no code implementations • ICML 2020 • Prathamesh Patil, Arpit Agarwal, Shivani Agarwal, Sanjeev Khanna
In this paper, we initiate the study of robustness in rank aggregation under the popular Bradley-Terry-Luce (BTL) model for pairwise comparisons.
no code implementations • 25 Sep 2024 • Akash Agrawal, Mayesh Mohapatra, Abhinav Raja, Paritosh Tiwari, Vishwajeet Pattanaik, Neeru Jaiswal, Arpit Agarwal, Punit Rathore
The current process for cyclone detection and intensity estimation involves physics-based simulation studies which are time-consuming, only using image features will automate the process for significantly faster and more accurate predictions.
no code implementations • 29 May 2024 • Arpit Agarwal, Nicolas Usunier, Alessandro Lazaric, Maximilian Nickel
In this paper we explore a new approach to recommender systems where we infer user utility based on their return probability to the platform rather than engagement signals.
no code implementations • 19 Mar 2024 • Joe Suk, Arpit Agarwal
The notion of regret here is tied to a notion of winner arm, most typically taken to be a so-called Condorcet winner or a Borda winner.
no code implementations • 21 Feb 2024 • Arpit Agarwal, Rad Niazadeh, Prathamesh Patil
Each user selects an item by first considering a prefix window of these ranked items and then picking the highest preferred item in that window (and the platform observes its payoff for this item).
no code implementations • 24 Dec 2023 • Arpit Agarwal, Rohan Ghuge, Viswanath Nagarajan
Stochastic optimization is a widely used approach for optimization under uncertainty, where uncertain input parameters are modeled by random variables.
1 code implementation • NeurIPS 2023 • Joe Suk, Arpit Agarwal
Specifically, we study the recent notion of significant shifts (Suk and Kpotufe, 2022), and ask whether one can design an adaptive algorithm for the dueling problem with $O(\sqrt{K\tilde{L}T})$ dynamic regret, where $\tilde{L}$ is the (unknown) number of significant shifts in preferences.
no code implementations • 12 Feb 2023 • Arpit Agarwal, William Brown
In each round, we show a menu of $k$ items (out of $n$ total) to the agent, who then chooses a single item, and we aim to minimize regret with respect to some $\textit{target set}$ (a subset of the item simplex) for adversarial losses over the agent's choices.
no code implementations • 25 Sep 2022 • Arpit Agarwal, Rohan Ghuge, Viswanath Nagarajan
}$ We answer this in the affirmative $\textit{under the Condorcet condition}$, a standard setting of the $K$-armed dueling bandit problem.
no code implementations • 20 Sep 2022 • Arpit Agarwal, William Brown
For this class, we give an algorithm for the Recommender which obtains $\tilde{O}(T^{3/4})$ regret against all item distributions satisfying two conditions: they are sufficiently diversified, and they are instantaneously realizable at any history by some distribution over menus.
no code implementations • 15 Jun 2022 • Arpit Agarwal, Sanjeev Khanna, Huan Li, Prathamesh Patil
At the heart of our algorithmic results is a view of the objective in terms of cuts in the graph, which allows us to use a relaxed notion of cut sparsifiers to do hierarchical clustering while introducing only a small distortion in the objective function.
no code implementations • 2 May 2022 • Arpit Agarwal, Sanjeev Khanna, Prathamesh Patil
In this paper we study the trade-off between memory and regret when $B$ passes over the stream are allowed, for any $B \geq 1$, and establish tight regret upper and lower bounds for any $B$-pass algorithm.
no code implementations • 22 Feb 2022 • Arpit Agarwal, Rohan Ghuge, Viswanath Nagarajan
The $K$-armed dueling bandit problem, where the feedback is in the form of noisy pairwise comparisons, has been widely studied.
no code implementations • 1 Oct 2021 • Arpit Agarwal
Another key question we try to answer in this paper is whether existing knowledge of the physics based models can be exploited to boost the accuracy of the ML classifiers.
1 code implementation • 24 Dec 2020 • Arpit Agarwal, Tim Man, Wenzhen Yuan
Tactile sensing has seen a rapid adoption with the advent of vision-based tactile sensors.
Robotics Graphics
no code implementations • NeurIPS 2020 • Arpit Agarwal, Nicholas Johnson, Shivani Agarwal
Here we study a natural generalization, that we term \emph{choice bandits}, where the learner plays a set of up to $k \geq 2$ arms and receives limited relative feedback in the form of a single multiway choice among the pulled arms, drawn from an underlying multiway choice model.
1 code implementation • 20 Nov 2018 • Ricson Cheng, Arpit Agarwal, Katerina Fragkiadaki
We propose hand/eye con-trollers that learn to move the camera to keep the object within the field of viewand visible, in coordination to manipulating it to achieve the desired goal, e. g., pushing it to a target location.
1 code implementation • 20 Nov 2018 • Arpit Agarwal, Katharina Muelling, Katerina Fragkiadaki
We propose an exploration method that incorporates look-ahead search over basic learnt skills and their dynamics, and use it for reinforcement learning (RL) of manipulation policies .
1 code implementation • ICML 2018 • Arpit Agarwal, Prathamesh Patil, Shivani Agarwal
In this paper, we design a provably faster spectral ranking algorithm, which we call accelerated spectral ranking (ASR), that is also consistent under the MNL/BTL models.