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07/31/2023
Pretrained deep models outperform GBDTs in Learning-To-Rank under label scarcity
While deep learning (DL) models are state-of-the-art in text and image d...
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06/15/2023
Logarithmic Bayes Regret Bounds
We derive the first finite-time logarithmic regret bounds for Bayesian b...
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01/30/2023
Understanding Self-Distillation in the Presence of Label Noise
Self-distillation (SD) is the process of first training a teacher model ...
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12/17/2022
Latent Variable Representation for Reinforcement Learning
Deep latent variable models have achieved significant empirical successe...
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11/15/2022
Bayesian Fixed-Budget Best-Arm Identification
Fixed-budget best-arm identification (BAI) is a bandit problem where the...
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09/19/2022
Toward Understanding Privileged Features Distillation in Learning-to-Rank
In learning-to-rank problems, a privileged feature is one that is availa...
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08/11/2022
On the Value of Behavioral Representations for Dense Retrieval
We consider text retrieval within dense representational space in real-w...
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05/23/2022
Beyond EM Algorithm on Over-specified Two-Component Location-Scale Gaussian Mixtures
The Expectation-Maximization (EM) algorithm has been predominantly used ...
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05/16/2022
An Exponentially Increasing Step-size for Parameter Estimation in Statistical Models
Using gradient descent (GD) with fixed or decaying step-size is standard...
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03/23/2022
Minimax Regret for Cascading Bandits
Cascading bandits model the task of learning to rank K out of L items ov...
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02/24/2022
Sample Efficiency of Data Augmentation Consistency Regularization
Data augmentation is popular in the training of large neural networks; c...
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02/09/2022
Improving Computational Complexity in Statistical Models with Second-Order Information
It is known that when the statistical models are singular, i.e., the Fis...
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10/15/2021
Towards Statistical and Computational Complexities of Polyak Step Size Gradient Descent
We study the statistical and computational complexities of the Polyak st...
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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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06/13/2021
DP-NormFedAvg: Normalizing Client Updates for Privacy-Preserving Federated Learning
In this paper, we focus on facilitating differentially private quantized...
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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 ...
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03/25/2021
Nearly Horizon-Free Offline Reinforcement Learning
We revisit offline reinforcement learning on episodic time-homogeneous t...
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03/03/2021
Combinatorial Bandits without Total Order for Arms
We consider the combinatorial bandits problem, where at each time step, ...
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03/03/2021
Linear Bandit Algorithms with Sublinear Time Complexity
We propose to accelerate existing linear bandit algorithms to achieve pe...
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12/07/2020
Improved Convergence Rates for Non-Convex Federated Learning with Compression
Federated learning is a new distributed learning paradigm that enables e...
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11/28/2020
On Generalization of Adaptive Methods for Over-parameterized Linear Regression
Over-parameterization and adaptive methods have played a crucial role in...
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11/20/2020
On the Benefits of Multiple Gossip Steps in Communication-Constrained Decentralized Optimization
In decentralized optimization, it is common algorithmic practice to have...
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04/01/2020
Extreme Multi-label Classification from Aggregated Labels
Extreme multi-label classification (XMC) is the problem of finding the r...
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01/10/2020
Choosing the Sample with Lowest Loss makes SGD Robust
The presence of outliers can potentially significantly skew the paramete...
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11/08/2019
Interaction Hard Thresholding: Consistent Sparse Quadratic Regression in Sub-quadratic Time and Space
Quadratic regression involves modeling the response as a (generalized) l...
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09/04/2019
Learning Distributions Generated by One-Layer ReLU Networks
We consider the problem of estimating the parameters of a d-dimensional ...
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07/27/2019
Blocking Bandits
We consider a novel stochastic multi-armed bandit setting, where playing...
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02/10/2019
Iterative Least Trimmed Squares for Mixed Linear Regression
Given a linear regression setting, Iterative Least Trimmed Squares (ILTS...
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11/16/2018
Minimum norm solutions do not always generalize well for over-parameterized problems
Stochastic gradient descent is the de facto algorithm for training deep ...
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10/28/2018
Sparse Logistic Regression Learns All Discrete Pairwise Graphical Models
We characterize the effectiveness of a natural and classic algorithm for...
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10/28/2018
Iteratively Learning from the Best
We study a simple generic framework to address the issue of bad training...
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06/26/2018
The Sparse Recovery Autoencoder
Linear encoding of sparse vectors is widely popular, but is most commonl...
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11/04/2017
Provable quantum state tomography via non-convex methods
With nowadays steadily growing quantum processors, it is required to dev...
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03/08/2017
Sparse Quadratic Logistic Regression in Sub-quadratic Time
We consider support recovery in the quadratic logistic regression settin...
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10/21/2016
Single Pass PCA of Matrix Products
In this paper we present a new algorithm for computing a low rank approx...
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10/04/2016
The Search Problem in Mixture Models
We consider the task of learning the parameters of a single component o...
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09/12/2016
Non-square matrix sensing without spurious local minima via the Burer-Monteiro approach
We consider the non-square matrix sensing problem, under restricted isom...
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08/19/2016
Solving a Mixture of Many Random Linear Equations by Tensor Decomposition and Alternating Minimization
We consider the problem of solving mixed random linear equations with k ...
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06/04/2016
Provable Burer-Monteiro factorization for a class of norm-constrained matrix problems
We study the projected gradient descent method on low-rank matrix proble...
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03/22/2016
Trading-off variance and complexity in stochastic gradient descent
Stochastic gradient descent is the method of choice for large-scale mach...
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09/14/2015
Dropping Convexity for Faster Semi-definite Optimization
We study the minimization of a convex function f(X) over the set of n× n...
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06/25/2015
The local convexity of solving systems of quadratic equations
This paper considers the recovery of a rank r positive semidefinite matr...
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06/08/2015
Convergence Rates of Active Learning for Maximum Likelihood Estimation
An active learner is given a class of models, a large set of unlabeled e...
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02/17/2015
A New Sampling Technique for Tensors
In this paper we propose new techniques to sample arbitrary third-order ...
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10/31/2014
Greedy Subspace Clustering
We consider the problem of subspace clustering: given points that lie on...
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10/28/2014
Non-convex Robust PCA
We propose a new method for robust PCA -- the task of recovering a low-r...
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10/14/2014
Tighter Low-rank Approximation via Sampling the Leveraged Element
In this work, we propose a new randomized algorithm for computing a low-...
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06/12/2013
Completing Any Low-rank Matrix, Provably
Matrix completion, i.e., the exact and provable recovery of a low-rank m...
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06/02/2013
Phase Retrieval using Alternating Minimization
Phase retrieval problems involve solving linear equations, but with miss...
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12/03/2012