Search Results for author: Avinava Dubey

Found 26 papers, 13 papers with code

Incremental Extractive Opinion Summarization Using Cover Trees

1 code implementation16 Jan 2024 Somnath Basu Roy Chowdhury, Nicholas Monath, Avinava Dubey, Manzil Zaheer, Andrew McCallum, Amr Ahmed, Snigdha Chaturvedi

In this work, we study the task of extractive opinion summarization in an incremental setting, where the underlying review set evolves over time.

Extractive Summarization Opinion Summarization

SARA-RT: Scaling up Robotics Transformers with Self-Adaptive Robust Attention

no code implementations4 Dec 2023 Isabel Leal, Krzysztof Choromanski, Deepali Jain, Avinava Dubey, Jake Varley, Michael Ryoo, Yao Lu, Frederick Liu, Vikas Sindhwani, Quan Vuong, Tamas Sarlos, Ken Oslund, Karol Hausman, Kanishka Rao

We present Self-Adaptive Robust Attention for Robotics Transformers (SARA-RT): a new paradigm for addressing the emerging challenge of scaling up Robotics Transformers (RT) for on-robot deployment.

Scalable Neural Network Kernels

1 code implementation20 Oct 2023 Arijit Sehanobish, Krzysztof Choromanski, Yunfan Zhao, Avinava Dubey, Valerii Likhosherstov

We introduce the concept of scalable neural network kernels (SNNKs), the replacements of regular feedforward layers (FFLs), capable of approximating the latter, but with favorable computational properties.

Enhancing Group Fairness in Online Settings Using Oblique Decision Forests

2 code implementations17 Oct 2023 Somnath Basu Roy Chowdhury, Nicholas Monath, Ahmad Beirami, Rahul Kidambi, Avinava Dubey, Amr Ahmed, Snigdha Chaturvedi

In the online setting, where the algorithm has access to a single instance at a time, estimating the group fairness objective requires additional storage and significantly more computation (e. g., forward/backward passes) than the task-specific objective at every time step.


FAVOR#: Sharp Attention Kernel Approximations via New Classes of Positive Random Features

no code implementations1 Feb 2023 Valerii Likhosherstov, Krzysztof Choromanski, Avinava Dubey, Frederick Liu, Tamas Sarlos, Adrian Weller

The problem of efficient approximation of a linear operator induced by the Gaussian or softmax kernel is often addressed using random features (RFs) which yield an unbiased approximation of the operator's result.

Karyotype AI for Precision Oncology

no code implementations20 Nov 2022 Zahra Shamsi, Drew Bryant, Jacob Wilson, Xiaoyu Qu, Avinava Dubey, Konik Kothari, Mostafa Dehghani, Mariya Chavarha, Valerii Likhosherstov, Brian Williams, Michael Frumkin, Fred Appelbaum, Krzysztof Choromanski, Ali Bashir, Min Fang

These individual chromosomes were used to train and assess deep learning models for classifying the 24 human chromosomes and identifying chromosomal aberrations.

Few-Shot Learning Inductive Bias

A Fourier Approach to Mixture Learning

no code implementations5 Oct 2022 Mingda Qiao, Guru Guruganesh, Ankit Singh Rawat, Avinava Dubey, Manzil Zaheer

Regev and Vijayaraghavan (2017) showed that with $\Delta = \Omega(\sqrt{\log k})$ separation, the means can be learned using $\mathrm{poly}(k, d)$ samples, whereas super-polynomially many samples are required if $\Delta = o(\sqrt{\log k})$ and $d = \Omega(\log k)$.

Unsupervised Opinion Summarization Using Approximate Geodesics

no code implementations15 Sep 2022 Somnath Basu Roy Chowdhury, Nicholas Monath, Avinava Dubey, Amr Ahmed, Snigdha Chaturvedi

We then use these representations to quantify the relevance of review sentences using a novel approximate geodesic distance based scoring mechanism.

Decoder Dictionary Learning +3

Chefs' Random Tables: Non-Trigonometric Random Features

1 code implementation30 May 2022 Valerii Likhosherstov, Krzysztof Choromanski, Avinava Dubey, Frederick Liu, Tamas Sarlos, Adrian Weller

We introduce chefs' random tables (CRTs), a new class of non-trigonometric random features (RFs) to approximate Gaussian and softmax kernels.

On Learning the Transformer Kernel

1 code implementation15 Oct 2021 Sankalan Pal Chowdhury, Adamos Solomou, Avinava Dubey, Mrinmaya Sachan

In this work we introduce KERNELIZED TRANSFORMER, a generic, scalable, data driven framework for learning the kernel function in Transformers.

Computational Efficiency

Distributed, partially collapsed MCMC for Bayesian Nonparametrics

no code implementations15 Jan 2020 Avinava Dubey, Michael Minyi Zhang, Eric P. Xing, Sinead A. Williamson

Bayesian nonparametric (BNP) models provide elegant methods for discovering underlying latent features within a data set, but inference in such models can be slow.

Discourse in Multimedia: A Case Study in Extracting Geometry Knowledge from Textbooks

no code implementations CL 2019 Mrinmaya Sachan, Avinava Dubey, Eduard H. Hovy, Tom M. Mitchell, Dan Roth, Eric P. Xing

At the same time, these help the readers pick up the structure of the discourse and comprehend the conveyed information.

Transformation Autoregressive Networks

no code implementations ICML 2018 Junier B. Oliva, Avinava Dubey, Manzil Zaheer, Barnabás Póczos, Ruslan Salakhutdinov, Eric P. Xing, Jeff Schneider

Further, through a comprehensive study over both real world and synthetic data, we show for that jointly leveraging transformations of variables and autoregressive conditional models, results in a considerable improvement in performance.

Density Estimation Outlier Detection

Personalized Survival Prediction with Contextual Explanation Networks

1 code implementation30 Jan 2018 Maruan Al-Shedivat, Avinava Dubey, Eric P. Xing

Accurate and transparent prediction of cancer survival times on the level of individual patients can inform and improve patient care and treatment practices.

Survival Prediction

The Intriguing Properties of Model Explanations

1 code implementation30 Jan 2018 Maruan Al-Shedivat, Avinava Dubey, Eric P. Xing

Linear approximations to the decision boundary of a complex model have become one of the most popular tools for interpreting predictions.

Contextual Explanation Networks

1 code implementation ICLR 2018 Maruan Al-Shedivat, Avinava Dubey, Eric P. Xing

Our results on image and text classification and survival analysis tasks demonstrate that CENs are not only competitive with the state-of-the-art methods but also offer additional insights behind each prediction, that can be valuable for decision support.

Image Classification Interpretability Techniques for Deep Learning +5

Parallel Markov Chain Monte Carlo for the Indian Buffet Process

no code implementations9 Mar 2017 Michael M. Zhang, Avinava Dubey, Sinead A. Williamson

In this paper we present a novel algorithm to perform asymptotically exact parallel Markov chain Monte Carlo inference for Indian Buffet Process models.

Bayesian Nonparametric Kernel-Learning

no code implementations29 Jun 2015 Junier Oliva, Avinava Dubey, Andrew G. Wilson, Barnabas Poczos, Jeff Schneider, Eric P. Xing

In this paper we introduce Bayesian nonparmetric kernel-learning (BaNK), a generic, data-driven framework for scalable learning of kernels.

Large-scale randomized-coordinate descent methods with non-separable linear constraints

no code implementations9 Sep 2014 Sashank Reddi, Ahmed Hefny, Carlton Downey, Avinava Dubey, Suvrit Sra

We develop randomized (block) coordinate descent (CD) methods for linearly constrained convex optimization.

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