Search Results for author: Ayush Jain

Found 26 papers, 9 papers with code

Does Social Pressure Drive Persuasion in Online Fora?

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.

Efficient List-Decodable Regression using Batches

no code implementations23 Nov 2022 Abhimanyu Das, Ayush Jain, Weihao Kong, Rajat Sen

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

regression

COGMEN: COntextualized GNN based Multimodal Emotion recognitioN

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.

Multimodal Emotion Recognition

Scalable Deep Reinforcement Learning Algorithms for Mean Field Games

no code implementations22 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.

reinforcement-learning

Deep Reinforcement Agent for Efficient Instant Search

no code implementations17 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.

TURF: A Two-factor, Universal, Robust, Fast Distribution Learning Algorithm

no code implementations15 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 $(t, d)\ne(1, 0)$.

Robust estimation algorithms don't need to know the corruption level

no code implementations11 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.

Bottom Up Top Down Detection Transformers for Language Grounding in Images and Point Clouds

1 code implementation16 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.

object-detection Object Detection +1

Robust Estimation for Random Graphs

no code implementations9 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.

Language Modulated Detection and Detection Modulated Language Grounding in 2D and 3D Scenes

no code implementations29 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.

object-detection Object Detection

Subset-of-Data Variational Inference for Deep Gaussian-Processes Regression

1 code implementation17 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.

Gaussian Processes regression +1

The Price of Tolerance in Distribution Testing

no code implementations25 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.

Variance Penalized On-Policy and Off-Policy Actor-Critic

1 code implementation3 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.

Deep learning via LSTM models for COVID-19 infection forecasting in India

1 code implementation28 Jan 2021 Rohitash Chandra, Ayush Jain, Divyanshu Singh Chauhan

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

Move to See Better: Self-Improving Embodied Object Detection

1 code implementation30 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.

object-detection Object Detection

Generalization to New Actions in Reinforcement Learning

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.

reinforcement-learning

Linear-Sample Learning of Low-Rank Distributions

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.

Collaborative Filtering Community Detection

NukeBERT: A Pre-trained language model for Low Resource Nuclear Domain

1 code implementation30 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.

Language Modelling Question Answering

SURF: A Simple, Universal, Robust, Fast Distribution Learning Algorithm

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.

Optimal Robust Learning of Discrete Distributions from Batches

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.

Collaborative Filtering Federated Learning

A deep learning system for differential diagnosis of skin diseases

no code implementations11 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).

Uniform Information Density Effects on Syntactic Choice in Hindi

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.

The Limits of Maxing, Ranking, and Preference Learning

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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