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ORCIDPranjal Awasthi
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2020 – today
- 2024
[j10]Pranjal Awasthi, Corinna Cortes, Mehryar Mohri
:
Best-effort adaptation. Ann. Math. Artif. Intell. 92(2): 393-438 (2024)- [c73]Pranjal Awasthi, Satyen Kale, Ankit Pensia:
Semi-supervised Group DRO: Combating Sparsity with Unlabeled Data. ALT 2024: 125-160 - [c72]Pranjal Awasthi, Nishanth Dikkala, Pritish Kamath, Raghu Meka:
Learning Neural Networks with Sparse Activations. COLT 2024: 406-425 - [c71]Pranjal Awasthi, Corinna Cortes, Yishay Mansour, Mehryar Mohri:
A Theory of Learning with Competing Objectives and User Feedback. ISAIM 2024: 10-49 - [i65]Hanna Mazzawi, Pranjal Awasthi, Xavi Gonzalvo, Srikumar Ramalingam:
Simulated Overparameterization. CoRR abs/2402.05033 (2024) - [i64]Maximilian Böther, Abraham Sebastian, Pranjal Awasthi, Ana Klimovic, Srikumar Ramalingam:
On Distributed Larger-Than-Memory Subset Selection With Pairwise Submodular Functions. CoRR abs/2402.16442 (2024) - [i63]Naman Agarwal, Pranjal Awasthi, Satyen Kale, Eric Zhao:
Stacking as Accelerated Gradient Descent. CoRR abs/2403.04978 (2024) - [i62]Hanseul Cho, Jaeyoung Cha, Pranjal Awasthi, Srinadh Bhojanapalli, Anupam Gupta, Chulhee Yun:
Position Coupling: Leveraging Task Structure for Improved Length Generalization of Transformers. CoRR abs/2405.20671 (2024) - [i61]Bernd Bohnet, Kevin Swersky, Rosanne Liu, Pranjal Awasthi, Azade Nova, Javier Snaider, Hanie Sedghi, Aaron T. Parisi, Michael Collins, Angeliki Lazaridou, Orhan Firat, Noah Fiedel:
Long-Span Question-Answering: Automatic Question Generation and QA-System Ranking via Side-by-Side Evaluation. CoRR abs/2406.00179 (2024) - [i60]Mehran Kazemi, Nishanth Dikkala, Ankit Anand, Petar Devic, Ishita Dasgupta, Fangyu Liu, Bahare Fatemi, Pranjal Awasthi, Dee Guo, Sreenivas Gollapudi, Ahmed Qureshi:
ReMI: A Dataset for Reasoning with Multiple Images. CoRR abs/2406.09175 (2024) - [i59]Pranjal Awasthi, Nishanth Dikkala, Pritish Kamath, Raghu Meka:
Learning Neural Networks with Sparse Activations. CoRR abs/2406.17989 (2024) - 2023
- [j9]Jordana J. George, Jie (Kevin) Yan, Dorothy E. Leidner, Pranjal Awasthi:
Does Engaging in Data Philanthropy Impact Business Value? Inf. Syst. Manag. 40(2): 112-126 (2023) - [c70]Pranjal Awasthi, Corinna Cortes, Christopher Mohri:
Theory and Algorithm for Batch Distribution Drift Problems. AISTATS 2023: 9826-9851 - [c69]Pranjal Awasthi, Anqi Mao, Mehryar Mohri, Yutao Zhong:
Theoretically Grounded Loss Functions and Algorithms for Adversarial Robustness. AISTATS 2023: 10077-10094 - [c68]Pranjal Awasthi, Nika Haghtalab, Eric Zhao:
Open Problem: The Sample Complexity of Multi-Distribution Learning for VC Classes. COLT 2023: 5943-5949 - [c67]Pranjal Awasthi, Christopher Jung, Jamie Morgenstern:
Distributionally Robust Data Join. FORC 2023: 10:1-10:15 - [c66]Pranjal Awasthi, Alex Tang, Aravindan Vijayaraghavan:
Agnostic Learning of General ReLU Activation Using Gradient Descent. ICLR 2023 - [i58]Pranjal Awasthi, Kush Bhatia, Sreenivas Gollapudi, Kostas Kollias:
Congested Bandits: Optimal Routing via Short-term Resets. CoRR abs/2301.09251 (2023) - [i57]Pranjal Awasthi, Corinna Cortes, Mehryar Mohri:
Best-Effort Adaptation. CoRR abs/2305.05816 (2023) - [i56]Pranjal Awasthi, Nika Haghtalab, Eric Zhao:
The Sample Complexity of Multi-Distribution Learning for VC Classes. CoRR abs/2307.12135 (2023) - [i55]Pranjal Awasthi, Anupam Gupta:
Improving Length-Generalization in Transformers via Task Hinting. CoRR abs/2310.00726 (2023) - [i54]Srikumar Ramalingam, Pranjal Awasthi, Sanjiv Kumar:
A Weighted K-Center Algorithm for Data Subset Selection. CoRR abs/2312.10602 (2023) - 2022
- [c65]Pranjal Awasthi, Abhimanyu Das, Sreenivas Gollapudi:
Beyond GNNs: An Efficient Architecture for Graph Problems. AAAI 2022: 6019-6027 - [c64]Pranjal Awasthi, Sivaraman Balakrishnan, Aravindan Vijayaraghavan:
Understanding Simultaneous Train and Test Robustness. ALT 2022: 34-69 - [c63]Patrick O'Reilly, Pranjal Awasthi, Aravindan Vijayaraghavan, Bryan Pardo:
Effective and Inconspicuous Over-the-Air Adversarial Examples with Adaptive Filtering. ICASSP 2022: 6607-6611 - [c62]Pranjal Awasthi, Abhimanyu Das, Rajat Sen, Ananda Theertha Suresh:
On the benefits of maximum likelihood estimation for Regression and Forecasting. ICLR 2022 - [c61]Jacob D. Abernethy, Pranjal Awasthi, Matthäus Kleindessner, Jamie Morgenstern, Chris Russell, Jie Zhang:
Active Sampling for Min-Max Fairness. ICML 2022: 53-65 - [c60]Saba Ahmadi, Pranjal Awasthi, Samir Khuller, Matthäus Kleindessner, Jamie Morgenstern, Pattara Sukprasert, Ali Vakilian:
Individual Preference Stability for Clustering. ICML 2022: 197-246 - [c59]Pranjal Awasthi, Kush Bhatia, Sreenivas Gollapudi, Kostas Kollias:
Congested Bandits: Optimal Routing via Short-term Resets. ICML 2022: 1078-1100 - [c58]Pranjal Awasthi, Nishanth Dikkala, Pritish Kamath:
Do More Negative Samples Necessarily Hurt In Contrastive Learning? ICML 2022: 1101-1116 - [c57]Pranjal Awasthi, Anqi Mao, Mehryar Mohri, Yutao Zhong:
H-Consistency Bounds for Surrogate Loss Minimizers. ICML 2022: 1117-1174 - [c56]Ziwei Ji, Kwangjun Ahn, Pranjal Awasthi, Satyen Kale, Stefani Karp:
Agnostic Learnability of Halfspaces via Logistic Loss. ICML 2022: 10068-10103 - [c55]Pranjal Awasthi, Abhimanyu Das, Weihao Kong, Rajat Sen:
Trimmed Maximum Likelihood Estimation for Robust Generalized Linear Model. NeurIPS 2022 - [c54]Pranjal Awasthi, Anqi Mao, Mehryar Mohri, Yutao Zhong:
Multi-Class $H$-Consistency Bounds. NeurIPS 2022 - [c53]Joan Puigcerver, Rodolphe Jenatton, Carlos Riquelme, Pranjal Awasthi, Srinadh Bhojanapalli:
On the Adversarial Robustness of Mixture of Experts. NeurIPS 2022 - [c52]Nived Rajaraman, Devvrit, Pranjal Awasthi:
Semi-supervised Active Linear Regression. NeurIPS 2022 - [i53]Ziwei Ji, Kwangjun Ahn, Pranjal Awasthi, Satyen Kale, Stefani Karp:
Agnostic Learnability of Halfspaces via Logistic Loss. CoRR abs/2201.13419 (2022) - [i52]Pranjal Awasthi, Christopher Jung, Jamie Morgenstern:
Distributionally Robust Data Join. CoRR abs/2202.05797 (2022) - [i51]Pranjal Awasthi, Nishanth Dikkala, Pritish Kamath:
Do More Negative Samples Necessarily Hurt in Contrastive Learning? CoRR abs/2205.01789 (2022) - [i50]Pranjal Awasthi, Anqi Mao, Mehryar Mohri, Yutao Zhong:
H-Consistency Estimation Error of Surrogate Loss Minimizers. CoRR abs/2205.08017 (2022) - [i49]Weihao Kong, Rajat Sen, Pranjal Awasthi, Abhimanyu Das:
Trimmed Maximum Likelihood Estimation for Robust Learning in Generalized Linear Models. CoRR abs/2206.04777 (2022) - [i48]Saba Ahmadi, Pranjal Awasthi, Samir Khuller, Matthäus Kleindessner, Jamie Morgenstern, Pattara Sukprasert, Ali Vakilian:
Individual Preference Stability for Clustering. CoRR abs/2207.03600 (2022) - [i47]Pranjal Awasthi, Alex Tang, Aravindan Vijayaraghavan:
Agnostic Learning of General ReLU Activation Using Gradient Descent. CoRR abs/2208.02711 (2022) - [i46]Joan Puigcerver, Rodolphe Jenatton, Carlos Riquelme, Pranjal Awasthi, Srinadh Bhojanapalli:
On the Adversarial Robustness of Mixture of Experts. CoRR abs/2210.10253 (2022) - 2021
- [c51]Flavien Prost, Pranjal Awasthi, Nick Blumm, Aditee Kumthekar, Trevor Potter, Li Wei, Xuezhi Wang, Ed H. Chi, Jilin Chen, Alex Beutel:
Measuring Model Fairness under Noisy Covariates: A Theoretical Perspective. AIES 2021: 873-883 - [c50]Naman Agarwal, Pranjal Awasthi, Satyen Kale:
A Deep Conditioning Treatment of Neural Networks. ALT 2021: 249-305 - [c49]Pranjal Awasthi, Vaggos Chatziafratis, Xue Chen, Aravindan Vijayaraghavan:
Adversarially Robust Low Dimensional Representations. COLT 2021: 237-325 - [c48]Pranjal Awasthi, George Yu, Chun-Sung Ferng, Andrew Tomkins, Da-Cheng Juan:
Adversarial Robustness Across Representation Spaces. CVPR 2021: 7608-7616 - [c47]Pranjal Awasthi, Alex Beutel, Matthäus Kleindessner, Jamie Morgenstern, Xuezhi Wang:
Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Information. FAccT 2021: 206-214 - [c46]Pranjal Awasthi, Natalie Frank, Mehryar Mohri:
On the Existence of The Adversarial Bayes Classifier. NeurIPS 2021: 2978-2990 - [c45]Zhilei Wang, Pranjal Awasthi, Christoph Dann, Ayush Sekhari, Claudio Gentile:
Neural Active Learning with Performance Guarantees. NeurIPS 2021: 7510-7521 - [c44]Pranjal Awasthi, Natalie Frank, Anqi Mao, Mehryar Mohri, Yutao Zhong:
Calibration and Consistency of Adversarial Surrogate Losses. NeurIPS 2021: 9804-9815 - [c43]Pranjal Awasthi, Alex Tang, Aravindan Vijayaraghavan:
Efficient Algorithms for Learning Depth-2 Neural Networks with General ReLU Activations. NeurIPS 2021: 13485-13496 - [c42]Pranjal Awasthi, Abhimanyu Das, Sreenivas Gollapudi:
A Convergence Analysis of Gradient Descent on Graph Neural Networks. NeurIPS 2021: 20385-20397 - [i45]Pranjal Awasthi, Alex Beutel, Matthäus Kleindessner, Jamie Morgenstern, Xuezhi Wang:
Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Information. CoRR abs/2102.08410 (2021) - [i44]Jacob D. Abernethy, Pranjal Awasthi, Satyen Kale:
A Multiclass Boosting Framework for Achieving Fast and Provable Adversarial Robustness. CoRR abs/2103.01276 (2021) - [i43]Pranjal Awasthi, Natalie Frank, Anqi Mao, Mehryar Mohri, Yutao Zhong:
Calibration and Consistency of Adversarial Surrogate Losses. CoRR abs/2104.09658 (2021) - [i42]Pranjal Awasthi, Anqi Mao, Mehryar Mohri, Yutao Zhong:
A Finer Calibration Analysis for Adversarial Robustness. CoRR abs/2105.01550 (2021) - [i41]Flavien Prost, Pranjal Awasthi, Nick Blumm, Aditee Kumthekar, Trevor Potter, Li Wei, Xuezhi Wang, Ed H. Chi, Jilin Chen, Alex Beutel:
Measuring Model Fairness under Noisy Covariates: A Theoretical Perspective. CoRR abs/2105.09985 (2021) - [i40]Pranjal Awasthi, Christoph Dann, Claudio Gentile, Ayush Sekhari, Zhilei Wang:
Neural Active Learning with Performance Guarantees. CoRR abs/2106.03243 (2021) - [i39]Devvrit, Nived Rajaraman, Pranjal Awasthi:
Semi-supervised Active Regression. CoRR abs/2106.06676 (2021) - [i38]Pranjal Awasthi, Abhimanyu Das, Rajat Sen, Ananda Theertha Suresh:
On the benefits of maximum likelihood estimation for Regression and Forecasting. CoRR abs/2106.10370 (2021) - [i37]Pranjal Awasthi, Alex Tang, Aravindan Vijayaraghavan:
Efficient Algorithms for Learning Depth-2 Neural Networks with General ReLU Activations. CoRR abs/2107.10209 (2021) - [i36]Pranjal Awasthi, Natalie S. Frank, Mehryar Mohri:
On the Existence of the Adversarial Bayes Classifier (Extended Version). CoRR abs/2112.01694 (2021) - 2020
- [j8]Pranjal Awasthi, Bahman Kalantari, Yikai Zhang:
Robust vertex enumeration for convex hulls in high dimensions. Ann. Oper. Res. 295(1): 37-73 (2020) - [c41]Pranjal Awasthi, Matthäus Kleindessner, Jamie Morgenstern:
Equalized odds postprocessing under imperfect group information. AISTATS 2020: 1770-1780 - [c40]Pranjal Awasthi, Jordana J. George:
A case for Data Democratization. AMCIS 2020 - [c39]Pranjal Awasthi, Xue Chen, Aravindan Vijayaraghavan:
Estimating Principal Components under Adversarial Perturbations. COLT 2020: 323-362 - [c38]Pranjal Awasthi, Jordana J. George:
The Impact of Data Philanthropy on Global Health When Mediated Through Digital Epidemiology. ICIS 2020 - [c37]Pranjal Awasthi, Natalie Frank, Mehryar Mohri:
Adversarial Learning Guarantees for Linear Hypotheses and Neural Networks. ICML 2020: 431-441 - [c36]Pranjal Awasthi, Himanshu Jain, Ankit Singh Rawat, Aravindan Vijayaraghavan:
Adversarial robustness via robust low rank representations. NeurIPS 2020 - [c35]Pranjal Awasthi, Satyen Kale, Stefani Karp, Mehryar Mohri:
PAC-Bayes Learning Bounds for Sample-Dependent Priors. NeurIPS 2020 - [c34]Chicheng Zhang, Jie Shen, Pranjal Awasthi:
Efficient active learning of sparse halfspaces with arbitrary bounded noise. NeurIPS 2020 - [i35]Naman Agarwal, Pranjal Awasthi, Satyen Kale:
A Deep Conditioning Treatment of Neural Networks. CoRR abs/2002.01523 (2020) - [i34]Chicheng Zhang, Jie Shen, Pranjal Awasthi:
Efficient active learning of sparse halfspaces with arbitrary bounded noise. CoRR abs/2002.04840 (2020) - [i33]Pranjal Awasthi, Natalie Frank, Mehryar Mohri:
Adversarial Learning Guarantees for Linear Hypotheses and Neural Networks. CoRR abs/2004.13617 (2020) - [i32]Pranjal Awasthi, Xue Chen, Aravindan Vijayaraghavan:
Estimating Principal Components under Adversarial Perturbations. CoRR abs/2006.00602 (2020) - [i31]Matthäus Kleindessner, Pranjal Awasthi, Jamie Morgenstern:
A Notion of Individual Fairness for Clustering. CoRR abs/2006.04960 (2020) - [i30]Jacob D. Abernethy, Pranjal Awasthi, Matthäus Kleindessner, Jamie Morgenstern, Jie Zhang:
Adaptive Sampling to Reduce Disparate Performance. CoRR abs/2006.06879 (2020) - [i29]Pranjal Awasthi, Himanshu Jain, Ankit Singh Rawat, Aravindan Vijayaraghavan:
Adversarial robustness via robust low rank representations. CoRR abs/2007.06555 (2020) - [i28]Pranjal Awasthi, Natalie Frank, Mehryar Mohri:
On the Rademacher Complexity of Linear Hypothesis Sets. CoRR abs/2007.11045 (2020) - [i27]Pranjal Awasthi, Corinna Cortes, Yishay Mansour, Mehryar Mohri:
Beyond Individual and Group Fairness. CoRR abs/2008.09490 (2020) - [i26]Pranjal Awasthi, George Yu, Chun-Sung Ferng, Andrew Tomkins, Da-Cheng Juan:
Adversarial Robustness Across Representation Spaces. CoRR abs/2012.00802 (2020)
2010 – 2019
- 2019
- [j7]Pranjal Awasthi, Vineet Goyal, Brian Y. Lu:
On the adaptivity gap in two-stage robust linear optimization under uncertain packing constraints. Math. Program. 173(1-2): 313-352 (2019) - [c33]Jie Shen, Pranjal Awasthi, Ping Li:
Robust Matrix Completion from Quantized Observations. AISTATS 2019: 397-407 - [c32]Haris Angelidakis, Pranjal Awasthi, Avrim Blum, Vaggos Chatziafratis, Chen Dan:
Bilu-Linial Stability, Certified Algorithms and the Independent Set Problem. ESA 2019: 7:1-7:16 - [c31]Pranjal Awasthi, Ainesh Bakshi, Maria-Florina Balcan, Colin White, David P. Woodruff:
Robust Communication-Optimal Distributed Clustering Algorithms. ICALP 2019: 18:1-18:16 - [c30]Matthäus Kleindessner, Pranjal Awasthi, Jamie Morgenstern:
Fair k-Center Clustering for Data Summarization. ICML 2019: 3448-3457 - [c29]Matthäus Kleindessner, Samira Samadi, Pranjal Awasthi, Jamie Morgenstern:
Guarantees for Spectral Clustering with Fairness Constraints. ICML 2019: 3458-3467 - [c28]Pranjal Awasthi, Abhratanu Dutta, Aravindan Vijayaraghavan:
On Robustness to Adversarial Examples and Polynomial Optimization. NeurIPS 2019: 13737-13747 - [i25]Matthäus Kleindessner, Pranjal Awasthi, Jamie Morgenstern:
Fair k-Center Clustering for Data Summarization. CoRR abs/1901.08628 (2019) - [i24]Matthäus Kleindessner, Samira Samadi, Pranjal Awasthi, Jamie Morgenstern:
Guarantees for Spectral Clustering with Fairness Constraints. CoRR abs/1901.08668 (2019) - [i23]Pranjal Awasthi, Matthäus Kleindessner, Jamie Morgenstern:
Effectiveness of Equalized Odds for Fair Classification under Imperfect Group Information. CoRR abs/1906.03284 (2019) - [i22]Pranjal Awasthi, Abhratanu Dutta, Aravindan Vijayaraghavan:
On Robustness to Adversarial Examples and Polynomial Optimization. CoRR abs/1911.04681 (2019) - [i21]Pranjal Awasthi, Vaggos Chatziafratis, Xue Chen, Aravindan Vijayaraghavan:
Adversarially Robust Low Dimensional Representations. CoRR abs/1911.13268 (2019) - 2018
- [c27]Pranjal Awasthi, Bahman Kalantari, Yikai Zhang:
Robust Vertex Enumeration for Convex Hulls in High Dimensions. AISTATS 2018: 1387-1396 - [c26]Pranjal Awasthi, Aravindan Vijayaraghavan:
Towards Learning Sparsely Used Dictionaries with Arbitrary Supports. FOCS 2018: 283-296 - [c25]Pranjal Awasthi, Aravindan Vijayaraghavan:
Clustering Semi-Random Mixtures of Gaussians. ICML 2018: 294-303 - [c24]Matthäus Kleindessner, Pranjal Awasthi:
Crowdsourcing with Arbitrary Adversaries. ICML 2018: 2713-2722 - [i20]Pranjal Awasthi, Bahman Kalantari, Yikai Zhang:
Robust Vertex Enumeration for Convex Hulls in High Dimensions. CoRR abs/1802.01515 (2018) - [i19]Pranjal Awasthi, Aravindan Vijayaraghavan:
Towards Learning Sparsely Used Dictionaries with Arbitrary Supports. CoRR abs/1804.08603 (2018) - [i18]Haris Angelidakis, Pranjal Awasthi, Avrim Blum, Vaggos Chatziafratis, Chen Dan:
Bilu-Linial stability, certified algorithms and the Independent Set problem. CoRR abs/1810.08414 (2018) - 2017
- [j6]Pranjal Awasthi, Maria-Florina Balcan, Philip M. Long:
The Power of Localization for Efficiently Learning Linear Separators with Noise. J. ACM 63(6): 50:1-50:27 (2017) - [j5]Pranjal Awasthi, Maria-Florina Balcan, Konstantin Voevodski:
Local algorithms for interactive clustering. J. Mach. Learn. Res. 18: 3:1-3:35 (2017) - [c23]Pranjal Awasthi, Avrim Blum, Nika Haghtalab, Yishay Mansour:
Efficient PAC Learning from the Crowd. COLT 2017: 127-150 - [i17]Pranjal Awasthi, Maria-Florina Balcan, Colin White:
General and Robust Communication-Efficient Algorithms for Distributed Clustering. CoRR abs/1703.00830 (2017) - [i16]Pranjal Awasthi, Avrim Blum, Nika Haghtalab, Yishay Mansour:
Efficient PAC Learning from the Crowd. CoRR abs/1703.07432 (2017) - [i15]Pranjal Awasthi, Aravindan Vijayaraghavan:
Clustering Semi-Random Mixtures of Gaussians. CoRR abs/1711.08841 (2017) - 2016
- [j4]Pranjal Awasthi, Madhav Jha, Marco Molinaro, Sofya Raskhodnikova:
Testing Lipschitz Functions on Hypergrid Domains. Algorithmica 74(3): 1055-1081 (2016) - [c22]Pranjal Awasthi, Maria-Florina Balcan, Nika Haghtalab, Hongyang Zhang:
Learning and 1-bit Compressed Sensing under Asymmetric Noise. COLT 2016: 152-192 - [c21]Pranjal Awasthi, Moses Charikar, Ravishankar Krishnaswamy, Ali Kemal Sinop:
Spectral Embedding of k-Cliques, Graph Partitioning and k-Means. ITCS 2016: 301-310 - [r1]Pranjal Awasthi:
Clustering Under Stability Assumptions. Encyclopedia of Algorithms 2016: 331-335 - 2015
- [c20]Pranjal Awasthi, Moses Charikar, Kevin A. Lai, Andrej Risteski:
Label optimal regret bounds for online local learning. COLT 2015: 150-166 - [c19]Pranjal Awasthi, Maria-Florina Balcan, Nika Haghtalab, Ruth Urner:
Efficient Learning of Linear Separators under Bounded Noise. COLT 2015: 167-190 - [c18]Pranjal Awasthi, Moses Charikar, Ravishankar Krishnaswamy, Ali Kemal Sinop:
The Hardness of Approximation of Euclidean k-Means. SoCG 2015: 754-767 - [c17]Pranjal Awasthi, Afonso S. Bandeira, Moses Charikar, Ravishankar Krishnaswamy, Soledad Villar, Rachel A. Ward:
Relax, No Need to Round: Integrality of Clustering Formulations. ITCS 2015: 191-200 - [c16]Pranjal Awasthi, Andrej Risteski:
On some provably correct cases of variational inference for topic models. NIPS 2015: 2098-2106 - [i14]Pranjal Awasthi, Moses Charikar, Ravishankar Krishnaswamy, Ali Kemal Sinop:
The Hardness of Approximation of Euclidean k-means. CoRR abs/1502.03316 (2015) - [i13]Pranjal Awasthi, Moses Charikar, Kevin A. Lai, Andrej Risteski:
Label optimal regret bounds for online local learning. CoRR abs/1503.02193 (2015) - [i12]Pranjal Awasthi, Maria-Florina Balcan, Nika Haghtalab, Ruth Urner:
Efficient Learning of Linear Separators under Bounded Noise. CoRR abs/1503.03594 (2015) - [i11]Pranjal Awasthi, Andrej Risteski:
On some provably correct cases of variational inference for topic models. CoRR abs/1503.06567 (2015) - 2014
- [j3]Pranjal Awasthi, Madhav Jha, Marco Molinaro, Sofya Raskhodnikova:
Limitations of Local Filters of Lipschitz and Monotone Functions. ACM Trans. Comput. Theory 7(1): 2:1-2:16 (2014) - [c15]Pranjal Awasthi, Maria-Florina Balcan, Konstantin Voevodski:
Local algorithms for interactive clustering. ICML 2014: 550-558 - [c14]Pranjal Awasthi, Avrim Blum, Or Sheffet, Aravindan Vijayaraghavan:
Learning Mixtures of Ranking Models. NIPS 2014: 2609-2617 - [c13]Pranjal Awasthi, Maria-Florina Balcan, Philip M. Long:
The power of localization for efficiently learning linear separators with noise. STOC 2014: 449-458 - [i10]Pranjal Awasthi, Afonso S. Bandeira, Moses Charikar, Ravishankar Krishnaswamy, Soledad Villar, Rachel A. Ward:
Relax, no need to round: integrality of clustering formulations. CoRR abs/1408.4045 (2014) - [i9]Pranjal Awasthi, Avrim Blum, Or Sheffet, Aravindan Vijayaraghavan:
Learning Mixtures of Ranking Models. CoRR abs/1410.8750 (2014) - 2013
- [b1]Pranjal Awasthi:
Approximation Algorithms and New Models for Clustering and Learning. Carnegie Mellon University, USA, 2013 - [c12]Pranjal Awasthi, Vitaly Feldman, Varun Kanade:
Learning Using Local Membership Queries. COLT 2013: 398-431 - [i8]Pranjal Awasthi, Maria-Florina Balcan, Philip M. Long:
The Power of Localization for Efficiently Learning Linear Separators with Malicious Noise. CoRR abs/1307.8371 (2013) - [i7]Pranjal Awasthi, Maria-Florina Balcan, Konstantin Voevodski:
Local algorithms for interactive clustering. CoRR abs/1312.6724 (2013) - 2012
- [j2]Pranjal Awasthi, Avrim Blum, Or Sheffet:
Center-based clustering under perturbation stability. Inf. Process. Lett. 112(1-2): 49-54 (2012) - [c11]Pranjal Awasthi, Avrim Blum, Jamie Morgenstern, Or Sheffet:
Additive Approximation for Near-Perfect Phylogeny Construction. APPROX-RANDOM 2012: 25-36 - [c10]Pranjal Awasthi, Or Sheffet:
Improved Spectral-Norm Bounds for Clustering. APPROX-RANDOM 2012: 37-49 - [c9]Pranjal Awasthi, Madhav Jha, Marco Molinaro, Sofya Raskhodnikova:
Limitations of Local Filters of Lipschitz and Monotone Functions. APPROX-RANDOM 2012: 374-386 - [c8]Pranjal Awasthi, Madhav Jha, Marco Molinaro, Sofya Raskhodnikova:
Testing Lipschitz Functions on Hypergrid Domains. APPROX-RANDOM 2012: 387-398 - [i6]Pranjal Awasthi, Or Sheffet:
Improved Spectral-Norm Bounds for Clustering. CoRR abs/1206.3204 (2012) - [i5]Pranjal Awasthi, Avrim Blum, Jamie Morgenstern, Or Sheffet:
Additive Approximation for Near-Perfect Phylogeny Construction. CoRR abs/1206.3334 (2012) - [i4]Pranjal Awasthi, Varun Kanade:
Learning using Local Membership Queries under Smooth Distributions. CoRR abs/1211.0996 (2012) - [i3]Pranjal Awasthi, Madhav Jha, Marco Molinaro, Sofya Raskhodnikova:
Limitations of Local Filters of Lipschitz and Monotone Functions. Electron. Colloquium Comput. Complex. TR12 (2012) - [i2]Pranjal Awasthi, Madhav Jha, Marco Molinaro, Sofya Raskhodnikova:
Testing Lipschitz Functions on Hypergrid Domains. Electron. Colloquium Comput. Complex. TR12 (2012) - 2011
- [j1]Venkatesan T. Chakaravarthy, Vinayaka Pandit, Sambuddha Roy, Pranjal Awasthi, Mukesh K. Mohania:
Decision trees for entity identification: Approximation algorithms and hardness results. ACM Trans. Algorithms 7(2): 15:1-15:22 (2011) - 2010
- [c7]Pranjal Awasthi, Avrim Blum, Or Sheffet:
Improved Guarantees for Agnostic Learning of Disjunctions. COLT 2010: 359-367 - [c6]Pranjal Awasthi, Avrim Blum, Or Sheffet:
Stability Yields a PTAS for k-Median and k-Means Clustering. FOCS 2010: 309-318 - [c5]Pranjal Awasthi, Reza Bosagh Zadeh:
Supervised Clustering. NIPS 2010: 91-99 - [c4]Pranjal Awasthi, Maria-Florina Balcan, Avrim Blum, Or Sheffet, Santosh S. Vempala:
On Nash-Equilibria of Approximation-Stable Games. SAGT 2010: 78-89 - [i1]Pranjal Awasthi, Avrim Blum, Or Sheffet:
Center-based Clustering under Perturbation Stability. CoRR abs/1009.3594 (2010)
2000 – 2009
- 2009
- [c3]Pranjal Awasthi, Tuomas Sandholm:
Online Stochastic Optimization in the Large: Application to Kidney Exchange. IJCAI 2009: 405-411 - 2007
- [c2]Pranjal Awasthi, Aakanksha Gagrani, Balaraman Ravindran:
Image Modeling Using Tree Structured Conditional Random Fields. IJCAI 2007: 2060-2065 - [c1]Venkatesan T. Chakaravarthy, Vinayaka Pandit, Sambuddha Roy, Pranjal Awasthi, Mukesh K. Mohania:
Decision trees for entity identification: approximation algorithms and hardness results. PODS 2007: 53-62
Coauthor Index
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