research
∙
09/04/2023
On Penalty Methods for Nonconvex Bilevel Optimization and First-Order Stochastic Approximation
In this work, we study first-order algorithms for solving Bilevel Optimi...
share
research
∙
06/15/2023
Feed Two Birds with One Scone: Exploiting Wild Data for Both Out-of-Distribution Generalization and Detection
Modern machine learning models deployed in the wild can encounter both c...
research
∙
02/14/2023
Algorithm Selection for Deep Active Learning with Imbalanced Datasets
Label efficiency has become an increasingly important objective in deep ...
research
∙
01/29/2023
SPEED: Experimental Design for Policy Evaluation in Linear Heteroscedastic Bandits
In this paper, we study the problem of optimal data collection for polic...
research
∙
01/26/2023
A Fully First-Order Method for Stochastic Bilevel Optimization
We consider stochastic unconstrained bilevel optimization problems when ...
research
∙
10/18/2022
OpenStack and Google Cloud performance comparison in Infrastructure as a Service model
Cloud computing is becoming common, and the choice of proper infrastruct...
research
∙
10/15/2022
Active Learning with Neural Networks: Insights from Nonparametric Statistics
Deep neural networks have great representation power, but typically requ...
research
∙
10/13/2022
Fast genomic optical map assembly algorithm using binary representation
Reducing the cost of sequencing genomes provided by next-generation sequ...
research
∙
07/07/2022
One for All: Simultaneous Metric and Preference Learning over Multiple Users
This paper investigates simultaneous preference and metric learning from...
research
∙
03/31/2022
Efficient Active Learning with Abstention
The goal of active learning is to achieve the same accuracy achievable b...
research
∙
03/09/2022
ReVar: Strengthening Policy Evaluation via Reduced Variance Sampling
This paper studies the problem of data collection for policy evaluation ...
research
∙
02/07/2022
Training OOD Detectors in their Natural Habitats
Out-of-distribution (OOD) detection is important for machine learning mo...
research
∙
02/03/2022
GALAXY: Graph-based Active Learning at the Extreme
Active learning is a label-efficient approach to train highly effective ...
research
∙
11/02/2021
Nearly Optimal Algorithms for Level Set Estimation
The level set estimation problem seeks to find all points in a domain X ...
research
∙
09/10/2021
Near Instance Optimal Model Selection for Pure Exploration Linear Bandits
The model selection problem in the pure exploration linear bandit settin...
research
∙
06/22/2021
Pure Exploration in Kernel and Neural Bandits
We study pure exploration in bandits, where the dimension of the feature...
research
∙
03/08/2021
Nearest Neighbor Search Under Uncertainty
Nearest Neighbor Search (NNS) is a central task in knowledge representat...
research
∙
02/12/2021
Pareto Optimal Model Selection in Linear Bandits
We study a model selection problem in the linear bandit setting, where t...
research
∙
12/15/2020
Generalized Chernoff Sampling for Active Learning and Structured Bandit Algorithms
Active learning and structured stochastic bandit problems are intimately...
research
∙
09/21/2020
Robust Outlier Arm Identification
We study the problem of Robust Outlier Arm Identification (ROAI), where ...
research
∙
06/26/2020
On Regret with Multiple Best Arms
We study regret minimization problem with the existence of multiple best...
research
∙
06/16/2020
Finding All ε-Good Arms in Stochastic Bandits
The pure-exploration problem in stochastic multi-armed bandits aims to f...
research
∙
12/07/2019
Tighter Confidence Intervals for Rating Systems
Rating systems are ubiquitous, with applications ranging from product re...
research
∙
06/06/2019
Should Adversarial Attacks Use Pixel p-Norm?
Adversarial attacks aim to confound machine learning systems, while rema...
research
∙
06/03/2019
MaxGap Bandit: Adaptive Algorithms for Approximate Ranking
This paper studies the problem of adaptively sampling from K distributio...
research
∙
05/30/2019
Learning Nearest Neighbor Graphs from Noisy Distance Samples
We consider the problem of learning the nearest neighbor graph of a data...
research
∙
03/09/2019
Linear Bandits with Feature Feedback
This paper explores a new form of the linear bandit problem in which the...
research
∙
01/08/2019
Bilinear Bandits with Low-rank Structure
We introduce the bilinear bandit problem with low-rank structure where a...
research
∙
07/10/2018
Scalable Sparse Subspace Clustering via Ordered Weighted ℓ_1 Regression
The main contribution of the paper is a new approach to subspace cluster...
research
∙
02/25/2018
Teacher Improves Learning by Selecting a Training Subset
We call a learner super-teachable if a teacher can trim down an iid trai...
research
∙
02/20/2018
Adaptive Sampling for Coarse Ranking
We consider the problem of active coarse ranking, where the goal is to s...
research
∙
09/18/2017
Learning Low-Dimensional Metrics
This paper investigates the theoretical foundations of metric learning, ...
research
∙
06/01/2017
Scalable Generalized Linear Bandits: Online Computation and Hashing
Generalized Linear Bandits (GLBs), a natural extension of the stochastic...
research
∙
09/03/2016
Graph-Based Active Learning: A New Look at Expected Error Minimization
In graph-based active learning, algorithms based on expected error minim...
research
∙
06/22/2016
Finite Sample Prediction and Recovery Bounds for Ordinal Embedding
The goal of ordinal embedding is to represent items as points in a low-d...
research
∙
03/14/2016
Active Algorithms For Preference Learning Problems with Multiple Populations
In this paper we model the problem of learning preferences of a populati...
research
∙
03/13/2016
On Learning High Dimensional Structured Single Index Models
Single Index Models (SIMs) are simple yet flexible semi-parametric model...
research
∙
06/30/2015
Learning Single Index Models in High Dimensions
Single Index Models (SIMs) are simple yet flexible semi-parametric model...
research
∙
06/29/2015
S2: An Efficient Graph Based Active Learning Algorithm with Application to Nonparametric Classification
This paper investigates the problem of active learning for binary label ...
research
∙
01/31/2015
Sparse Dueling Bandits
The dueling bandit problem is a variation of the classical multi-armed b...
research
∙
04/28/2014
Data Requirement for Phylogenetic Inference from Multiple Loci: A New Distance Method
We consider the problem of estimating the evolutionary history of a set ...
research
∙
02/18/2014
Classification with Sparse Overlapping Groups
Classification with a sparsity constraint on the solution plays a centra...
research
∙
12/27/2013
lil' UCB : An Optimal Exploration Algorithm for Multi-Armed Bandits
The paper proposes a novel upper confidence bound (UCB) procedure for id...
research
∙
11/20/2013
Sparse Overlapping Sets Lasso for Multitask Learning and its Application to fMRI Analysis
Multitask learning can be effective when features useful in one task are...
research
∙
06/17/2013
On Finding the Largest Mean Among Many
Sampling from distributions to find the one with the largest mean arises...
research
∙
04/17/2013
A Junction Tree Framework for Undirected Graphical Model Selection
An undirected graphical model is a joint probability distribution define...
research
∙
09/14/2012
Signal Recovery in Unions of Subspaces with Applications to Compressive Imaging
In applications ranging from communications to genetics, signals can be ...
research
∙
04/10/2012
Robust Spatio-Temporal Signal Recovery from Noisy Counts in Social Media
Many real-world phenomena can be represented by a spatio-temporal signal...
research
∙
12/23/2011
High-Rank Matrix Completion and Subspace Clustering with Missing Data
This paper considers the problem of completing a matrix with many missin...
research
∙
07/29/2011
