Anchit Gupta

EDBT 2026 Demo / reviewers in the wild / expert

dblp:176/5620 · DBLP profile ↗

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12ranked-venue papers

2as first author

9since 2021 · last 2024

—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 10 · 1 first-author · 7 since 2021Graphics, computer vision, multimedia, augmented reality and games · 4 · 2 first-author · 3 since 2021Systems, architecture and hardware · 2 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence 5 papers		Motion planning and robot control · 27% Language models and text generation · 16% Learning paradigms · 9%
Theoretical computer science 1 paper		Computational complexity · 77% Algorithms and data structures · 23%

Topics — the 12 heaviest of 14, each with the papers that count most for it

Topic	Weight	Papers	Last	Evidence papers
Robotics › Motion planning and robot control › robot learning › robot policy learning generalist robot policy	0.8	1	2024	Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration · ICRA 2024
Robotics › Motion planning and robot control robot learning	0.8	1	2024	Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration · ICRA 2024
Machine learning › Transfer learning and domain adaptation fine-tuning	0.5	1	2021	Better Fine-Tuning by Reducing Representational Collapse · ICLR 2021
Natural language and speech › Language models and text generation large language model fine-tuning	0.5	1	2021	Better Fine-Tuning by Reducing Representational Collapse · ICLR 2021
Machine learning › Learning paradigms multi-task learning	0.5	1	2021	Muppet: Massive Multi-task Representations with Pre-Finetuning · EMNLP (1) 2021
Machine learning › Deep learning architectures and training › regularization noise-based regularization	0.5	1	2021	Better Fine-Tuning by Reducing Representational Collapse · ICLR 2021
Machine learning › Representation and self-supervised learning transferable representation	0.5	1	2021	Muppet: Massive Multi-task Representations with Pre-Finetuning · EMNLP (1) 2021
Natural language and speech › Question answering and dialogue systems › conversational agents conversational assistant	0.4	1	2020	Sound Natural: Content Rephrasing in Dialog Systems · EMNLP (1) 2020
Natural language and speech › Language models and text generation text generation	0.4	1	2020	Sound Natural: Content Rephrasing in Dialog Systems · EMNLP (1) 2020
Knowledge, reasoning and agents › Planning, search and constraint satisfaction › planning under uncertainty › probabilistic planning markov decision process planning	0.3	1	2017	Improved Strong Worst-case Upper Bounds for MDP Planning · IJCAI 2017
Machine learning › Reinforcement learning › dynamic programming policy iteration	0.3	1	2017	Improved Strong Worst-case Upper Bounds for MDP Planning · IJCAI 2017
Algorithms and data structures combinatorial algorithms	0.1	1	2017	Improved Strong Worst-case Upper Bounds for MDP Planning · IJCAI 2017

Methods — techniques the papers use, named apart from their topics

fine-tuning · 1.0transformer policy · 0.8large-scale pretraining · 0.8regularization · 0.5pre-training · 0.5knowledge distillation · 0.4copy-pointer · 0.4copy loss · 0.4LSTM seq2seq · 0.4BART · 0.4policy iteration · 0.3fibonacci seesaw · 0.3

Year	Publication	Venue	Position
2024	Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration abstract Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, this has led to a consolidation of pretrained models, with general pretrained backbones serving as a starting point for many applications. Can such a consolidation happen in robotics? Conventionally, robotic learning methods train a separate model for every application, every robot, and even every environment. Can we instead train "generalist" X-robot policy that can be adapted efficiently to new robots, tasks, and environments? In this paper, we provide datasets in standardized data formats and models to make it possible to explore this possibility in the context of robotic manipulation, alongside experimental results that provide an example of effective X-robot policies. We assemble a dataset from 22 different robots collected through a collaboration between 21 institutions, demonstrating 527 skills (160266 tasks). We show that a high-capacity model trained on this data, which we call RT-X, exhibits positive transfer and improves the capabilities of multiple robots by leveraging experience from other platforms. The project website is robotics-transformer-x.github.io. Abigail O'Neill, Abhiram Maddukuri, Abhishek Gupta 0004, Abhishek Padalkar, Abraham Lee, Acorn Pooley, Agrim Gupta, Ajay Mandlekar, Ajinkya Jain, Albert Tung, Alex Bewley, Alex Irpan, Alexander Khazatsky, Anant Rai, Anchit Gupta, Andrew E. Wang, Anikait Singh, Animesh Garg, Aniruddha Kembhavi, Annie Xie, Anthony Brohan, Antonin Raffin, Archit Sharma, Arefeh Yavary, Arhan Jain, Ashwin Balakrishna, Ayzaan Wahid, Ben Burgess-Limerick, Bernhard Schölkopf, Blake Wulfe, Brian Ichter, Cewu Lu, Charles Xu 0003, Charlotte Le, Chelsea Finn, Chen Wang 0053, Chenfeng Xu, Cheng Chi 0001, Chenguang Huang, Christine Chan, Christopher Agia, Chuer Pan, Chuyuan Fu, Coline Devin, Danfei Xu, Daniel Morton, Danny Drieß, Daphne Chen, Deepak Pathak, Dhruv Shah, Dieter Büchler, Dinesh Jayaraman, Dmitry Kalashnikov, Dorsa Sadigh, Edward Johns, Ethan Paul Foster, Fangchen Liu, Federico Ceola, Fei Xia 0002, Feiyu Zhao, Freek Stulp, Gaoyue Zhou, Gaurav S. Sukhatme, Gautam Salhotra, Gilbert Feng, Giulio Schiavi, Glen Berseth, Gregory Kahn, Guanzhi Wang, Hao Su 0001, Haoshu Fang, Henghui Bao, Heni Ben Amor, Henrik I. Christensen, Hiroki Furuta, Homer Walke, Hongjie Fang, Huy Ha, Igor Mordatch, Ilija Radosavovic, Isabel Leal, Jacky Liang, Jad Abou-Chakra, Jaehyung Kim 0001, Jaimyn Drake, Jan Peters 0001, Jan Schneider 0007, Jasmine Hsu, Jeannette Bohg, Jeffrey T. Bingham, Jensen Gao, Jiaheng Hu, Jiajun Wu 0001, Jiankai Sun, Jianlan Luo, Jiayuan Gu, Jie Tan 0001, Jihoon Oh, Jimmy Wu, Jingpei Lu, Jitendra Malik, João Silvério, Joey Hejna, Jonathan Booher, Jonathan Tompson, Jonathan Yang, Jordi Salvador, Joseph J. Lim, Junhyek Han, Kanishka Rao, Karl Pertsch, Karol Hausman, Keegan Go, Keerthana Gopalakrishnan, Kenneth Y. Goldberg, Kendra Byrne, Kenneth Oslund, Kento Kawaharazuka, Kevin Black, Kevin Zhang 0002, Kiana Ehsani, Kiran Lekkala, Kirsty Ellis, Krishan Rana, Krishnan Srinivasan, Kuan Fang, Kunal Pratap Singh, Kuo-Hao Zeng, Kyle Hatch, Kyle Hsu, Laurent Itti, Yunliang Chen 0001, Lerrel Pinto, Li Fei-Fei 0001, Liam Tan, Linxi Fan, Lionel Ott, Lisa Lee, Luca Weihs, Magnum Chen, Marion Lepert, Marius Memmel, Masayoshi Tomizuka, Masha Itkina, Mateo Guaman Castro, Max Spero, Maximilian Du, Michael Ahn, Michael C. Yip, Mingtong Zhang 0003, Mingyu Ding, Minho Heo, Mohan Kumar Srirama, Mohit Sharma 0001, Moo Jin Kim, Naoaki Kanazawa, Nicklas Hansen 0001, Nicolas Heess, Nikhil J. Joshi, Niko Sünderhauf, Norman Di Palo, Nur Muhammad Shafiullah, Oier Mees, Oliver Kroemer, Osbert Bastani, Pannag R. Sanketi, Patrick Tree Miller, Patrick Yin, Paul Wohlhart, Peng Xu 0010, Peter David Fagan, Peter Mitrano, Pierre Sermanet, Pieter Abbeel, Priya Sundaresan, Qiuyu Chen, Rafael Rafailov, Ria Doshi, Roberto Martin Martin, Rohan Baijal, Rosario Scalise, Rose Hendrix, Roy Lin, Runjia Qian, Russell Mendonca, Rutav Shah, Ryan Hoque, Ryan Julian, Samuel Bustamante-Gomez, Sean Kirmani, Sergey Levine, Sherry Moore, Shikhar Bahl, Shivin Dass, Shubham D. Sonawani, Shuran Song, Sichun Xu, Siddhant Haldar, Siddharth Karamcheti, Simeon Adebola, Simon Guist, Soroush Nasiriany, Stefan Schaal, Stefan Welker, Stephen Tian, Subramanian Ramamoorthy, Sudeep Dasari, Suneel Belkhale, Sungjae Park, Suraj Nair 0003, Suvir Mirchandani, Takayuki Osa, Tanmay Gupta, Tatsuya Harada, Tatsuya Matsushima, Ted Xiao, Thomas Kollar, Tianhe Yu, Tianli Ding, Todor Davchev, Tony Z. Zhao, Travis Armstrong, Trevor Darrell, Trinity Chung, Vidhi Jain, Vincent Vanhoucke, Wolfram Burgard, Xiaolong Wang 0004, Xinghao Zhu, Xinyang Geng, Liangwei Xu, Yecheng Jason Ma 0001, Yejin Kim 0003, Yevgen Chebotar, Yilin Wu 0003, Yonatan Bisk, Yoonyoung Cho, Youngwoon Lee, Yuchen Cui, Yueh-Hua Wu, Yujin Tang, Yuke Zhu, Yunchu Zhang, Yunfan Jiang 0001, Yunshuang Li, Yunzhu Li, Yusuke Iwasawa, Yutaka Matsuo, Zehan Ma, Zichen Jeff Cui, Zichen Zhang 0016, Zipeng Lin	ICRA	17
2023	Retrieve-and-Fill for Scenario-based Task-Oriented Semantic Parsing abstract Akshat Shrivastava, Shrey Desai, Anchit Gupta, Ali Elkahky, Aleksandr Livshits, Alexander Zotov, Ahmed Aly. Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics. 2023. Akshat Shrivastava, Shrey Desai, Anchit Gupta, Ali Elkahky, Aleksandr Livshits, Alexander Zotov, Ahmed Aly	EACL	3
2023	Towards Generating Ultra-High Resolution Talking-Face Videos with Lip synchronization abstract Talking-face video generation works have achieved state-of-the-art results in synthesizing videos with lip synchronization. However, most of the previous works deal with low-resolution talking-face videos (up to 256×256 pixels), thus, generating extremely high-resolution videos still remains a challenge. We take a giant leap in this work and propose a novel method to synthesize talking-face videos at resolutions as high as 4K! Our task presents several key challenges: (i) Scaling the existing methods to such high resolutions is resource-constrained, both in terms of compute and the availability of very high-resolution datasets, (ii) The synthesized videos need to be spatially and temporally coherent. The sheer number of pixels that the model needs to generate while maintaining the temporal consistency at the video level makes this task non-trivial and has never been attempted before in literature. To address these issues, we propose to train the lip-sync generator in a compact Vector Quantized (VQ) space for the first time. Our core idea to encode the faces in a compact 16× 16 representation allows us to model high-resolution videos. In our framework, we learn the lip movements in the quantized space on the newly collected 4K Talking Faces (4KTF) dataset. Our approach is speaker agnostic and can handle various languages and voices. We benchmark our technique against several competitive works and show that we can achieve a remarkable 64-times more pixels than the current state-of-the-art! Our supplementary demo video depicts additional qualitative results, comparisons, and several real-world applications, like professional movie editing enabled by our model. Anchit Gupta, Rudrabha Mukhopadhyay, Sindhu Balachandra, Faizan Farooq Khan, Vinay P. Namboodiri, C. V. Jawahar	WACV	1
2023	Unsupervised Audio-Visual Lecture Segmentation abstract Over the last decade, online lecture videos have become increasingly popular and have experienced a meteoric rise during the pandemic. However, video-language research has primarily focused on instructional videos or movies, and tools to help students navigate the growing online lectures are lacking. Our first contribution is to facilitate research in the educational domain by introducing AVLectures, a large-scale dataset consisting of 86 courses with over 2,350 lectures covering various STEM subjects. Each course contains video lectures, transcripts, OCR outputs for lecture frames, and optionally lecture notes, slides, assignments, and related educational content that can inspire a variety of tasks. Our second contribution is introducing video lecture segmentation that splits lectures into bite-sized topics. Lecture clip representations leverage visual, textual, and OCR cues and are trained on a pretext self-supervised task of matching the narration with the temporally aligned visual content. We formulate lecture segmentation as an unsupervised task and use these representations to generate segments using a temporally consistent 1-nearest neighbor algorithm, TW-FINCH [44]. We evaluate our method on 15 courses and compare it against various visual and textual baselines, outperforming all of them. Our comprehensive ablation studies also identify the key factors driving the success of our approach. Darshan Singh S, Anchit Gupta, C. V. Jawahar, Makarand Tapaswi	WACV	2
2022	Compressing Video Calls using Synthetic Talking Heads Madhav Agarwal, Anchit Gupta, Rudrabha Mukhopadhyay, Vinay P. Namboodiri, C. V. Jawahar	BMVC	2
2022	Simple Local Attentions Remain Competitive for Long-Context Tasks abstract Wenhan Xiong, Barlas Oguz, Anchit Gupta, Xilun Chen, Diana Liskovich, Omer Levy, Scott Yih, Yashar Mehdad. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2022. Wenhan Xiong, Barlas Oguz, Anchit Gupta, Xilun Chen 0002, Diana Liskovich, Omer Levy, Scott Yih, Yashar Mehdad	NAACL-HLT	3
2021	MTOP: A Comprehensive Multilingual Task-Oriented Semantic Parsing Benchmark abstract Haoran Li, Abhinav Arora, Shuohui Chen, Anchit Gupta, Sonal Gupta, Yashar Mehdad. Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume. 2021. Haoran Li 0007, Abhinav Arora, Shuohui Chen, Anchit Gupta, Sonal Gupta, Yashar Mehdad	EACL	4
2021	Muppet: Massive Multi-task Representations with Pre-Finetuning abstract We propose pre-finetuning, an additional largescale learning stage between language model pre-training and fine-tuning.Pre-finetuning is massively multi-task learning (around 50 datasets, over 4.8 million total labeled examples), and is designed to encourage learning of representations that generalize better to many different tasks.We show that prefinetuning consistently improves performance for pretrained discriminators (e.g.RoBERTa) and generation models (e.g.BART) on a wide range of tasks (sentence prediction, commonsense reasoning, MRC, etc.), while also significantly improving sample efficiency during fine-tuning.We also show that large-scale multi-tasking is crucial; pre-finetuning can hurt performance when few tasks are used up until a critical point (usually above 15) after which performance improves linearly in the number of tasks. Armen Aghajanyan, Anchit Gupta, Akshat Shrivastava, Xilun Chen 0002, Luke Zettlemoyer, Sonal Gupta	EMNLP (1)	2
2021	Better Fine-Tuning by Reducing Representational Collapse Armen Aghajanyan, Akshat Shrivastava, Anchit Gupta, Naman Goyal 0001, Luke Zettlemoyer, Sonal Gupta	ICLR	3
2020	Sound Natural: Content Rephrasing in Dialog Systems abstract We introduce a new task of rephrasing for a more natural virtual assistant.Currently, virtual assistants work in the paradigm of intentslot tagging and the slot values are directly passed as-is to the execution engine.However, this setup fails in some scenarios such as messaging when the query given by the user needs to be changed before repeating it or sending it to another user.For example, for queries like 'ask my wife if she can pick up the kids' or 'remind me to take my pills', we need to rephrase the content to 'can you pick up the kids' and 'take your pills'.In this paper, we study the problem of rephrasing with messaging as a use case and release a dataset of 3000 pairs of original query and rephrased query.We show that BART, a pre-trained transformers-based masked language model with auto-regressive decoding, is a strong baseline for the task, and show improvements by adding a copy-pointer and copy loss to it.We analyze different tradeoffs of BART-based and LSTM-based seq2seq models, and propose a distilled LSTM-based seq2seq as the best practical model. Arash Einolghozati, Anchit Gupta, Keith Diedrick, Sonal Gupta	EMNLP (1)	2
2019	Scaling Robot Supervision to Hundreds of Hours with RoboTurk: Robotic Manipulation Dataset through Human Reasoning and Dexterity abstract Large, richly annotated datasets have accelerated progress in fields such as computer vision and natural language processing, but replicating these successes in robotics has been challenging. While prior data collection methodologies such as self-supervision have resulted in large datasets, the data can have poor signal-to-noise ratio. By contrast, previous efforts to collect task demonstrations with humans provide better quality data, but they cannot reach the same data magnitude. Furthermore, neither approach places guarantees on the diversity of the data collected, in terms of solution strategies. In this work, we leverage and extend the RoboTurk platform to scale up data collection for robotic manipulation using remote teleoperation. The primary motivation for our platform is two-fold: (1) to address the shortcomings of prior work and increase the total quantity of manipulation data collected through human supervision by an order of magnitude without sacrificing the quality of the data and (2) to collect data on challenging manipulation tasks across several operators and observe a diverse set of emergent behaviors and solutions. We collected over 111 hours of robot manipulation data across 54 users and 3 challenging manipulation tasks in 1 week, resulting in the largest robot dataset collected via remote teleoperation. We evaluate the quality of our platform, the diversity of demonstrations in our dataset, and the utility of our dataset via quantitative and qualitative analysis. For additional results, supplementary videos, and to download our dataset, visit http;//roboturk.stanford.edu/realrobotdataset. Ajay Mandlekar, Jonathan Booher, Max Spero, Albert Tung, Anchit Gupta, Yuke Zhu, Animesh Garg, Silvio Savarese, Li Fei-Fei 0001	IROS	5
2017	Improved Strong Worst-case Upper Bounds for MDP Planning abstract The Markov Decision Problem (MDP) plays a central role in AI as an abstraction of sequential decision making. We contribute to the theoretical analysis of MDP PLANNING, which is the problem of computing an optimal policy for a given MDP. Specifically, we furnish improved STRONG WORST-CASE upper bounds on the running time of MDP planning. Strong bounds are those that depend only on the number of states n and the number of actions k in the specified MDP; they have no dependence on affiliated variables such as the discount factor and the number of bits needed to represent the MDP. Worst-case bounds apply to EVERY run of an algorithm; randomised algorithms can typically yield faster EXPECTED running times. While the special case of 2-action MDPs (that is, k = 2) has recently received some attention, bounds for general k have remained to be improved for several decades. Our contributions are to this general case. For k >= 3, the tightest strong upper bound shown to date for MDP planning belongs to a family of algorithms called Policy Iteration. This bound is only a polynomial improvement over a trivial bound of poly(n, k) k^{n} [Mansour and Singh, 1999]. In this paper, we generalise a contrasting algorithm called the Fibonacci Seesaw, and derive a bound of poly(n, k) k^{0.6834n}. The key construct we use is a template to map algorithms for the 2-action setting to the general setting. Interestingly, this idea can also be used to design Policy Iteration algorithms with a running time upper bound of poly(n, k) k^{0.7207n}. Both our results improve upon bounds that have stood for several decades. Anchit Gupta, Shivaram Kalyanakrishnan	IJCAI	1