Search Results for author: Karthik Abinav Sankararaman

Found 13 papers, 1 papers with code

Analyzing the effect of neural network architecture on training performance

no code implementations ICML 2020 Karthik Abinav Sankararaman, Soham De, Zheng Xu, W. Ronny Huang, Tom Goldstein

Through novel theoretical and experimental results, we show how the neural net architecture affects gradient confusion, and thus the efficiency of training.

Bandits with Knapsacks beyond the Worst Case

no code implementations NeurIPS 2021 Karthik Abinav Sankararaman, Aleksandrs Slivkins

Third, we provide a "generalreduction" from BwK to bandits which takes advantage of some known helpful structure, and apply this reduction to combinatorial semi-bandits, linear contextual bandits, and multinomial-logit bandits.

Multi-Armed Bandits

Stochastic Bandits for Multi-platform Budget Optimization in Online Advertising

no code implementations16 Mar 2021 Vashist Avadhanula, Riccardo Colini-Baldeschi, Stefano Leonardi, Karthik Abinav Sankararaman, Okke Schrijvers

We modify the algorithm proposed in Badanidiyuru \emph{et al.,} to extend it to the case of multiple platforms to obtain an algorithm for both the discrete and continuous bid-spaces.

Beyond $\log^2(T)$ Regret for Decentralized Bandits in Matching Markets

no code implementations12 Mar 2021 Soumya Basu, Karthik Abinav Sankararaman, Abishek Sankararaman

We design decentralized algorithms for regret minimization in the two-sided matching market with one-sided bandit feedback that significantly improves upon the prior works (Liu et al. 2020a, 2020b, Sankararaman et al. 2020).

Robust Identifiability in Linear Structural Equation Models of Causal Inference

no code implementations14 Jul 2020 Karthik Abinav Sankararaman, Anand Louis, Navin Goyal

First, for a large and well-studied class of LSEMs, namely ``bow free'' models, we provide a sufficient condition on model parameters under which robust identifiability holds, thereby removing the restriction of paths required by prior work.

Causal Inference

Dominate or Delete: Decentralized Competing Bandits in Serial Dictatorship

no code implementations26 Jun 2020 Abishek Sankararaman, Soumya Basu, Karthik Abinav Sankararaman

Online learning in a two-sided matching market, with demand side agents continuously competing to be matched with supply side (arms), abstracts the complex interactions under partial information on matching platforms (e. g. UpWork, TaskRabbit).

online learning

Bandits with Knapsacks beyond the Worst-Case

no code implementations1 Feb 2020 Karthik Abinav Sankararaman, Aleksandrs Slivkins

Third, we provide a general "reduction" from BwK to bandits which takes advantage of some known helpful structure, and apply this reduction to combinatorial semi-bandits, linear contextual bandits, and multinomial-logit bandits.

Multi-Armed Bandits

Balancing the Tradeoff between Profit and Fairness in Rideshare Platforms During High-Demand Hours

1 code implementation18 Dec 2019 Vedant Nanda, Pan Xu, Karthik Abinav Sankararaman, John P. Dickerson, Aravind Srinivasan

Moreover, if in such a scenario, the assignment of requests to drivers (by the platform) is made only to maximize profit and/or minimize wait time for riders, requests of a certain type (e. g. from a non-popular pickup location, or to a non-popular drop-off location) might never be assigned to a driver.

Fairness

Stability of Linear Structural Equation Models of Causal Inference

no code implementations16 May 2019 Karthik Abinav Sankararaman, Anand Louis, Navin Goyal

First we prove that under a sufficient condition, for a certain sub-class of $\LSEM$ that are \emph{bow-free} (Brito and Pearl (2002)), the parameter recovery is stable.

Causal Inference

Adversarial Bandits with Knapsacks

no code implementations28 Nov 2018 Nicole Immorlica, Karthik Abinav Sankararaman, Robert Schapire, Aleksandrs Slivkins

We suggest a new algorithm for the stochastic version, which builds on the framework of regret minimization in repeated games and admits a substantially simpler analysis compared to prior work.

Multi-Armed Bandits

Attenuate Locally, Win Globally: An Attenuation-based Framework for Online Stochastic Matching with Timeouts

no code implementations22 Apr 2018 Brian Brubach, Karthik Abinav Sankararaman, Aravind Srinivasan, Pan Xu

On the upper bound side, we show that this framework, combined with a black-box adapted from Bansal et al., (Algorithmica, 2012), yields an online algorithm which nearly doubles the ratio to 0. 46.

Combinatorial Semi-Bandits with Knapsacks

no code implementations23 May 2017 Karthik Abinav Sankararaman, Aleksandrs Slivkins

We unify two prominent lines of work on multi-armed bandits: bandits with knapsacks (BwK) and combinatorial semi-bandits.

Multi-Armed Bandits

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