210 citations · 283 across the 20 of their papers we have counts for
10 papers · 1 filter
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…
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…
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…