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About Adaptive Coding on Countable Alphabets
, 2012
"... This paper sheds light on universal coding with respect to classes of memoryless sources over a countable alphabet defined by an envelope function with finite and non-decreasing hazard rate. We prove that the auto-censuring (AC) code introduced by Bontemps (2011) is adaptive with respect to the coll ..."
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Cited by 5 (1 self)
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This paper sheds light on universal coding with respect to classes of memoryless sources over a countable alphabet defined by an envelope function with finite and non-decreasing hazard rate. We prove that the auto-censuring (AC) code introduced by Bontemps (2011) is adaptive with respect to the collection of such classes. The analysis builds on the tight characterization of universal redundancy rate in terms of metric entropy by Haussler and Opper (1997) and on a careful analysis of the performance of the AC-coding algorithm. The latter relies on non-asymptotic bounds for maxima of samples from discrete distributions with finite and non-decreasing hazard rate.
Poissonization and universal compression of envelope classes
"... Abstract—Poisson sampling is a method for eliminating de-pendence among symbols in a random sequence. It helps im-prove algorithm design, strengthen bounds, and simplify proofs. We relate the redundancy of fixed-length and Poisson-sampled sequences, use this result to derive a simple formula for the ..."
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Abstract—Poisson sampling is a method for eliminating de-pendence among symbols in a random sequence. It helps im-prove algorithm design, strengthen bounds, and simplify proofs. We relate the redundancy of fixed-length and Poisson-sampled sequences, use this result to derive a simple formula for the redundancy of general envelope classes, and apply this formula to obtain simple and tight bounds on the redundancy of power-law and exponential envelope classes, in particular answering a question posed in [1]. I.
1Large Alphabet Compression and Predictive Distributions through Poissonization and Tilting
"... This paper introduces a convenient strategy for coding and predicting sequences of independent, identically distributed random variables generated from a large alphabet of size m. In particular, the size of the sample is allowed to be variable. The employment of a Poisson model and tilting method si ..."
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This paper introduces a convenient strategy for coding and predicting sequences of independent, identically distributed random variables generated from a large alphabet of size m. In particular, the size of the sample is allowed to be variable. The employment of a Poisson model and tilting method simplifies the implementation and analysis through independence. The resulting strategy is optimal within the class of distributions satisfying a moment condition, and is close to optimal for the class of all i.i.d distributions on strings of a given length. Moreover, the method can be used to code and predict strings with a condition on the tail of the ordered counts. It can also be applied to distributions in an envelope class.
Universal Compression of Envelope Classes: Tight Characterization via Poisson Sampling∗
, 2014
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TABLE OF CONTENTS
, 2014
"... Copyright Jayadev Acharya, 2014 All rights reserved. The dissertation of Jayadev Acharya is approved, and it is acceptable in quality and form for publication on microfilm and electronically: ..."
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Copyright Jayadev Acharya, 2014 All rights reserved. The dissertation of Jayadev Acharya is approved, and it is acceptable in quality and form for publication on microfilm and electronically:
Universal Compression of Envelope Classes: Tight Characterization via Poisson Sampling∗
, 2014
"... ar ..."
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1 About Adaptive Coding on Countable Alphabets §
, 2014
"... Abstract—This paper sheds light on adaptive coding with respect to classes of memoryless sources over a countable alphabet defined by an envelope function with finite and non-decreasing hazard rate (log-concave envelope distributions). We prove that the auto-censuring (AC) code introduced by Bontemp ..."
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Abstract—This paper sheds light on adaptive coding with respect to classes of memoryless sources over a countable alphabet defined by an envelope function with finite and non-decreasing hazard rate (log-concave envelope distributions). We prove that the auto-censuring (AC) code introduced by Bontemps (2011) is adaptive with respect to the collection of such classes. The analysis builds on the tight characterization of universal redundancy rate in terms of metric entropy by Haussler and Opper (1997) and on a careful analysis of the performance of the ACcoding algorithm. The latter relies on non-asymptotic bounds for maxima of samples from discrete distributions with finite and non-decreasing hazard rate. Index Terms—countable alphabets, redundancy, adaptive compression, minimax.