Search Results for author:
Found 17 papers, 5 papers with code
Reinforcement Learning of Active Vision for Manipulating Objects under Occlusions
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.
Simulation of Vision-based Tactile Sensors using Physics based Rendering
Tactile sensing has seen a rapid adoption with the advent of vision-based tactile sensors.
Robotics Graphics
Model Learning for Look-ahead Exploration in Continuous Control
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 .
Accelerated Spectral Ranking
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.
Rank Aggregation from Pairwise Comparisons in the Presence of Adversarial Corruptions
In this paper, we initiate the study of robustness in rank aggregation under the popular Bradley-Terry-Luce (BTL) model for pairwise comparisons.
Choice Bandits
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.
Machine learning models for prediction of droplet collision outcomes
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.
Batched Dueling Bandits
The $K$-armed dueling bandit problem, where the feedback is in the form of noisy pairwise comparisons, has been widely studied.
A Sharp Memory-Regret Trade-Off for Multi-Pass Streaming Bandits
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.
Sublinear Algorithms for Hierarchical Clustering
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.
An Asymptotically Optimal Batched Algorithm for the Dueling Bandit Problem
}$ We answer this in the affirmative $\textit{under the Condorcet condition}$, a standard setting of the $K$-armed dueling bandit problem.
Diversified Recommendations for Agents with Adaptive Preferences
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.
When Can We Track Significant Preference Shifts in Dueling Bandits?
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.
Online Recommendations for Agents with Discounted Adaptive Preferences
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.
Semi-Bandit Learning for Monotone Stochastic Optimization
Stochastic optimization is a widely used approach for optimization under uncertainty, where uncertain input parameters are modeled by random variables.
Misalignment, Learning, and Ranking: Harnessing Users Limited Attention
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).
Optimal and Adaptive Non-Stationary Dueling Bandits Under a Generalized Borda Criterion
In dueling bandits, the learner receives preference feedback between arms, and the regret of an arm is defined in terms of its suboptimality to a winner arm.
