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07/05/2023
Meta-Learning Adversarial Bandit Algorithms
We study online meta-learning with bandit feedback, with the goal of imp...
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04/06/2023
Reliable Learning for Test-time Attacks and Distribution Shift
Machine learning algorithms are often used in environments which are not...
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03/25/2023
Learning with Explanation Constraints
While supervised learning assumes the presence of labeled data, we may h...
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02/28/2023
Bicriteria Multidimensional Mechanism Design with Side Information
We develop a versatile new methodology for multidimensional mechanism de...
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02/22/2023
Learning Revenue Maximizing Menus of Lotteries and Two-Part Tariffs
We study learnability of two important classes of mechanisms, menus of l...
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10/07/2022
Label Propagation with Weak Supervision
Semi-supervised learning and weakly supervised learning are important pa...
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07/20/2022
Provably tuning the ElasticNet across instances
An important unresolved challenge in the theory of regularization is to ...
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05/27/2022
Meta-Learning Adversarial Bandits
We study online learning with bandit feedback across multiple tasks, wit...
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04/15/2022
Structural Analysis of Branch-and-Cut and the Learnability of Gomory Mixed Integer Cuts
The incorporation of cutting planes within the branch-and-bound algorith...
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04/07/2022
Faster algorithms for learning to link, align sequences, and price two-part tariffs
Data-driven algorithm configuration is a promising, learning-based appro...
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03/08/2022
Robustly-reliable learners under poisoning attacks
Data poisoning attacks, in which an adversary corrupts a training set wi...
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02/18/2022
Learning Predictions for Algorithms with Predictions
A burgeoning paradigm in algorithm design is the field of algorithms wit...
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11/18/2021
Improved Learning Bounds for Branch-and-Cut
Branch-and-cut is the most widely used algorithm for solving integer pro...
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08/19/2021
Learning-to-learn non-convex piecewise-Lipschitz functions
We analyze the meta-learning of the initialization and step-size of lear...
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06/08/2021
Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing
Tuning hyperparameters is a crucial but arduous part of the machine lear...
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06/08/2021
Sample Complexity of Tree Search Configuration: Cutting Planes and Beyond
Cutting-plane methods have enabled remarkable successes in integer progr...
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03/18/2021
Data driven algorithms for limited labeled data learning
We consider a novel data driven approach for designing learning algorith...
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12/24/2020
Generalization in portfolio-based algorithm selection
Portfolio-based algorithm selection has seen tremendous practical succes...
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12/19/2020
Scalable and Provably Accurate Algorithms for Differentially Private Distributed Decision Tree Learning
This paper introduces the first provably accurate algorithms for differe...
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11/14/2020
Data-driven Algorithm Design
Data driven algorithm design is an important aspect of modern data scien...
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10/10/2020
Noise in Classification
This chapter considers the computational and statistical aspects of lear...
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06/21/2020
Refined bounds for algorithm configuration: The knife-edge of dual class approximability
Automating algorithm configuration is growing increasingly necessary as ...
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04/16/2020
Geometry-Aware Gradient Algorithms for Neural Architecture Search
Many recent state-of-the-art methods for neural architecture search (NAS...
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08/08/2019
How much data is sufficient to learn high-performing algorithms?
Algorithms for scientific analysis typically have tunable parameters tha...
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07/22/2019
Online optimization of piecewise Lipschitz functions in changing environments
In an online optimization problem we are required to choose a sequence o...
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07/01/2019
Learning to Link
Clustering is an important part of many modern data analysis pipelines, ...
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06/06/2019
Adaptive Gradient-Based Meta-Learning Methods
We build a theoretical framework for understanding practical meta-learni...
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05/26/2019
Learning to Optimize Computational Resources: Frugal Training with Generalization Guarantees
Algorithms typically come with tunable parameters that have a considerab...
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04/18/2019
Semi-bandit Optimization in the Dispersed Setting
In this work, we study the problem of online optimization of piecewise L...
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02/27/2019
Provable Guarantees for Gradient-Based Meta-Learning
We study the problem of meta-learning through the lens of online convex ...
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02/25/2019
Estimating Approximate Incentive Compatibility
In practice, most mechanisms for selling, buying, matching, voting, and ...
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10/18/2018
Testing Matrix Rank, Optimally
We show that for the problem of testing if a matrix A ∈ F^n × n has rank...
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09/23/2018
Envy-Free Classification
In classic fair division problems such as cake cutting and rent division...
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09/19/2018
Data-Driven Clustering via Parameterized Lloyd's Families
Algorithms for clustering points in metric spaces is a long-studied area...
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03/27/2018
Learning to Branch
Tree search algorithms, such as branch-and-bound, are the most widely us...
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04/27/2017
Optimal Sample Complexity for Matrix Completion and Related Problems via ℓ_2-Regularization
We study the strong duality of non-convex matrix factorization: we show ...
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03/22/2017
S-Concave Distributions: Towards Broader Distributions for Noise-Tolerant and Sample-Efficient Learning Algorithms
We provide new results concerning noise-tolerant and sample-efficient le...
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11/14/2016
Learning-Theoretic Foundations of Algorithm Configuration for Combinatorial Partitioning Problems
Max-cut, clustering, and many other partitioning problems that are of si...
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02/23/2016
An Improved Gap-Dependency Analysis of the Noisy Power Method
We consider the noisy power method algorithm, which has wide application...
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02/01/2016
Active Learning Algorithms for Graphical Model Selection
The problem of learning the structure of a high dimensional graphical mo...
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12/15/2015
Data Driven Resource Allocation for Distributed Learning
In distributed machine learning, data is dispatched to multiple machines...
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06/21/2015
Communication Efficient Distributed Agnostic Boosting
We consider the problem of learning from distributed data in the agnosti...
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08/09/2014
Efficient Clustering with Limited Distance Information
Given a point set S and an unknown metric d on S, we study the problem o...
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07/21/2014
Scalable Kernel Methods via Doubly Stochastic Gradients
The general perception is that kernel methods are not scalable, and neur...
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04/09/2014
A Distributed Frank-Wolfe Algorithm for Communication-Efficient Sparse Learning
Learning sparse combinations is a frequent theme in machine learning. In...
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12/08/2013
Budgeted Influence Maximization for Multiple Products
The typical algorithmic problem in viral marketing aims to identify a se...
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07/31/2013
The Power of Localization for Efficiently Learning Linear Separators with Noise
We introduce a new approach for designing computationally efficient lear...
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07/11/2013
Statistical Active Learning Algorithms for Noise Tolerance and Differential Privacy
We describe a framework for designing efficient active learning algorith...
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06/03/2013
Distributed k-Means and k-Median Clustering on General Topologies
This paper provides new algorithms for distributed clustering for two po...
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11/06/2012
