Search Results for author:
Found 13 papers, 10 papers with code
How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy
However, while some adoption of DP has happened in industry, attempts to apply DP to real world complex ML models are still few and far between.
Fast as CHITA: Neural Network Pruning with Combinatorial Optimization
Our approach, CHITA, extends the classical Optimal Brain Surgeon framework and results in significant improvements in speed, memory, and performance over existing optimization-based approaches for network pruning.
Mind the (optimality) Gap: A Gap-Aware Learning Rate Scheduler for Adversarial Nets
We run large-scale experiments to study the effectiveness of the scheduler on two popular applications: GANs for image generation and adversarial nets for domain adaptation.
Benchmarking Robustness to Adversarial Image Obfuscations
We evaluate 33 pretrained models on the benchmark and train models with different augmentations, architectures and training methods on subsets of the obfuscations to measure generalization.
Flexible Modeling and Multitask Learning using Differentiable Tree Ensembles
We therefore propose a novel tensor-based formulation of differentiable trees that allows for efficient vectorization on GPUs.
L0Learn: A Scalable Package for Sparse Learning using L0 Regularization
We introduce L0Learn: an open-source package for sparse regression and classification using L0 regularization.
DSelect-k: Differentiable Selection in the Mixture of Experts with Applications to Multi-Task Learning
State-of-the-art MoE models use a trainable sparse gate to select a subset of the experts for each input example.
Grouped Variable Selection with Discrete Optimization: Computational and Statistical Perspectives
Our algorithmic framework consists of approximate and exact algorithms.
Sparse Regression at Scale: Branch-and-Bound rooted in First-Order Optimization
In this work, we present a new exact MIP framework for $\ell_0\ell_2$-regularized regression that can scale to $p \sim 10^7$, achieving speedups of at least $5000$x, compared to state-of-the-art exact methods.
The Tree Ensemble Layer: Differentiability meets Conditional Computation
We aim to combine these advantages by introducing a new layer for neural networks, composed of an ensemble of differentiable decision trees (a. k. a.
Learning Sparse Classifiers: Continuous and Mixed Integer Optimization Perspectives
We aim to bridge this gap in computation times by developing new MIP-based algorithms for $\ell_0$-regularized classification.
Learning Hierarchical Interactions at Scale: A Convex Optimization Approach
In addition, we introduce a specialized active-set strategy with gradient screening for avoiding costly gradient computations.
Fast Best Subset Selection: Coordinate Descent and Local Combinatorial Optimization Algorithms
In spite of the usefulness of $L_0$-based estimators and generic MIO solvers, there is a steep computational price to pay when compared to popular sparse learning algorithms (e. g., based on $L_1$ regularization).
