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Journal Articles
- 2023
[j7]Varun Kanade
, Frederik Mallmann-Trenn, Thomas Sauerwald:
On Coalescence Time in Graphs: When Is Coalescing as Fast as Meeting? ACM Trans. Algorithms 19(2): 18:1-18:46 (2023)- 2021
- [j6]Pascale Gourdeau, Varun Kanade, Marta Kwiatkowska, James Worrell:
On the Hardness of Robust Classification. J. Mach. Learn. Res. 22: 273:1-273:29 (2021) - 2019
- [j5]Vincent Cohen-Addad, Varun Kanade, Frederik Mallmann-Trenn, Claire Mathieu:
Hierarchical Clustering: Objective Functions and Algorithms. J. ACM 66(4): 26:1-26:42 (2019) - [j4]Varun Kanade, Andrea Rocchetto, Simone Severini:
mu - biased quantum Fourier sampling. Quantum Inf. Comput. 19(15&16): 1261-1278 (2019) - 2016
- [j3]Varun Kanade, Elchanan Mossel, Tselil Schramm:
Global and Local Information in Clustering Labeled Block Models. IEEE Trans. Inf. Theory 62(10): 5906-5917 (2016) - 2014
- [j2]Varun Kanade, Thomas Steinke:
Learning Hurdles for Sleeping Experts. ACM Trans. Comput. Theory 6(3): 11:1-11:16 (2014) - 2012
- [j1]Adam Tauman Kalai, Varun Kanade, Yishay Mansour:
Reliable agnostic learning. J. Comput. Syst. Sci. 78(5): 1481-1495 (2012)
Conference and Workshop Papers
- 2024
- [c47]Satwik Bhattamishra, Arkil Patel, Phil Blunsom, Varun Kanade:
Understanding In-Context Learning in Transformers and LLMs by Learning to Learn Discrete Functions. ICLR 2024 - 2023
- [c46]Satwik Bhattamishra, Arkil Patel, Varun Kanade, Phil Blunsom:
Simplicity Bias in Transformers and their Ability to Learn Sparse Boolean Functions. ACL (1) 2023: 5767-5791 - [c45]Elad Hazan, Adam Tauman Kalai, Varun Kanade, Clara Mohri, Y. Jennifer Sun:
Partial Matrix Completion. NeurIPS 2023 - 2022
- [c44]Pascale Gourdeau, Varun Kanade, Marta Kwiatkowska, James Worrell:
Sample Complexity Bounds for Robustly Learning Decision Lists against Evasion Attacks. IJCAI 2022: 3022-3028 - [c43]Pascale Gourdeau, Varun Kanade, Marta Kwiatkowska, James Worrell:
When are Local Queries Useful for Robust Learning? NeurIPS 2022 - 2021
- [c42]Vincent Cohen-Addad, Benjamin Guedj, Varun Kanade, Guy Rom:
Online k-means Clustering. AISTATS 2021: 1126-1134 - [c41]Adam Tauman Kalai, Varun Kanade:
Efficient Learning with Arbitrary Covariate Shift. ALT 2021: 850-864 - [c40]Amartya Sanyal, Puneet K. Dokania, Varun Kanade, Philip H. S. Torr:
How Benign is Benign Overfitting ? ICLR 2021 - [c39]Adam Kalai, Varun Kanade:
Towards optimally abstaining from prediction with OOD test examples. NeurIPS 2021: 12774-12785 - 2020
- [c38]Limor Gultchin, Matt J. Kusner, Varun Kanade, Ricardo Silva:
Differentiable Causal Backdoor Discovery. AISTATS 2020: 3970-3979 - [c37]Qiong Wu, Felix Ming Fai Wong, Yanhua Li, Zhenming Liu, Varun Kanade:
Adaptive Reduced Rank Regression. NeurIPS 2020 - [c36]Tomas Vaskevicius, Varun Kanade, Patrick Rebeschini:
The Statistical Complexity of Early-Stopped Mirror Descent. NeurIPS 2020 - 2019
- [c35]Quentin Berthet, Varun Kanade:
Statistical Windows in Testing for the Initial Distribution of a Reversible Markov Chain. AISTATS 2019: 246-255 - [c34]Tomas Vaskevicius, Varun Kanade, Patrick Rebeschini:
Implicit Regularization for Optimal Sparse Recovery. NeurIPS 2019: 2968-2979 - [c33]David Martínez-Rubio, Varun Kanade, Patrick Rebeschini:
Decentralized Cooperative Stochastic Bandits. NeurIPS 2019: 4531-4542 - [c32]Pascale Gourdeau, Varun Kanade, Marta Kwiatkowska, James Worrell:
On the Hardness of Robust Classification. NeurIPS 2019: 7444-7453 - [c31]Varun Kanade, Frederik Mallmann-Trenn, Thomas Sauerwald:
On coalescence time in graphs: When is coalescing as fast as meeting?: Extended Abstract. SODA 2019: 956-965 - 2018
- [c30]Amartya Sanyal, Matt J. Kusner, Adrià Gascón, Varun Kanade:
TAPAS: Tricks to Accelerate (encrypted) Prediction As a Service. ICML 2018: 4497-4506 - [c29]Vincent Cohen-Addad, Varun Kanade, Frederik Mallmann-Trenn:
Clustering Redemption-Beyond the Impossibility of Kleinberg's Axioms. NeurIPS 2018: 8526-8535 - [c28]Vincent Cohen-Addad, Varun Kanade, Frederik Mallmann-Trenn, Claire Mathieu:
Hierarchical Clustering: Objective Functions and Algorithms. SODA 2018: 378-397 - 2017
- [c27]Vincent Cohen-Addad, Varun Kanade:
Online Optimization of Smoothed Piecewise Constant Functions. AISTATS 2017: 412-420 - [c26]Surbhi Goel, Varun Kanade, Adam R. Klivans, Justin Thaler:
Reliably Learning the ReLU in Polynomial Time. COLT 2017: 1004-1042 - [c25]Cheng Li, Felix Ming Fai Wong, Zhenming Liu, Varun Kanade:
From which world is your graph. NIPS 2017: 1469-1479 - [c24]Vincent Cohen-Addad, Varun Kanade, Frederik Mallmann-Trenn:
Hierarchical Clustering Beyond the Worst-Case. NIPS 2017: 6201-6209 - [c23]Varun Kanade, Frederik Mallmann-Trenn, Victor Verdugo:
Brief Announcement: How Large is your Graph? PODC 2017: 195-197 - [c22]Varun Kanade, Frederik Mallmann-Trenn, Victor Verdugo:
How Large Is Your Graph?. DISC 2017: 34:1-34:16 - 2016
- [c21]Varun Kanade, Nikos Leonardos, Frédéric Magniez:
Stable Matching with Evolving Preferences. APPROX-RANDOM 2016: 36:1-36:13 - [c20]Varun Kanade, Reut Levi, Zvi Lotker, Frederik Mallmann-Trenn, Claire Mathieu:
Distance in the Forest Fire Model How far are you from Eve? SODA 2016: 1602-1620 - 2015
- [c19]Steve Hanneke, Varun Kanade, Liu Yang:
Learning with a Drifting Target Concept. ALT 2015: 149-164 - [c18]Varun Kanade, Elchanan Mossel:
MCMC Learning. COLT 2015: 1101-1128 - 2014
- [c17]Varun Kanade, Elchanan Mossel, Tselil Schramm:
Global and Local Information in Clustering Labeled Block Models. APPROX-RANDOM 2014: 779-792 - [c16]Varun Kanade, Justin Thaler:
Distribution-independent Reliable Learning. COLT 2014: 3-24 - [c15]Yasin Abbasi-Yadkori, Peter L. Bartlett, Varun Kanade:
Tracking Adversarial Targets. ICML 2014: 369-377 - [c14]Elaine Angelino, Varun Kanade:
Attribute-efficient evolvability of linear functions. ITCS 2014: 287-300 - 2013
- [c13]Pranjal Awasthi, Vitaly Feldman, Varun Kanade:
Learning Using Local Membership Queries. COLT 2013: 398-431 - [c12]Yasin Abbasi-Yadkori, Peter L. Bartlett, Varun Kanade, Yevgeny Seldin, Csaba Szepesvári:
Online Learning in Markov Decision Processes with Adversarially Chosen Transition Probability Distributions. NIPS 2013: 2508-2516 - 2012
- [c11]Varun Kanade, Thomas Steinke:
Learning hurdles for sleeping experts. ITCS 2012: 11-18 - [c10]Varun Kanade, Zhenming Liu, Bozidar Radunovic:
Distributed Non-Stochastic Experts. NIPS 2012: 260-268 - [c9]Vitaly Feldman, Varun Kanade:
Computational Bounds on Statistical Query Learning. COLT 2012: 16.1-16.22 - 2011
- [c8]Varun Kanade:
Evolution with Recombination. FOCS 2011: 837-846 - [c7]Sham M. Kakade, Adam Kalai, Varun Kanade, Ohad Shamir:
Efficient Learning of Generalized Linear and Single Index Models with Isotonic Regression. NIPS 2011: 927-935 - 2010
- [c6]Varun Kanade, Leslie G. Valiant, Jennifer Wortman Vaughan:
Evolution with Drifting Targets. COLT 2010: 155-167 - 2009
- [c5]Adam Tauman Kalai, Varun Kanade, Yishay Mansour:
Reliable Agnostic Learning. COLT 2009 - [c4]Adam Kalai, Varun Kanade:
Potential-Based Agnostic Boosting. NIPS 2009: 880-888 - [c3]Varun Kanade, H. Brendan McMahan, Brent Bryan:
Sleeping Experts and Bandits with Stochastic Action Availability and Adversarial Rewards. AISTATS 2009: 272-279 - 2007
- [c2]Varun Kanade, Santosh S. Vempala:
Life (and routing) on the Wireless Manifold. HotNets 2007 - [c1]David A. Bader, Varun Kanade, Kamesh Madduri:
SWARM: A Parallel Programming Framework for Multicore Processors. IPDPS 2007: 1-8
Informal and Other Publications
- 2024
- [i42]Satwik Bhattamishra, Michael Hahn, Phil Blunsom, Varun Kanade:
Separations in the Representational Capabilities of Transformers and Recurrent Architectures. CoRR abs/2406.09347 (2024) - 2023
- [i41]Satwik Bhattamishra, Arkil Patel, Phil Blunsom, Varun Kanade:
Understanding In-Context Learning in Transformers and LLMs by Learning to Learn Discrete Functions. CoRR abs/2310.03016 (2023) - 2022
- [i40]Varun Kanade, Patrick Rebeschini, Tomas Vaskevicius:
Exponential Tail Local Rademacher Complexity Risk Bounds Without the Bernstein Condition. CoRR abs/2202.11461 (2022) - [i39]Pascale Gourdeau, Varun Kanade, Marta Kwiatkowska, James Worrell:
Sample Complexity Bounds for Robustly Learning Decision Lists against Evasion Attacks. CoRR abs/2205.06127 (2022) - [i38]Limor Gultchin, Vincent Cohen-Addad, Sophie Giffard-Roisin, Varun Kanade, Frederik Mallmann-Trenn:
Beyond Impossibility: Balancing Sufficiency, Separation and Accuracy. CoRR abs/2205.12327 (2022) - [i37]Varun Kanade, Elad Hazan, Adam Tauman Kalai:
Partial Matrix Completion. CoRR abs/2208.12063 (2022) - [i36]Pascale Gourdeau, Varun Kanade, Marta Kwiatkowska, James Worrell:
When are Local Queries Useful for Robust Learning? CoRR abs/2210.06089 (2022) - [i35]Satwik Bhattamishra, Arkil Patel, Varun Kanade, Phil Blunsom:
Simplicity Bias in Transformers and their Ability to Learn Sparse Boolean Functions. CoRR abs/2211.12316 (2022) - 2021
- [i34]Adam Kalai, Varun Kanade:
Efficient Learning with Arbitrary Covariate Shift. CoRR abs/2102.07802 (2021) - [i33]Adam Tauman Kalai, Varun Kanade:
Towards optimally abstaining from prediction. CoRR abs/2105.14119 (2021) - 2020
- [i32]Tomas Vaskevicius, Varun Kanade, Patrick Rebeschini:
The Statistical Complexity of Early Stopped Mirror Descent. CoRR abs/2002.00189 (2020) - [i31]Limor Gultchin, Matt J. Kusner, Varun Kanade, Ricardo Silva:
Differentiable Causal Backdoor Discovery. CoRR abs/2003.01461 (2020) - [i30]Amartya Sanyal, Puneet K. Dokania, Varun Kanade, Philip H. S. Torr:
How benign is benign overfitting? CoRR abs/2007.04028 (2020) - [i29]Bryn Elesedy, Varun Kanade, Yee Whye Teh:
Lottery Tickets in Linear Models: An Analysis of Iterative Magnitude Pruning. CoRR abs/2007.08243 (2020) - 2019
- [i28]Qiong Wu, Felix Ming Fai Wong, Zhenming Liu, Yanhua Li, Varun Kanade:
Adaptive Reduced Rank Regression. CoRR abs/1905.11566 (2019) - [i27]Tomas Vaskevicius, Varun Kanade, Patrick Rebeschini:
Implicit Regularization for Optimal Sparse Recovery. CoRR abs/1909.05122 (2019) - [i26]Pascale Gourdeau, Varun Kanade, Marta Kwiatkowska, James Worrell:
On the Hardness of Robust Classification. CoRR abs/1909.05822 (2019) - [i25]Vincent Cohen-Addad, Benjamin Guedj, Varun Kanade, Guy Rom:
Online k-means Clustering. CoRR abs/1909.06861 (2019) - [i24]Pascale Gourdeau, Varun Kanade, Marta Kwiatkowska, James Worrell:
On the Hardness of Robust Classification. Electron. Colloquium Comput. Complex. TR19 (2019) - 2018
- [i23]Varun Kanade, Andrea Rocchetto, Simone Severini:
Learning DNFs under product distributions via μ-biased quantum Fourier sampling. CoRR abs/1802.05690 (2018) - [i22]Amartya Sanyal, Varun Kanade, Philip H. S. Torr:
Low Rank Structure of Learned Representations. CoRR abs/1804.07090 (2018) - [i21]Amartya Sanyal, Matt J. Kusner, Adrià Gascón, Varun Kanade:
TAPAS: Tricks to Accelerate (encrypted) Prediction As a Service. CoRR abs/1806.03461 (2018) - [i20]Quentin Berthet, Varun Kanade:
Statistical Windows in Testing for the Initial Distribution of a Reversible Markov Chain. CoRR abs/1808.01857 (2018) - [i19]David Martínez-Rubio, Varun Kanade, Patrick Rebeschini:
Decentralized Cooperative Stochastic Multi-armed Bandits. CoRR abs/1810.04468 (2018) - 2017
- [i18]Varun Kanade, Frederik Mallmann-Trenn, Victor Verdugo:
How large is your graph? CoRR abs/1702.03959 (2017) - [i17]Vincent Cohen-Addad, Varun Kanade, Frederik Mallmann-Trenn, Claire Mathieu:
Hierarchical Clustering: Objective Functions and Algorithms. CoRR abs/1704.02147 (2017) - [i16]Cheng Li, Felix Ming Fai Wong, Zhenming Liu, Varun Kanade:
From which world is your graph? CoRR abs/1711.00982 (2017) - 2016
- [i15]Vincent Cohen-Addad, Varun Kanade:
Online Optimization of Smoothed Piecewise Constant Functions. CoRR abs/1604.01999 (2016) - [i14]Varun Kanade, Frederik Mallmann-Trenn, Thomas Sauerwald:
On coalescence time in graphs-When is coalescing as fast as meeting? CoRR abs/1611.02460 (2016) - [i13]Surbhi Goel, Varun Kanade, Adam R. Klivans, Justin Thaler:
Reliably Learning the ReLU in Polynomial Time. CoRR abs/1611.10258 (2016) - 2015
- [i12]Steve Hanneke, Varun Kanade, Liu Yang:
Learning with a Drifting Target Concept. CoRR abs/1505.05215 (2015) - [i11]Varun Kanade, Nikos Leonardos, Frédéric Magniez:
Stable Matching with Evolving Preferences. CoRR abs/1509.01988 (2015) - 2014
- [i10]Varun Kanade, Justin Thaler:
Distribution-Independent Reliable Learning. CoRR abs/1402.5164 (2014) - [i9]Varun Kanade, Elchanan Mossel, Tselil Schramm:
Global and Local Information in Clustering Labeled Block Models. CoRR abs/1404.6325 (2014) - 2013
- [i8]Varun Kanade, Elchanan Mossel:
MCMC Learning. CoRR abs/1307.3617 (2013) - [i7]Elaine Angelino, Varun Kanade:
Attribute-Efficient Evolvability of Linear Functions. CoRR abs/1309.4132 (2013) - 2012
- [i6]Pranjal Awasthi, Varun Kanade:
Learning using Local Membership Queries under Smooth Distributions. CoRR abs/1211.0996 (2012) - [i5]Varun Kanade, Zhenming Liu, Bozidar Radunovic:
Distributed Non-Stochastic Experts. CoRR abs/1211.3212 (2012) - 2011
- [i4]Sham M. Kakade, Adam Tauman Kalai, Varun Kanade, Ohad Shamir:
Efficient Learning of Generalized Linear and Single Index Models with Isotonic Regression. CoRR abs/1104.2018 (2011) - [i3]Varun Kanade:
Computational Bottlenecks for Evolvability. Electron. Colloquium Comput. Complex. TR11 (2011) - [i2]Varun Kanade, Thomas Steinke:
Learning Hurdles for Sleeping Experts. Electron. Colloquium Comput. Complex. TR11 (2011) - 2010
- [i1]Varun Kanade, Leslie G. Valiant, Jennifer Wortman Vaughan:
Evolution with Drifting Targets. CoRR abs/1005.3566 (2010)
Coauthor Index
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