Tsachy (Itschak) Weissman
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Papers

A fairly updated list can be found here. Please email me if you would like a pdf of any paper you are not able to access online.

Recent Papers

[1] B Isik, K Choi, X Zheng, T Weissman, S Ermon, and ... Neural network compression for noisy storage devices. arXiv preprint arXiv …, 2021. [ bib | http ]
[2] B Isik, A No, and T Weissman. Successive pruning for model compression via rate distortion theory. arXiv preprint arXiv:2102.08329, 2021. [ bib | http ]
[3] Y Han, K Tatwawadi, GR Kurri, Z Zhou, and ... Optimal communication rates and combinatorial properties for common randomness generation. IEEE Transactions …, 2021. [ bib | http ]
[4] P Tandon, S Chandak, P Pataranutaporn, Y Liu, and ... Txt2vid: Ultra-low bitrate compression of talking-head videos via text. arXiv preprint arXiv …, 2021. [ bib | http ]
[5] W Zhang, B Kitts, Y Han, Z Zhou, T Mao, H He, and ... Meow: A space-efficient nonparametric bid shading algorithm. Proceedings of the 27th …, 2021. [ bib | DOI | http ]
[6] BT Lau, S Chandak, S Roy, K Tatawadi, M Wootters, and ... Magnetic dna random access memory with nanopore readouts and exponentially-scaled combinatorial addressing. bioRxiv, 2021. [ bib | DOI | http ]
[7] Q Meng, S Chandak, Y Zhu, and T Weissman. Nanospring: reference-free lossless compression of nanopore sequencing reads using an approximate assembly approach. bioRxiv, 2021. [ bib | DOI | http ]
[8] DS Pavlichin, HJ Lee, SU Greer, SM Grimes, and ... Kmerkeys: a web resource for searching indexed genome assemblies and variants. bioRxiv, 2021. [ bib | DOI | http ]
[9] B Isik, A No, and T Weissman. Rate-distortion theoretic model compression: Successive refinement for pruning. arXiv preprint arXiv:2102.08329, 2021. [ bib | http ]
[10] Y Han, J Jiao, T Weissman, and Y Wu. Optimal rates of entropy estimation over lipschitz balls. The Annals of Statistics, 2020. [ bib | DOI | http ]
[11] Y Han, J Jiao, and T Weissman. Minimax estimation of divergences between discrete distributions. IEEE Journal on Selected Areas in …, 2020. [ bib | http ]
[12] J Mardia, J Jiao, E Tánczos, RD Nowak, and ... Concentration inequalities for the empirical distribution of discrete distributions: beyond the method of types. … and Inference: A …, 2020. [ bib | http ]
[13] S Chandak, J Neu, K Tatwawadi, and ... Overcoming high nanopore basecaller error rates for dna storage via basecaller-decoder integration and convolutional codes. ICASSP 2020-2020 …, 2020. [ bib | http ]
[14] S Chandak, K Tatwawadi, S Sridhar, and ... Impact of lossy compression of nanopore raw signal data on basecalling and consensus accuracy. Bioinformatics, 2020. [ bib | http ]
[15] S Chandak, K Tatwawadi, C Wen, and ... Lfzip: Lossy compression of multivariate floating-point time series data via improved prediction. 2020 Data …, 2020. [ bib | http ]
[16] Y Han, Z Zhou, A Flores, E Ordentlich, and ... Learning to bid optimally and efficiently in adversarial first-price auctions. arXiv preprint arXiv …, 2020. [ bib | http ]
[17] Y Han, Z Zhou, and T Weissman. Optimal no-regret learning in repeated first-price auctions. arXiv preprint arXiv:2003.09795, 2020. [ bib | http ]
[18] R Prabhakar, S Chandak, C Chiu, R Liang, and ... Reducing latency and bandwidth for video streaming using keypoint extraction and digital puppetry. arXiv preprint arXiv …, 2020. [ bib | http ]
[19] B Isik, K Choi, X Zheng, HSP Wong, S Ermon, and ... Noisy Neural Network Compression for Analog Storage Devices. openreview.net, 2020. [ bib | http ]
[20] S Chandak, KS Tatwawadi, T Weissman, and ... Systems and methods for compressing genetic sequencing data and uses thereof. US Patent App. 16 …, 2020. [ bib | http ]
[21] S Chandak, K Tatwawadi, I Ochoa, M Hernaez, and ... Spring: a next-generation compressor for fastq data. …, 2019. [ bib | http ]
[22] DS Pavlichin, J Jiao, and T Weissman. Approximate profile maximum likelihood. J. Mach. Learn. Res., 2019. [ bib | .pdf ]
[23] K Choi, K Tatwawadi, A Grover, and ... Neural joint source-channel coding. International …, 2019. [ bib | .html ]
[24] M Hernaez, D Pavlichin, T Weissman, and ... Genomic data compression. Annual Review of …, 2019. [ bib | DOI | http ]
[25] S Chandak, K Tatwawadi, B Lau, and ... Improved read/write cost tradeoff in dna-based data storage using ldpc codes. 2019 57th Annual …, 2019. [ bib | http ]
[26] J Jiao, Y Han, I Fischer-Hwang, and ... Estimating the fundamental limits is easier than achieving the fundamental limits. IEEE Transactions on …, 2019. [ bib | http ]
[27] I Fischer-Hwang, I Ochoa, T Weissman, and M Hernaez. Denoising of aligned genomic data. Scientific reports, 2019. [ bib | http ]
[28] I Fischer-Hwang, Z Lu, J Zou, and T Weissman. Cross-linked rna secondary structure analysis using network techniques. bioRxiv, 2019. [ bib | DOI | http ]
[29] DS Pavlichin, Y Quek, and T Weissman. Minimum power to maintain a nonequilibrium distribution of a markov chain. arXiv preprint arXiv:1907.01582, 2019. [ bib | http ]
[30] Y Han, K Tatwawadi, GR Kurri, Z Zhou, and ... Optimal communication rates and combinatorial properties for distributed simulation. arXiv preprint arXiv …, 2019. [ bib | http ]
[31] A Bhown, S Mukherjee, S Yang, and ... Humans are still the best lossy image compressors. 2019 Data …, 2019. [ bib | http ]
[32] Y Han, K Tatwawadi, Z Zhou, GR Kurri, and ... Optimal communication rates for zero-error distributed simulation under blackboard communication protocols. CoRR, 2019. [ bib ]

Information Measure Estimation

Information and Estimation

Multi-terminal Source Coding

Actions in Information Theory

Feedback Communication

Denoising

Toward Universal and Practical Lossy Source Coding

Directed Information

Delay and Complexity Constrained Information Theory

Prediction, Learning, and Sequential Decision Making

Entropy

Shannon Theory