Inderjit S. Dhillon

research

∙ 11/01/2022

Preserving In-Context Learning ability in Large Language Model Fine-tuning

Pretrained large language models (LLMs) are strong in-context learners t...

5 Yihan Wang, et al. ∙

research

∙ 10/16/2022

End-to-End Learning to Index and Search in Large Output Spaces

Extreme multi-label classification (XMC) is a popular framework for solv...

22 Nilesh Gupta, et al. ∙

research

∙ 06/22/2022

FINGER: Fast Inference for Graph-based Approximate Nearest Neighbor Search

Approximate K-Nearest Neighbor Search (AKNNS) has now become ubiquitous ...

21 Patrick H. Chen, et al. ∙

research

∙ 04/22/2022

Counterfactual Learning To Rank for Utility-Maximizing Query Autocompletion

Conventional methods for query autocompletion aim to predict which compl...

8 Adam Block, et al. ∙

research

∙ 02/24/2022

Sample Efficiency of Data Augmentation Consistency Regularization

Data augmentation is popular in the training of large neural networks; c...

7 Shuo Yang, et al. ∙

research

∙ 10/29/2021

Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Prediction

Learning on graphs has attracted significant attention in the learning c...

19 Eli Chien, et al. ∙

research

∙ 10/01/2021

Fast Multi-Resolution Transformer Fine-tuning for Extreme Multi-label Text Classification

Extreme multi-label text classification (XMC) seeks to find relevant lab...

9 Jiong Zhang, et al. ∙

research

∙ 06/24/2021

Label Disentanglement in Partition-based Extreme Multilabel Classification

Partition-based methods are increasingly-used in extreme multi-label cla...

0 Xuanqing Liu, et al. ∙

research

∙ 06/23/2021

Extreme Multi-label Learning for Semantic Matching in Product Search

We consider the problem of semantic matching in product search: given a ...

4 Wei-Cheng Chang, 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

∙ 06/13/2021

DP-NormFedAvg: Normalizing Client Updates for Privacy-Preserving Federated Learning

In this paper, we focus on facilitating differentially private quantized...

20 Rudrajit Das, et al. ∙

research

∙ 06/01/2021

Enabling Efficiency-Precision Trade-offs for Label Trees in Extreme Classification

Extreme multi-label classification (XMC) aims to learn a model that can ...

26 Tavor Z. Baharav, et al. ∙

research

∙ 03/03/2021

Combinatorial Bandits without Total Order for Arms

We consider the combinatorial bandits problem, where at each time step, ...

5 Shuo Yang, et al. ∙

research

∙ 03/03/2021

Linear Bandit Algorithms with Sublinear Time Complexity

We propose to accelerate existing linear bandit algorithms to achieve pe...

18 Shuo Yang, et al. ∙

research

∙ 12/09/2020

Session-Aware Query Auto-completion using Extreme Multi-label Ranking

Query auto-completion is a fundamental feature in search engines where t...

0 Nishant Yadav, et al. ∙

research

∙ 12/07/2020

Improved Convergence Rates for Non-Convex Federated Learning with Compression

Federated learning is a new distributed learning paradigm that enables e...

14 Rudrajit Das, et al. ∙

research

∙ 10/12/2020

PECOS: Prediction for Enormous and Correlated Output Spaces

Many challenging problems in modern applications amount to finding relev...

0 Hsiang-Fu Yu, et al. ∙

research

∙ 04/24/2020

Non-Exhaustive, Overlapping Co-Clustering: An Extended Analysis

The goal of co-clustering is to simultaneously identify a clustering of ...

1 Joyce Jiyoung Whang, et al. ∙

research

∙ 08/27/2019

Multiresolution Transformer Networks: Recurrence is Not Essential for Modeling Hierarchical Structure

The architecture of Transformer is based entirely on self-attention, and...

18 Vikas K. Garg, et al. ∙

research

∙ 06/18/2019

Inverting Deep Generative models, One layer at a time

We study the problem of inverting a deep generative model with ReLU acti...

23 Qi Lei, et al. ∙

research

∙ 06/06/2019

Primal-Dual Block Frank-Wolfe

We propose a variant of the Frank-Wolfe algorithm for solving a class of...

1 Qi Lei, et al. ∙

research

∙ 05/08/2019

AutoAssist: A Framework to Accelerate Training of Deep Neural Networks

Deep neural networks have yielded superior performance in many applicati...

28 Jiong Zhang, et al. ∙

research

∙ 03/29/2019

SysML: The New Frontier of Machine Learning Systems

Machine learning (ML) techniques are enjoying rapidly increasing adoptio...

0 Alexander Ratner, et al. ∙

research

∙ 01/15/2019

The Limitations of Adversarial Training and the Blind-Spot Attack

The adversarial training procedure proposed by Madry et al. (2018) is on...

22 Huan Zhang, et al. ∙

research

∙ 12/01/2018

Discrete Attacks and Submodular Optimization with Applications to Text Classification

Adversarial examples are carefully constructed modifications to an input...

0 Qi Lei, 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

∙ 04/25/2018

Towards Fast Computation of Certified Robustness for ReLU Networks

Verifying the robustness property of a general Rectified Linear Unit (Re...

0 Tsui-Wei Weng, et al. ∙

research

∙ 03/25/2018

Stabilizing Gradients for Deep Neural Networks via Efficient SVD Parameterization

Vanishing and exploding gradients are two of the main obstacles in train...

0 Jiong Zhang, et al. ∙

research

∙ 03/17/2018

Learning Long Term Dependencies via Fourier Recurrent Units

It is a known fact that training recurrent neural networks for tasks tha...

0 Jiong Zhang, et al. ∙

research

∙ 11/08/2017

Learning Non-overlapping Convolutional Neural Networks with Multiple Kernels

In this paper, we consider parameter recovery for non-overlapping convol...

0 Kai Zhong, et al. ∙

research

∙ 06/10/2017

Recovery Guarantees for One-hidden-layer Neural Networks

In this paper, we consider regression problems with one-hidden-layer neu...

0 Kai Zhong, et al. ∙

research

∙ 02/12/2017

Similarity Preserving Representation Learning for Time Series Analysis

A considerable amount of machine learning algorithms take instance-featu...

0 Qi Lei, et al. ∙

research

∙ 06/02/2016

Generalized Root Models: Beyond Pairwise Graphical Models for Univariate Exponential Families

We present a novel k-way high-dimensional graphical model called the Gen...

0 David I. Inouye, et al. ∙

research

∙ 05/31/2016

Extreme Stochastic Variational Inference: Distributed and Asynchronous

We propose extreme stochastic variational inference (ESVI), an asynchron...

0 Jiong Zhang, et al. ∙

research

∙ 03/11/2016

Square Root Graphical Models: Multivariate Generalizations of Univariate Exponential Families that Permit Positive Dependencies

We develop Square Root Graphical Models (SQR), a novel class of parametr...

0 David I. Inouye, et al. ∙

research

∙ 09/28/2015

High-dimensional Time Series Prediction with Missing Values

High-dimensional time series prediction is needed in applications as div...

0 Hsiang-Fu Yu, et al. ∙

research

∙ 05/07/2015

Optimal Decision-Theoretic Classification Using Non-Decomposable Performance Metrics

We provide a general theoretical analysis of expected out-of-sample util...

0 Nagarajan Natarajan, et al. ∙

research

∙ 11/22/2014

PU Learning for Matrix Completion

In this paper, we consider the matrix completion problem when the observ...

0 Cho-Jui Hsieh, et al. ∙

research

∙ 06/27/2014

Proximal Quasi-Newton for Computationally Intensive L1-regularized M-estimators

We consider the class of optimization problems arising from computationa...

0 Kai Zhong, et al. ∙

research

∙ 06/13/2013

Sparse Inverse Covariance Matrix Estimation Using Quadratic Approximation

The L1-regularized Gaussian maximum likelihood estimator (MLE) has been ...

0 Cho-Jui Hsieh, et al. ∙

research

∙ 06/04/2013

Provable Inductive Matrix Completion

Consider a movie recommendation system where apart from the ratings info...

0 Prateek Jain, et al. ∙

research

∙ 06/14/2011

Orthogonal Matching Pursuit with Replacement

In this paper, we consider the problem of compressed sensing where the g...

0 Prateek Jain, et al. ∙

research

∙ 10/30/2009

Metric and Kernel Learning using a Linear Transformation

Metric and kernel learning are important in several machine learning app...

0 Prateek Jain, et al. ∙

Inderjit S. Dhillon

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