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ORCIDJonathan Scarlett
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- affiliation: National University of Singapore, Singapore
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2020 – today
- 2024
[j44]Nelvin Tan, Pablo Pascual Cobo, Jonathan Scarlett, Ramji Venkataramanan
:
Approximate Message Passing with Rigorous Guarantees for Pooled Data and Quantitative Group Testing. SIAM J. Math. Data Sci. 6(4): 1027-1054 (2024)- [j43]Millen Kanabar, Jonathan Scarlett:
Mismatched Rate-Distortion Theory: Ensembles, Bounds, and General Alphabets. IEEE Trans. Inf. Theory 70(3): 1525-1539 (2024) - [j42]Yan Hao Ling, Jonathan Scarlett:
Maxflow-Based Bounds for Low-Rate Information Propagation Over Noisy Networks. IEEE Trans. Inf. Theory 70(6): 3840-3854 (2024) - [j41]Thach V. Bui, Jonathan Scarlett:
Concomitant Group Testing. IEEE Trans. Inf. Theory 70(10): 7179-7192 (2024) - [j40]Yan Hao Ling, Jonathan Scarlett:
Optimal 1-bit Error Exponent for 2-Hop Relaying With Binary-Input Channels. IEEE Trans. Inf. Theory 70(11): 7599-7615 (2024) - [j39]Zihan Li, Jonathan Scarlett:
Regret Bounds for Noise-Free Cascaded Kernelized Bandits. Trans. Mach. Learn. Res. 2024 (2024) - [c85]Xu Cai, Jonathan Scarlett:
Kernelized Normalizing Constant Estimation: Bridging Bayesian Quadrature and Bayesian Optimization. AAAI 2024: 11150-11158 - [c84]Arpan Losalka, Jonathan Scarlett:
No-Regret Algorithms for Safe Bayesian Optimization with Monotonicity Constraints. AISTATS 2024: 3232-3240 - [c83]Yan Hao Ling, Jonathan Scarlett:
Exact Error Exponents for a Concatenated Coding Based Class of DNA Storage Codes. ISIT 2024: 3426-3431 - [c82]Zihan Li, Pasin Manurangsi, Jonathan Scarlett, Warut Suksompong:
Complexity of Round-Robin Allocation with Potentially Noisy Queries. SAGT 2024: 520-537 - [i97]Junren Chen, Jonathan Scarlett:
Exact Thresholds for Noisy Non-Adaptive Group Testing. CoRR abs/2401.04884 (2024) - [i96]Xu Cai, Jonathan Scarlett:
Kernelized Normalizing Constant Estimation: Bridging Bayesian Quadrature and Bayesian Optimization. CoRR abs/2401.05716 (2024) - [i95]Zihan Li, Pasin Manurangsi, Jonathan Scarlett, Warut Suksompong:
Complexity of Round-Robin Allocation with Potentially Noisy Queries. CoRR abs/2404.19402 (2024) - [i94]Arpan Losalka, Jonathan Scarlett:
No-Regret Algorithms for Safe Bayesian Optimization with Monotonicity Constraints. CoRR abs/2406.03264 (2024) - [i93]Qianli Shen, Yezhen Wang, Zhouhao Yang, Xiang Li, Haonan Wang, Yang Zhang, Jonathan Scarlett, Zhanxing Zhu, Kenji Kawaguchi:
Memory-Efficient Gradient Unrolling for Large-Scale Bi-level Optimization. CoRR abs/2406.14095 (2024) - [i92]Junren Chen, Zhaoqiang Liu, Michael Kwok-Po Ng, Jonathan Scarlett:
Robust Instance Optimal Phase-Only Compressed Sensing. CoRR abs/2408.06275 (2024) - [i91]Yan Hao Ling, Jonathan Scarlett:
Exact Error Exponents of Concatenated Codes for DNA Storage. CoRR abs/2409.01223 (2024) - [i90]Recep Can Yavas, Yuqi Huang, Vincent Y. F. Tan, Jonathan Scarlett:
A General Framework for Clustering and Distribution Matching with Bandit Feedback. CoRR abs/2409.05072 (2024) - [i89]Xu Cai, Jonathan Scarlett:
Lower Bounds for Time-Varying Kernelized Bandits. CoRR abs/2410.16692 (2024) - 2023
- [j38]Nelvin Tan, Way Tan, Jonathan Scarlett:
Performance Bounds for Group Testing With Doubly-Regular Designs. IEEE Trans. Inf. Theory 69(2): 1224-1243 (2023) - [j37]Yan Hao Ling, Jonathan Scarlett:
Multi-Bit Relaying Over a Tandem of Channels. IEEE Trans. Inf. Theory 69(6): 3511-3524 (2023) - [j36]Xu Cai, Chi Thanh Lam, Jonathan Scarlett:
On Average-Case Error Bounds for Kernel-Based Bayesian Quadrature. Trans. Mach. Learn. Res. 2023 (2023) - [c81]Ivan Lau, Yan Hao Ling, Mayank Shrivastava, Jonathan Scarlett:
Max-Quantile Grouped Infinite-Arm Bandits. ALT 2023: 909-945 - [c80]Jonathan Scarlett, Nicholas Teh, Yair Zick:
For One and All: Individual and Group Fairness in the Allocation of Indivisible Goods. AAMAS 2023: 2466-2468 - [c79]Prathamesh Mayekar, Jonathan Scarlett, Vincent Y. F. Tan:
Communication-Constrained Bandits under Additive Gaussian Noise. ICML 2023: 24236-24250 - [c78]Junren Chen, Jonathan Scarlett, Michael Ng, Zhaoqiang Liu:
A Unified Framework for Uniform Signal Recovery in Nonlinear Generative Compressed Sensing. NeurIPS 2023 - [c77]Arpan Losalka, Jonathan Scarlett:
Benefits of monotonicity in safe exploration with Gaussian processes. UAI 2023: 1304-1314 - [e1]Andreas Krause, Emma Brunskill, Kyunghyun Cho, Barbara Engelhardt, Sivan Sabato, Jonathan Scarlett:
International Conference on Machine Learning, ICML 2023, 23-29 July 2023, Honolulu, Hawaii, USA. Proceedings of Machine Learning Research 202, PMLR 2023 [contents] - [i88]Jonathan Scarlett, Nicholas Teh, Yair Zick:
For One and All: Individual and Group Fairness in the Allocation of Indivisible Goods. CoRR abs/2302.06958 (2023) - [i87]Yan Hao Ling, Jonathan Scarlett:
Maxflow-Based Bounds for Low-Rate Information Propagation over Noisy Networks. CoRR abs/2304.02226 (2023) - [i86]Prathamesh Mayekar, Jonathan Scarlett, Vincent Y. F. Tan:
Communication-Constrained Bandits under Additive Gaussian Noise. CoRR abs/2304.12680 (2023) - [i85]Thach V. Bui, Jonathan Scarlett:
Concomitant Group Testing. CoRR abs/2309.04221 (2023) - [i84]Nelvin Tan, Jonathan Scarlett, Ramji Venkataramanan:
Approximate Message Passing with Rigorous Guarantees for Pooled Data and Quantitative Group Testing. CoRR abs/2309.15507 (2023) - [i83]Junren Chen, Jonathan Scarlett, Michael Kwok-Po Ng, Zhaoqiang Liu:
A Unified Framework for Uniform Signal Recovery in Nonlinear Generative Compressed Sensing. CoRR abs/2310.03758 (2023) - [i82]Yan Hao Ling, Jonathan Scarlett:
Optimal 1-bit Error Exponent for 2-hop Relaying with Binary-Input Channels. CoRR abs/2311.14251 (2023) - 2022
- [j35]Paul Hand, Reinhard Heckel, Jonathan Scarlett:
Guest Editorial. IEEE J. Sel. Areas Inf. Theory 3(3): 432 (2022) - [j34]Jonathan Scarlett, Reinhard Heckel, Miguel R. D. Rodrigues, Paul Hand, Yonina C. Eldar:
Theoretical Perspectives on Deep Learning Methods in Inverse Problems. IEEE J. Sel. Areas Inf. Theory 3(3): 433-453 (2022) - [j33]Oliver Gebhard, Max Hahn-Klimroth, Olaf Parczyk, Manuel Penschuck, Maurice Rolvien, Jonathan Scarlett, Nelvin Tan:
Near-Optimal Sparsity-Constrained Group Testing: Improved Bounds and Algorithms. IEEE Trans. Inf. Theory 68(5): 3253-3280 (2022) - [j32]Bernard Teo, Jonathan Scarlett:
Noisy Adaptive Group Testing via Noisy Binary Search. IEEE Trans. Inf. Theory 68(5): 3340-3353 (2022) - [j31]Yan Hao Ling, Jonathan Scarlett:
Simple Coding Techniques for Many-Hop Relaying. IEEE Trans. Inf. Theory 68(11): 7043-7053 (2022) - [j30]Zexin Wang, Vincent Y. F. Tan, Jonathan Scarlett:
Tight Regret Bounds for Noisy Optimization of a Brownian Motion. IEEE Trans. Signal Process. 70: 1072-1087 (2022) - [j29]Ivan Lau, Jonathan Scarlett, Yang Sun:
Model-Based and Graph-Based Priors for Group Testing. IEEE Trans. Signal Process. 70: 6035-6050 (2022) - [c76]Zhenlin Wang, Jonathan Scarlett:
Max-Min Grouped Bandits. AAAI 2022: 8603-8611 - [c75]Zihan Li, Jonathan Scarlett:
Gaussian Process Bandit Optimization with Few Batches. AISTATS 2022: 92-107 - [c74]Yang Sun, Jonathan Scarlett:
Data-Driven Algorithms for Gaussian Measurement Matrix Design in Compressive Sensing. ICASSP 2022: 5523-5527 - [c73]Zhaoqiang Liu, Jiulong Liu, Subhroshekhar Ghosh, Jun Han, Jonathan Scarlett:
Generative Principal Component Analysis. ICLR 2022 - [c72]Eric Han, Jonathan Scarlett:
Adversarial Attacks on Gaussian Process Bandits. ICML 2022: 8304-8329 - [c71]Sattar Vakili, Jonathan Scarlett, Da-Shan Shiu, Alberto Bernacchia:
Improved Convergence Rates for Sparse Approximation Methods in Kernel-Based Learning. ICML 2022: 21960-21983 - [c70]Thach V. Bui, Yeow Meng Chee, Jonathan Scarlett, Van Khu Vu:
Group Testing with Blocks of Positives. ISIT 2022: 1082-1087 - [c69]Millen Kanabar, Jonathan Scarlett:
Multi-User Random Coding Techniques for Mismatched Rate-Distortion Theory. ISIT 2022: 1425-1429 - [c68]Yan Hao Ling, Jonathan Scarlett:
A Simple Coding Scheme Attaining Positive Information Velocity. ISIT 2022: 3215-3219 - [c67]Sidhant Bansal, Arnab Bhattacharyya, Anamay Chaturvedi, Jonathan Scarlett:
Universal 1-Bit Compressive Sensing for Bounded Dynamic Range Signals. ISIT 2022: 3280-3284 - [c66]Ilija Bogunovic, Zihan Li, Andreas Krause, Jonathan Scarlett:
A Robust Phased Elimination Algorithm for Corruption-Tolerant Gaussian Process Bandits. NeurIPS 2022 - [i81]Nelvin Tan, Way Tan, Jonathan Scarlett:
Performance Bounds for Group Testing With Doubly-Regular Designs. CoRR abs/2201.03745 (2022) - [i80]Ilija Bogunovic, Zihan Li, Andreas Krause, Jonathan Scarlett:
A Robust Phased Elimination Algorithm for Corruption-Tolerant Gaussian Process Bandits. CoRR abs/2202.01850 (2022) - [i79]Sattar Vakili, Jonathan Scarlett, Da-Shan Shiu, Alberto Bernacchia:
Improved Convergence Rates for Sparse Approximation Methods in Kernel-Based Learning. CoRR abs/2202.04005 (2022) - [i78]Sidhant Bansal, Arnab Bhattacharyya, Anamay Chaturvedi, Jonathan Scarlett:
Universal 1-Bit Compressive Sensing for Bounded Dynamic Range Signals. CoRR abs/2202.10611 (2022) - [i77]Xu Cai, Chi Thanh Lam, Jonathan Scarlett:
Order-Optimal Error Bounds for Noisy Kernel-Based Bayesian Quadrature. CoRR abs/2202.10615 (2022) - [i76]Zhaoqiang Liu, Jiulong Liu, Subhroshekhar Ghosh, Jun Han, Jonathan Scarlett:
Generative Principal Component Analysis. CoRR abs/2203.09693 (2022) - [i75]Millen Kanabar, Jonathan Scarlett:
Mismatched Rate-Distortion Theory: Ensembles, Bounds, and General Alphabets. CoRR abs/2203.15193 (2022) - [i74]Ivan Lau, Jonathan Scarlett, Yang Sun:
Model-Based and Graph-Based Priors for Group Testing. CoRR abs/2205.11838 (2022) - [i73]Jonathan Scarlett, Reinhard Heckel, Miguel R. D. Rodrigues, Paul Hand, Yonina C. Eldar:
Theoretical Perspectives on Deep Learning Methods in Inverse Problems. CoRR abs/2206.14373 (2022) - [i72]Yan Hao Ling, Jonathan Scarlett:
Multi-Bit Relaying over a Tandem of Channels. CoRR abs/2208.02003 (2022) - [i71]Ivan Lau, Yan Hao Ling, Mayank Shrivastava, Jonathan Scarlett:
Max-Quantile Grouped Infinite-Arm Bandits. CoRR abs/2210.01295 (2022) - [i70]Arpan Losalka, Jonathan Scarlett:
Benefits of Monotonicity in Safe Exploration with Gaussian Processes. CoRR abs/2211.01561 (2022) - [i69]Zihan Li, Jonathan Scarlett:
Regret Bounds for Noise-Free Cascaded Kernelized Bandits. CoRR abs/2211.05430 (2022) - 2021
- [j28]Yang Sun, Hangdong Zhao, Jonathan Scarlett:
On Architecture Selection for Linear Inverse Problems with Untrained Neural Networks. Entropy 23(11): 1481 (2021) - [j27]Steffen Bondorf, Binbin Chen, Jonathan Scarlett, Haifeng Yu, Yuda Zhao:
Sublinear-Time Non-Adaptive Group Testing With O(k log n) Tests via Bit-Mixing Coding. IEEE Trans. Inf. Theory 67(3): 1559-1570 (2021) - [j26]Yan Hao Ling, Jonathan Scarlett:
Optimal Rates of Teaching and Learning Under Uncertainty. IEEE Trans. Inf. Theory 67(11): 7067-7080 (2021) - [c65]Eric Han, Ishank Arora, Jonathan Scarlett:
High-Dimensional Bayesian Optimization via Tree-Structured Additive Models. AAAI 2021: 7630-7638 - [c64]Ilija Bogunovic, Arpan Losalka, Andreas Krause, Jonathan Scarlett:
Stochastic Linear Bandits Robust to Adversarial Attacks. AISTATS 2021: 991-999 - [c63]Sattar Vakili, Jonathan Scarlett, Tara Javidi:
Open Problem: Tight Online Confidence Intervals for RKHS Elements. COLT 2021: 4647-4652 - [c62]Xu Cai, Selwyn Gomes, Jonathan Scarlett:
Lenient Regret and Good-Action Identification in Gaussian Process Bandits. ICML 2021: 1183-1192 - [c61]Xu Cai, Jonathan Scarlett:
On Lower Bounds for Standard and Robust Gaussian Process Bandit Optimization. ICML 2021: 1216-1226 - [c60]Yan Hao Ling, Jonathan Scarlett:
Optimal Rates of Teaching and Learning Under Binary Symmetric Noise. ISIT 2021: 1356-1360 - [c59]Nelvin Tan, Jonathan Scarlett:
An Analysis of the DD Algorithm for Group Testing with Size-Constrained Tests. ISIT 2021: 1967-1972 - [c58]Zhaoqiang Liu, Subhroshekhar Ghosh, Jonathan Scarlett:
Robust 1-bit Compressive Sensing with Partial Gaussian Circulant Matrices and Generative Priors. ITW 2021: 1-6 - [c57]Zhaoqiang Liu, Subhroshekhar Ghosh, Jonathan Scarlett:
Towards Sample-Optimal Compressive Phase Retrieval with Sparse and Generative Priors. NeurIPS 2021: 17656-17668 - [i68]Xu Cai, Selwyn Gomes, Jonathan Scarlett:
Lenient Regret and Good-Action Identification in Gaussian Process Bandits. CoRR abs/2102.05793 (2021) - [i67]Yan Hao Ling, Jonathan Scarlett:
Optimal Rates of Teaching and Learning Under Uncertainty. CoRR abs/2104.06565 (2021) - [i66]Eric Price, Jonathan Scarlett, Nelvin Tan:
Fast Splitting Algorithms for Sparsity-Constrained and Noisy Group Testing. CoRR abs/2106.00308 (2021) - [i65]Bernard Teo, Jonathan Scarlett:
Noisy Adaptive Group Testing via Noisy Binary Search. CoRR abs/2106.12193 (2021) - [i64]Zhaoqiang Liu, Subhroshekhar Ghosh, Jonathan Scarlett:
Towards Sample-Optimal Compressive Phase Retrieval with Sparse and Generative Priors. CoRR abs/2106.15358 (2021) - [i63]Zhaoqiang Liu, Subhroshekhar Ghosh, Jun Han, Jonathan Scarlett:
Robust 1-bit Compressive Sensing with Partial Gaussian Circulant Matrices and Generative Priors. CoRR abs/2108.03570 (2021) - [i62]Zihan Li, Jonathan Scarlett:
Gaussian Process Bandit Optimization with Few Batches. CoRR abs/2110.07788 (2021) - [i61]Eric Han, Jonathan Scarlett:
Adversarial Attacks on Gaussian Process Bandits. CoRR abs/2110.08449 (2021) - [i60]Sattar Vakili, Jonathan Scarlett, Tara Javidi:
Open Problem: Tight Online Confidence Intervals for RKHS Elements. CoRR abs/2110.15458 (2021) - [i59]Zhenlin Wang, Jonathan Scarlett:
Max-Min Grouped Bandits. CoRR abs/2111.08862 (2021) - [i58]Yan Hao Ling, Jonathan Scarlett:
Simple Coding Techniques for Many-Hop Relaying. CoRR abs/2112.07120 (2021) - 2020
- [j25]Lan V. Truong, Jonathan Scarlett:
On Gap-Based Lower Bounding Techniques for Best-Arm Identification. Entropy 22(7): 788 (2020) - [j24]Jonathan Scarlett, Albert Guillén i Fàbregas, Anelia Somekh-Baruch, Alfonso Martinez:
Information-Theoretic Foundations of Mismatched Decoding. Found. Trends Commun. Inf. Theory 17(2-3): 149-401 (2020) - [j23]Zhaoqiang Liu, Jonathan Scarlett:
Information-Theoretic Lower Bounds for Compressive Sensing With Generative Models. IEEE J. Sel. Areas Inf. Theory 1(1): 292-303 (2020) - [j22]Lan V. Truong, Matthew Aldridge, Jonathan Scarlett:
On the All-or-Nothing Behavior of Bernoulli Group Testing. IEEE J. Sel. Areas Inf. Theory 1(3): 669-680 (2020) - [j21]Jonathan Scarlett, Oliver Johnson:
Noisy Non-Adaptive Group Testing: A (Near-)Definite Defectives Approach. IEEE Trans. Inf. Theory 66(6): 3775-3797 (2020) - [j20]Lan V. Truong, Jonathan Scarlett:
Support Recovery in the Phase Retrieval Model: Information-Theoretic Fundamental Limit. IEEE Trans. Inf. Theory 66(12): 7887-7910 (2020) - [c56]Lorenzo Ciampiconi, Bishwamittra Ghosh, Jonathan Scarlett, Kuldeep S. Meel:
A MaxSAT-Based Framework for Group Testing. AAAI 2020: 10144-10152 - [c55]Ilija Bogunovic, Andreas Krause, Jonathan Scarlett:
Corruption-Tolerant Gaussian Process Bandit Optimization. AISTATS 2020: 1071-1081 - [c54]Anamay Chaturvedi, Jonathan Scarlett:
Learning Gaussian Graphical Models via Multiplicative Weights. AISTATS 2020: 1104-1114 - [c53]Eric Price, Jonathan Scarlett:
A Fast Binary Splitting Approach to Non-Adaptive Group Testing. APPROX-RANDOM 2020: 13:1-13:20 - [c52]Abdul Fatir Ansari, Jonathan Scarlett, Harold Soh:
A Characteristic Function Approach to Deep Implicit Generative Modeling. CVPR 2020: 7476-7484 - [c51]Zhaoqiang Liu, Selwyn Gomes, Avtansh Tiwari, Jonathan Scarlett:
Sample Complexity Bounds for 1-bit Compressive Sensing and Binary Stable Embeddings with Generative Priors. ICML 2020: 6216-6225 - [c50]Nelvin Tan, Jonathan Scarlett:
Near-Optimal Sparse Adaptive Group Testing. ISIT 2020: 1420-1425 - [c49]Zhaoqiang Liu, Jonathan Scarlett:
The Generalized Lasso with Nonlinear Observations and Generative Priors. NeurIPS 2020 - [c48]Zhaoqiang Liu, Jonathan Scarlett:
Sample Complexity Lower Bounds for Compressive Sensing with Generative Models. SPCOM 2020: 1-5 - [i57]Zexin Wang, Vincent Y. F. Tan, Jonathan Scarlett:
Tight Regret Bounds for Noisy Optimization of a Brownian Motion. CoRR abs/2001.09327 (2020) - [i56]Lan V. Truong, Jonathan Scarlett:
On the All-Or-Nothing Behavior of Bernoulli Group Testing. CoRR abs/2001.10137 (2020) - [i55]Zhaoqiang Liu, Selwyn Gomes, Avtansh Tiwari, Jonathan Scarlett:
Sample Complexity Bounds for 1-bit Compressive Sensing and Binary Stable Embeddings with Generative Priors. CoRR abs/2002.01697 (2020) - [i54]Anamay Chaturvedi, Jonathan Scarlett:
Learning Gaussian Graphical Models via Multiplicative Weights. CoRR abs/2002.08663 (2020) - [i53]Ilija Bogunovic, Andreas Krause, Jonathan Scarlett:
Corruption-Tolerant Gaussian Process Bandit Optimization. CoRR abs/2003.01971 (2020) - [i52]Jonathan Scarlett, Albert Guillén i Fàbregas, Anelia Somekh-Baruch, Alfonso Martinez:
Information-Theoretic Foundations of Mismatched Decoding. CoRR abs/2003.11694 (2020) - [i51]Nelvin Tan, Jonathan Scarlett:
Improved Bounds and Algorithms for Sparsity-Constrained Group Testing. CoRR abs/2004.03119 (2020) - [i50]Bay Wei Heng, Jonathan Scarlett:
Non-Adaptive Group Testing in the Linear Regime: Strong Converse and Approximate Recovery. CoRR abs/2006.01325 (2020) - [i49]Eric Price, Jonathan Scarlett:
A Fast Binary Splitting Approach to Non-Adaptive Group Testing. CoRR abs/2006.10268 (2020) - [i48]Zhaoqiang Liu, Jonathan Scarlett:
The Generalized Lasso with Nonlinear Observations and Generative Priors. CoRR abs/2006.12415 (2020) - [i47]Ilija Bogunovic, Arpan Losalka, Andreas Krause, Jonathan Scarlett:
Stochastic Linear Bandits Robust to Adversarial Attacks. CoRR abs/2007.03285 (2020) - [i46]Xu Cai, Jonathan Scarlett:
On Lower Bounds for Standard and Robust Gaussian Process Bandit Optimization. CoRR abs/2008.08757 (2020) - [i45]Eric Han, Ishank Arora, Jonathan Scarlett:
High-Dimensional Bayesian Optimization via Tree-Structured Additive Models. CoRR abs/2012.13088 (2020)
2010 – 2019
- 2019
- [j19]Matthew Aldridge, Oliver Johnson, Jonathan Scarlett:
Group Testing: An Information Theory Perspective. Found. Trends Commun. Inf. Theory 15(3-4): 196-392 (2019) - [j18]Oliver Johnson, Matthew Aldridge, Jonathan Scarlett:
Performance of Group Testing Algorithms With Near-Constant Tests Per Item. IEEE Trans. Inf. Theory 65(2): 707-723 (2019) - [j17]Anelia Somekh-Baruch, Jonathan Scarlett, Albert Guillen i Fabregas:
Generalized Random Gilbert-Varshamov Codes. IEEE Trans. Inf. Theory 65(6): 3452-3469 (2019) - [j16]Jonathan Scarlett:
Noisy Adaptive Group Testing: Bounds and Algorithms. IEEE Trans. Inf. Theory 65(6): 3646-3661 (2019) - [c47]Steffen Bondorf, Binbin Chen, Jonathan Scarlett, Haifeng Yu, Yuda Zhao:
Cross-sender bit-mixing coding. IPSN 2019: 205-216 - [c46]Jonathan Scarlett, Ilija Bogunovic, Volkan Cevher:
Overlapping Multi-Bandit Best Arm Identification. ISIT 2019: 2544-2548 - [c45]Jonathan Scarlett:
An Efficient Algorithm for Capacity-Approaching Noisy Adaptive Group Testing. ISIT 2019: 2679-2683 - [c44]Anelia Somekh-Baruch, Jonathan Scarlett, Albert Guillén i Fàbregas:
A Recursive Cost-Constrained Construction that Attains the Expurgated Exponent. ISIT 2019: 2938-2942 - [c43]Lan V. Truong, Jonathan Scarlett:
On the Information-Theoretic Limits of Noisy Sparse Phase Retrieval. ITW 2019: 1-5 - [c42]Zihan Li, Matthias Fresacher, Jonathan Scarlett:
Learning Erdos-Renyi Random Graphs via Edge Detecting Queries. NeurIPS 2019: 402-412 - [i44]Jonathan Scarlett, Volkan Cevher:
An Introductory Guide to Fano's Inequality with Applications in Statistical Estimation. CoRR abs/1901.00555 (2019) - [i43]Lan V. Truong, Jonathan Scarlett:
Support Recovery in the Phase Retrieval Model: Information-Theoretic Fundamental Limits. CoRR abs/1901.10647 (2019) - [i42]Matthew Aldridge, Oliver Johnson, Jonathan Scarlett:
Group testing: an information theory perspective. CoRR abs/1902.06002 (2019) - [i41]Steffen Bondorf, Binbin Chen, Jonathan Scarlett, Haifeng Yu, Yuda Zhao:
Sublinear-Time Non-Adaptive Group Testing with O(k log n) Tests via Bit-Mixing Coding. CoRR abs/1904.10102 (2019) - [i40]Zihan Li, Matthias Fresacher, Jonathan Scarlett:
Learning Erdős-Rényi Random Graphs via Edge Detecting Queries. CoRR abs/1905.03410 (2019) - [i39]Zhaoqiang Liu, Jonathan Scarlett:
Information-Theoretic Lower Bounds for Compressive Sensing with Generative Models. CoRR abs/1908.10744 (2019) - [i38]Abdul Fatir Ansari, Jonathan Scarlett, Harold Soh:
A Characteristic Function Approach to Deep Implicit Generative Modeling. CoRR abs/1909.07425 (2019) - [i37]Jonathan Scarlett:
An Efficient Algorithm for Capacity-Approaching Noisy Adaptive Group Testing. CoRR abs/1911.02764 (2019) - 2018
- [j15]Jonathan Scarlett, Volkan Cevher:
Near-Optimal Noisy Group Testing via Separate Decoding of Items. IEEE J. Sel. Top. Signal Process. 12(5): 902-915 (2018) - [j14]Jonathan Scarlett, Alfonso Martinez, Albert Guillen i Fabregas:
Mismatched Multi-Letter Successive Decoding for the Multiple-Access Channel. IEEE Trans. Inf. Theory 64(4): 2253-2266 (2018) - [j13]Baran Gozcu, Rabeeh Karimi Mahabadi, Yen-Huan Li, Efe Ilicak, Tolga Çukur, Jonathan Scarlett, Volkan Cevher:
Learning-Based Compressive MRI. IEEE Trans. Medical Imaging 37(6): 1394-1406 (2018) - [c41]Paul Rolland, Jonathan Scarlett, Ilija Bogunovic, Volkan Cevher:
High-Dimensional Bayesian Optimization via Additive Models with Overlapping Groups. AISTATS 2018: 298-307 - [c40]Anelia Somekh-Baruch, Jonathan Scarlett, Albert Guillen i Fabregas:
The error exponent of random gilbert-varshamov codes. CISS 2018: 1-2 - [c39]Jonathan Scarlett:
Tight Regret Bounds for Bayesian Optimization in One Dimension. ICML 2018: 4507-4515 - [c38]Jonathan Scarlett, Volkan Cevher:
Near-Optimal Noisy Group Testing via Separate Decoding of Items. ISIT 2018: 2311-2315 - [c37]Anelia Somekh-Baruch, Jonathan Scarlett, Albert Guillen i Fabregas:
The Error Exponent of Generalized Random-Gilbert Varshamov Codes. ISIT 2018: 2361-2365 - [c36]Ilija Bogunovic, Jonathan Scarlett, Stefanie Jegelka, Volkan Cevher:
Adversarially Robust Optimization with Gaussian Processes. NeurIPS 2018: 5765-5775 - [i36]Paul Rolland, Jonathan Scarlett, Ilija Bogunovic, Volkan Cevher:
High-Dimensional Bayesian Optimization via Additive Models with Overlapping Groups. CoRR abs/1802.07028 (2018) - [i35]Jonathan Scarlett:
Noisy Adaptive Group Testing: Bounds and Algorithms. CoRR abs/1803.05099 (2018) - [i34]Baran Gözcü, Rabeeh Karimi Mahabadi, Yen-Huan Li, Efe Ilicak, Tolga Çukur, Jonathan Scarlett, Volkan Cevher:
Learning-Based Compressive MRI. CoRR abs/1805.01266 (2018) - [i33]Anelia Somekh-Baruch, Jonathan Scarlett, Albert Guillén i Fàbregas:
Generalized Random Gilbert-Varshamov Codes. CoRR abs/1805.02515 (2018) - [i32]Jonathan Scarlett:
Tight Regret Bounds for Bayesian Optimization in One Dimension. CoRR abs/1805.11792 (2018) - [i31]Steffen Bondorf, Binbin Chen, Jonathan Scarlett, Haifeng Yu, Yuda Zhao:
Cross-Sender Bit-Mixing Coding. CoRR abs/1807.04449 (2018) - [i30]Jonathan Scarlett, Oliver Johnson:
Noisy Non-Adaptive Group Testing: A (Near-)Definite Defectives Approach. CoRR abs/1808.09143 (2018) - [i29]Ilija Bogunovic, Jonathan Scarlett, Stefanie Jegelka, Volkan Cevher:
Adversarially Robust Optimization with Gaussian Processes. CoRR abs/1810.10775 (2018) - 2017
- [j12]Jonathan Scarlett, Vincent Y. F. Tan, Giuseppe Durisi:
The Dispersion of Nearest-Neighbor Decoding for Additive Non-Gaussian Channels. IEEE Trans. Inf. Theory 63(1): 81-92 (2017) - [j11]Jonathan Scarlett, Volkan Cevher:
Limits on Support Recovery With Probabilistic Models: An Information-Theoretic Framework. IEEE Trans. Inf. Theory 63(1): 593-620 (2017) - [c35]Jonathan Scarlett, Volkan Cevher:
Lower Bounds on Active Learning for Graphical Model Selection. AISTATS 2017: 55-64 - [c34]Ilija Bogunovic, Slobodan Mitrovic, Jonathan Scarlett, Volkan Cevher:
A distributed algorithm for partitioned robust submodular maximization. CAMSAP 2017: 1-5 - [c33]Jonathan Scarlett, Ilija Bogunovic, Volkan Cevher:
Lower Bounds on Regret for Noisy Gaussian Process Bandit Optimization. COLT 2017: 1723-1742 - [c32]Jonathan Scarlett, Volkan Cevher:
How little does non-exact recovery help in group testing? ICASSP 2017: 6090-6094 - [c31]Ilija Bogunovic, Slobodan Mitrovic, Jonathan Scarlett, Volkan Cevher:
Robust Submodular Maximization: A Non-Uniform Partitioning Approach. ICML 2017: 508-516 - [c30]Jonathan Scarlett, Alfonso Martinez, Albert Guillen i Fabregas:
Expurgated joint source-channel coding bounds and error exponents. ISIT 2017: 908-912 - [c29]Jonathan Scarlett, Volkan Cevher:
Phase Transitions in the Pooled Data Problem. NIPS 2017: 377-385 - [c28]Volkan Cevher, Michael Kapralov, Jonathan Scarlett, Amir Zandieh:
An adaptive sublinear-time block sparse fourier transform. STOC 2017: 702-715 - [i28]Volkan Cevher, Michael Kapralov, Jonathan Scarlett, Amir Zandieh:
An Adaptive Sublinear-Time Block Sparse Fourier Transform. CoRR abs/1702.01286 (2017) - [i27]Jonathan Scarlett, Ilija Bogunovic, Volkan Cevher:
Lower Bounds on Regret for Noisy Gaussian Process Bandit Optimization. CoRR abs/1706.00090 (2017) - [i26]Ilija Bogunovic, Slobodan Mitrovic, Jonathan Scarlett, Volkan Cevher:
Robust Submodular Maximization: A Non-Uniform Partitioning Approach. CoRR abs/1706.04918 (2017) - [i25]Jonathan Scarlett, Volkan Cevher:
Phase Transitions in the Pooled Data Problem. CoRR abs/1710.06766 (2017) - [i24]Jonathan Scarlett, Volkan Cevher:
Efficient and Near-Optimal Noisy Group Testing: An Information-Theoretic Framework. CoRR abs/1710.08704 (2017) - 2016
- [j10]Luca Baldassarre, Yen-Huan Li, Jonathan Scarlett, Baran Gozcu, Ilija Bogunovic, Volkan Cevher:
Learning-Based Compressive Subsampling. IEEE J. Sel. Top. Signal Process. 10(4): 809-822 (2016) - [j9]Jonathan Scarlett, Alfonso Martinez, Albert Guillén i Fàbregas:
Multiuser Random Coding Techniques for Mismatched Decoding. IEEE Trans. Inf. Theory 62(7): 3950-3970 (2016) - [j8]Jonathan Scarlett, Volkan Cevher:
On the Difficulty of Selecting Ising Models With Approximate Recovery. IEEE Trans. Signal Inf. Process. over Networks 2(4): 625-638 (2016) - [c27]Jonathan Scarlett, Volkan Cevher:
Limits on Sparse Support Recovery via Linear Sketching with Random Expander Matrices. AISTATS 2016: 149-158 - [c26]Ilija Bogunovic, Jonathan Scarlett, Volkan Cevher:
Time-Varying Gaussian Process Bandit Optimization. AISTATS 2016: 314-323 - [c25]Matthew Aldridge, Oliver Johnson, Jonathan Scarlett:
Improved group testing rates with constant column weight designs. ISIT 2016: 1381-1385 - [c24]Jonathan Scarlett, Volkan Cevher:
Partial recovery bounds for the sparse stochastic block model. ISIT 2016: 1904-1908 - [c23]Jonathan Scarlett, Vincent Y. F. Tan, Giuseppe Durisi:
The dispersion of nearest-neighbor decoding for additive non-Gaussian channels. ISIT 2016: 2664-2668 - [c22]Jonathan Scarlett, Volkan Cevher:
Converse bounds for noisy group testing with arbitrary measurement matrices. ISIT 2016: 2868-2872 - [c21]Ilija Bogunovic, Jonathan Scarlett, Andreas Krause, Volkan Cevher:
Truncated Variance Reduction: A Unified Approach to Bayesian Optimization and Level-Set Estimation. NIPS 2016: 1507-1515 - [c20]Jonathan Scarlett, Volkan Cevher:
Phase Transitions in Group Testing. SODA 2016: 40-53 - [i23]Ilija Bogunovic, Jonathan Scarlett, Volkan Cevher:
Time-Varying Gaussian Process Bandit Optimization. CoRR abs/1601.06650 (2016) - [i22]Jonathan Scarlett, Volkan Cevher:
Converse Bounds for Noisy Group Testing with Arbitrary Measurement Matrices. CoRR abs/1602.00875 (2016) - [i21]Jonathan Scarlett, Volkan Cevher:
Partial Recovery Bounds for the Sparse Stochastic Block Model. CoRR abs/1602.00877 (2016) - [i20]Matthew Aldridge, Oliver Johnson, Jonathan Scarlett:
Improved group testing rates with constant column weight designs. CoRR abs/1602.03471 (2016) - [i19]Jonathan Scarlett, Volkan Cevher:
On the Difficulty of Selecting Ising Models with Approximate Recovery. CoRR abs/1602.03647 (2016) - [i18]Jonathan Scarlett, Volkan Cevher:
Lower Bounds on Active Learning for Graphical Model Selection. CoRR abs/1607.02413 (2016) - [i17]Ilija Bogunovic, Jonathan Scarlett, Andreas Krause, Volkan Cevher:
Truncated Variance Reduction: A Unified Approach to Bayesian Optimization and Level-Set Estimation. CoRR abs/1610.07379 (2016) - [i16]Jonathan Scarlett, Alfonso Martinez, Albert Guillén i Fàbregas:
Mismatched Multi-letter Successive Decoding for the Multiple-Access Channel. CoRR abs/1612.00211 (2016) - [i15]Oliver Johnson, Matthew Aldridge, Jonathan Scarlett:
Performance of group testing algorithms with constant tests-per-item. CoRR abs/1612.07122 (2016) - 2015
- [j7]Jonathan Scarlett, Alfonso Martinez, Albert Guillen i Fabregas:
Second-Order Rate Region of Constant-Composition Codes for the Multiple-Access Channel. IEEE Trans. Inf. Theory 61(1): 157-172 (2015) - [j6]Jonathan Scarlett:
On the Dispersions of the Gel'fand-Pinsker Channel and Dirty Paper Coding. IEEE Trans. Inf. Theory 61(9): 4569-4586 (2015) - [j5]Jonathan Scarlett, Anelia Somekh-Baruch, Alfonso Martinez, Albert Guillen i Fabregas:
A Counter-Example to the Mismatched Decoding Converse for Binary-Input Discrete Memoryless Channels. IEEE Trans. Inf. Theory 61(10): 5387-5395 (2015) - [j4]Jonathan Scarlett, Vincent Y. F. Tan:
Second-Order Asymptotics for the Gaussian MAC With Degraded Message Sets. IEEE Trans. Inf. Theory 61(12): 6700-6718 (2015) - [c19]Yen-Huan Li, Jonathan Scarlett, Pradeep Ravikumar, Volkan Cevher:
Sparsistency of 1-Regularized M-Estimators. AISTATS 2015 - [c18]Ilija Bogunovic, Volkan Cevher, Jarvis D. Haupt, Jonathan Scarlett:
Active learning of self-concordant like multi-index functions. ICASSP 2015: 2189-2193 - [c17]Jonathan Scarlett, Alfonso Martinez, Albert Guillen i Fabregas:
The likelihood decoder: Error exponents and mismatch. ISIT 2015: 86-90 - [c16]Jonathan Scarlett, Volkan Cevher:
Limits on support recovery with probabilistic models: An information-theoretic framework. ISIT 2015: 2331-2335 - [c15]Jonathan Scarlett, Alfonso Martinez, Albert Guillen i Fabregas:
Refinements of the third-order term in the fixed error asymptotics of constant-composition codes. ISIT 2015: 2954-2958 - [c14]Jonathan Scarlett, Vincent Y. F. Tan:
Second-order asymptotics for the discrete memoryless MAC with degraded message sets. ISIT 2015: 2964-2968 - [i14]Jonathan Scarlett, Volkan Cevher:
Limits on Support Recovery with Probabilistic Models: An Information-Spectrum Approach. CoRR abs/1501.07440 (2015) - [i13]Jonathan Scarlett, Anelia Somekh-Baruch, Alfonso Martinez, Albert Guillén i Fàbregas:
A Counter-Example to the Mismatched Decoding Converse for Binary-Input Discrete Memoryless Channels. CoRR abs/1508.02374 (2015) - [i12]Luca Baldassarre, Yen-Huan Li, Jonathan Scarlett, Baran Gözcü, Ilija Bogunovic, Volkan Cevher:
Learning-based Compressive Subsampling. CoRR abs/1510.06188 (2015) - [i11]Jonathan Scarlett, Vincent Y. F. Tan, Giuseppe Durisi:
The Dispersion of Nearest-Neighbor Decoding for Additive Non-Gaussian Channels. CoRR abs/1512.06618 (2015) - 2014
- [j3]Jonathan Scarlett, Alfonso Martinez, Albert Guillen i Fabregas:
Mismatched Decoding: Error Exponents, Second-Order Rates and Saddlepoint Approximations. IEEE Trans. Inf. Theory 60(5): 2647-2666 (2014) - [j2]Jonathan Scarlett, Li Peng, Neri Merhav, Alfonso Martinez, Albert Guillen i Fabregas:
Expurgated Random-Coding Ensembles: Exponents, Refinements, and Connections. IEEE Trans. Inf. Theory 60(8): 4449-4462 (2014) - [c13]Jonathan Scarlett, Vincent Y. F. Tan:
Second-order asymptotics for the gaussian MAC with degraded message sets. ISIT 2014: 461-465 - [c12]Jonathan Scarlett, Alfonso Martinez, Albert Guillen i Fabregas:
The saddlepoint approximation: Unified random coding asymptotics for fixed and varying rates. ISIT 2014: 1892-1896 - [c11]Jonathan Scarlett:
On the dispersion of dirty paper coding. ISIT 2014: 2282-2286 - [c10]Jonathan Scarlett, Alfonso Martinez, Albert Guillen i Fabregas:
Mismatched multi-letter successive decoding for the multiple-access channel. ISIT 2014: 2539-2543 - [c9]Alfonso Martinez, Jonathan Scarlett, Marco Dalai, Albert Guillen i Fabregas:
A complex-integration approach to the saddlepoint approximation for random-coding bounds. ISWCS 2014: 618-621 - [i10]Jonathan Scarlett, Alfonso Martinez, Albert Guillén i Fàbregas:
The Saddlepoint Approximation: A Unification of Exponents, Dispersions and Moderate Deviations. CoRR abs/1402.3941 (2014) - [i9]Jonathan Scarlett, Vincent Y. F. Tan:
Second-Order Asymptotics for the Discrete Memoryless MAC with Degraded Message Sets. CoRR abs/1408.1119 (2014) - 2013
- [j1]Jonathan Scarlett, Jamie S. Evans, Subhrakanti Dey:
Compressed Sensing With Prior Information: Information-Theoretic Limits and Practical Decoders. IEEE Trans. Signal Process. 61(2): 427-439 (2013) - [c8]Jonathan Scarlett, Alfonso Martinez, Albert Guillen i Fabregas:
Second-order rate region of constant-composition codes for the multiple-access channel. Allerton 2013: 588-593 - [c7]Jonathan Scarlett, Alfonso Martinez, Albert Guillen i Fabregas:
A derivation of the asymptotic random-coding prefactor. Allerton 2013: 956-961 - [c6]Jonathan Scarlett, Alfonso Martinez, Albert Guillen i Fabregas:
Superposition codes for mismatched decoding. ISIT 2013: 81-85 - [c5]Jonathan Scarlett, Alfonso Martinez, Albert Guillen i Fabregas:
The mismatched multiple-access channel: General alphabets. ISIT 2013: 86-90 - [c4]Jonathan Scarlett, Alfonso Martinez, Albert Guillen i Fabregas:
Cost-constrained random coding and applications. ITA 2013: 1-7 - [i8]Jonathan Scarlett, Alfonso Martinez, Albert Guillen i Fabregas:
Mismatched Decoding: Finite-Length Bounds, Error Exponents and Approximations. CoRR abs/1303.6166 (2013) - [i7]Jonathan Scarlett, Alfonso Martinez, Albert Guillen i Fabregas:
Second-Order Rate Region of Constant-Composition Codes for the Multiple-Access Channel. CoRR abs/1303.6167 (2013) - [i6]Jonathan Scarlett, Alfonso Martinez, Albert Guillen i Fabregas:
A Derivation of the Asymptotic Random-Coding Prefactor. CoRR abs/1306.6203 (2013) - [i5]Jonathan Scarlett, Li Peng, Neri Merhav, Alfonso Martinez, Albert Guillen i Fabregas:
Expurgated Random-Coding Ensembles: Exponents, Refinements and Connections. CoRR abs/1307.6679 (2013) - [i4]Jonathan Scarlett:
Second-Order Rate of Constant-Composition Codes for the Gel'fand-Pinsker Channel. CoRR abs/1309.6200 (2013) - [i3]Jonathan Scarlett, Vincent Y. F. Tan:
Second-Order Asymptotics for the Gaussian MAC with Degraded Message Sets. CoRR abs/1310.1197 (2013) - [i2]Jonathan Scarlett, Alfonso Martinez, Albert Guillén i Fàbregas:
Multiuser Coding Techniques for Mismatched Decoding. CoRR abs/1311.6635 (2013) - 2012
- [c3]Jonathan Scarlett, Alfonso Martinez, Albert Guillen i Fabregas:
Ensemble-tight error exponents for mismatched decoders. Allerton Conference 2012: 1951-1958 - [c2]Jonathan Scarlett, Albert Guillen i Fabregas:
An achievable error exponent for the mismatched multiple-access channel. Allerton Conference 2012: 1975-1982 - 2011
- [c1]Jonathan Scarlett, Jamie S. Evans, Subhrakanti Dey:
How much training is needed in fading multiple access channels? ISWCS 2011: 527-531 - [i1]Jonathan Scarlett, Jamie S. Evans, Subhrakanti Dey:
On the Tradeoff Between Multiuser Diversity and Training Overhead in Multiple Access Channels. CoRR abs/1105.3531 (2011)
Coauthor Index
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