltechAUTHORS:ABUieeetnn01b
Authors: {'items': [{'id': 'Abu-Mostafa-Y-S', 'name': {'family': 'Abu-Mostafa', 'given': 'Yaser S.'}}]}
Year: 2001
DOI: 10.1109/72.935092
We introduce a technique for forcing the calibration of a financial model to produce valid parameters. The technique is based on learning from hints. It converts simple curve fitting into genuine calibration, where broad conclusions can be inferred from parameter values. The technique augments the error function of curve fitting with consistency hint error functions based on the Kullback-Leibler distance. We introduce an efficient EM-type optimization algorithm tailored to this technique. We also introduce other consistency hints, and balance their weights using canonical errors. We calibrate the correlated multifactor Vasicek model of interest rates, and apply it successfully to Japanese Yen swaps market and US dollar yield market.https://authors.library.caltech.eduhttps://authors.library.caltech.edu/records/7hwe0-aqc33The Multilevel Classification Problem and a Monotonicity Hint
https://resolver.caltech.edu/CaltechAUTHORS:20190702-152246968
Authors: {'items': [{'id': 'Magdon-Ismail-M', 'name': {'family': 'Magdon-Ismail', 'given': 'Malik'}}, {'id': 'Chen-Hung-Ching', 'name': {'family': 'Chen', 'given': 'Hung-Ching'}}, {'id': 'Abu-Mostafa-Y-S', 'name': {'family': 'Abu-Mostafa', 'given': 'Yaser S.'}}]}
Year: 2002
DOI: 10.1007/3-540-45675-9_61
We introduce and formalize the multilevel classification problem, in which each category can be subdivided into different levels. We analyze the framework in a Bayesian setting using Normal class conditional densities. Within this framework, a natural monotonicity hint converts the problem into a nonlinear programming task, with non-linear constraints. We present Monte Carlo and gradient based techniques for addressing this task, and show the results of simulations. Incorporation of monotonicity yields a systematic improvement in performance.https://authors.library.caltech.eduhttps://authors.library.caltech.edu/records/v3q22-et309Emergent Specialization in Swarm Systems
https://resolver.caltech.edu/CaltechAUTHORS:20190702-150156265
Authors: {'items': [{'id': 'Li-Ling', 'name': {'family': 'Li', 'given': 'Ling'}}, {'id': 'Martinoli-A', 'name': {'family': 'Martinoli', 'given': 'Alcherio'}}, {'id': 'Abu-Mostafa-Y-S', 'name': {'family': 'Abu-Mostafa', 'given': 'Yaser S.'}}]}
Year: 2002
DOI: 10.1007/3-540-45675-9_43
Distributed learning is the learning process of multiple autonomous agents in a varying environment, where each agent has only partial information about the global task. In this paper, we investigate the influence of different reinforcement signals (local and global) and team diversity (homogeneous and heterogeneous agents) on the learned solutions. We compare the learned solutions with those obtained by systematic search in a simple case study in which pairs of agents have to collaborate in order to solve the task without any explicit communication. The results show that policies which allow teammates to specialize find an adequate diversity of the team and, in general, achieve similar or better performances than policies which force homogeneity. However, in this specific case study, the achieved team performances appear to be independent of the locality or globality of the reinforcement signal.https://authors.library.caltech.eduhttps://authors.library.caltech.edu/records/mqcjd-3wx35CGBoost: Conjugate Gradient in Function Space
https://resolver.caltech.edu/CaltechCSTR:2003.007
Authors: {'items': [{'id': 'Li-L', 'name': {'family': 'Li', 'given': 'Ling'}}, {'id': 'Abu-Mostafa-Y-S', 'name': {'family': 'Abu-Mostafa', 'given': 'Yaser S.'}}, {'id': 'Pratap-A', 'name': {'family': 'Pratap', 'given': 'Amrit'}}]}
Year: 2003
DOI: 10.7907/yryfk-z8a36
The superior out-of-sample performance of AdaBoost has been attributed to the fact that it minimizes a cost function based on margin, in that it can be viewed as a special case of AnyBoost, an abstract gradient descent algorithm. In this paper, we provide a more sophisticated abstract boosting algorithm, CGBoost, based on conjugate gradient in function space. When the AdaBoost exponential cost function is optimized, CGBoost generally yields much lower cost and training error but higher test error, which implies that the exponential cost is vulnerable to overfitting. With the optimization power of CGBoost, we can adopt more "regularized" cost functions that have better out-of-sample performance but are difficult to optimize. Our experiments demonstrate that CGBoost generally outperforms AnyBoost in cost reduction. With suitable cost functions, CGBoost can have better out-of-sample performance.https://authors.library.caltech.eduhttps://authors.library.caltech.edu/records/yryfk-z8a36The maximum drawdown of the Brownian motion
https://resolver.caltech.edu/CaltechAUTHORS:MAGcife03
Authors: {'items': [{'id': 'Magon-Ismail-M', 'name': {'family': 'Magdon-Ismail', 'given': 'Malik'}}, {'id': 'Atiya-A-F', 'name': {'family': 'Atiya', 'given': 'Amir'}}, {'id': 'Pratap-A', 'name': {'family': 'Pratap', 'given': 'Amrit'}}, {'id': 'Abu-Mostafa-Y-S', 'name': {'family': 'Abu-Mostafa', 'given': 'Yaser'}}]}
Year: 2003
DOI: 10.1109/CIFER.2003.1196267
The MDD is defined as the maximum loss incurred from peak to bottom during a specified period of time. It is often preferred over some of the other risk measures because of the tight relationship between large drawdowns and fund redemptions. Also, a large drawdown can even indicate the start of a deterioration of an otherwise successful trading system, for example due to a market regime switch. Overall, the MDD is a very important risk measure. To be able to use it more insightfully, its analytical properties have to be understood. As a step towards this direction, we have presented in this article some analytic results that we have developed. We hope more and more results will come out from the research community analyzing this important measure.https://authors.library.caltech.eduhttps://authors.library.caltech.edu/records/2aprk-1ee98On the Maximum Drawdown of a Brownian Motion
https://resolver.caltech.edu/CaltechAUTHORS:20190702-143758047
Authors: {'items': [{'id': 'Magdon-Ismail-M', 'name': {'family': 'Magdon-Ismail', 'given': 'Malik'}}, {'id': 'Atiya-A-F', 'name': {'family': 'Atiya', 'given': 'Amir F.'}}, {'id': 'Pratap-A', 'name': {'family': 'Pratap', 'given': 'Amrit'}}, {'id': 'Abu-Mostafa-Y-S', 'name': {'family': 'Abu-Mostafa', 'given': 'Yaser S.'}}]}
Year: 2004
DOI: 10.1239/jap/1077134674
The maximum drawdown at time T of a random process on [0,T] can be defined informally as the largest drop from a peak to a trough. In this paper, we investigate the behaviour of this statistic for a Brownian motion with drift. In particular, we give an infinite series representation of its distribution and consider its expected value. When the drift is zero, we give an analytic expression for the expected value, and for nonzero drift, we give an infinite series representation. For all cases, we compute the limiting (T → ∞) behaviour, which can be logarithmic (for positive drift), square root (for zero drift) or linear (for negative drift).https://authors.library.caltech.eduhttps://authors.library.caltech.edu/records/nx99z-mnz54The Bin Model
https://resolver.caltech.edu/CaltechCSTR:2004.002
Authors: {'items': [{'id': 'Abu-Mostafa-Y-S', 'name': {'family': 'Abu-Mostafa', 'given': 'Yaser'}}, {'id': 'Song-Xubo', 'name': {'family': 'Song', 'given': 'Xubo'}}, {'id': 'Nicholson-A', 'name': {'family': 'Nicholson', 'given': 'Alexander'}}, {'id': 'Magdon-Ismail-M', 'name': {'family': 'Magdon-Ismail', 'given': 'Malik'}}]}
Year: 2004
DOI: 10.7907/Z9222RR7
We propose a novel theoretical framework for understanding learning and generalization which we will call the bin model. Using the bin model, a closed form is derived for the generalization error that estimates the out-of-sample performance in terms of the in-sample performance. We address the problem of overfitting, and show that using a simple exhaustive learning algorithm it does not arise. This is independent of the target function, input distribution and learning model, and remains true even with noisy data sets. We apply our analysis to both classification and regression problems and give an example of how it may be used effectively in practice.https://authors.library.caltech.eduhttps://authors.library.caltech.edu/records/n7j7x-tmq04Learning and Measuring Specialization in Collaborative Swarm Systems
https://resolver.caltech.edu/CaltechAUTHORS:20190702-144335632
Authors: {'items': [{'id': 'Li-Ling', 'name': {'family': 'Li', 'given': 'Ling'}}, {'id': 'Martinoli-A', 'name': {'family': 'Martinoli', 'given': 'Alcherio'}}, {'id': 'Abu-Mostafa-Y-S', 'name': {'family': 'Abu-Mostafa', 'given': 'Yaser S.'}}]}
Year: 2004
DOI: 10.1177/105971230401200306
This paper addresses qualitative and quantitative diversity and specialization issues in the framework of self-organizing, distributed, artificial systems. Both diversity and specialization are obtained via distributed learning from initially homogeneous swarms. While measuring diversity essentially quantifies differences among the individuals, assessing the degree of specialization implies correlation between the swarm's heterogeneity with its overall performance. Starting from the stick-pulling experiment in collective robotics, a task that requires the collaboration of two robots, we abstract and generalize in simulation the task constraints to k robots collaborating sequentially or in parallel. We investigate quantitatively the influence of task constraints and types of reinforcement signals on performance, diversity, and specialization in these collaborative experiments. Results show that, though diversity is not explicitly rewarded in our learning algorithm, even in scenarios without explicit communication among agents the swarm becomes specialized after learning. The degrees of both diversity and specialization are affected strongly by environmental conditions and task constraints. While the specialization measure reveals characteristics related to performance and learning in a clearer way than diversity does, the latter measure appears to be less sensitive to different noise conditions and learning parameters.https://authors.library.caltech.eduhttps://authors.library.caltech.edu/records/dbbv3-t8n54Improving Generalization by Data Categorization
https://resolver.caltech.edu/CaltechAUTHORS:20190702-142717858
Authors: {'items': [{'id': 'Li-Ling', 'name': {'family': 'Li', 'given': 'Ling'}}, {'id': 'Pratap-A', 'name': {'family': 'Pratap', 'given': 'Amrit'}}, {'id': 'Lin-Hsuan-Tien', 'name': {'family': 'Lin', 'given': 'Hsuan-Tien'}}, {'id': 'Abu-Mostafa-Y-S', 'name': {'family': 'Abu-Mostafa', 'given': 'Yaser S.'}}]}
Year: 2005
DOI: 10.1007/11564126_19
In most of the learning algorithms, examples in the training set are treated equally. Some examples, however, carry more reliable or critical information about the target than the others, and some may carry wrong information. According to their intrinsic margin, examples can be grouped into three categories: typical, critical, and noisy. We propose three methods, namely the selection cost, SVM confidence margin, and AdaBoost data weight, to automatically group training examples into these three categories. Experimental results on artificial datasets show that, although the three methods have quite different nature, they give similar and reasonable categorization. Results with real-world datasets further demonstrate that treating the three data categories differently in learning can improve generalization.https://authors.library.caltech.eduhttps://authors.library.caltech.edu/records/4q0t9-sqp86Pruning training sets for learning of object categories
https://resolver.caltech.edu/CaltechAUTHORS:ANGcvpr05
Authors: {'items': [{'id': 'Angelova-A', 'name': {'family': 'Angelova', 'given': 'Anelia'}}, {'id': 'Abu-Mostafa-Y-S', 'name': {'family': 'Abu-Mostafa', 'given': 'Yaser S.'}}, {'id': 'Perona-P', 'name': {'family': 'Perona', 'given': 'Pietro'}, 'orcid': '0000-0002-7583-5809'}]}
Year: 2005
DOI: 10.1109/CVPR.2005.283
Training datasets for learning of object categories are often contaminated or imperfect. We explore an approach to automatically identify examples that are noisy or troublesome for learning and exclude them from the training set. The problem is relevant to learning in semi-supervised or unsupervised setting, as well as to learning when the training data is contaminated with wrongly labeled examples or when correctly labeled, but hard to learn examples, are present. We propose a fully automatic mechanism for noise cleaning, called 'data pruning', and demonstrate its success on learning of human faces. It is not assumed that the data or the noise can be modeled or that additional training examples are available. Our experiments show that data pruning can improve on generalization performance for algorithms with various robustness to noise. It outperforms methods with regularization properties and is superior to commonly applied aggregation methods, such as bagging.https://authors.library.caltech.eduhttps://authors.library.caltech.edu/records/5feec-dr959Data complexity in machine learning
https://resolver.caltech.edu/CaltechCSTR:2006.004
Authors: {'items': [{'id': 'Li-Ling', 'name': {'family': 'Li', 'given': 'Ling'}}, {'id': 'Abu-Mostafa-Y-S', 'name': {'family': 'Abu-Mostafa', 'given': 'Yaser S.'}}]}
Year: 2006
DOI: 10.7907/Z9319SW2
We investigate the role of data complexity in the context of binary classification problems. The universal data complexity is defined for a data set as the Kolmogorov complexity of the mapping enforced by the data set. It is closely related to several existing principles used in machine learning such as Occam's razor, the minimum description length, and the Bayesian approach. The data complexity can also be defined based on a learning model, which is more realistic for applications. We demonstrate the application of the data complexity in two learning problems, data decomposition and data pruning. In data decomposition, we illustrate that a data set is best approximated by its principal subsets which are Pareto optimal with respect to the complexity and the set size. In data pruning, we show that outliers usually have high complexity contributions, and propose methods for estimating the complexity contribution. Since in practice we have to approximate the ideal data complexity measures, we also discuss the impact of such approximations.https://authors.library.caltech.eduhttps://authors.library.caltech.edu/records/ypj0s-xex59Machines that Think for Themselves
https://resolver.caltech.edu/CaltechAUTHORS:20120627-141125402
Authors: {'items': [{'id': 'Abu-Mostafa-Y-S', 'name': {'family': 'Abu-Mostafa', 'given': 'Yaser S.'}}]}
Year: 2012
DOI: 10.1038/scientificamerican0712-78
New techniques for teaching computers how to learn are beating the experts.https://authors.library.caltech.eduhttps://authors.library.caltech.edu/records/ree7p-z3287Mismatched Training and Test Distributions Can Outperform Matched Ones
https://resolver.caltech.edu/CaltechAUTHORS:20141218-110007751
Authors: {'items': [{'id': 'González-C-R', 'name': {'family': 'González', 'given': 'Carlos R.'}}, {'id': 'Abu-Mostafa-Y-S', 'name': {'family': 'Abu-Mostafa', 'given': 'Yaser S.'}}]}
Year: 2015
DOI: 10.1162/NECO_a_00697
In learning theory, the training and test sets are assumed to be drawn from the same probability distribution. This assumption is also followed in practical situations, where matching the training and test distributions is considered desirable. Contrary to conventional wisdom, we show that mismatched training and test distributions in supervised learning can in fact outperform matched distributions in terms of the bottom line, the out-of-sample performance, independent of the target function in question. This surprising result has theoretical and algorithmic ramifications that we discuss.https://authors.library.caltech.eduhttps://authors.library.caltech.edu/records/x442b-zwk70County-Specific, Real-Time Projection of the Effect of Business Closures on the COVID-19 Pandemic
https://resolver.caltech.edu/CaltechAUTHORS:20210212-100829452
Authors: {'items': [{'id': 'Yurk-Dominic', 'name': {'family': 'Yurk', 'given': 'Dominic'}}, {'id': 'Abu-Mostafa-Y-S', 'name': {'family': 'Abu-Mostafa', 'given': 'Yaser'}}]}
Year: 2021
DOI: 10.1101/2021.02.10.21251533
Public health policies such as business closures have been one of our most effective tools in slowing the spread of COVID-19, but they also impose costs. This has created demand from policy makers for models which can predict when and where such policies will be most effective to head off a surge and where they could safely be loosened. No current model combines data-driven, real-time policy effect predictions with county-level granularity. We present a neural net-based model for predicting the effect of business closures or re-openings on the COVID-19 time-varying reproduction number Rt in real time for every county in California. When trained on data from May through September the model accurately captured relative county dynamics during the October/November California COVID-19 surge (r2=0.76), indicating robust out-of-sample performance. To showcase the model's potential utility we present a case study of various counties in mid-October. Even when counties imposed similar restrictions at the time, our model successfully distinguished counties in need of drastic and immediate action to head off a surge from counties in less dire need of intervention. While this study focuses on business closures in California, the presented model architecture could be applied to other policies around world.https://authors.library.caltech.eduhttps://authors.library.caltech.edu/records/tehhm-n2q81The United States COVID-19 Forecast Hub dataset
https://resolver.caltech.edu/CaltechAUTHORS:20211105-194210514
Authors: {'items': [{'id': 'Cramer-Estee-Y', 'name': {'family': 'Cramer', 'given': 'Estee Y.'}, 'orcid': '0000-0003-1373-3177'}, {'id': 'Huang-Yuxin', 'name': {'family': 'Huang', 'given': 'Yuxin'}}, {'id': 'Wang-Yijin', 'name': {'family': 'Wang', 'given': 'Yijin'}}, {'id': 'Ray-Evan-L', 'name': {'family': 'Ray', 'given': 'Evan L.'}}, {'id': 'Cornell-Matthew', 'name': {'family': 'Cornell', 'given': 'Matthew'}}, {'id': 'Bracher-Johannes', 'name': {'family': 'Bracher', 'given': 'Johannes'}, 'orcid': '0000-0002-3777-1410'}, {'id': 'Brennen-Andrea', 'name': {'family': 'Brennen', 'given': 'Andrea'}}, {'id': 'Castro-Rivadeneira-Alvaro-J', 'name': {'family': 'Castro Rivadeneira', 'given': 'Alvaro J.'}}, {'id': 'Gerding-Aaron', 'name': {'family': 'Gerding', 'given': 'Aaron'}}, {'id': 'House-Katie', 'name': {'family': 'House', 'given': 'Katie'}}, {'id': 'Jayawardena-Dasuni', 'name': {'family': 'Jayawardena', 'given': 'Dasuni'}}, {'id': 'Kanji-Abdul-Hannan', 'name': {'family': 'Kanji', 'given': 'Abdul Hannan'}}, {'id': 'Khandelwal-Ayush', 'name': {'family': 'Khandelwal', 'given': 'Ayush'}}, {'id': 'Le-Khoa', 'name': {'family': 'Le', 'given': 'Khoa'}}, {'id': 'Mody-Vidhi', 'name': {'family': 'Mody', 'given': 'Vidhi'}}, {'id': 'Mody-Vrushti', 'name': {'family': 'Mody', 'given': 'Vrushti'}}, {'id': 'Niemi-Jarad', 'name': {'family': 'Niemi', 'given': 'Jarad'}, 'orcid': '0000-0002-5079-158X'}, {'id': 'Stark-Ariane', 'name': {'family': 'Stark', 'given': 'Ariane'}}, {'id': 'Shah-Apurv', 'name': {'family': 'Shah', 'given': 'Apurv'}}, {'id': 'Wattanachit-Nutcha', 'name': {'family': 'Wattanachit', 'given': 'Nutcha'}}, {'id': 'Zorn-Martha-W', 'name': {'family': 'Zorn', 'given': 'Martha W.'}}, {'id': 'Reich-Nicholas-G', 'name': {'family': 'Reich', 'given': 'Nicholas G.'}, 'orcid': '0000-0003-3503-9899'}, {'id': 'Abu-Mostafa-Y-S', 'name': {'family': 'Abu-Mostafa', 'given': 'Yaser S.'}}, {'id': 'Bathwal-Rahil', 'name': {'family': 'Bathwal', 'given': 'Rahil'}}, {'id': 'Chang-Nicholas-A', 'name': {'family': 'Chang', 'given': 'Nicholas A.'}}, {'id': 'Chitta-Pavan', 'name': {'family': 'Chitta', 'given': 'Pavan'}}, {'id': 'Erickson-Anne', 'name': {'family': 'Erickson', 'given': 'Anne'}}, {'id': 'Goel-Sumit', 'name': {'family': 'Goel', 'given': 'Sumit'}, 'orcid': '0000-0003-3266-9035'}, {'id': 'Gowda-Jethin', 'name': {'family': 'Gowda', 'given': 'Jethin'}}, {'id': 'Jin-Qixuan', 'name': {'family': 'Jin', 'given': 'Qixuan'}}, {'id': 'Jo-HyeongChan', 'name': {'family': 'Jo', 'given': 'HyeongChan'}}, {'id': 'Kim-Juhyun', 'name': {'family': 'Kim', 'given': 'Juhyun'}}, {'id': 'Kulkarni-Pranav-D', 'name': {'family': 'Kulkarni', 'given': 'Pranav'}, 'orcid': '0000-0002-1461-0948'}, {'id': 'Lushtak-Samuel-M', 'name': {'family': 'Lushtak', 'given': 'Samuel M.'}}, {'id': 'Mann-Ethan', 'name': {'family': 'Mann', 'given': 'Ethan'}}, {'id': 'Popken-Max', 'name': {'family': 'Popken', 'given': 'Max'}}, {'id': 'Soohoo-Connor', 'name': {'family': 'Soohoo', 'given': 'Connor'}}, {'id': 'Tirumala-Kushal', 'name': {'family': 'Tirumala', 'given': 'Kushal'}}, {'id': 'Tseng-Albert', 'name': {'family': 'Tseng', 'given': 'Albert'}}, {'id': 'Varadarajan-Vignesh', 'name': {'family': 'Varadarajan', 'given': 'Vignesh'}}, {'id': 'Vytheeswaran-Jagath', 'name': {'family': 'Vytheeswaran', 'given': 'Jagath'}, 'orcid': '0000-0002-5250-7714'}, {'id': 'Wang-Christopher', 'name': {'family': 'Wang', 'given': 'Christopher'}}, {'id': 'Yeluri-Akshay', 'name': {'family': 'Yeluri', 'given': 'Akshay'}, 'orcid': '0000-0001-8654-1673'}, {'id': 'Yurk-Dominic', 'name': {'family': 'Yurk', 'given': 'Dominic'}, 'orcid': '0000-0002-2276-4189'}, {'id': 'Zhang-Michael', 'name': {'family': 'Zhang', 'given': 'Michael'}}, {'id': 'Zlokapa-Alexander-M', 'name': {'family': 'Zlokapa', 'given': 'Alexander'}, 'orcid': '0000-0002-4153-8646'}]}
Year: 2022
DOI: 10.1038/s41597-022-01517-w
PMCID: PMC8236414
Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages.https://authors.library.caltech.eduhttps://authors.library.caltech.edu/records/ys28f-z5094