210 citations · 283 across the 20 of their papers we have counts for
22 papers
Model Monotonicity in Autobidding Auctions: When Do Better Predictions Lead to Better Outcomes?
Ashwinkumar Badanidiyuru
Online advertising platforms rely on machine learning models to predict click-through rates (pCTR) and conversion rates (pCVR) for auction mechanisms. We introduce a novel framewor…
Selling Joint Ads: A Regret Minimization Perspective
Gagan Aggarwal, Ashwinkumar Badanidiyuru, Paul Dütting +1
Motivated by online retail, we consider the problem of selling one item (e.g., an ad slot) to two non-excludable buyers (say, a merchant and a brand). This problem captures, for ex…
Auto-bidding and Auctions in Online Advertising: A Survey
Gagan Aggarwal, Ashwinkumar Badanidiyuru, Santiago R. Balseiro +23
In this survey, we summarize recent developments in research fueled by the growing adoption of automated bidding strategies in online advertising. We explore the challenges and opp…
Learning from Aggregate responses: Instance Level versus Bag Level Loss Functions
Adel Javanmard, Lin Chen, Vahab Mirrokni +2
Due to the rise of privacy concerns, in many practical applications the training data is aggregated before being shared with the learner, in order to protect privacy of users' sens…
Optimal Unbiased Randomizers for Regression with Label Differential Privacy
Ashwinkumar Badanidiyuru, Badih Ghazi, Pritish Kamath +5
We propose a new family of label randomizers for training regression models under the constraint of label differential privacy (DP). In particular, we leverage the trade-offs betwe…
Follow-ups Also Matter: Improving Contextual Bandits via Post-serving Contexts
Chaoqi Wang, Ziyu Ye, Zhe Feng +2
Standard contextual bandit problem assumes that all the relevant contexts are observed before the algorithm chooses an arm. This modeling paradigm, while useful, often falls short…
Incrementality Bidding via Reinforcement Learning under Mixed and Delayed Rewards
Ashwinkumar Badanidiyuru, Zhe Feng, Tianxi Li +1
Incrementality, which is used to measure the causal effect of showing an ad to a potential customer (e.g. a user in an internet platform) versus not, is a central object for advert…
Auctioning with Strategically Reticent Bidders
Jibang Wu, Ashwinkumar Badanidiyuru, Haifeng Xu
We propose and study a novel mechanism design setup where each bidder holds two kinds of private information: (1) type variable, which can be misreported; (2) information variable,…
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…
Online Learning via Offline Greedy Algorithms: Applications in Market Design and Optimization
Rad Niazadeh, Negin Golrezaei, Joshua Wang +2
Motivated by online decision-making in time-varying combinatorial environments, we study the problem of transforming offline algorithms to their online counterparts. We focus on of…
Handling many conversions per click in modeling delayed feedback
Ashwinkumar Badanidiyuru, Andrew Evdokimov, Vinodh Krishnan +3
Predicting the expected value or number of post-click conversions (purchases or other events) is a key task in performance-based digital advertising. In training a conversion optim…
Submodular Maximization Through Barrier Functions
Ashwinkumar Badanidiyuru, Amin Karbasi, Ehsan Kazemi +1
In this paper, we introduce a novel technique for constrained submodular maximization, inspired by barrier functions in continuous optimization. This connection not only improves t…
Response Prediction for Low-Regret Agents
Saeed Alaei, Ashwinkumar Badanidiyuru, Mohammad Mahdian +1
Companies like Google and Microsoft run billions of auctions every day to sell advertising opportunities. Any change to the rules of these auctions can have a tremendous effect on…
Targeting and Signaling in Ad Auctions
Ashwinkumar Badanidiyuru, Kshipra Bhawalkar, Haifeng Xu
Modern ad auctions allow advertisers to target more specific segments of the user population. Unfortunately, this is not always in the best interest of the ad platform. In this pap…
Locally Adaptive Optimization: Adaptive Seeding for Monotone Submodular Functions
Ashwinkumar Badanidiyuru, Christos Papadimitriou, Aviad Rubinstein +2
The Adaptive Seeding problem is an algorithmic challenge motivated by influence maximization in social networks: One seeks to select among certain accessible nodes in a network, an…
Lazier Than Lazy Greedy
Baharan Mirzasoleiman, Ashwinkumar Badanidiyuru, Amin Karbasi +2
Is it possible to maximize a monotone submodular function faster than the widely used lazy greedy algorithm (also known as accelerated greedy), both in theory and practice? In this…
Resourceful Contextual Bandits
Ashwinkumar Badanidiyuru, John Langford, Aleksandrs Slivkins
We study contextual bandits with ancillary constraints on resources, which are common in real-world applications such as choosing ads or dynamic pricing of items. We design the fir…
Bandits with Knapsacks
Ashwinkumar Badanidiyuru, Robert Kleinberg, Aleksandrs Slivkins
Multi-armed bandit problems are the predominant theoretical model of exploration-exploitation tradeoffs in learning, and they have countless applications ranging from medical trial…
Approximating Low-Dimensional Coverage Problems
Ashwinkumar Badanidiyuru, Robert Kleinberg, Hooyeon Lee
We study the complexity of the maximum coverage problem, restricted to set systems of bounded VC-dimension. Our main result is a fixed-parameter tractable approximation scheme: an…
Optimization with Demand Oracles
Ashwinkumar Badanidiyuru, Shahar Dobzinski, Sigal Oren
We study \emph{combinatorial procurement auctions}, where a buyer with a valuation function and budget wishes to buy a set of items. Each item has a cost and the…
Buyback Problem - Approximate matroid intersection with cancellation costs
B. V. Ashwinkumar
In the buyback problem, an algorithm observes a sequence of bids and must decide whether to accept each bid at the moment it arrives, subject to some constraints on the set of acce…
Randomized Online Algorithms for the Buyback Problem
Ashwinkumar B. V., Robert Kleinberg
In the matroid buyback problem, an algorithm observes a sequence of bids and must decide whether to accept each bid at the moment it arrives, subject to a matroid constraint on the…