no code implementations • 25 Jan 2024 • Saumya Saxena, Mohit Sharma, Oliver Kroemer
Leveraging sensing modalities across diverse spatial and temporal resolutions can improve performance of robotic manipulation tasks.
no code implementations • 16 Dec 2023 • Mohit Sharma, Amit Deshpande
We further generalize it to arbitrary data distributions and arbitrary hypothesis classes, i. e., we prove that for any data distribution, if the optimally accurate classifier in a given hypothesis class is fair and robust, then it can be recovered through fair classification with equal opportunity constraints on the biased distribution whenever the bias parameters satisfy certain simple conditions.
no code implementations • 5 Sep 2023 • Homanga Bharadhwaj, Jay Vakil, Mohit Sharma, Abhinav Gupta, Shubham Tulsiani, Vikash Kumar
The grand aim of having a single robot that can manipulate arbitrary objects in diverse settings is at odds with the paucity of robotics datasets.
no code implementations • 13 Apr 2023 • Mohit Sharma, Claudio Fantacci, Yuxiang Zhou, Skanda Koppula, Nicolas Heess, Jon Scholz, Yusuf Aytar
We demonstrate that appropriate placement of our parameter efficient adapters can significantly reduce the performance gap between frozen pretrained representations and full end-to-end fine-tuning without changes to the original representation and thus preserving original capabilities of the pretrained model.
1 code implementation • 12 Feb 2023 • Mohit Sharma, Amit Deshpande, Rajiv Ratn Shah
In this paper, we consider a theoretical model for injecting data bias, namely, under-representation and label bias (Blum & Stangl, 2019).
1 code implementation • 13 Oct 2021 • Mohit Sharma, Raj Patra, Harshal Desai, Shruti Vyas, Yogesh Rawat, Rajiv Ratn Shah
We present this as a benchmark dataset in noisy learning for video understanding.
no code implementations • 18 Mar 2021 • Mohit Sharma, Oliver Kroemer
We empirically evaluate our approach on multiple different manipulation tasks and show its ability to generalize to large variance in object size, shape and geometry.
no code implementations • 4 Mar 2021 • Navyata Sanghvi, Shinnosuke Usami, Mohit Sharma, Joachim Groeger, Kris Kitani
Various methods for solving the inverse reinforcement learning (IRL) problem have been developed independently in machine learning and economics.
no code implementations • 15 Dec 2020 • Saurav Manchanda, Mohit Sharma, George Karypis
Slot-filling refers to the task of annotating individual terms in a query with the corresponding intended product characteristics (product type, brand, gender, size, color, etc.).
no code implementations • 3 Dec 2020 • Mohit Sharma, Oliver Kroemer
Our work is motivated by the intuition that many complex manipulation tasks, with multiple objects, can be simplified by focusing on less complex pairwise object relations.
no code implementations • 9 Nov 2020 • Mohit Sharma, Jacky Liang, Jialiang Zhao, Alex LaGrassa, Oliver Kroemer
Manipulation tasks can often be decomposed into multiple subtasks performed in parallel, e. g., sliding an object to a goal pose while maintaining contact with a table.
no code implementations • 27 Sep 2019 • Kevin Zhang, Mohit Sharma, Manuela Veloso, Oliver Kroemer
In this paper, we propose using vibrations and force-torque feedback from the interactions to adapt the slicing motions and monitor for contact events.
no code implementations • 25 Sep 2019 • Nirbhay Modhe, Prithvijit Chattopadhyay, Mohit Sharma, Abhishek Das, Devi Parikh, Dhruv Batra, Ramakrishna Vedantam
We learn to identify decision states, namely the parsimonious set of states where decisions meaningfully affect the future states an agent can reach in an environment.
1 code implementation • 22 Aug 2019 • Saurav Manchanda, Mohit Sharma, George Karypis
Moreover, for the tasks of identifying the important terms in a query and for predicting the additional terms that represent product intent, experiments illustrate that our approaches outperform the non-contextual baselines.
no code implementations • 24 Jul 2019 • Nirbhay Modhe, Prithvijit Chattopadhyay, Mohit Sharma, Abhishek Das, Devi Parikh, Dhruv Batra, Ramakrishna Vedantam
We propose a novel framework to identify sub-goals useful for exploration in sequential decision making tasks under partial observability.
1 code implementation • 9 Jul 2019 • Siddharth Gururani, Mohit Sharma, Alexander Lerch
While the automatic recognition of musical instruments has seen significant progress, the task is still considered hard for music featuring multiple instruments as opposed to single instrument recordings.
no code implementations • 22 Apr 2019 • Mohit Sharma, F. Maxwell Harper, George Karypis
Our analysis of these ratings shows that though the majority of the users provide the average of the ratings on a set's constituent items as the rating on the set, there exists a significant number of users that tend to consistently either under- or over-rate the sets.
1 code implementation • 22 Apr 2019 • Mohit Sharma, George Karypis
In this work, we show that the skewed distribution of ratings in the user-item rating matrix of real-world datasets affects the accuracy of matrix-completion-based approaches.
no code implementations • 22 Apr 2019 • Mohit Sharma, Jiayu Zhou, Junling Hu, George Karypis
The user personalized non-collaborative methods based on item features can be used to address this item cold-start problem.
no code implementations • ICLR 2019 • Arjun Sharma, Mohit Sharma, Nicholas Rhinehart, Kris M. Kitani
The use of imitation learning to learn a single policy for a complex task that has multiple modes or hierarchical structure can be challenging.
no code implementations • 22 Sep 2017 • Mohit Sharma, Kris M. Kitani, Joachim Groeger
We make an important connection to existing results in econometrics to describe an alternative formulation of inverse reinforcement learning (IRL).
no code implementations • NeurIPS 2010 • Yuzong Liu, Mohit Sharma, Charles Gaona, Jonathan Breshears, Jarod Roland, Zachary Freudenburg, Eric Leuthardt, Kilian Q. Weinberger
For successful upper limb BCIs, it is important to decode finger movements from brain activity.