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Pritish Kamath
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2020 – today
- 2025
- [j12]Alex Berke, Badih Ghazi, Enrico Bacis, Pritish Kamath, Ravi Kumar, Robin Lassonde, Pasin Manurangsi, Umar Syed:
How Unique is Whose Web Browser? The role of demographics in browser fingerprinting among US users. Proc. Priv. Enhancing Technol. 2025(1): 720-758 (2025) - 2024
- [j11]Hidayet Aksu, Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Adam Sealfon, Avinash V. Varadarajan:
Summary Reports Optimization in the Privacy Sandbox Attribution Reporting API. Proc. Priv. Enhancing Technol. 2024(4): 605-621 (2024) - [c44]Lynn Chua, Qiliang Cui, Badih Ghazi, Charlie Harrison, Pritish Kamath, Walid Krichene, Ravi Kumar, Pasin Manurangsi, Nicolas Mayoraz, Hema Venkata Krishna Giri Narra, Steffen Rendle, Amer Sinha, Avinash V. Varadarajan, Chiyuan Zhang:
Training Differentially Private Ad Prediction Models With Semi-Sensitive Features. AdKDD@KDD 2024 - [c43]Pranjal Awasthi, Nishanth Dikkala, Pritish Kamath, Raghu Meka:
Learning Neural Networks with Sparse Activations. COLT 2024: 406-425 - [c42]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Raghu Meka, Chiyuan Zhang:
On Convex Optimization with Semi-Sensitive Features. COLT 2024: 1916-1938 - [c41]Badih Ghazi, Yangsibo Huang, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Chiyuan Zhang:
LabelDP-Pro: Learning with Label Differential Privacy via Projections. ICLR 2024 - [c40]Lynn Chua, Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Amer Sinha, Chiyuan Zhang:
How Private are DP-SGD Implementations? ICML 2024 - [c39]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Adam Sealfon:
Individualized Privacy Accounting via Subsampling with Applications in Combinatorial Optimization. ICML 2024 - [i52]Lynn Chua, Qiliang Cui, Badih Ghazi, Charlie Harrison, Pritish Kamath, Walid Krichene, Ravi Kumar, Pasin Manurangsi, Krishna Giri Narra, Amer Sinha, Avinash V. Varadarajan, Chiyuan Zhang:
Training Differentially Private Ad Prediction Models with Semi-Sensitive Features. CoRR abs/2401.15246 (2024) - [i51]Lynn Chua, Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Amer Sinha, Chiyuan Zhang:
How Private is DP-SGD? CoRR abs/2403.17673 (2024) - [i50]Badih Ghazi, Cristóbal Guzmán, Pritish Kamath, Ravi Kumar, Pasin Manurangsi:
Differentially Private Optimization with Sparse Gradients. CoRR abs/2404.10881 (2024) - [i49]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Adam Sealfon:
Individualized Privacy Accounting via Subsampling with Applications in Combinatorial Optimization. CoRR abs/2405.18534 (2024) - [i48]Lynn Chua, Badih Ghazi, Yangsibo Huang, Pritish Kamath, Daogao Liu, Pasin Manurangsi, Amer Sinha, Chiyuan Zhang:
Mind the Privacy Unit! User-Level Differential Privacy for Language Model Fine-Tuning. CoRR abs/2406.14322 (2024) - [i47]Lynn Chua, Badih Ghazi, Yangsibo Huang, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Amer Sinha, Chulin Xie, Chiyuan Zhang:
Crosslingual Capabilities and Knowledge Barriers in Multilingual Large Language Models. CoRR abs/2406.16135 (2024) - [i46]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Adam Sealfon:
On Computing Pairwise Statistics with Local Differential Privacy. CoRR abs/2406.16305 (2024) - [i45]Pranjal Awasthi, Nishanth Dikkala, Pritish Kamath, Raghu Meka:
Learning Neural Networks with Sparse Activations. CoRR abs/2406.17989 (2024) - [i44]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Raghu Meka, Chiyuan Zhang:
On Convex Optimization with Semi-Sensitive Features. CoRR abs/2406.19040 (2024) - [i43]Alex Berke, Enrico Bacis, Badih Ghazi, Pritish Kamath, Ravi Kumar, Robin Lassonde, Pasin Manurangsi, Umar Syed:
How Unique is Whose Web Browser? The role of demographics in browser fingerprinting among US users. CoRR abs/2410.06954 (2024) - [i42]Yangsibo Huang, Daogao Liu, Lynn Chua, Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Milad Nasr, Amer Sinha, Chiyuan Zhang:
Unlearn and Burn: Adversarial Machine Unlearning Requests Destroy Model Accuracy. CoRR abs/2410.09591 (2024) - 2023
- [j10]Ankit Shah
, Pritish Kamath, Shen Li, Patrick L. Craven, Kevin J. Landers, Kevin Oden, Julie Shah:
Supervised Bayesian specification inference from demonstrations. Int. J. Robotics Res. 42(14): 1245-1264 (2023) - [c38]Matthew Dawson, Badih Ghazi, Pritish Kamath, Kapil Kumar, Ravi Kumar, Bo Luan, Pasin Manurangsi, Nishanth Mundru, Harikesh Nair, Adam Sealfon, Shengyu Zhu:
Optimizing Hierarchical Queries for the Attribution Reporting API. AdKDD@KDD 2023 - [c37]Carson Denison, Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Krishna Giri Narra, Amer Sinha, Avinash V. Varadarajan, Chiyuan Zhang:
Private Ad Modeling with DP-SGD. AdKDD@KDD 2023 - [c36]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Ayush Sekhari, Chiyuan Zhang:
Ticketed Learning-Unlearning Schemes. COLT 2023: 5110-5139 - [c35]Badih Ghazi, Rahul Ilango, Pritish Kamath, Ravi Kumar, Pasin Manurangsi:
Towards Separating Computational and Statistical Differential Privacy. FOCS 2023: 580-599 - [c34]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Kewen Wu:
On Differentially Private Counting on Trees. ICALP 2023: 66:1-66:18 - [c33]Badih Ghazi, Pritish Kamath, Ravi Kumar, Ethan Leeman, Pasin Manurangsi, Avinash V. Varadarajan, Chiyuan Zhang:
Regression with Label Differential Privacy. ICLR 2023 - [c32]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Raghu Meka, Chiyuan Zhang:
On User-Level Private Convex Optimization. ICML 2023: 11283-11299 - [c31]Badih Ghazi, Yangsibo Huang, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Amer Sinha, Chiyuan Zhang:
Sparsity-Preserving Differentially Private Training of Large Embedding Models. NeurIPS 2023 - [c30]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Raghu Meka, Chiyuan Zhang:
User-Level Differential Privacy With Few Examples Per User. NeurIPS 2023 - [c29]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Adam Sealfon:
On Computing Pairwise Statistics with Local Differential Privacy. NeurIPS 2023 - [c28]Ashwinkumar Badanidiyuru Varadaraja, Badih Ghazi, Pritish Kamath, Ravi Kumar, Ethan Leeman, Pasin Manurangsi, Avinash V. Varadarajan, Chiyuan Zhang:
Optimal Unbiased Randomizers for Regression with Label Differential Privacy. NeurIPS 2023 - [i41]Badih Ghazi, Rahul Ilango, Pritish Kamath, Ravi Kumar, Pasin Manurangsi:
Separating Computational and Statistical Differential Privacy (Under Plausible Assumptions). CoRR abs/2301.00104 (2023) - [i40]Badih Ghazi, Pritish Kamath, Ravi Kumar, Raghu Meka, Pasin Manurangsi, Chiyuan Zhang:
On User-Level Private Convex Optimization. CoRR abs/2305.04912 (2023) - [i39]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Ayush Sekhari, Chiyuan Zhang:
Ticketed Learning-Unlearning Schemes. CoRR abs/2306.15744 (2023) - [i38]Matthew Dawson, Badih Ghazi, Pritish Kamath, Kapil Kumar, Ravi Kumar, Bo Luan, Pasin Manurangsi, Nishanth Mundru, Harikesh Nair, Adam Sealfon, Shengyu Zhu:
Optimizing Hierarchical Queries for the Attribution Reporting API. CoRR abs/2308.13510 (2023) - [i37]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Raghu Meka, Chiyuan Zhang:
User-Level Differential Privacy With Few Examples Per User. CoRR abs/2309.12500 (2023) - [i36]Badih Ghazi, Yangsibo Huang, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Amer Sinha, Chiyuan Zhang:
Sparsity-Preserving Differentially Private Training of Large Embedding Models. CoRR abs/2311.08357 (2023) - [i35]Hidayet Aksu, Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Adam Sealfon, Avinash V. Varadarajan:
Summary Reports Optimization in the Privacy Sandbox Attribution Reporting API. CoRR abs/2311.13586 (2023) - [i34]Ashwinkumar Badanidiyuru, Badih Ghazi, Pritish Kamath, Ravi Kumar, Ethan Leeman, Pasin Manurangsi, Avinash V. Varadarajan, Chiyuan Zhang:
Optimal Unbiased Randomizers for Regression with Label Differential Privacy. CoRR abs/2312.05659 (2023) - 2022
- [j9]Siyao Guo, Pritish Kamath, Alon Rosen
Limits on the Efficiency of (Ring) LWE-Based Non-interactive Key Exchange. J. Cryptol. 35(1): 1 (2022) - [j8]Vadym Doroshenko, Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi:
Connect the Dots: Tighter Discrete Approximations of Privacy Loss Distributions. Proc. Priv. Enhancing Technol. 2022(4): 552-570 (2022) - [c27]Pranjal Awasthi, Nishanth Dikkala, Pritish Kamath:
Do More Negative Samples Necessarily Hurt In Contrastive Learning? ICML 2022: 1101-1116 - [c26]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi:
Faster Privacy Accounting via Evolving Discretization. ICML 2022: 7470-7483 - [c25]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi:
Anonymized Histograms in Intermediate Privacy Models. NeurIPS 2022 - [c24]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi:
Private Isotonic Regression. NeurIPS 2022 - [c23]Gene Li, Pritish Kamath, Dylan J. Foster, Nati Srebro:
Understanding the Eluder Dimension. NeurIPS 2022 - [c22]Klim Efremenko
Circuits resilient to short-circuit errors. STOC 2022: 582-594 - [i33]Pranjal Awasthi, Nishanth Dikkala, Pritish Kamath:
Do More Negative Samples Necessarily Hurt in Contrastive Learning? CoRR abs/2205.01789 (2022) - [i32]Vadym Doroshenko, Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi:
Connect the Dots: Tighter Discrete Approximations of Privacy Loss Distributions. CoRR abs/2207.04380 (2022) - [i31]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi:
Faster Privacy Accounting via Evolving Discretization. CoRR abs/2207.04381 (2022) - [i30]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi:
Private Isotonic Regression. CoRR abs/2210.15175 (2022) - [i29]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi:
Anonymized Histograms in Intermediate Privacy Models. CoRR abs/2210.15178 (2022) - [i28]Carson Denison, Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Krishna Giri Narra, Amer Sinha, Avinash V. Varadarajan, Chiyuan Zhang:
Private Ad Modeling with DP-SGD. CoRR abs/2211.11896 (2022) - [i27]Badih Ghazi, Pritish Kamath, Ravi Kumar, Ethan Leeman, Pasin Manurangsi, Avinash V. Varadarajan, Chiyuan Zhang:
Regression with Label Differential Privacy. CoRR abs/2212.06074 (2022) - [i26]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Kewen Wu:
On Differentially Private Counting on Trees. CoRR abs/2212.11967 (2022) - [i25]Klim Efremenko, Bernhard Haeupler, Yael Kalai, Pritish Kamath, Gillat Kol, Nicolas Resch, Raghuvansh Saxena:
Circuits Resilient to Short-Circuit Errors. Electron. Colloquium Comput. Complex. TR22 (2022) - 2021
- [c21]Pritish Kamath, Akilesh Tangella, Danica J. Sutherland, Nathan Srebro:
Does Invariant Risk Minimization Capture Invariance? AISTATS 2021: 4069-4077 - [c20]Eran Malach, Pritish Kamath, Emmanuel Abbe, Nathan Srebro:
Quantifying the Benefit of Using Differentiable Learning over Tangent Kernels. ICML 2021: 7379-7389 - [c19]Emmanuel Abbe, Pritish Kamath, Eran Malach, Colin Sandon, Nathan Srebro:
On the Power of Differentiable Learning versus PAC and SQ Learning. NeurIPS 2021: 24340-24351 - [i24]Pritish Kamath, Akilesh Tangella, Danica J. Sutherland
Does Invariant Risk Minimization Capture Invariance? CoRR abs/2101.01134 (2021) - [i23]Eran Malach, Pritish Kamath, Emmanuel Abbe, Nathan Srebro:
Quantifying the Benefit of Using Differentiable Learning over Tangent Kernels. CoRR abs/2103.01210 (2021) - [i22]Gene Li, Pritish Kamath, Dylan J. Foster, Nathan Srebro:
Eluder Dimension and Generalized Rank. CoRR abs/2104.06970 (2021) - [i21]Ankit J. Shah, Pritish Kamath, Shen Li, Patrick L. Craven, Kevin J. Landers, Kevin Oden, Julie Shah:
Supervised Bayesian Specification Inference from Demonstrations. CoRR abs/2107.02912 (2021) - [i20]Emmanuel Abbe, Pritish Kamath, Eran Malach, Colin Sandon, Nathan Srebro:
On the Power of Differentiable Learning versus PAC and SQ Learning. CoRR abs/2108.04190 (2021) - 2020
- [j7]Mohammad Bavarian, Badih Ghazi, Elad Haramaty, Pritish Kamath, Ronald L. Rivest, Madhu Sudan:
Optimality of Correlated Sampling Strategies. Theory Comput. 16: 1-18 (2020) - [j6]Ankit Garg, Mika Göös, Pritish Kamath, Dmitry Sokolov
Monotone Circuit Lower Bounds from Resolution. Theory Comput. 16: 1-30 (2020) - [c18]Mika Göös, Pritish Kamath, Katerina Sotiraki
On the Complexity of Modulo-q Arguments and the Chevalley - Warning Theorem. CCC 2020: 19:1-19:42 - [c17]Pritish Kamath, Omar Montasser, Nathan Srebro:
Approximate is Good Enough: Probabilistic Variants of Dimensional and Margin Complexity. COLT 2020: 2236-2262 - [c16]Siyao Guo, Pritish Kamath, Alon Rosen, Katerina Sotiraki:
Limits on the Efficiency of (Ring) LWE Based Non-interactive Key Exchange. Public Key Cryptography (1) 2020: 374-395 - [i19]Pritish Kamath, Omar Montasser, Nathan Srebro:
Approximate is Good Enough: Probabilistic Variants of Dimensional and Margin Complexity. CoRR abs/2003.04180 (2020) - [i18]Siyao Guo, Pritish Kamath, Alon Rosen, Katerina Sotiraki
Limits on the Efficiency of (Ring) LWE based Non-Interactive Key Exchange. IACR Cryptol. ePrint Arch. 2020: 1555 (2020)
2010 – 2019
- 2019
- [j5]Mika Göös, Pritish Kamath, Toniann Pitassi, Thomas Watson:
Query-to-Communication Lifting for P NP. Comput. Complex. 28(1): 113-144 (2019) - [j4]Mika Göös, Pritish Kamath, Toniann Pitassi, Thomas Watson:
Correction to: Query-to-Communication Lifting for P NP. Comput. Complex. 28(3): 543-544 (2019) - [c15]Mika Göös, Pritish Kamath, Robert Robere, Dmitry Sokolov
Adventures in Monotone Complexity and TFNP. ITCS 2019: 38:1-38:19 - [i17]Mika Göös, Pritish Kamath, Katerina Sotiraki, Manolis Zampetakis:
On the Complexity of Modulo-q Arguments and the Chevalley-Warning Theorem. CoRR abs/1912.04467 (2019) - 2018
- [c14]Badih Ghazi, Pritish Kamath, Prasad Raghavendra:
Dimension Reduction for Polynomials over Gaussian Space and Applications. CCC 2018: 28:1-28:37 - [c13]Ankit Shah, Pritish Kamath, Julie A. Shah, Shen Li:
Bayesian Inference of Temporal Task Specifications from Demonstrations. NeurIPS 2018: 3808-3817 - [c12]Ankit Garg, Mika Göös, Pritish Kamath, Dmitry Sokolov
Monotone circuit lower bounds from resolution. STOC 2018: 902-911 - [i16]Mika Göös, Pritish Kamath, Robert Robere, Dmitry Sokolov:
Adventures in Monotone Complexity and TFNP. Electron. Colloquium Comput. Complex. TR18 (2018) - 2017
- [j3]Ankit Gupta, Pritish Kamath, Neeraj Kayal, Ramprasad Saptharishi
Unexpected power of low-depth arithmetic circuits. Commun. ACM 60(6): 93-100 (2017) - [c11]Mika Göös, Pritish Kamath, Toniann Pitassi, Thomas Watson:
Query-to-Communication Lifting for P^NP. CCC 2017: 12:1-12:16 - [c10]Badih Ghazi, Elad Haramaty, Pritish Kamath, Madhu Sudan:
Compression in a Distributed Setting. ITCS 2017: 19:1-19:22 - [c9]Jayadev Acharya, Arnab Bhattacharyya, Pritish Kamath:
Improved bounds for universal one-bit compressive sensing. ISIT 2017: 2353-2357 - [i15]Jayadev Acharya, Arnab Bhattacharyya, Pritish Kamath:
Improved Bounds for Universal One-Bit Compressive Sensing. CoRR abs/1705.00763 (2017) - [i14]Badih Ghazi, Pritish Kamath, Prasad Raghavendra:
Dimension Reduction for Polynomials over Gaussian Space and Applications. CoRR abs/1708.03808 (2017) - [i13]Ankit Garg, Mika Göös, Pritish Kamath, Dmitry Sokolov:
Monotone Circuit Lower Bounds from Resolution. Electron. Colloquium Comput. Complex. TR17 (2017) - [i12]Badih Ghazi, Pritish Kamath, Prasad Raghavendra:
Dimension Reduction for Polynomials over Gaussian Space and Applications. Electron. Colloquium Comput. Complex. TR17 (2017) - [i11]Mika Göös, Pritish Kamath, Toniann Pitassi, Thomas Watson:
Query-to-Communication Lifting for P^NP. Electron. Colloquium Comput. Complex. TR17 (2017) - 2016
- [j2]Ankit Gupta, Pritish Kamath, Neeraj Kayal, Ramprasad Saptharishi
Arithmetic Circuits: A Chasm at Depth 3. SIAM J. Comput. 45(3): 1064-1079 (2016) - [c8]Badih Ghazi, Pritish Kamath, Madhu Sudan:
Decidability of Non-interactive Simulation of Joint Distributions. FOCS 2016: 545-554 - [c7]Badih Ghazi, Pritish Kamath, Madhu Sudan:
Communication Complexity of Permutation-Invariant Functions. SODA 2016: 1902-1921 - [i10]Badih Ghazi, Pritish Kamath, Madhu Sudan:
Decidability of Non-Interactive Simulation of Joint Distributions. CoRR abs/1607.04322 (2016) - [i9]Mohammad Bavarian, Badih Ghazi, Elad Haramaty, Pritish Kamath, Ronald L. Rivest, Madhu Sudan:
The Optimality of Correlated Sampling. CoRR abs/1612.01041 (2016) - [i8]Mohammad Bavarian, Badih Ghazi, Elad Haramaty, Pritish Kamath, Ronald L. Rivest, Madhu Sudan:
The Optimality of Correlated Sampling. Electron. Colloquium Comput. Complex. TR16 (2016) - [i7]Badih Ghazi, Pritish Kamath, Madhu Sudan:
Decidability of Non-Interactive Simulation of Joint Distributions. Electron. Colloquium Comput. Complex. TR16 (2016) - 2015
- [c6]Bernhard Haeupler
Communication with Partial Noiseless Feedback. APPROX-RANDOM 2015: 881-897 - [i6]Badih Ghazi, Pritish Kamath, Madhu Sudan:
Communication Complexity of Permutation-Invariant Functions. CoRR abs/1506.00273 (2015) - [i5]Badih Ghazi, Pritish Kamath, Madhu Sudan:
Communication Complexity of Permutation-Invariant Functions. Electron. Colloquium Comput. Complex. TR15 (2015) - 2014
- [j1]Ankit Gupta, Pritish Kamath, Neeraj Kayal, Ramprasad Saptharishi
Approaching the Chasm at Depth Four. J. ACM 61(6): 33:1-33:16 (2014) - 2013
- [c5]Ankit Gupta, Pritish Kamath, Neeraj Kayal, Ramprasad Saptharishi
Approaching the Chasm at Depth Four. CCC 2013: 65-73 - [c4]Ankit Gupta, Pritish Kamath, Neeraj Kayal, Ramprasad Saptharishi
Arithmetic Circuits: A Chasm at Depth Three. FOCS 2013: 578-587 - [i4]Ankit Gupta, Pritish Kamath, Neeraj Kayal, Ramprasad Saptharishi:
Arithmetic circuits: A chasm at depth three. Electron. Colloquium Comput. Complex. TR13 (2013) - 2012
- [c3]Krishnendu Chatterjee
Faster Algorithms for Alternating Refinement Relations. CSL 2012: 167-182 - [c2]Abhisekh Sankaran, Bharat Adsul, Vivek Madan, Pritish Kamath, Supratik Chakraborty
Preservation under Substructures modulo Bounded Cores. WoLLIC 2012: 291-305 - [i3]Krishnendu Chatterjee, Siddhesh Chaubal, Pritish Kamath:
Faster Algorithms for Alternating Refinement Relations. CoRR abs/1201.4449 (2012) - [i2]Abhisekh Sankaran, Bharat Adsul, Vivek Madan, Pritish Kamath, Supratik Chakraborty:
Preservation under Substructures modulo Bounded Cores. CoRR abs/1205.1358 (2012) - [i1]Ankit Gupta, Pritish Kamath, Neeraj Kayal, Ramprasad Saptharishi:
An exponential lower bound for homogeneous depth four arithmetic circuits with bounded bottom fanin. Electron. Colloquium Comput. Complex. TR12 (2012) - 2011
- [c1]Noël Malod-Dognin
Using Dominances for Solving the Protein Family Identification Problem. WABI 2011: 201-212
Coauthor Index
[j12] [j11] [c44] [c42] [c41] [c40] [c39] [i52] [i51] [i50] [i49] [i48] [i47] [i46] [i44] [i43] [i42] [c38] [c37] [c36] [c35] [c34] [c33] [c32] [c31] [c30] [c29] [c28] [i41] [i40] [i39] [i38] [i37] [i36] [i35] [i34] [j8] [c26] [c25] [c24] [i32] [i31] [i30] [i29] [i28] [i27] [i26] [j7] [c14] [c10] [i14] [i12] [c8] [c7] [i10] [i9] [i8] [i7] [i6] [i5]
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