269 citations · 337 across the 21 of their papers we have counts for
26 papers
Optimal and Efficient Online Inverse Optimization
Anupam Gupta, Guru Guruganesh, Honghao Lin +3
In online inverse linear optimization, a learner recommends an action and then observes the choice of an expert who maximizes a fixed, unknown linear objective on ;…
Efficient Online Inverse Optimization with Regret
Yang Cai, Anupam Gupta, Vineet Gupta +7
We give a deterministic algorithm for online inverse linear optimization with regret , uniform in the horizon and time per round. A bound of this order was obtaine…
Gemma 4 Technical Report
Gemma Team, Sherif El Abd, Vaibhav Aggarwal +320
We introduce Gemma 4, a new generation of open-weight, natively multimodal language models in the Gemma model family. Designed to advance compute efficiency and reasoning, the Gemm…
Combinatorial Optimization using Comparison Oracles
Vincent Cohen-Addad, Tommaso d'Orsi, Anupam Gupta +7
In linear combinatorial optimization, we aim to find for a family over a ground set…
Contracting with a Learning Agent
Guru Guruganesh, Yoav Kolumbus, Jon Schneider +4
Many real-life contractual relations differ completely from the clean, static model at the heart of principal-agent theory. Typically, they involve repeated strategic interactions…
Functional Interpolation for Relative Positions Improves Long Context Transformers
Shanda Li, Chong You, Guru Guruganesh +7
Preventing the performance decay of Transformers on inputs longer than those used for training has been an important challenge in extending the context length of these models. Thou…
The Power of Menus in Contract Design
Guru Guruganesh, Jon Schneider, Joshua Wang +1
We study the power of menus of contracts in principal-agent problems with adverse selection (agents can be one of several types) and moral hazard (we cannot observe agent actions d…
A Fourier Approach to Mixture Learning
Mingda Qiao, Guru Guruganesh, Ankit Singh Rawat +2
We revisit the problem of learning mixtures of spherical Gaussians. Given samples from mixture , the goal is to estimate the means $…
Prior-Independent Auctions for Heterogeneous Bidders
Guru Guruganesh, Aranyak Mehta, Di Wang +1
We study the design of prior-independent auctions in a setting with heterogeneous bidders. In particular, we consider the setting of selling to bidders whose values are drawn f…
Maximizing revenue in the presence of intermediaries
Gagan Aggarwal, Kshipra Bhawalkar, Guru Guruganesh +1
We study the mechanism design problem of selling items to unit-demand buyers with private valuations for the items. A buyer either participates directly in the auction or is re…
Margin-Independent Online Multiclass Learning via Convex Geometry
Guru Guruganesh, Allen Liu, Jon Schneider +1
We consider the problem of multi-class classification, where a stream of adversarially chosen queries arrive and must be assigned a label online. Unlike traditional bounds which se…
Learning to Bid in Contextual First Price Auctions
Ashwinkumar Badanidiyuru, Zhe Feng, Guru Guruganesh
In this paper, we investigate the problem about how to bid in repeated contextual first price auctions. We consider a single bidder (learner) who repeatedly bids in the first price…
Contextual Recommendations and Low-Regret Cutting-Plane Algorithms
Sreenivas Gollapudi, Guru Guruganesh, Kostas Kollias +3
We consider the following variant of contextual linear bandits motivated by routing applications in navigational engines and recommendation systems. We wish to learn a hidden -d…
Contracts under Moral Hazard and Adverse Selection
Guru Guruganesh, Jon Schneider, Joshua Wang
In the classical principal-agent problem, a principal must design a contract to incentivize an agent to perform an action on behalf of the principal. We study the classical princip…
Scalable Hierarchical Agglomerative Clustering
Nicholas Monath, Avinava Dubey, Guru Guruganesh +9
The applicability of agglomerative clustering, for inferring both hierarchical and flat clustering, is limited by its scalability. Existing scalable hierarchical clustering methods…
Convergence Analysis of No-Regret Bidding Algorithms in Repeated Auctions
Zhe Feng, Guru Guruganesh, Christopher Liaw +2
The connection between games and no-regret algorithms has been widely studied in the literature. A fundamental result is that when all players play no-regret strategies, this produ…
Big Bird: Transformers for Longer Sequences
Manzil Zaheer, Guru Guruganesh, Avinava Dubey +8
Transformers-based models, such as BERT, have been one of the most successful deep learning models for NLP. Unfortunately, one of their core limitations is the quadratic dependency…
Dimension-Free Bounds on Chasing Convex Functions
C. J. Argue, Anupam Gupta, Guru Guruganesh
We consider the problem of chasing convex functions, where functions arrive over time. The player takes actions after seeing the function, and the goal is to achieve a small functi…
Chasing Convex Bodies with Linear Competitive Ratio
C. J. Argue, Anupam Gupta, Guru Guruganesh +1
We study the problem of chasing convex bodies online: given a sequence of convex bodies the algorithm must respond with points in an online…
Stochastic Online Metric Matching
Anupam Gupta, Guru Guruganesh, Binghui Peng +1
We study the minimum-cost metric perfect matching problem under online i.i.d arrivals. We are given a fixed metric with a server at each of the points, and then requests arrive onl…
Sticky Brownian Rounding and its Applications to Constraint Satisfaction Problems
Sepehr Abbasi-Zadeh, Nikhil Bansal, Guru Guruganesh +3
Semidefinite programming is a powerful tool in the design and analysis of approximation algorithms for combinatorial optimization problems. In particular, the random hyperplane rou…
Fully-Dynamic Bin Packing with Limited Repacking
Anupam Gupta, Guru Guruganesh, Amit Kumar +1
We study the classic Bin Packing problem in a fully-dynamic setting, where new items can arrive and old items may depart. We want algorithms with low asymptotic competitive ratio \…
Understanding the Correlation Gap for Matchings
Guru Guruganesh, Euiwoong Lee
Given a set of vertices with , a weight vector , and a probability vector in the matchi…
Single-sink Fractionally Subadditive Network Design
Guru Guruganesh, Jennifer Iglesias, R. Ravi +1
We study a generalization of the Steiner tree problem, where we are given a weighted network together with a collection of subsets of its vertices and a root . We wish t…