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06/29/2023
Sparse Model Soups: A Recipe for Improved Pruning via Model Averaging
Neural networks can be significantly compressed by pruning, leading to s...
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05/25/2023
Accelerated Methods for Riemannian Min-Max Optimization Ensuring Bounded Geometric Penalties
In this work, we study optimization problems of the form min_x max_y f(x...
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04/04/2023
Online Learning for Scheduling MIP Heuristics
Mixed Integer Programming (MIP) is NP-hard, and yet modern solvers often...
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11/26/2022
Accelerated Riemannian Optimization: Handling Constraints with a Prox to Bound Geometric Penalties
We propose a globally-accelerated, first-order method for the optimizati...
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08/23/2022
Convex integer optimization with Frank-Wolfe methods
Mixed-integer nonlinear optimization is a broad class of problems that f...
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07/04/2022
Approximate Vanishing Ideal Computations at Scale
The approximate vanishing ideal of a set of points X = {𝐱_1, …, 𝐱_m}⊆ [0...
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06/01/2022
Merlin-Arthur Classifiers: Formal Interpretability with Interactive Black Boxes
We present a new theoretical framework for making black box classifiers ...
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05/24/2022
Compression-aware Training of Neural Networks using Frank-Wolfe
Many existing Neural Network pruning approaches either rely on retrainin...
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04/11/2022
Optimizing a low-dimensional convex function over a high-dimensional cube
For a matrix W ∈ℤ^m × n, m ≤ n, and a convex function g: ℝ^m →ℝ, we are ...
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03/04/2022
The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights
Combinatorial optimization is a well-established area in operations rese...
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02/23/2022
Training Characteristic Functions with Reinforcement Learning: XAI-methods play Connect Four
One of the goals of Explainable AI (XAI) is to determine which input com...
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11/01/2021
Back to Basics: Efficient Network Compression via IMP
Network pruning is a widely used technique for effectively compressing D...
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10/15/2021
Interpretable Neural Networks with Frank-Wolfe: Sparse Relevance Maps and Relevance Orderings
We study the effects of constrained optimization formulations and Frank-...
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05/28/2021
Efficient Online-Bandit Strategies for Minimax Learning Problems
Several learning problems involve solving min-max problems, e.g., empiri...
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05/28/2021
Simple steps are all you need: Frank-Wolfe and generalized self-concordant functions
Generalized self-concordance is a key property present in the objective ...
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03/18/2021
Learning to Schedule Heuristics in Branch-and-Bound
Primal heuristics play a crucial role in exact solvers for Mixed Integer...
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03/10/2021
Linear Bandits on Uniformly Convex Sets
Linear bandit algorithms yield 𝒪̃(n√(T)) pseudo-regret bounds on compact...
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02/12/2021
Parameter-free Locally Accelerated Conditional Gradients
Projection-free conditional gradient (CG) methods are the algorithms of ...
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02/09/2021
Local and Global Uniform Convexity Conditions
We review various characterizations of uniform convexity and smoothness ...
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01/27/2021
Adversaries in Online Learning Revisited: with applications in Robust Optimization and Adversarial training
We revisit the concept of "adversary" in online learning, motivated by s...
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01/25/2021
Complexity of Linear Minimization and Projection on Some Sets
The Frank-Wolfe algorithm is a method for constrained optimization that ...
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01/07/2021
CINDy: Conditional gradient-based Identification of Non-linear Dynamics – Noise-robust recovery
Governing equations are essential to the study of nonlinear dynamics, of...
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10/14/2020
Deep Neural Network Training with Frank-Wolfe
This paper studies the empirical efficacy and benefits of using projecti...
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09/29/2020
Projection-Free Adaptive Gradients for Large-Scale Optimization
The complexity in large-scale optimization can lie in both handling the ...
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09/16/2020
Accelerating Domain Propagation: an Efficient GPU-Parallel Algorithm over Sparse Matrices
Fast domain propagation of linear constraints has become a crucial compo...
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06/15/2020
Walking in the Shadow: A New Perspective on Descent Directions for Constrained Minimization
Descent directions such as movement towards Frank-Wolfe vertices, away s...
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03/13/2020
Boosting Frank-Wolfe by Chasing Gradients
The Frank-Wolfe algorithm has become a popular first-order optimization ...
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02/20/2020
Second-order Conditional Gradients
Constrained second-order convex optimization algorithms are the method o...
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02/11/2020
IPBoost – Non-Convex Boosting via Integer Programming
Recently non-convex optimization approaches for solving machine learning...
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02/10/2020
On the Unreasonable Effectiveness of the Greedy Algorithm: Greedy Adapts to Sharpness
Submodular maximization has been widely studied over the past decades, m...
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11/11/2019
Revisiting the Approximate Carathéodory Problem via the Frank-Wolfe Algorithm
The approximate Carathéodory theorem states that given a polytope P, eac...
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06/19/2019
Locally Accelerated Conditional Gradients
Conditional gradient methods form a class of projection-free first-order...
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04/28/2019
Blended Matching Pursuit
Matching pursuit algorithms are an important class of algorithms in sign...
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10/30/2018
An Online-Learning Approach to Inverse Optimization
In this paper, we demonstrate how to learn the objective function of a d...
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10/07/2018
Principled Deep Neural Network Training through Linear Programming
Deep Learning has received significant attention due to its impressive p...
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07/25/2018
Efficient algorithms for robust submodular maximization under matroid constraints
In this work, we consider robust submodular maximization with matroid co...
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07/06/2018
Limits of Treewidth-based tractability in Optimization
Sparse structures are frequently sought when pursuing tractability in op...
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05/18/2018
Blended Conditional Gradients: the unconditioning of conditional gradients
We present a blended conditional gradient approach for minimizing a smoo...
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06/15/2017
Reinforcement Learning under Model Mismatch
We study reinforcement learning under model misspecification, where we d...
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03/16/2017
Conditional Accelerated Lazy Stochastic Gradient Descent
In this work we introduce a conditional accelerated lazy stochastic grad...
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09/01/2015
Sequential Information Guided Sensing
We study the value of information in sequential compressed sensing by ch...
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01/26/2015
Sequential Sensing with Model Mismatch
We characterize the performance of sequential information guided sensing...
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07/02/2014
