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2021-05
Repurposing Pretrained Models for Robust Out-of-domain Few-Shot Learning
2020-11
Geometry-Aware Universal Mirror-Prox.
On the Convergence of Continuous Constrained Optimization for Structure Learning.
2020-09
Flight-connection Prediction for Airline Crew Scheduling to Construct Initial Clusters for OR Optimizer.
Machine learning in airline crew pairing to construct initial clusters for dynamic constraint aggregation
Machine Learning in Airline Crew Pairing to Construct Initial Clusters for Dynamic Constraint Aggregation
2020-08
Implicit Regularization in Deep Learning: A View from Function Space
2020-07
Are Few-Shot Learning Benchmarks too Simple ? Solving them without Task Supervision at Test-Time.
arXiv: Learning
(2020-07-27)
Stochastic Hamiltonian Gradient Methods for Smooth Games
Differentiable Causal Discovery from Interventional Data.
Adversarial Example Games.
2020-06
To Each Optimizer a Norm, To Each Norm its Generalization.
Adaptive Gradient Methods Converge Faster with Over-Parameterization (and you can do a line-search).
Fast and Furious Convergence: Stochastic Second Order Methods under Interpolation.
AISTATS 2020
(2020-06-03)
proceedings.mlr.pressPDF[Also on arXiv preprint arXiv:1910.04920 (2019-10-11)]2020-05
An Analysis of the Adaptation Speed of Causal Models
2020-04
Gradient-Based Neural DAG Learning
A Closer Look at the Optimization Landscapes of Generative Adversarial Networks
2020-02
Stochastic Polyak Step-size for SGD: An Adaptive Learning Rate for Fast Convergence.
Machine learning in airline crew pairing to construct initial clusters for dynamic constraint aggregation
2020-01
Differentiable Causal Discovery from Interventional Data
Adversarial Example Games
GAIT: A Geometric Approach to Information Theory.
A Tight and Unified Analysis of Gradient-Based Methods for a Whole Spectrum of Differentiable Games.
Accelerating Smooth Games by Manipulating Spectral Shapes.
AISTATS 2020
(2020-01-01)
proceedings.mlr.press[LATEST on arXiv preprint arXiv:2001.00602 (2020-01-02)]2019-12
Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates
Implicit Regularization of Discrete Gradient Dynamics in Deep Linear Neural Networks
Reducing Noise in GAN Training with Variance Reduced Extragradient
2019-09
Are Few-shot Learning Benchmarks Too Simple ?
Scattering Networks for Hybrid Representation Learning
2019-06
GEAR: Geometry-Aware Rényi Information.
GAIT: A Geometric Approach to Information Theory
A Tight and Unified Analysis of Gradient-Based Methods for a Whole Spectrum of Games
A Tight and Unified Analysis of Extragradient for a Whole Spectrum of Differentiable Games.
2019-05
2019-04
Implicit Regularization of Discrete Gradient Dynamics in Linear Neural Networks.
Negative Momentum for Improved Game Dynamics
Flight-connection prediction for airline crew scheduling to construct initial clusters for OR optimizer
2019-02
Centroid Networks for Few-Shot Clustering and Unsupervised Few-Shot Classification.
2018-12
Quantifying Learning Guarantees for Convex but Inconsistent Surrogates
2018-09
A Modern Take on the Bias-Variance Tradeoff in Neural Networks
Learning from Narrated Instruction Videos
IEEE Transactions on Pattern Analysis and Machine Intelligence
(2018-09-01)
hal.archives-ouvertes.frPDF2018-08
2018-07
Predicting Tactical Solutions to Operational Planning Problems under Imperfect Information
Predicting Solution Summaries to Integer Linear Programs under Imperfect Information with Machine Learning.
Negative Momentum for Improved Game Dynamics.
2018-03
Frank-Wolfe Splitting via Augmented Lagrangian Method
AISTATS 2018
(2018-03-31)
proceedings.mlr.pressPDF[LATEST on arXiv preprint arXiv:1804.03176 (2018-04-09)]2018-02
A Variational Inequality Perspective on Generative Adversarial Nets
SEARNN: Training RNNs with global-local losses
Parametric Adversarial Divergences are Good Task Losses for Generative Modeling
2018-01
A3T: Adversarially Augmented Adversarial Training.
Improved asynchronous parallel optimization analysis for stochastic incremental methods
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