b'Prateek Jain'

research

∙ 06/09/2023

End-to-End Neural Network Compression via ℓ_1/ℓ_2 Regularized Latency Surrogates

Neural network (NN) compression via techniques such as pruning, quantiza...

0 Anshul Nasery, et al. ∙

research

∙ 05/30/2023

AdANNS: A Framework for Adaptive Semantic Search

Web-scale search systems learn an encoder to embed a given query which i...

0 Aniket Rege, et al. ∙

research

∙ 02/01/2023

Simplicity Bias in 1-Hidden Layer Neural Networks

Recent works have demonstrated that neural networks exhibit extreme simp...

0 Depen Morwani, et al. ∙

research

∙ 02/01/2023

iPAL: A Machine Learning Based Smart Healthcare Framework For Automatic Diagnosis Of Attention Deficit/Hyperactivity Disorder (ADHD)

ADHD is a prevalent disorder among the younger population. Standard eval...

0 Abhishek Sharma, et al. ∙

research

∙ 01/30/2023

Near Optimal Private and Robust Linear Regression

We study the canonical statistical estimation problem of linear regressi...

0 Xiyang Liu, et al. ∙

research

∙ 01/17/2023

Optimal Algorithms for Latent Bandits with Cluster Structure

We consider the problem of latent bandits with cluster structure where t...

4 Soumyabrata Pal, et al. ∙

research

∙ 12/15/2022

Hybrid Quantum Generative Adversarial Networks for Molecular Simulation and Drug Discovery

In molecular research, simulation & design of molecules are key areas wi...

0 Prateek Jain, et al. ∙

research

∙ 11/15/2022

Variational Quantum Algorithms for Chemical Simulation and Drug Discovery

Quantum computing has gained a lot of attention recently, and scientists...

0 Hasan Mustafa, et al. ∙

research

∙ 10/11/2022

Multi-User Reinforcement Learning with Low Rank Rewards

In this work, we consider the problem of collaborative multi-user reinfo...

16 Naman Agarwal, et al. ∙

research

∙ 10/07/2022

Private and Efficient Meta-Learning with Low Rank and Sparse Decomposition

Meta-learning is critical for a variety of practical ML systems – like p...

7 Soumyabrata Pal, et al. ∙

research

∙ 10/04/2022

Learning an Invertible Output Mapping Can Mitigate Simplicity Bias in Neural Networks

Deep Neural Networks are known to be brittle to even minor distribution ...

5 Sravanti Addepalli, et al. ∙

research

∙ 09/08/2022

Online Low Rank Matrix Completion

We study the problem of online low-rank matrix completion with 𝖬 users, ...

3 Prateek Jain, et al. ∙

research

∙ 09/06/2022

Surya Namaskar: real-time advanced yoga pose recognition and correction for smart healthcare

Nowadays, yoga has gained worldwide attention because of increasing leve...

26 Abhishek Sharma, et al. ∙

research

∙ 08/19/2022

DAFT: Distilling Adversarially Fine-tuned Models for Better OOD Generalization

We consider the problem of OOD generalization, where the goal is to trai...

11 Anshul Nasery, et al. ∙

research

∙ 07/11/2022

(Nearly) Optimal Private Linear Regression via Adaptive Clipping

We study the problem of differentially private linear regression where e...

5 Prateek Varshney, et al. ∙

research

∙ 06/17/2022

MET: Masked Encoding for Tabular Data

We consider the task of self-supervised representation learning (SSL) fo...

4 Kushal Majmundar, et al. ∙

research

∙ 05/27/2022

DP-PCA: Statistically Optimal and Differentially Private PCA

We study the canonical statistical task of computing the principal compo...

4 Xiyang Liu, et al. ∙

research

∙ 05/26/2022

Matryoshka Representations for Adaptive Deployment

Learned representations are a central component in modern ML systems, se...

10 Aditya Kusupati, et al. ∙

research

∙ 02/09/2022

Reproducibility in Optimization: Theoretical Framework and Limits

We initiate a formal study of reproducibility in optimization. We define...

5 Kwangjun Ahn, et al. ∙

research

∙ 01/19/2022

Real-time Recognition of Yoga Poses using computer Vision for Smart Health Care

Nowadays, yoga has become a part of life for many people. Exercises and ...

8 Abhishek Sharma, et al. ∙

research

∙ 11/23/2021

Node-Level Differentially Private Graph Neural Networks

Graph Neural Networks (GNNs) are a popular technique for modelling graph...

4 Ameya Daigavane, et al. ∙

research

∙ 10/19/2021

Neural Network Compatible Off-Policy Natural Actor-Critic Algorithm

Learning optimal behavior from existing data is one of the most importan...

8 Raghuram Bharadwaj Diddigi, et al. ∙

research

∙ 10/16/2021

Online Target Q-learning with Reverse Experience Replay: Efficiently finding the Optimal Policy for Linear MDPs

Q-learning is a popular Reinforcement Learning (RL) algorithm which is w...

5 Naman Agarwal, et al. ∙

research

∙ 09/28/2021

IGLU: Efficient GCN Training via Lazy Updates

Graph Convolution Networks (GCN) are used in numerous settings involving...

2 S Deepak Narayanan, et al. ∙

research

∙ 06/16/2021

Robust Training in High Dimensions via Block Coordinate Geometric Median Descent

Geometric median (Gm) is a classical method in statistics for achieving ...

12 Anish Acharya, et al. ∙

research

∙ 06/02/2021

LLC: Accurate, Multi-purpose Learnt Low-dimensional Binary Codes

Learning binary representations of instances and classes is a classical ...

8 Aditya Kusupati, et al. ∙

research

∙ 05/24/2021

Near-optimal Offline and Streaming Algorithms for Learning Non-Linear Dynamical Systems

We consider the setting of vector valued non-linear dynamical systems X_...

8 Prateek Jain, et al. ∙

research

∙ 05/18/2021

Sample Efficient Linear Meta-Learning by Alternating Minimization

Meta-learning synthesizes and leverages the knowledge from a given set o...

7 Kiran Koshy Thekumparampil, et al. ∙

research

∙ 03/10/2021

Streaming Linear System Identification with Reverse Experience Replay

We consider the problem of estimating a stochastic linear time-invariant...

19 Prateek Jain, et al. ∙

research

∙ 02/25/2021

Do Input Gradients Highlight Discriminative Features?

Interpretability methods that seek to explain instance-specific model pr...

3 Harshay Shah, et al. ∙

research

∙ 02/15/2021

Learning Accurate Decision Trees with Bandit Feedback via Quantized Gradient Descent

Decision trees provide a rich family of highly non-linear but efficient ...

8 Ajaykrishna Karthikeyan, et al. ∙

research

∙ 10/05/2020

Projection Efficient Subgradient Method and Optimal Nonsmooth Frank-Wolfe Method

We consider the classical setting of optimizing a nonsmooth Lipschitz co...

21 Kiran Koshy Thekumparampil, et al. ∙

research

∙ 07/14/2020

Programming by Rewards

We formalize and study “programming by rewards” (PBR), a new approach fo...

13 Nagarajan Natarajan, et al. ∙

research

∙ 06/25/2020

Globally-convergent Iteratively Reweighted Least Squares for Robust Regression Problems

We provide the first global model recovery results for the IRLS (iterati...

8 Bhaskar Mukhoty, et al. ∙

research

∙ 06/16/2020

Least Squares Regression with Markovian Data: Fundamental Limits and Algorithms

We study the problem of least squares linear regression where the data-p...

10 Guy Bresler, et al. ∙

research

∙ 06/13/2020

The Pitfalls of Simplicity Bias in Neural Networks

Several works have proposed Simplicity Bias (SB)—the tendency of standar...

0 Harshay Shah, et al. ∙

research

∙ 04/03/2020

COVID-19: Strategies for Allocation of Test Kits

With the increasing spread of COVID-19, it is important to systematicall...

0 Arpita Biswas, et al. ∙

research

∙ 02/28/2020

DROCC: Deep Robust One-Class Classification

Classical approaches for one-class problems such as one-class SVM (Schol...

13 Sachin Goyal, et al. ∙

research

∙ 02/27/2020

RNNPool: Efficient Non-linear Pooling for RAM Constrained Inference

Pooling operators are key components in most Convolutional Neural Networ...

15 Oindrila Saha, et al. ∙

research

∙ 02/08/2020

Soft Threshold Weight Reparameterization for Learnable Sparsity

Sparsity in Deep Neural Networks (DNNs) is studied extensively with the ...

3 Aditya Kusupati, et al. ∙

research

∙ 01/28/2020

Rich-Item Recommendations for Rich-Users via GCNN: Exploiting Dynamic and Static Side Information

We study the standard problem of recommending relevant items to users; a...

0 Amar Budhiraja, et al. ∙

research

∙ 11/26/2019

OASIS: ILP-Guided Synthesis of Loop Invariants

Finding appropriate inductive loop invariants for a program is a key cha...

0 Sahil Bhatia, et al. ∙

research

∙ 07/12/2019

Learning Functions over Sets via Permutation Adversarial Networks

In this paper, we consider the problem of learning functions over sets, ...

0 Chirag Pabbaraju, et al. ∙

research

∙ 07/02/2019

Efficient Algorithms for Smooth Minimax Optimization

This paper studies first order methods for solving smooth minimax optimi...

10 Kiran Koshy Thekumparampil, et al. ∙

research

∙ 04/29/2019

Making the Last Iterate of SGD Information Theoretically Optimal

Stochastic gradient descent (SGD) is one of the most widely used algorit...

0 Prateek Jain, et al. ∙

research

∙ 03/19/2019

Adaptive Hard Thresholding for Near-optimal Consistent Robust Regression

We study the problem of robust linear regression with response variable ...

0 Arun Sai Suggala, et al. ∙

research

∙ 03/04/2019

SGD without Replacement: Sharper Rates for General Smooth Convex Functions

We study stochastic gradient descent without replacement () for smooth ...

12 Prateek Jain, et al. ∙

research

∙ 01/08/2019

FastGRNN: A Fast, Accurate, Stable and Tiny Kilobyte Sized Gated Recurrent Neural Network

This paper develops the FastRNN and FastGRNN algorithms to address the t...

0 Aditya Kusupati, et al. ∙

research

∙ 05/26/2018

Nonlinear Inductive Matrix Completion based on One-layer Neural Networks

The goal of a recommendation system is to predict the interest of a user...

0 Kai Zhong, et al. ∙

research

∙ 04/03/2018

Neural-Guided Deductive Search for Real-Time Program Synthesis from Examples

Synthesizing user-intended programs from a small number of input-output ...

0 Ashwin J. Vijayakumar, et al. ∙

Prateek Jain

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