Stop the war!Остановите войну!
for scientists:
Google
Google Scholar
Semantic Scholar
IA Scholar
CiteSeerX
ORCIDAarti Singh
Person information
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2022
[i69]Yusha Liu, Yichong Xu, Nihar B. Shah, Aarti Singh:
Integrating Rankings into Quantized Scores in Peer Review. CoRR abs/2204.03505 (2022)- [i68]Dhruv Malik, Yuanzhi Li, Aarti Singh:
Complete Policy Regret Bounds for Tallying Bandits. CoRR abs/2204.11174 (2022) - 2021
- [j23]Ivan Stelmakh, Nihar B. Shah, Aarti Singh:
PeerReview4All: Fair and Accurate Reviewer Assignment in Peer Review. J. Mach. Learn. Res. 22: 163:1-163:66 (2021) - [j22]Zeyuan Allen-Zhu, Yuanzhi Li, Aarti Singh, Yining Wang:
Near-optimal discrete optimization for experimental design: a regret minimization approach. Math. Program. 186(1): 439-478 (2021) - [j21]Ivan Stelmakh, Nihar B. Shah, Aarti Singh, Hal Daumé III:
Prior and Prejudice: The Novice Reviewers' Bias against Resubmissions in Conference Peer Review. Proc. ACM Hum. Comput. Interact. 5(CSCW1): 1-17 (2021) - [c79]Ivan Stelmakh, Nihar B. Shah, Aarti Singh, Hal Daumé III:
A Novice-Reviewer Experiment to Address Scarcity of Qualified Reviewers in Large Conferences. AAAI 2021: 4785-4793 - [c78]Ivan Stelmakh, Nihar B. Shah, Aarti Singh:
Catch Me if I Can: Detecting Strategic Behaviour in Peer Assessment. AAAI 2021: 4794-4802 - [c77]Ojash Neopane, Aaditya Ramdas, Aarti Singh:
Best Arm Identification under Additive Transfer Bandits. ACSCC 2021: 464-470 - [c76]Yusha Liu, Yining Wang, Aarti Singh:
Smooth Bandit Optimization: Generalization to Holder Space. AISTATS 2021: 2206-2214 - [c75]Aarti Singh, Neal Patwari:
Range-based Collision Prediction for Dynamic Motion. CCNC 2021: 1-6 - [c74]Stefani Karp, Ezra Winston, Yuanzhi Li, Aarti Singh:
Local Signal Adaptivity: Provable Feature Learning in Neural Networks Beyond Kernels. NeurIPS 2021: 24883-24897 - [i67]Ojash Neopane, Aaditya Ramdas, Aarti Singh:
Best Arm Identification under Additive Transfer Bandits. CoRR abs/2112.04083 (2021) - 2020
- [j20]Yichong Xu, Sivaraman Balakrishnan, Aarti Singh, Artur Dubrawski:
Regression with Comparisons: Escaping the Curse of Dimensionality with Ordinal Information. J. Mach. Learn. Res. 21: 162:1-162:54 (2020) - [c73]Yichong Xu, Xi Chen, Aarti Singh, Artur Dubrawski:
Thresholding Bandit Problem with Both Duels and Pulls. AISTATS 2020: 2591-2600 - [c72]Charvi Rastogi, Sivaraman Balakrishnan, Nihar B. Shah, Aarti Singh:
Two-Sample Testing on Pairwise Comparison Data and the Role of Modeling Assumptions. ISIT 2020: 1271-1276 - [c71]Yichong Xu, Ruosong Wang, Lin F. Yang, Aarti Singh, Artur Dubrawski:
Preference-based Reinforcement Learning with Finite-Time Guarantees. NeurIPS 2020 - [c70]Yichong Xu, Aparna Joshi, Aarti Singh, Artur Dubrawski:
Zeroth Order Non-convex optimization with Dueling-Choice Bandits. UAI 2020: 899-908 - [i66]Yichong Xu, Ruosong Wang, Lin F. Yang, Aarti Singh, Artur Dubrawski:
Preference-based Reinforcement Learning with Finite-Time Guarantees. CoRR abs/2006.08910 (2020) - [i65]Charvi Rastogi, Sivaraman Balakrishnan, Nihar B. Shah, Aarti Singh:
Two-Sample Testing on Ranked Preference Data and the Role of Modeling Assumptions. CoRR abs/2006.11909 (2020) - [i64]Ivan Stelmakh, Nihar B. Shah, Aarti Singh:
Catch Me if I Can: Detecting Strategic Behaviour in Peer Assessment. CoRR abs/2010.04041 (2020) - [i63]Ivan Stelmakh, Nihar B. Shah, Aarti Singh, Hal Daumé III:
Prior and Prejudice: The Novice Reviewers' Bias against Resubmissions in Conference Peer Review. CoRR abs/2011.14646 (2020) - [i62]Ivan Stelmakh, Nihar B. Shah, Aarti Singh, Hal Daumé III:
A Novice-Reviewer Experiment to Address Scarcity of Qualified Reviewers in Large Conferences. CoRR abs/2011.15050 (2020) - [i61]Ivan Stelmakh, Charvi Rastogi, Nihar B. Shah, Aarti Singh, Hal Daumé III:
A Large Scale Randomized Controlled Trial on Herding in Peer-Review Discussions. CoRR abs/2011.15083 (2020) - [i60]Yusha Liu, Yining Wang, Aarti Singh:
Smooth Bandit Optimization: Generalization to Hölder Space. CoRR abs/2012.06076 (2020)
2010 – 2019
- 2019
- [j19]Aarti Singh, Dimple Juneja, Rashmi Singh, Saurabh Mukherjee:
A clustered neighbourhood consensus algorithm for a generic agent interaction protocol. Int. J. Adv. Intell. Paradigms 12(3/4): 305-316 (2019) - [j18]Yining Wang, Jialei Wang, Sivaraman Balakrishnan, Aarti Singh:
Rate optimal estimation and confidence intervals for high-dimensional regression with missing covariates. J. Multivar. Anal. 174 (2019) - [j17]Yining Wang
, Yu-Xiang Wang, Aarti Singh:
A Theoretical Analysis of Noisy Sparse Subspace Clustering on Dimensionality-Reduced Data. IEEE Trans. Inf. Theory 65(2): 685-706 (2019) - [j16]Yining Wang, Sivaraman Balakrishnan, Aarti Singh:
Optimization of Smooth Functions With Noisy Observations: Local Minimax Rates. IEEE Trans. Inf. Theory 65(11): 7350-7366 (2019) - [c69]Yifan Wu, Barnabás Póczos, Aarti Singh:
Towards Understanding the Generalization Bias of Two Layer Convolutional Linear Classifiers with Gradient Descent. AISTATS 2019: 1070-1078 - [c68]Ivan Stelmakh, Nihar B. Shah, Aarti Singh:
PeerReview4All: Fair and Accurate Reviewer Assignment in Peer Review. ALT 2019: 827-855 - [c67]Simon S. Du, Xiyu Zhai, Barnabás Póczos, Aarti Singh:
Gradient Descent Provably Optimizes Over-parameterized Neural Networks. ICLR (Poster) 2019 - [c66]Ivan Stelmakh, Nihar B. Shah, Aarti Singh:
On Testing for Biases in Peer Review. NeurIPS 2019: 5287-5297 - [i59]Yichong Xu, Xi Chen, Aarti Singh, Artur Dubrawski:
Thresholding Bandit Problem with Both Duels and Pulls. CoRR abs/1910.06368 (2019) - [i58]Yuexin Wu, Yichong Xu, Aarti Singh, Yiming Yang, Artur Dubrawski:
Active Learning for Graph Neural Networks via Node Feature Propagation. CoRR abs/1910.07567 (2019) - [i57]Yichong Xu, Aparna Joshi, Aarti Singh, Artur Dubrawski:
Zeroth Order Non-convex optimization with Dueling-Choice Bandits. CoRR abs/1911.00980 (2019) - 2018
- [j15]Aarti Singh, Anu Sharma:
A clustering-based recommendation engine for restaurants. Int. J. Adv. Intell. Paradigms 11(3/4): 272-283 (2018) - [j14]Martin Azizyan, Akshay Krishnamurthy, Aarti Singh:
Extreme Compressive Sampling for Covariance Estimation. IEEE Trans. Inf. Theory 64(12): 7613-7635 (2018) - [c65]Yichong Xu, Sivaraman Balakrishnan, Aarti Singh, Artur Dubrawski:
Interactive Linear Regression with Pairwise Comparisons. ACSSC 2018: 636-640 - [c64]Yining Wang, Simon S. Du, Sivaraman Balakrishnan, Aarti Singh:
Stochastic Zeroth-order Optimization in High Dimensions. AISTATS 2018: 1356-1365 - [c63]Yining Wang, Aarti Singh:
Linear Quantization by Effective-Resistance Sampling. ICASSP 2018: 6927-6930 - [c62]Simon S. Du, Jason D. Lee, Yuandong Tian, Aarti Singh, Barnabás Póczos:
Gradient Descent Learns One-hidden-layer CNN: Don't be Afraid of Spurious Local Minima. ICML 2018: 1338-1347 - [c61]Yichong Xu, Hariank Muthakana, Sivaraman Balakrishnan, Aarti Singh, Artur Dubrawski:
Nonparametric Regression with Comparisons: Escaping the Curse of Dimensionality with Ordinal Information. ICML 2018: 5469-5478 - [c60]Simon S. Du, Yining Wang, Xiyu Zhai, Sivaraman Balakrishnan, Ruslan Salakhutdinov, Aarti Singh:
How Many Samples are Needed to Estimate a Convolutional Neural Network? NeurIPS 2018: 371-381 - [c59]Yining Wang, Sivaraman Balakrishnan, Aarti Singh:
Optimization of Smooth Functions with Noisy Observations: Local Minimax Rates. NeurIPS 2018: 4343-4354 - [c58]Sivaraman Balakrishnan, Yo Joong Choe, Aarti Singh, Jean M. Vettel, Timothy D. Verstynen:
Local White Matter Architecture Defines Functional Brain Dynamics. SMC 2018: 595-602 - [i56]Yifan Wu, Barnabás Póczos, Aarti Singh:
Towards Understanding the Generalization Bias of Two Layer Convolutional Linear Classifiers with Gradient Descent. CoRR abs/1802.04420 (2018) - [i55]Siheng Chen, Aarti Singh, Jelena Kovacevic:
Multiresolution Representations for Piecewise-Smooth Signals on Graphs. CoRR abs/1803.02944 (2018) - [i54]Yining Wang, Sivaraman Balakrishnan, Aarti Singh:
Optimization of Smooth Functions with Noisy Observations: Local Minimax Rates. CoRR abs/1803.08586 (2018) - [i53]Simon S. Du, Yining Wang, Xiyu Zhai, Sivaraman Balakrishnan, Ruslan Salakhutdinov, Aarti Singh:
How Many Samples are Needed to Learn a Convolutional Neural Network? CoRR abs/1805.07883 (2018) - [i52]Simon S. Du, Yining Wang, Sivaraman Balakrishnan, Pradeep Ravikumar, Aarti Singh:
Robust Nonparametric Regression under Huber's ε-contamination Model. CoRR abs/1805.10406 (2018) - [i51]Yichong Xu, Hariank Muthakana, Sivaraman Balakrishnan, Aarti Singh, Artur Dubrawski:
Nonparametric Regression with Comparisons: Escaping the Curse of Dimensionality with Ordinal Information. CoRR abs/1806.03286 (2018) - [i50]Ivan Stelmakh, Nihar B. Shah, Aarti Singh:
PeerReview4All: Fair and Accurate Reviewer Assignment in Peer Review. CoRR abs/1806.06237 (2018) - [i49]Simon S. Du, Xiyu Zhai, Barnabás Póczos, Aarti Singh:
Gradient Descent Provably Optimizes Over-parameterized Neural Networks. CoRR abs/1810.02054 (2018) - [i48]Yining Wang, Erva Ulu, Aarti Singh, Levent Burak Kara:
Efficient Load Sampling for Worst-Case Structural Analysis Under Force Location Uncertainty. CoRR abs/1810.10977 (2018) - 2017
- [j13]Dimple Juneja, Aarti Singh, Rashmi Singh, Saurabh Mukherjee:
A Thorough Insight into Theoretical and Practical Developments in MultiAgent Systems. Int. J. Ambient Comput. Intell. 8(1): 23-49 (2017) - [j12]Aarti Singh, Dimple Juneja, Manisha Malhotra:
A novel agent based autonomous and service composition framework for cost optimization of resource provisioning in cloud computing. J. King Saud Univ. Comput. Inf. Sci. 29(1): 19-28 (2017) - [j11]Yining Wang, Adams Wei Yu, Aarti Singh:
On Computationally Tractable Selection of Experiments in Measurement-Constrained Regression Models. J. Mach. Learn. Res. 18: 143:1-143:41 (2017) - [j10]Yining Wang, Aarti Singh:
Provably Correct Algorithms for Matrix Column Subset Selection with Selectively Sampled Data. J. Mach. Learn. Res. 18: 156:1-156:42 (2017) - [j9]Siheng Chen, Yaoqing Yang, Shi Zong, Aarti Singh, Jelena Kovacevic:
Detecting Localized Categorical Attributes on Graphs. IEEE Trans. Signal Process. 65(10): 2725-2740 (2017) - [c57]Sivaraman Balakrishnan, Simon S. Du, Jerry Li, Aarti Singh:
Computationally Efficient Robust Sparse Estimation in High Dimensions. COLT 2017: 169-212 - [c56]Zeyuan Allen-Zhu, Yuanzhi Li, Aarti Singh, Yining Wang:
Near-Optimal Design of Experiments via Regret Minimization. ICML 2017: 126-135 - [c55]Pengtao Xie, Aarti Singh, Eric P. Xing:
Uncorrelation and Evenness: a New Diversity-Promoting Regularizer. ICML 2017: 3811-3820 - [c54]Simon S. Du, Yining Wang, Aarti Singh:
On the Power of Truncated SVD for General High-rank Matrix Estimation Problems. NIPS 2017: 445-455 - [c53]Simon S. Du, Jayanth Koushik, Aarti Singh, Barnabás Póczos:
Hypothesis Transfer Learning via Transformation Functions. NIPS 2017: 574-584 - [c52]Simon S. Du, Chi Jin, Jason D. Lee, Michael I. Jordan, Aarti Singh, Barnabás Póczos:
Gradient Descent Can Take Exponential Time to Escape Saddle Points. NIPS 2017: 1067-1077 - [c51]Yichong Xu, Hongyang Zhang, Aarti Singh, Artur Dubrawski, Kyle Miller:
Noise-Tolerant Interactive Learning Using Pairwise Comparisons. NIPS 2017: 2431-2440 - [e1]Aarti Singh, Xiaojin (Jerry) Zhu:
Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, AISTATS 2017, 20-22 April 2017, Fort Lauderdale, FL, USA. Proceedings of Machine Learning Research 54, PMLR 2017 [contents] - [i47]Yining Wang, Jialei Wang, Sivaraman Balakrishnan, Aarti Singh:
Rate Optimal Estimation and Confidence Intervals for High-dimensional Regression with Missing Covariates. CoRR abs/1702.02686 (2017) - [i46]Simon S. Du, Yining Wang, Aarti Singh:
On the Power of Truncated SVD for General High-rank Matrix Estimation Problems. CoRR abs/1702.06861 (2017) - [i45]Simon S. Du, Sivaraman Balakrishnan, Aarti Singh:
Computationally Efficient Robust Estimation of Sparse Functionals. CoRR abs/1702.07709 (2017) - [i44]Simon S. Du, Chi Jin, Jason D. Lee, Michael I. Jordan, Barnabás Póczos, Aarti Singh:
Gradient Descent Can Take Exponential Time to Escape Saddle Points. CoRR abs/1705.10412 (2017) - [i43]Yining Wang, Simon S. Du, Sivaraman Balakrishnan, Aarti Singh:
Stochastic Zeroth-order Optimization in High Dimensions. CoRR abs/1710.10551 (2017) - [i42]Zeyuan Allen-Zhu, Yuanzhi Li, Aarti Singh, Yining Wang:
Near-Optimal Discrete Optimization for Experimental Design: A Regret Minimization Approach. CoRR abs/1711.05174 (2017) - [i41]Simon S. Du, Jason D. Lee, Yuandong Tian, Barnabás Póczos, Aarti Singh:
Gradient Descent Learns One-hidden-layer CNN: Don't be Afraid of Spurious Local Minima. CoRR abs/1712.00779 (2017) - 2016
- [j8]Fang-Cheng Yeh, Jean M. Vettel, Aarti Singh, Barnabás Póczos, Scott T. Grafton, Kirk I. Erickson, Wen-Yih Isaac Tseng, Timothy D. Verstynen:
Quantifying Differences and Similarities in Whole-Brain White Matter Architecture Using Local Connectome Fingerprints. PLoS Comput. Biol. 12(11) (2016) - [j7]Wahiba Ben Abdessalem Karaa, Zeineb Ben Azzouz, Aarti Singh, Nilanjan Dey, Amira S. Ashour, Henda Ben Ghézala:
Automatic builder of class diagram (ABCD): an application of UML generation from functional requirements. Softw. Pract. Exp. 46(11): 1443-1458 (2016) - [j6]Siheng Chen, Rohan Varma, Aarti Singh, Jelena Kovacevic:
Signal Recovery on Graphs: Fundamental Limits of Sampling Strategies. IEEE Trans. Signal Inf. Process. over Networks 2(4): 539-554 (2016) - [j5]James Sharpnack, Alessandro Rinaldo, Aarti Singh:
Detecting Anomalous Activity on Networks With the Graph Fourier Scan Statistic. IEEE Trans. Signal Process. 64(2): 364-379 (2016) - [c50]Yining Wang, Aarti Singh:
Noise-Adaptive Margin-Based Active Learning and Lower Bounds under Tsybakov Noise Condition. AAAI 2016: 2180-2186 - [c49]Yining Wang, Yu-Xiang Wang, Aarti Singh:
Graph Connectivity in Noisy Sparse Subspace Clustering. AISTATS 2016: 538-546 - [c48]Gautam Dasarathy, Aarti Singh, Maria-Florina Balcan, Jong Hyuk Park:
Active Learning Algorithms for Graphical Model Selection. AISTATS 2016: 1356-1364 - [c47]Siheng Chen, Yaoqing Yang, Aarti Singh, Jelena Kovacevic:
Signal detection on graphs: Bernoulli noise model. GlobalSIP 2016: 395-399 - [c46]Siheng Chen, Rohan Varma, Aarti Singh, Jelena Kovacevic:
Representations of piecewise smooth signals on graphs. ICASSP 2016: 6370-6374 - [c45]Aarti Singh, Kavita Gupta:
Optimal Cluster Head Election Algorithm for Mobile Wireless Sensor Networks. ICTCS 2016: 132:1-132:6 - [c44]Aaditya Ramdas, David Isenberg, Aarti Singh, Larry A. Wasserman:
Minimax lower bounds for linear independence testing. ISIT 2016: 965-969 - [c43]Siheng Chen, Rohan Varma, Aarti Singh, Jelena Kovacevic:
A statistical perspective of sampling scores for linear regression. ISIT 2016: 1556-1560 - [c42]Bo Li, Yining Wang, Aarti Singh, Yevgeniy Vorobeychik:
Data Poisoning Attacks on Factorization-Based Collaborative Filtering. NIPS 2016: 1885-1893 - [i40]Yining Wang, Aarti Singh:
Minimax Subsampling for Estimation and Prediction in Low-Dimensional Linear Regression. CoRR abs/1601.02068 (2016) - [i39]Aaditya Ramdas, David Isenberg, Aarti Singh, Larry A. Wasserman:
Minimax Lower Bounds for Linear Independence Testing. CoRR abs/1601.06259 (2016) - [i38]Gautam Dasarathy, Aarti Singh, Maria-Florina Balcan, Jong Hyuk Park:
Active Learning Algorithms for Graphical Model Selection. CoRR abs/1602.00354 (2016) - [i37]Aaditya Ramdas, Aarti Singh, Larry A. Wasserman:
Classification Accuracy as a Proxy for Two Sample Testing. CoRR abs/1602.02210 (2016) - [i36]Siheng Chen, Yaoqing Yang, Shi Zong, Aarti Singh, Jelena Kovacevic:
Detecting Structure-correlated Attributes on Graphs. CoRR abs/1604.00657 (2016) - [i35]Bo Li, Yining Wang, Aarti Singh, Yevgeniy Vorobeychik:
Data Poisoning Attacks on Factorization-Based Collaborative Filtering. CoRR abs/1608.08182 (2016) - [i34]Yining Wang, Yu-Xiang Wang, Aarti Singh:
A Theoretical Analysis of Noisy Sparse Subspace Clustering on Dimensionality-Reduced Data. CoRR abs/1610.07650 (2016) - [i33]Simon Shaolei Du, Jayanth Koushik, Aarti Singh, Barnabás Póczos:
Transformation Function Based Methods for Model Shift. CoRR abs/1612.01020 (2016) - 2015
- [j4]Aarti Singh, Manisha Malhotra:
Agent based Resource Allocation Mechanism Focusing Cost Optimization in Cloud Computing. Int. J. Cloud Appl. Comput. 5(3): 53-61 (2015) - [j3]Aarti Singh, Anu Sharma, Nilanjan Dey:
Semantics and Agents Oriented Web Personalization: State of the Art. Int. J. Serv. Sci. Manag. Eng. Technol. 6(2): 35-49 (2015) - [c41]Aaditya Ramdas, Sashank Jakkam Reddi, Barnabás Póczos, Aarti Singh, Larry A. Wasserman:
On the Decreasing Power of Kernel and Distance Based Nonparametric Hypothesis Tests in High Dimensions. AAAI 2015: 3571-3577 - [c40]Martin Azizyan, Aarti Singh, Larry A. Wasserman:
Efficient Sparse Clustering of High-Dimensional Non-spherical Gaussian Mixtures. AISTATS 2015 - [c39]Sashank J. Reddi, Aaditya Ramdas, Barnabás Póczos, Aarti Singh, Larry A. Wasserman:
On the High Dimensional Power of a Linear-Time Two Sample Test under Mean-shift Alternatives. AISTATS 2015 - [c38]Yining Wang, Aarti Singh:
Column Subset Selection with Missing Data via Active Sampling. AISTATS 2015 - [c37]Yining Wang, Aarti Singh:
An empirical comparison of sampling techniques for matrix column subset selection. Allerton 2015: 1069-1074 - [c36]Yining Wang, Yu-Xiang Wang, Aarti Singh:
A Deterministic Analysis of Noisy Sparse Subspace Clustering for Dimensionality-reduced Data. ICML 2015: 1422-1431 - [c35]Yining Wang, Yu-Xiang Wang, Aarti Singh:
Differentially private subspace clustering. NIPS 2015: 1000-1008 - [i32]Yining Wang, Yu-Xiang Wang, Aarti Singh:
Clustering Consistent Sparse Subspace Clustering. CoRR abs/1504.01046 (2015) - [i31]Siheng Chen, Rohan Varma, Aarti Singh, Jelena Kovacevic:
Signal Recovery on Graphs: Random versus Experimentally Designed Sampling. CoRR abs/1504.05427 (2015) - [i30]Martin Azizyan, Yen-Chi Chen, Aarti Singh, Larry A. Wasserman:
Risk Bounds For Mode Clustering. CoRR abs/1505.00482 (2015) - [i29]Aaditya Ramdas, Aarti Singh:
Algorithmic Connections Between Active Learning and Stochastic Convex Optimization. CoRR abs/1505.04214 (2015) - [i28]Aaditya Ramdas, Barnabás Póczos, Aarti Singh, Larry A. Wasserman:
An Analysis of Active Learning With Uniform Feature Noise. CoRR abs/1505.04215 (2015) - [i27]Yining Wang, Aarti Singh:
Provably Correct Active Sampling Algorithms for Matrix Column Subset Selection with Missing Data. CoRR abs/1505.04343 (2015) - [i26]Martin Azizyan, Akshay Krishnamurthy, Aarti Singh:
Extreme Compressive Sampling for Covariance Estimation. CoRR abs/1506.00898 (2015) - [i25]Aaditya Ramdas, Sashank J. Reddi, Barnabás Póczos, Aarti Singh, Larry A. Wasserman:
Adaptivity and Computation-Statistics Tradeoffs for Kernel and Distance based High Dimensional Two Sample Testing. CoRR abs/1508.00655 (2015) - [i24]Siheng Chen, Rohan Varma, Aarti Singh, Jelena Kovacevic:
Signal Recovery on Graphs: Fundamental Limits of Sampling Strategies. CoRR abs/1512.05405 (2015) - [i23]Siheng Chen, Rohan Varma, Aarti Singh, Jelena Kovacevic:
Signal Representations on Graphs: Tools and Applications. CoRR abs/1512.05406 (2015) - 2014
- [c34]Martin Azizyan, Akshay Krishnamurthy, Aarti Singh:
Subspace learning from extremely compressed measurements. ACSSC 2014: 311-315 - [c33]Junier B. Oliva, Barnabás Póczos, Timothy D. Verstynen, Aarti Singh, Jeff G. Schneider, Fang-Cheng Yeh, Wen-Yih Isaac Tseng:
FuSSO: Functional Shrinkage and Selection Operator. AISTATS 2014: 715-723 - [c32]Aaditya Ramdas, Barnabás Póczos, Aarti Singh, Larry A. Wasserman:
An Analysis of Active Learning with Uniform Feature Noise. AISTATS 2014: 805-813 - [i22]Akshay Krishnamurthy, Martin Azizyan, Aarti Singh:
Subspace Learning from Extremely Compressed Measurements. CoRR abs/1404.0751 (2014) - [i21]Sashank J. Reddi, Aaditya Ramdas, Barnabás Póczos, Aarti Singh, Larry A. Wasserman:
Kernel MMD, the Median Heuristic and Distance Correlation in High Dimensions. CoRR abs/1406.2083 (2014) - [i20]Yining Wang, Aarti Singh:
Noise-adaptive Margin-based Active Learning for Multi-dimensional Data. CoRR abs/1406.5383 (2014) - [i19]Akshay Krishnamurthy, Aarti Singh:
On the Power of Adaptivity in Matrix Completion and Approximation. CoRR abs/1407.3619 (2014) - [i18]Aaditya Ramdas, Sashank J. Reddi, Barnabás Póczos, Aarti Singh, Larry A. Wasserman:
On the High-dimensional Power of Linear-time Kernel Two-Sample Testing under Mean-difference Alternatives. CoRR abs/1411.6314 (2014) - 2013
- [c31]Akshay Krishnamurthy, James Sharpnack, Aarti Singh:
Recovering graph-structured activations using adaptive compressive measurements. ACSSC 2013: 765-769 - [c30]Barnabás Póczos, Aarti Singh, Alessandro Rinaldo, Larry A. Wasserman:
Distribution-Free Distribution Regression. AISTATS 2013: 507-515 - [c29]