Aarti Singh
Professor
Machine Learning Department
Carnegie Mellon University
Director
AI institute for Societal Decision Making
| RESEARCH | PUBLICATIONS | GROUP | TEACHING | OUTREACH | CONTACT/BIO |
NEW/PRE-PRINTS:
Weighted Tallying Bandits: Overcoming Intractability via Repeated Exposure Optimality
D. Malik, Y. Li and A. Singh.
International Conference on Machine Learning, ICML'23.
Virtues of Laziness in Model-based RL: A Unified Objective and Algorithms (ArXiv)
A. Vemula, Y. Song, A. Singh, J. A. Bagnell, S. Choudhury.
International Conference on Machine Learning, ICML'23.
Adaptation to Misspecified Kernel Regularization in Kernelised Bandits (ArXiv)
Y. Liu and A. Singh.
Artificial Intelligence and Statistics, AISTATS'23.
PUBLISHED/ARXIVED:
Complete Policy Regret Bounds for Tallying Bandits (ArXiv)
D. Malik, Y. Li and A. Singh.
Conference on Learning Theory, COLT, 2022.
Integrating Rankings into Quantized Scores in Peer Review (ArXiv)
Y. Liu, Y. Xu, N. B. Shah and A. Singh.
Transactions on Machine Learning Research (TMLR), October 2022.
ICLR workshop on ML Evaluation Standards, 2022.
Outstanding Paper Award and People's Choice Award
Gaussian Processes for Episodic Experimental Design (link)
A. Fiorino, O. Neopane and A. Singh.
ICML workshop on Adaptive Experimental Design and Active Learning in the Real World, 2022.
Threshold Bandit Problem with Link Assumption between Pulls and Duels
K. Narayan and A. Singh.
ICML workshop on complex feedback in online learning, 2022.
Two-Sample Testing with Pairwise Comparison Data and the Role of Modeling Assumptions (ArXiv)
C. Rastogi, S. Balakrishnan, N. B. Shah and A. Singh.
Journal of Machine Learning Research (JMLR), vol.23, pp. 1-48, 2022.
Artificial Intelligence for Materials Research at Extremes (link)
B. Maruyama, J. Hattrick-Simpers, W. Musinski, L. Graham-Brady, K. Li, J. Hollenbach, A. Singh and M. L. Taheri.
MRS Bulletin, vol. 47, pp. 1154-1164, 2022, invited paper
Employing Artificial Intelligence to Accelerate Development and Implementation of Materials and Manufacturing Innovations (link)
The Minerals, Metals & Materials Society (TMS) study, 2022.
Local Signal Adaptivity: Provable Feature Learning in Neural Networks Beyond Kernels (link)
S. Karp, E.Winston, Y. Li and A. Singh.
Neural Information Processing Systems, NeurIPS, 2021.
Best Arm Identification under Additive Transfer Bandits (ArXiv)
O. Neopane, A. Ramdas and A. Singh.
Asilomar Conference on Signals, Systems, and Computers, 2021.
Best student paper award
Smooth Bandit Optimization: Generalization to Holder Space (ArXiv)
Y. Liu, Y. Wang, A. Singh.
Artificial Intelligence and Statistics, AISTATS, 2021.
A Large Scale Randomized Controlled Trial on Herding in Peer-Review Discussions (ArXiv)
I. Stelmakh, C. Rastogi, N. Shah, A. Singh and H. Daume III.
Peer Review Congress, 2022.
Catch Me if I Can: Detecting Strategic Behaviour in Peer Assessment (ArXiv)
I. Stelmakh, N. Shah and A. Singh.
AAAI Conference on Artificial Intelligence, 2021.
A Novice-Reviewer Experiment to Address Scarcity of Qualified Reviewers in Large Conferences (ArXiv)
I. Stelmakh, N. Shah, A. Singh and H. Daume III.
AAAI Conference on Artificial Intelligence, 2021.
Prior and Prejudice: The Novice Reviewers' Bias against Resubmissions in Conference Peer Review (ArXiv)
I. Stelmakh, N. Shah, A. Singh and H. Daume III.
ACM Conference on Computer-Supported Cooperative Work and Social Computing, CSCW, 2021.
Preference-based Reinforcement Learning with Finite-Time Guarantees (ArXiv)
Y. Xu, R. Wang, L. F. Yang, A. Singh and A. Dubrawski.
Neural Information Processing Systems, NeurIPS 2020.
Evidential Reasoning with Expert-Guided Machine Learning (link)
X. Ding, G. Atulya, A. Singh, A. Davis, and S. Fazzio.
NeurIPS workshop on Human And Machine in-the-Loop Evaluation and Learning Strategies, HAMLETS, 2020.
Classification accuracy as a proxy for two-sample testing (ArXiv)
I. Kim, A. Ramdas, A. Singh, and L. Wasserman.
Annals of Statistics, vol. 49, no. 1, pp. 411-434, 2021.
Zeroth Order Non-convex optimization with Dueling-Choice Bandits (ArXiv)
Y. Xu, A. Joshi, A. Singh and A. Dubrawski.
Uncertainty in Artificial Intelligence, UAI 2020.
Thresholding Bandit Problem with both Duels and Pulls (ArXiv)
Y. Xu, X. Chen, A. Singh and A. Dubrawski.
Artificial Intelligence and Statistics, AISTATS 2020.
Two-Sample Testing with Pairwise Comparison Data and the Role of Modeling Assumptions (ArXiv)
C. Rastogi, S. Balakrishnan, N. B. Shah and A. Singh.
IEEE International Symposium on Information Theory, ISIT 2020.
Parametric Analysis to Quantify Process Input Influence on the Printed Densities of Binder Jetted Alumina Ceramics (link)
E. M. Jimenez, D. Ding, L. Su, A. R. Joshi, A. Singh, B. Reeja-Jayan and J. Beuth.
Additive Manufacturing, vol. 30, 100864, 2019.
On testing for biases in peer review (ArXiv)
I. Stelmakh, N. B. Shah and A. Singh.
Neural Information Processing Systems, NeurIPS 2019.
PeerReview4All: Fair and Accurate Reviewer Assignment in Peer Review (ArXiv)
I. Stelmakh, N. B. Shah and A. Singh.
Algorithmic Learning Theory, ALT 2019.
Journal of Machine Learning Research (JMLR), vol. 22, no. 163, pp. 1-66, 2021.
Gradient Descent Provably Optimizes Over-parameterized Neural Networks (ArXiv)
S. S. Du*, X. Zhai*, B. Poczos and A. Singh.
International Conference on Learning Representations, ICLR 2019.
Towards Understanding the Generalization Bias of Two Layer Convolutional Linear Classifiers with Gradient Descent (ArXiv)
Y.Wu, B. Poczos and A. Singh.
Artificial Intelligence in Statistics, AISTATS 2019.
Rate Optimal Estimation and Confidence Intervals for High-dimensional Regression with Missing Covariates (ArXiv)
Y. Wang, J. Wang, S. Balakrishnan and A. Singh.
Journal of Multivariate Analysis, vol. 174, pp. 104526, 2019.
A Theoretical Analysis of Noisy Sparse Subspace Clustering on Dimensionality-Reduced Data. (ArXiv)
Y. Wang, Y.-X. Wang and A. Singh.
IEEE Transactions on Information Theory, vol. 65, no. 2, pp. 685-706, 2019.
Optimization of Smooth Functions with Noisy Observations: Local Minimax Rates (ArXiv)
Y. Wang, S. Balakrishnan and A. Singh.
Neural Information Processing Systems, NeurIPS 2018.
IEEE Transactions on Information Theory, vol. 65, no. 11, pp.7350-7366, 2019.
How Many Samples are Needed to Learn a Convolutional Neural Network? (ArXiv)
S. Du*, Y. Wang*, X. Zhai, S. Balakrishnan, R. Salakhutdinov and A. Singh.
Neural Information Processing Systems, NeurIPS 2018.
NVIDIA Pioneer Award
Interactive Linear Regression with Pairwise Comparisons (pdf)
Y. Xu, S. Balakrishnan, A. Singh and A. Dubrawski.
Asilomar Conference on Signals, Systems and Computers 2018, invited paper.
Efficient Load Sampling for Worst-Case Structural Analysis under Force Location Uncertainty (pdf)
Y. Wang, E. Ulu, A. Singh and L. B. Kara
ASME 2018 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference, IDETC 2018.
Local White Matter Architecture Defines Functional Brain Dynamics (ArXiv)
Y. J. Choe, S. Balakrishnan, A. Singh, J. M. Vettel and T. Verstynen.
IEEE International Conference on Systems, Man and Cybernetics, SMC 2018.
Franklin V. Taylor Memorial Award
Nonparametric Regression with Comparisons: Escaping the Curse of Dimensionality with Ordinal Information (ArXiv)
Y. Xu, H. Muthakana, S. Balakrishnan, A. Singh and A. Dubrawski.
International Conference on Machine Learning, ICML 2018.
A longer journal version appeared in Journal of Machine Learning Research, JMLR, vol. 21, no. 162, pp. 1-54, 2020.
Gradient Descent Learns One-hidden-layer CNN: Don't be Afraid of Spurious Local Minima (ArXiv)
S. Du, J. D. Lee, Y. Tian, B. Poczos and A. Singh.
International Conference of Machine Learning, ICML 2018.
Experiments in using nonlinear regression for business activity normalization in the Energy Star benchmarking method
X. Lei, M. Berges, B. Akinci and A. Singh.
International Conference on Computing in Civil and Building Engineering, ICCCBE 2018.
Stochastic Zeroth-order Optimization in High Dimensions (ArXiv)
Y. Wang, S. Du, S. Balakrishnan and A. Singh.
Artificial Intelligence and Statistics, AISTATS 2018, oral presentation.
Linear quantization by effective resistance sampling (pdf)
Y. Wang and A. Singh.
IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2018, invited paper.
Extreme Compressive Sampling for Covariance Estimation (ArXiv)
M. Azizyan, A. Krishnamurthy and A. Singh.
IEEE Transactions on Information Theory, vol. 64, no. 2, pp. 7613-7635, 2018.
Gradient Descent Can Take Exponential Time to Escape Saddle Points (ArXiv)
S. Du, C. Jin, J. Lee, M. Jordan, B. Poczos and A. Singh.
Neural Information Processing Systems, NIPS 2017, spotlight presentation.
Noise-Tolerant Interactive Learning Using Pairwise Comparisons (ArXiv)
Y. Xu, H. Zhang, K. Miller, A. Singh and A. Dubrawski.
Neural Information Processing Systems, NIPS 2017.
On the Power of Truncated SVD for General High-rank Matrix Estimation Problems (ArXiv)
S. Du, Y. Wang and A. Singh.
Neural Information Processing Systems, NIPS 2017.
Hypothesis Transfer Learning via Transformation Functions (ArXiv)
S. Du, J. Koushik, A. Singh and B. Poczos.
Neural Information Processing Systems, NIPS 2017.
Near-Optimal Design of Experiments via Regret Minimization (pdf, ArXiv)
Z. A.-Zhu, Y. Li, A. Singh, and Y. Wang.
International Conference on Machine Learning, ICML 2017.
An abridged version also appeared in NIPS 2017 Workshop on Discrete Structures in Machine Learning.
A longer journal version appeared in Mathematical Programming, vol. 186, pp. 439-478, 2021.
Uncorrelation and Evenness: A New Diversity-Promoting Regularizer
P. Xie, A. Singh, and E. Xing.
International Conference on Machine Learning, ICML 2017.
A Brain Phenotype for Stressor-Evoked Blood Pressure Reactivity (link)
P. J. Gianaros, L. K. Sheu, F. Uyar, J. Koushik, J. R. Jennings, T. Wager, A. Singh and T. Verstynen.
Journal of the American Heart Association, JAHA, e006053, vol. 6, no. 9, 2017.
Computationally Efficient Robust Sparse Estimation in High Dimensions (proceedings)
S. Balakrishnan, S. S. Du, J. Li, and A. Singh.
Conference on Learning Theory, COLT 2017.
On Computationally Tractable Selection of Experiments in Measurement-Constrained Regression Models (ArXiv)
Y. Wang, A. W. Yu and A. Singh.
Journal of Machine Learning Research, JMLR, vol. 18, no. 143, pp. 1-41, 2017.
Detecting localized categorical attributes on graphs (IEEE, ArXiv)
S. Chen, Y. Yang, S. Zong, A. Singh, and J. Kovac̆ević.
IEEE Transactions on Signal Processing, vol. 65, no. 10, pp. 2725-2740, 2017.
Signal detection on graphs: Bernoulli noise model (pdf)
S. Chen, Y. Yang, A. Singh, and J. Kovac̆ević.
IEEE Global Conference on Signal and Information Processing, GLOBALSIP, 2016.
Quantifying Differences and Similarities in Whole-Brain White Matter Architecture Using Local Connectome Fingerprints (PubMed)
F.-C. Yeh, J. Vettel, A. Singh, B. Poczos, S. Grafton, K. Erickson, W.-Y. Tseng, and T. Verstynen.
PLOS Computational Biology, e1005203, vol. 12, no. 11, pp. 1-17, 2016.
Minimax Lower Bounds for Linear Independence Testing (ArXiv)
D. Isenberg, A. Ramdas, A. Singh and L. Wasserman.
IEEE International Symposium on Information Theory, ISIT 2016.
A statistical perspective of sampling scores for linear regression (ArXiv)
S. Chen, R. Varma, A. Singh and J. Kovac̆ević.
IEEE International Symposium on Information Theory, ISIT 2016.
Active Learning Algorithms for Graphical Model Selection (ArXiv)
G. Dasarathy, A. Singh, M.-F. Balcan and J.H. Park
Artificial Intelligence and Statistics, AISTATS 2016, oral presentation.
Graph Connectivity in Noisy Sparse Subspace Clustering (ArXiv)
Y. Wang, Y.-X. Wang and A. Singh.
Artificial Intelligence and Statistics, AISTATS 2016.
Detecting Anomalous Activity on Networks with the Graph Fourier Scan Statistic (ArXiv)
J. Sharpnack, A. Singh and A. Rinaldo.
IEEE Transactions on Signal Processing, vol. 64, no. 2, 364-379, 2016.
Minimax Linear Regression under Measurement Constraints (pdf)
Y. Wang and A. Singh.
ICML 2016 Workshop on Data-Efficient Machine Learning.
Novel Quantization Strategies for Linear Prediction with Guarantees (pdf)
S. Du, Y. Xu, H. Zhang, C. Li, P. Grover and A. Singh.
ICML 2016 Workshop on On-Device Intelligence.
Frequency bands are an organizational force of intrinsic brain networks
S. Mowlaei, A. Singh and A. Ghuman.
Society for Neuroscience, 2016.
Noise-adaptive Margin-based Active Learning for Multi-dimensional Data and Lower Bounds under Tsybakov Noise Condition (pdf, arXiv)
Y. Wang and A. Singh.
AAAI Conference on Artificial Intelligence, AAAI 2016.
Representations of piecewise smooth signals on graphs (pdf)
S. Chen, R. Varma, A. Singh and J. Kovac̆ević.
IEEE International Conference on Acoustic, Speech and Signal Processing, ICASSP 2016.
Adaptivity & Computation-Statistics Tradeoffs for Kernel & Distance based High-dimensional Two Sample Testing (ArXiv)
A. Ramdas, S. J. Reddi, B. Poczos, A. Singh and L. Wasserman.
Provably Correct Active Sampling Algorithms for Matrix Column Subset Selection with Missing Data (arXiv)
Y. Wang and A. Singh.
Journal of Machine Learning Research, JMLR, vol. 18, no. 156, pp. 1-42, 2018.
Differentially Private Subspace Clustering (pdf, proceedings)
Y. Wang, Y.-X. Wang and A. Singh.
Neural Information Processing Systems, NIPS 2015.
A Deterministic Analysis of Noisy Sparse Subspace Clustering for Dimensionality-reduced Data (pdf)
Y. Wang, Y.-X. Wang and A. Singh.
International Conference on Machine Learning, ICML 2015.
An Empirical Comparison of Sampling Techniques for Matrix Column Subset Selection (pdf)
Y. Wang and A. Singh.
Allerton Conference on Communication, Control and Computing, 2015.
Signal recovery on graphs: Random versus experimentally designed sampling (arXiv_long, arXiv_short)
S. Chen, R. Varma, A. Singh and J. Kovac̆ević.
IEEE Transactions on Signal and Information Processing over Networks, special issue on Inference and Learning over Networks, vol. 2, no. 4, pp. 539-554, 2016.
An abridged version also appeared in Sampling Theory and Applications, SampTA 2015, invited paper.
Column Subset Selection with Missing Data via Active Sampling (pdf)
Y. Wang and A. Singh.
Artificial Intelligence and Statistics, AISTATS 2015.
On the High Dimensional Power of a Linear-Time Two Sample Test under Mean-shift Alternatives (pdf)
S. Reddi and A. Ramdas and B. Poczos and A. Singh and L. Wasserman.
Artificial Intelligence and Statistics, AISTATS 2015.
Efficient Sparse Clustering of High-Dimensional Non-spherical Gaussian Mixtures (arXiv)
M. Azizyan, A. Singh and L. Wasserman.
Artificial Intelligence and Statistics, AISTATS 2015.
On the Decreasing Power of Kernel and Distance based Nonparametric Hypothesis Tests in High Dimensions (arXiv)
S. Reddi, A. Ramdas, B. Poczos, A. Singh and L. Wasserman.
AAAI Conference on Artificial Intelligence, 2015.
Interpretability and Informativeness of Clustering Methods for Exploratory Analysis of Clinical Data (pdf)
M. Azizyan, A. Singh and W. Wu.
2nd NIPS 2014 Workshop on Machine Learning for Clinical Data Analysis, Healthcare and Genomics.
Subspace Learning from Extremely Compressed Measurements (arXiv, Code)
A. Krishnamurthy, M. Azizyan and A. Singh.
Asilomar Conference on Signals, Systems, and Computers, 2014, invited paper, finalist for best student paper award.
The predictive value of functional connectivity (Poster)
M. Clute, A. Singh, B. Poczos and T. Verstynen.
Annual Meeting of the Organization for Human Brain Mapping, OHBM 2014.
Confidence Sets For Persistence Diagrams (arXiv)
B. Fasy, F. Lecci, A. Rinaldo, L. Wasserman, S. Balakrishnan and A. Singh.
Annals of Statistics, Vol. 42, No. 6, 2301-2339, 2014.
An Analysis of Active Learning with Uniform Feature Noise (pdf)
A. Ramdas, B. Poczos, A. Singh and L. Wasserman.
Artificial Intelligence and Statistics, AISTATS 2014, oral presentation.
FuSSO: Functional Shrinkage and Selection Operator (pdf)
J. Oliva, B. Poczos, T. Verstynen, A. Singh, J. Schneider, F.-C. Yeh and E.-Y. Tseng.
Artificial Intelligence and Statistics, AISTATS 2014.
A preliminary version appeared in NIPS 2013 workshop on Modern Nonparametric Methods in Machine Learning (pdf).
Feature Selection For High-Dimensional Clustering (arXiv)
L. Wasserman, M. Azizyan and A. Singh.
On the Power of Adaptivity in Matrix Completion and Approximation (arXiv)
A. Krishnamurthy and A. Singh.
Low-Rank Matrix and Tensor Completion via Adaptive Sampling (pdf, arXiv, Code)
A. Krishnamurthy and A. Singh.
Neural Information Processing Systems, NIPS 2013.
A related longer version of the paper with some improvements for matrix completion and approximation is available at arXiv.
Minimax Theory for High-dimensional Gaussian Mixtures with Sparse Mean Separation (pdf, arXiv)
M. Azizyan, A. Singh and L. Wasserman.
Neural Information Processing Systems, NIPS 2013.
Near-optimal Anomaly Detection in Graphs using Lovasz Extended Scan Statistic (pdf, arXiv)
J. Sharpnack, A. Krishnamurthy and A. Singh.
Neural Information Processing Systems, NIPS 2013.
An abridged version summarizing our NIPS 2013 and two AISTATS 2013 papers on this topic appeared in GlobalSIP 2013, invited paper (pdf).
Cluster Trees on Manifolds (pdf, arXiv)
S. Balakrishnan, S. Narayanan, A. Rinaldo, A. Singh and L. Wasserman.
Neural Information Processing Systems, NIPS 2013.
Recovering Graph-Structured Activations using Adaptive Compressive Measurements (pdf, arXiv)
A. Krishnamurthy, J. Sharpnack and A. Singh.
Asilomar Conference on Signals, Systems, and Computers, 2013, invited paper, Best student paper award.
Recovering Block-Structured Activations using Compressive Measurements (EJS, arXiv)
S. Balakrishnan, M. Kolar, A. Rinaldo and A. Singh.
Electronic Journal of Statistics, vol. 11, no. 1, pp. 2647-2678, 2017.
On the Bootstrap for Persistence Diagrams and Landscapes (arXiv)
F. Chazal, B. Fasy, F. Lecci, A. Rinaldo, A. Singh and L. Wasserman.
Modeling and Analysis of Information Systems, vol. 20, no. 6, pp. 111-120, 2013.
Tight Lower Bounds for Homology Inference (arXiv)
S. Balakrishnan, A. Rinaldo, A. Singh and L. Wasserman.
Algorithmic Connections between Active Learning and Stochastic Convex Optimization (pdf)
A. Ramdas and A. Singh.
Algorithmic Learning Theory, ALT 2013.
Abridged versions of the ALT and ICML papers have appeared at NIPS 2013 workshop on Optimization for Machine Learning (pdf), and GlobalSIP 2013 as invited paper (pdf).
Optimal rates for stochastic convex optimization under Tsybakov noise condition (pdf)
A. Ramdas and A. Singh.
International Conference on Machine Learning, ICML 2013, oral presentation.
An older version is available on arXiv.
Detecting Activations over Graphs using Spanning Tree Wavelet Bases (arXiv, pdf)
J. Sharpnack, A. Krishnamurthy and A. Singh.
Artificial Intelligence and Statistics, AISTATS 2013, oral presentation.
Changepoint Detection over Graphs with the Spectral Scan Statistic (arXiv, pdf)
J. Sharpnack, A. Rinaldo and A. Singh.
Artificial Intelligence and Statistics, AISTATS 2013.
Distribution-free Distribution Regression (arXiv, pdf)
B. Poczos, A. Rinaldo, A. Singh and L. Wasserman.
Artificial Intelligence and Statistics, AISTATS 2013, oral presentation.
Density-sensitive Semisupervised Inference (arXiv, Code)
M. Azizyan, A. Singh and L. Wasserman.
Annals of Statistics, vol. 41, no. 2, pp. 751-771, 2013.
Subspace Detection of High-Dimensional Vectors Using Compressive Sampling (pdf, revision)
M. Azizyan, and A. Singh.
IEEE Statistical Signal Processing Workshop, SSP 2012.
Completion of high-rank ultrametric matrices using selective entries (pdf)
A. Singh, A. Krishnamurthy, S. Balakrishnan and M. Xu.
International Conference on Signal Processing and Communications, SPCOM 2012, invited paper.
Efficient Active Algorithms for Hierarchical Clustering (pdf, Code)
A. Krishnamurthy, S. Balakrishnan, M. Xu and A. Singh.
International Conference on Machine Learning, ICML 2012.
Sparsistency of the Edge Lasso over Graphs (pdf)
J. Sharpnack, A. Rinaldo, and A. Singh.
Artifical Intelligence and Statistics, AISTATS 2012.
Minimax rates for homology inference (arXiv)
S. Balakrishnan, A. Rinaldo, D. Sheehy, A. Singh, and L. Wasserman.
Artifical Intelligence and Statistics, AISTATS 2012, oral presentation.
Stability of Density-Based Clustering (pdf, arXiv)
A. Rinaldo, A. Singh, R. Nugent, and L. Wasserman.
Journal of Machine Learning Research, JMLR, Vol. 13, pages 905-948, 2012.
Robust Multi-Source Network Tomography Using Selective Probes (pdf, Supplementary file, Code)
A. Krishnamurthy and A. Singh.
IEEE International Conference on Computer Communications, INFOCOM 2012.
Noise Thresholds for Spectral Clustering (pdf)
S. Balakrishnan, M. Xu, A. Krishnamurthy, and A. Singh.
Neural Information Processing Systems, NIPS 2011, spotlight presentation.
Minimax Localization of Structural Information in Large Noisy Matrices (pdf)
M. Kolar, S. Balakrishnan, A. Rinaldo, and A. Singh.
Neural Information Processing Systems, NIPS 2011, spotlight presentation.
Statistical and computational tradeoffs in biclustering (pdf)
S. Balakrishnan, M. Kolar, A. Rinaldo, A. Singh, and L. Wasserman.
NIPS 2011 Workshop on Computational Trade-offs in Statistical Learning.
Active Clustering: Robust and Efficient Hierarchical Clustering using Adaptively Selected Similarities (pdf, arXiv)
B. Eriksson, G. Dasarathy, A. Singh, and R. Nowak.
Artificial Intelligence and Statistics, AISTATS 2011.
Identifying graph-structured activation patterns in networks (pdf)
J. Sharpnack, and A. Singh.
Neural Information Processing Systems, NIPS 2010, oral presentation.
Detecting weak but hierarchically-structured patterns in networks (pdf, arXiv)
A. Singh, R. Nowak, and R. Calderbank.
Artificial Intelligence and Statistics, AISTATS 2010, oral presentation.
Multi-manifold semi-supervised learning (pdf)
A. Goldberg, X. Zhu, A. Singh, Z. Xu, and R. Nowak.
Artificial Intelligence and Statistics, AISTATS 2009.
Unlabeled data: Now it helps, now it doesn't (pdf) [Errata]
A. Singh, R. Nowak and X. Zhu.
Neural Information Processing Systems, NIPS 2008, oral presentation.
Extended version available as Technical Report No. ECE-08-03, ECE Department, University of Wisconsin – Madison.
Adaptive Hausdorff Estimation of Density Level Sets (pdf)
A. Singh, C. Scott and R. Nowak.
Annals of Statistics, vol. 37, no. 5B, pp. 2760-2782, 2009.
Extended version available as Technical Report No. ECE-07-06, ECE Department, University of Wisconsin – Madison.
A shorter version of this paper appeared in Conference on Learning Theory, COLT 2008, pdf
Controlling the error in fMRI: Hypothesis testing or Set estimation? (pdf)
Z. Harmany, R. Willett, A. Singh and R. Nowak.
IEEE International Symposium on Biomedical Imaging, ISBI 2008.
Delay-differentiated Gossiping in Delay Tolerant Networks (pdf)
P. Ramanathan and A. Singh.
IEEE International Conference on Communications, ICC 2008.
Active Learning for Adaptive Mobile Sensing Networks (pdf)
A. Singh, R. Nowak and P. Ramanathan.
ACM/IEEE Interntional Conference on Information Processing in Sensor Networks, IPSN 2006.
Decentralized Compression and Predistribution via Randomized Gossiping (pdf)
M. Rabbat, J. Haupt, A. Singh and R. Nowak.
ACM/IEEE Interntional Conference on Information Processing in Sensor Networks, IPSN 2006.
Spatial Reuse through Adaptive Interference Cancellation in Multi-Antenna Wireless Networks (pdf)
A. Singh, P. Ramanathan and B. D. Van Veen.
IEEE Global Telecommunications Conference, GLOBECOM 2005.
Second- and Third-order Signal Predistortion for Nonlinear Distortion Suppression in a Traveling Wave Tube (pdf)
A. Singh, J. E. Scharer, J. H. Booske and J. G. Wöhlbier.
IEEE Trans. on Electron Devices, Special Issue on Vacuum Electron Devices, pp. 709-717, vol. 52, No. 5, May 2005.
Experimental Verification of the Mechanisms for Nonlinear Harmonic Growth and Suppression by Harmonic Injection in a Traveling Wave Tube (pdf)
A. Singh, J. G. Wöhlbier, J. H. Booske and J. E. Scharer.
Physical Review Letters, 92(20), Article 205005, 2004.
Sensitivity of Harmonic Injection and its Spatial Evolution for Nonlinear Distortion Suppression in a TWT (pdf)
A. Singh, J. E. Scharer, J. G. Wöhlbier and J. H. Booske.
IEEE International Vacuum Electronics Conference, IVEC 2004.
Injection Schemes for TWT Linearization (pdf)
A. Singh, J. G. Wöhlbier, J. E. Scharer and J. H. Booske.
IEEE International Vacuum Electronics Conference, IVEC 2003.
Intermodulation Suppression in a Broad Band TWT (pdf)
A. Singh, J. E. Scharer, M. Wirth, S. Bhattacharjee and J. H. Booske.
IEEE International Vacuum Electronics Conference, IVEC 2002.
Book Section: :
Active Techniques in Chapter 9 "How to Achieve Linear Amplification", Modern Microwave and Millimeter-Wave Power Electronics, John Wiley and IEEE Press, April 2005 (url)
A. Singh, J. Scharer and J. Booske.
Miscellaneous:
Nonparametric set estimation problems in statistical inference and learning (pdf)
Ph.D. Thesis, University of Wisconsin - Madison, August 2008.
Experimental investigation of TWT nonlinearities and distortion suppression by signal injection (pdf)
M.S. Thesis, University of Wisconsin - Madison, Dec 2003.
Nonlinear behavior and intermodulation suppression in a TWT amplifier (presentation)
MURI Teleconference presentation, University of Wisconsin - Madison, 2003.
Adaptive noise cancellation and its applications (pdf)
Undergraduate B.E. Project Report, Netaji Subhas Institute of Technology, 2001.
Study of MDS matrix used in Twofish AES (Advanced Encryption Standard) Algorithm and its VHDL Implementation (pdf)
Technical report, Central Electronics Engineering Research Institute, 2000.