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no code implementations • EMNLP 2021 • Ayush Jain, Shashank Srivastava

Online forums such as ChangeMyView have been explored to research aspects of persuasion and argumentative quality in language.

no code implementations • SIGDIAL (ACL) 2020 • Ayush Jain, Maria Leonor Pacheco, Steven Lancette, Mahak Goindani, Dan Goldwasser

In this work, we study collaborative online conversations.

no code implementations • 23 Nov 2022 • Abhimanyu Das, Ayush Jain, Weihao Kong, Rajat Sen

We begin the study of list-decodable linear regression using batches.

1 code implementation • NAACL 2022 • Abhinav Joshi, Ashwani Bhat, Ayush Jain, Atin Vikram Singh, Ashutosh Modi

Emotions are an inherent part of human interactions, and consequently, it is imperative to develop AI systems that understand and recognize human emotions.

Ranked #1 on Multimodal Emotion Recognition on IEMOCAP

no code implementations • 22 Mar 2022 • Mathieu Laurière, Sarah Perrin, Sertan Girgin, Paul Muller, Ayush Jain, Theophile Cabannes, Georgios Piliouras, Julien Pérolat, Romuald Élie, Olivier Pietquin, Matthieu Geist

One limiting factor to further scale up using RL is that existing algorithms to solve MFGs require the mixing of approximated quantities such as strategies or $q$-values.

no code implementations • 17 Mar 2022 • Ravneet Singh Arora, Sreejith Menon, Ayush Jain, Nehil Jain

Instant Search is a paradigm where a search system retrieves answers on the fly while typing.

no code implementations • 15 Feb 2022 • Yi Hao, Ayush Jain, Alon Orlitsky, Vaishakh Ravindrakumar

We derive a near-linear-time and essentially sample-optimal estimator that establishes $c_{t, d}=2$ for all $(t, d)\ne(1, 0)$.

no code implementations • 11 Feb 2022 • Ayush Jain, Alon Orlitsky, Vaishakh Ravindrakumar

However, their vast majority approach optimal accuracy only when given a tight upper bound on the fraction of corrupt data.

1 code implementation • 16 Dec 2021 • Ayush Jain, Nikolaos Gkanatsios, Ishita Mediratta, Katerina Fragkiadaki

We propose a language grounding model that attends on the referential utterance and on the object proposal pool computed from a pre-trained detector to decode referenced objects with a detection head, without selecting them from the pool.

no code implementations • 9 Nov 2021 • Jayadev Acharya, Ayush Jain, Gautam Kamath, Ananda Theertha Suresh, Huanyu Zhang

We study the problem of robustly estimating the parameter $p$ of an Erd\H{o}s-R\'enyi random graph on $n$ nodes, where a $\gamma$ fraction of nodes may be adversarially corrupted.

1 code implementation • ICLR 2022 • Ayush Jain, Norio Kosaka, Kyung-Min Kim, Joseph J Lim

Intelligent agents can solve tasks in a variety of ways depending on the action set at their disposal.

no code implementations • 29 Sep 2021 • Ayush Jain, Nikolaos Gkanatsios, Ishita Mediratta, Katerina Fragkiadaki

Object detectors are typically trained on a fixed vocabulary of objects and attributes that is often too restrictive for open-domain language grounding, where the language utterance may refer to visual entities in various levels of abstraction, such as a cat, the leg of a cat, or the stain on the front leg of the chair.

1 code implementation • 17 Jul 2021 • Ayush Jain, P. K. Srijith, Mohammad Emtiyaz Khan

Deep Gaussian Processes (DGPs) are multi-layer, flexible extensions of Gaussian processes but their training remains challenging.

no code implementations • 25 Jun 2021 • Clément L. Canonne, Ayush Jain, Gautam Kamath, Jerry Li

Specifically, we show the sample complexity to be \[\tilde \Theta\left(\frac{\sqrt{n}}{\varepsilon_2^{2}} + \frac{n}{\log n} \cdot \max \left\{\frac{\varepsilon_1}{\varepsilon_2^2},\left(\frac{\varepsilon_1}{\varepsilon_2^2}\right)^{\!\! 2}\right\}\right),\] providing a smooth tradeoff between the two previously known cases.

1 code implementation • 3 Feb 2021 • Arushi Jain, Gandharv Patil, Ayush Jain, Khimya Khetarpal, Doina Precup

Reinforcement learning algorithms are typically geared towards optimizing the expected return of an agent.

1 code implementation • 28 Jan 2021 • Rohitash Chandra, Ayush Jain, Divyanshu Singh Chauhan

Deep learning models such as recurrent neural networks are well suited for modelling spatiotemporal sequences.

1 code implementation • 30 Nov 2020 • Zhaoyuan Fang, Ayush Jain, Gabriel Sarch, Adam W. Harley, Katerina Fragkiadaki

Experiments on both indoor and outdoor datasets show that (1) our method obtains high-quality 2D and 3D pseudo-labels from multi-view RGB-D data; (2) fine-tuning with these pseudo-labels improves the 2D detector significantly in the test environment; (3) training a 3D detector with our pseudo-labels outperforms a prior self-supervised method by a large margin; (4) given weak supervision, our method can generate better pseudo-labels for novel objects.

2 code implementations • ICML 2020 • Ayush Jain, Andrew Szot, Joseph J. Lim

A fundamental trait of intelligence is the ability to achieve goals in the face of novel circumstances, such as making decisions from new action choices.

no code implementations • NeurIPS 2020 • Ayush Jain, Alon Orlitsky

Many latent-variable applications, including community detection, collaborative filtering, genomic analysis, and NLP, model data as generated by low-rank matrices.

1 code implementation • 30 Mar 2020 • Ayush Jain, Dr. N. M. Meenachi, Dr. B. Venkatraman

This paper contributes to research in understanding nuclear domain knowledge which is then evaluated on Nuclear Question Answering Dataset (NQuAD) created by nuclear domain experts as part of this research.

no code implementations • NeurIPS 2020 • Ayush Jain, Alon Orlitsky

In many applications, data is collected in batches, some of which are corrupt or even adversarial.

no code implementations • NeurIPS 2020 • Yi Hao, Ayush Jain, Alon Orlitsky, Vaishakh Ravindrakumar

Sample- and computationally-efficient distribution estimation is a fundamental tenet in statistics and machine learning.

no code implementations • ICML 2020 • Ayush Jain, Alon Orlitsky

Previous estimators for this setting ran in exponential time, and for some regimes required a suboptimal number of batches.

no code implementations • 11 Sep 2019 • Yuan Liu, Ayush Jain, Clara Eng, David H. Way, Kang Lee, Peggy Bui, Kimberly Kanada, Guilherme de Oliveira Marinho, Jessica Gallegos, Sara Gabriele, Vishakha Gupta, Nalini Singh, Vivek Natarajan, Rainer Hofmann-Wellenhof, Greg S. Corrado, Lily H. Peng, Dale R. Webster, Dennis Ai, Susan Huang, Yun Liu, R. Carter Dunn, David Coz

In this paper, we developed a deep learning system (DLS) to provide a differential diagnosis of skin conditions for clinical cases (skin photographs and associated medical histories).

no code implementations • WS 2018 • Ayush Jain, Vishal Singh, Sidharth Ranjan, Rajakrishnan Rajkumar, Sumeet Agarwal

According to the UNIFORM INFORMATION DENSITY (UID) hypothesis (Levy and Jaeger, 2007; Jaeger, 2010), speakers tend to distribute information density across the signal uniformly while producing language.

no code implementations • ICML 2018 • Moein Falahatgar, Ayush Jain, Alon Orlitsky, Venkatadheeraj Pichapati, Vaishakh Ravindrakumar

We present a comprehensive understanding of three important problems in PAC preference learning: maximum selection (maxing), ranking, and estimating all pairwise preference probabilities, in the adaptive setting.

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