Improved Region-Growing and Combinatorial Algorithms for $k$-Route Cut Problems. - Guru Guruganesh; Laura Sanità; Chaitanya Swamy (CoRR, 2014)

Improved Region-Growing and Combinatorial Algorithms for k-Route Cut Problems (Extended Abstract). - Guru Guruganesh; Laura Sanità; Chaitanya Swamy (SODA, 2015)

Approximation Algorithms for Aversion k-Clustering via Local k-Median. - Anupam Gupta 001; Guru Guruganesh; Melanie Schmidt 001 (ICALP, 2016)

Single-Sink Fractionally Subadditive Network Design. - Guru Guruganesh; Jennifer Iglesias; R. Ravi 001; Laura Sanità (ESA, 2017)

Understanding the Correlation Gap For Matchings. - Guru Guruganesh; Euiwoong Lee (FSTTCS, 2017)

Fully-Dynamic Bin Packing with Limited Repacking. - Anupam Gupta 001; Guru Guruganesh; Amit Kumar 001; David Wajc (CoRR, 2017)

Fully-Dynamic Bin Packing with Little Repacking. - Björn Feldkord; Matthias Feldotto; Anupam Gupta 001; Guru Guruganesh; Amit Kumar 001; Sören Riechers; David Wajc (ICALP, 2018)

On the Lovász Theta Function for Independent Sets in Sparse Graphs. - Nikhil Bansal 001; Anupam Gupta 001; Guru Guruganesh (SIAM J. Comput., 2018)

Stochastic Online Metric Matching. - Anupam Gupta 001; Guru Guruganesh; Binghui Peng; David Wajc (ICALP, 2019)

Dimension-Free Bounds for Chasing Convex Functions. - C. J. Argue; Anupam Gupta 001; Guru Guruganesh (COLT, 2020)

Big Bird: Transformers for Longer Sequences. - Manzil Zaheer; Guru Guruganesh; Kumar Avinava Dubey; Joshua Ainslie; Chris Alberti; Santiago Ontañón; Philip Pham; Anirudh Ravula; Qifan Wang 001; Li Yang; Amr Ahmed 001 (NeurIPS, 2020)

Scalable Bottom-Up Hierarchical Clustering. - Nicholas Monath; Avinava Dubey; Guru Guruganesh; Manzil Zaheer; Amr Ahmed 001; Andrew McCallum; Gökhan Mergen; Marc Najork; Mert Terzihan; Bryon Tjanaka; Yuan Wang; Yuchen Wu (CoRR, 2020)

Convergence Analysis of No-Regret Bidding Algorithms in Repeated Auctions. - Zhe Feng 004; Guru Guruganesh; Christopher Liaw; Aranyak Mehta; Abhishek Sethi (AAAI, 2021)

Scalable Hierarchical Agglomerative Clustering. - Nicholas Monath; Kumar Avinava Dubey; Guru Guruganesh; Manzil Zaheer; Amr Ahmed 001; Andrew McCallum; Gökhan Mergen; Marc Najork; Mert Terzihan; Bryon Tjanaka; Yuan Wang; Yuchen Wu (KDD, 2021)

Contextual Recommendations and Low-Regret Cutting-Plane Algorithms. - Sreenivas Gollapudi; Guru Guruganesh; Kostas Kollias; Pasin Manurangsi; Renato Paes Leme; Jon Schneider (NeurIPS, 2021)

Margin-Independent Online Multiclass Learning via Convex Geometry. - Guru Guruganesh; Allen Liu; Jon Schneider; Joshua R. Wang (NeurIPS, 2021)

Contracts under Moral Hazard and Adverse Selection. - Guru Guruganesh; Jon Schneider; Joshua R. Wang (EC, 2021)

Chasing convex bodies with linear competitive ratio (invited paper). - C. J. Argue; Anupam Gupta 001; Guru Guruganesh; Ziye Tang (STOC, 2021)

Chasing Convex Bodies with Linear Competitive Ratio. - C. J. Argue; Anupam Gupta 001; Ziye Tang; Guru Guruganesh (J. ACM, 2021)

Maximizing Revenue in the Presence of Intermediaries. - Gagan Aggarwal; Kshipra Bhawalkar; Guru Guruganesh; Andrés Perlroth (ITCS, 2022)

A Fourier Approach to Mixture Learning. - Mingda Qiao; Guru Guruganesh; Ankit Singh Rawat; Kumar Avinava Dubey; Manzil Zaheer (NeurIPS, 2022)

Research Challenges in Internet Ad Markets: Vignettes on Complex Environments. - Kshipra Bhawalkar; Guru Guruganesh; Sébastien Lahaie; Andrés Perlroth; Balasubramanian Sivan (SIGecom Exch., 2022)

Sticky Brownian Rounding and its Applications to Constraint Satisfaction Problems. - Sepehr Abbasi Zadeh; Nikhil Bansal 001; Guru Guruganesh; Aleksandar Nikolov; Roy Schwartz 002; Mohit Singh (ACM Trans. Algorithms, 2022)

Optimal No-Regret Learning for One-Sided Lipschitz Functions. - Paul Duetting; Guru Guruganesh; Jon Schneider; Joshua Ruizhi Wang (ICML, 2023)

The Power of Menus in Contract Design. - Guru Guruganesh; Jon Schneider; Joshua R. Wang; Junyao Zhao 001 (EC, 2023)

Learning to Bid in Contextual First Price Auctions✱. - Ashwinkumar Badanidiyuru; Zhe Feng 004; Guru Guruganesh (WWW, 2023)

Functional Interpolation for Relative Positions improves Long Context Transformers. - Shanda Li; Chong You; Guru Guruganesh; Joshua Ainslie; Santiago Ontañón; Manzil Zaheer; Sumit Sanghai; Yiming Yang; Sanjiv Kumar; Srinadh Bhojanapalli (ICLR, 2024)

Contracting with a Learning Agent. - Guru Guruganesh; Yoav Kolumbus; Jon Schneider; Inbal Talgam-Cohen; Emmanouil-Vasileios Vlatakis-Gkaragkounis; Joshua R. Wang; S. Matthew Weinberg (NeurIPS, 2024)

Prior-Independent Auctions for Heterogeneous Bidders. - Guru Guruganesh; Aranyak Mehta; Di Wang 005; Kangning Wang 001 (SODA, 2024)

Prior-Free Mechanism with Welfare Guarantees. - Guru Guruganesh; Jon Schneider; Joshua R. Wang (WWW, 2024)