Life (and routing) on the Wireless Manifold. - Varun Kanade; Santosh S. Vempala (HotNets, 2007)

SWARM: A Parallel Programming Framework for Multicore Processors. - David A. Bader; Varun Kanade; Kamesh Madduri (IPDPS, 2007)

Potential-Based Agnostic Boosting. - Adam Kalai; Varun Kanade (NIPS, 2009)

Sleeping Experts and Bandits with Stochastic Action Availability and Adversarial Rewards. - Varun Kanade; H. Brendan McMahan; Brent Bryan (AISTATS, 2009)

Evolution with Drifting Targets. - Varun Kanade; Leslie G. Valiant; Jennifer Wortman Vaughan (COLT, 2010)

Evolution with Recombination. - Varun Kanade (FOCS, 2011)

Efficient Learning of Generalized Linear and Single Index Models with Isotonic Regression. - Sham M. Kakade; Adam Kalai; Varun Kanade; Ohad Shamir (NIPS, 2011)

Computational Bottlenecks for Evolvability. - Varun Kanade (Electron. Colloquium Comput. Complex., 2011)

Learning Hurdles for Sleeping Experts. - Varun Kanade; Thomas Steinke 002 (Electron. Colloquium Comput. Complex., 2011)

Distributed Non-Stochastic Experts. - Varun Kanade; Zhenming Liu; Bozidar Radunovic (NIPS, 2012)

Learning using Local Membership Queries under Smooth Distributions - Pranjal Awasthi; Varun Kanade (CoRR, 2012)

Reliable agnostic learning. - Adam Tauman Kalai; Varun Kanade; Yishay Mansour (J. Comput. Syst. Sci., 2012)

Computational Bounds on Statistical Query Learning. - Vitaly Feldman; Varun Kanade (COLT, 2012)

Learning Using Local Membership Queries. - Pranjal Awasthi; Vitaly Feldman; Varun Kanade (COLT, 2013)

Online Learning in Markov Decision Processes with Adversarially Chosen Transition Probability Distributions. - Yasin Abbasi-Yadkori; Peter L. Bartlett; Varun Kanade; Yevgeny Seldin; Csaba Szepesvári (NIPS, 2013)

Distribution-independent Reliable Learning. - Varun Kanade; Justin Thaler (COLT, 2014)

Tracking Adversarial Targets. - Yasin Abbasi-Yadkori; Peter L. Bartlett; Varun Kanade (ICML, 2014)

Attribute-efficient evolvability of linear functions. - Elaine Angelino; Varun Kanade (ITCS, 2014)

Learning with a Drifting Target Concept. - Steve Hanneke; Varun Kanade; Liu Yang 001 (ALT, 2015)

MCMC Learning. - Varun Kanade; Elchanan Mossel (COLT, 2015)

Stable Matching with Evolving Preferences. - Varun Kanade; Nikos Leonardos; Frédéric Magniez (APPROX-RANDOM, 2016)

Distance in the Forest Fire Model How far are you from Eve? - Varun Kanade; Reut Levi; Zvi Lotker; Frederik Mallmann-Trenn; Claire Mathieu (SODA, 2016)

Global and Local Information in Clustering Labeled Block Models. - Varun Kanade; Elchanan Mossel; Tselil Schramm (IEEE Trans. Inf. Theory, 2016)

Online Optimization of Smoothed Piecewise Constant Functions. - Vincent Cohen-Addad; Varun Kanade (AISTATS, 2017)

Reliably Learning the ReLU in Polynomial Time. - Surbhi Goel; Varun Kanade; Adam R. Klivans; Justin Thaler (COLT, 2017)

Hierarchical Clustering Beyond the Worst-Case. - Vincent Cohen-Addad; Varun Kanade; Frederik Mallmann-Trenn (NIPS, 2017)

From which world is your graph. - Cheng Li; Felix Ming Fai Wong; Zhenming Liu; Varun Kanade (NIPS, 2017)

Brief Announcement: How Large is your Graph? - Varun Kanade; Frederik Mallmann-Trenn; Victor Verdugo (PODC, 2017)

How Large Is Your Graph?. - Varun Kanade; Frederik Mallmann-Trenn; Victor Verdugo (DISC, 2017)

TAPAS: Tricks to Accelerate (encrypted) Prediction As a Service. - Amartya Sanyal; Matt J. Kusner; Adrià Gascón; Varun Kanade (ICML, 2018)

Clustering Redemption-Beyond the Impossibility of Kleinberg's Axioms. - Vincent Cohen-Addad; Varun Kanade; Frederik Mallmann-Trenn (NeurIPS, 2018)

Learning DNFs under product distributions via μ-biased quantum Fourier sampling. - Varun Kanade; Andrea Rocchetto; Simone Severini (CoRR, 2018)

Low Rank Structure of Learned Representations. - Amartya Sanyal; Varun Kanade; Philip H. S. Torr (CoRR, 2018)

Decentralized Cooperative Stochastic Multi-armed Bandits. - David Martínez-Rubio; Varun Kanade; Patrick Rebeschini (CoRR, 2018)

Statistical Windows in Testing for the Initial Distribution of a Reversible Markov Chain. - Quentin Berthet; Varun Kanade (AISTATS, 2019)

Decentralized Cooperative Stochastic Bandits. - David Martínez-Rubio; Varun Kanade; Patrick Rebeschini (NeurIPS, 2019)

Implicit Regularization for Optimal Sparse Recovery. - Tomas Vaskevicius; Varun Kanade; Patrick Rebeschini (NeurIPS, 2019)

On coalescence time in graphs: When is coalescing as fast as meeting?: Extended Abstract. - Varun Kanade; Frederik Mallmann-Trenn; Thomas Sauerwald (SODA, 2019)

On the Hardness of Robust Classification. - Pascale Gourdeau; Varun Kanade; Marta Kwiatkowska; James Worrell 001 (Electron. Colloquium Comput. Complex., 2019)

Hierarchical Clustering: Objective Functions and Algorithms. - Vincent Cohen-Addad; Varun Kanade; Frederik Mallmann-Trenn; Claire Mathieu (J. ACM, 2019)

mu - biased quantum Fourier sampling. - Varun Kanade; Andrea Rocchetto; Simone Severini (Quantum Inf. Comput., 2019)

Differentiable Causal Backdoor Discovery. - Limor Gultchin; Matt J. Kusner; Varun Kanade; Ricardo Silva 001 (AISTATS, 2020)

Adaptive Reduced Rank Regression. - Qiong Wu 008; Felix Ming Fai Wong; Yanhua Li; Zhenming Liu; Varun Kanade (NeurIPS, 2020)

The Statistical Complexity of Early-Stopped Mirror Descent. - Tomas Vaskevicius; Varun Kanade; Patrick Rebeschini (NeurIPS, 2020)

Lottery Tickets in Linear Models: An Analysis of Iterative Magnitude Pruning. - Bryn Elesedy; Varun Kanade; Yee Whye Teh (CoRR, 2020)

Online k-means Clustering. - Vincent Cohen-Addad; Benjamin Guedj; Varun Kanade; Guy Rom (AISTATS, 2021)

Efficient Learning with Arbitrary Covariate Shift. - Adam Tauman Kalai; Varun Kanade (ALT, 2021)

How Benign is Benign Overfitting ? - Amartya Sanyal; Puneet K. Dokania; Varun Kanade; Philip H. S. Torr (ICLR, 2021)

Towards optimally abstaining from prediction with OOD test examples. - Adam Kalai; Varun Kanade (NeurIPS, 2021)

Towards optimally abstaining from prediction. - Adam Tauman Kalai; Varun Kanade (CoRR, 2021)

Sample Complexity Bounds for Robustly Learning Decision Lists against Evasion Attacks. - Pascale Gourdeau; Varun Kanade; Marta Kwiatkowska; James Worrell 001 (IJCAI, 2022)

When are Local Queries Useful for Robust Learning? - Pascale Gourdeau; Varun Kanade; Marta Kwiatkowska; James Worrell 001 (NeurIPS, 2022)

Exponential Tail Local Rademacher Complexity Risk Bounds Without the Bernstein Condition. - Varun Kanade; Patrick Rebeschini; Tomas Vaskevicius (CoRR, 2022)

Beyond Impossibility: Balancing Sufficiency, Separation and Accuracy. - Limor Gultchin; Vincent Cohen-Addad; Sophie Giffard-Roisin; Varun Kanade; Frederik Mallmann-Trenn (CoRR, 2022)

Partial Matrix Completion. - Varun Kanade; Elad Hazan; Adam Tauman Kalai (CoRR, 2022)

Simplicity Bias in Transformers and their Ability to Learn Sparse Boolean Functions. - Satwik Bhattamishra; Arkil Patel; Varun Kanade; Phil Blunsom (ACL (1), 2023)

Partial Matrix Completion. - Elad Hazan; Adam Tauman Kalai; Varun Kanade; Clara Mohri; Y. Jennifer Sun (NeurIPS, 2023)

On Coalescence Time in Graphs: When Is Coalescing as Fast as Meeting? - Varun Kanade; Frederik Mallmann-Trenn; Thomas Sauerwald (ACM Trans. Algorithms, 2023)

Understanding In-Context Learning in Transformers and LLMs by Learning to Learn Discrete Functions. - Satwik Bhattamishra; Arkil Patel; Phil Blunsom; Varun Kanade (ICLR, 2024)

Separations in the Representational Capabilities of Transformers and Recurrent Architectures. - Satwik Bhattamishra; Michael Hahn 001; Phil Blunsom; Varun Kanade (NeurIPS, 2024)

How Global Calibration Strengthens Multiaccuracy. - Sílvia Casacuberta; Parikshit Gopalan; Varun Kanade; Omer Reingold (CoRR, 2025)

Pause Tokens Strictly Increase the Expressivity of Constant-Depth Transformers. - Charles London; Varun Kanade (CoRR, 2025)