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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...
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05/30/2023
AdANNS: A Framework for Adaptive Semantic Search
Web-scale search systems learn an encoder to embed a given query which i...
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02/01/2023
Simplicity Bias in 1-Hidden Layer Neural Networks
Recent works have demonstrated that neural networks exhibit extreme simp...
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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...
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01/30/2023
Near Optimal Private and Robust Linear Regression
We study the canonical statistical estimation problem of linear regressi...
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01/17/2023
Optimal Algorithms for Latent Bandits with Cluster Structure
We consider the problem of latent bandits with cluster structure where t...
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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...
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11/15/2022
Variational Quantum Algorithms for Chemical Simulation and Drug Discovery
Quantum computing has gained a lot of attention recently, and scientists...
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10/11/2022
Multi-User Reinforcement Learning with Low Rank Rewards
In this work, we consider the problem of collaborative multi-user reinfo...
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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...
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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 ...
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09/08/2022
Online Low Rank Matrix Completion
We study the problem of online low-rank matrix completion with 𝖬 users, ...
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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...
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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...
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07/11/2022
(Nearly) Optimal Private Linear Regression via Adaptive Clipping
We study the problem of differentially private linear regression where e...
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06/17/2022
MET: Masked Encoding for Tabular Data
We consider the task of self-supervised representation learning (SSL) fo...
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05/27/2022
DP-PCA: Statistically Optimal and Differentially Private PCA
We study the canonical statistical task of computing the principal compo...
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05/26/2022
Matryoshka Representations for Adaptive Deployment
Learned representations are a central component in modern ML systems, se...
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02/09/2022
Reproducibility in Optimization: Theoretical Framework and Limits
We initiate a formal study of reproducibility in optimization. We define...
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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 ...
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11/23/2021
Node-Level Differentially Private Graph Neural Networks
Graph Neural Networks (GNNs) are a popular technique for modelling graph...
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10/19/2021
Neural Network Compatible Off-Policy Natural Actor-Critic Algorithm
Learning optimal behavior from existing data is one of the most importan...
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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...
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09/28/2021
IGLU: Efficient GCN Training via Lazy Updates
Graph Convolution Networks (GCN) are used in numerous settings involving...
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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 ...
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06/02/2021
LLC: Accurate, Multi-purpose Learnt Low-dimensional Binary Codes
Learning binary representations of instances and classes is a classical ...
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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_...
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05/18/2021
Sample Efficient Linear Meta-Learning by Alternating Minimization
Meta-learning synthesizes and leverages the knowledge from a given set o...
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03/10/2021
Streaming Linear System Identification with Reverse Experience Replay
We consider the problem of estimating a stochastic linear time-invariant...
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02/25/2021
Do Input Gradients Highlight Discriminative Features?
Interpretability methods that seek to explain instance-specific model pr...
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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 ...
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10/05/2020
Projection Efficient Subgradient Method and Optimal Nonsmooth Frank-Wolfe Method
We consider the classical setting of optimizing a nonsmooth Lipschitz co...
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07/14/2020
Programming by Rewards
We formalize and study “programming by rewards” (PBR), a new approach fo...
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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...
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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...
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06/13/2020
The Pitfalls of Simplicity Bias in Neural Networks
Several works have proposed Simplicity Bias (SB)—the tendency of standar...
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04/03/2020
COVID-19: Strategies for Allocation of Test Kits
With the increasing spread of COVID-19, it is important to systematicall...
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02/28/2020
DROCC: Deep Robust One-Class Classification
Classical approaches for one-class problems such as one-class SVM (Schol...
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02/27/2020
RNNPool: Efficient Non-linear Pooling for RAM Constrained Inference
Pooling operators are key components in most Convolutional Neural Networ...
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02/08/2020
Soft Threshold Weight Reparameterization for Learnable Sparsity
Sparsity in Deep Neural Networks (DNNs) is studied extensively with the ...
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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...
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11/26/2019
OASIS: ILP-Guided Synthesis of Loop Invariants
Finding appropriate inductive loop invariants for a program is a key cha...
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07/12/2019
Learning Functions over Sets via Permutation Adversarial Networks
In this paper, we consider the problem of learning functions over sets, ...
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07/02/2019
Efficient Algorithms for Smooth Minimax Optimization
This paper studies first order methods for solving smooth minimax optimi...
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04/29/2019
Making the Last Iterate of SGD Information Theoretically Optimal
Stochastic gradient descent (SGD) is one of the most widely used algorit...
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03/19/2019
Adaptive Hard Thresholding for Near-optimal Consistent Robust Regression
We study the problem of robust linear regression with response variable ...
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03/04/2019
SGD without Replacement: Sharper Rates for General Smooth Convex Functions
We study stochastic gradient descent without replacement () for smooth ...
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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...
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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...
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04/03/2018