133 citations · 298 across the 14 of their papers we have counts for
16 papers
Lookback for Learning to Branch
Prateek Gupta, Elias B. Khalil, Didier Chetélat +4
The expressive and computationally inexpensive bipartite Graph Neural Networks (GNN) have been shown to be an important component of deep learning based Mixed-Integer Linear Progra…
IBP Regularization for Verified Adversarial Robustness via Branch-and-Bound
Alessandro De Palma, Rudy Bunel, Krishnamurthy Dvijotham +2
Recent works have tried to increase the verifiability of adversarially trained networks by running the attacks over domains larger than the original perturbations and adding variou…
In Defense of the Unitary Scalarization for Deep Multi-Task Learning
Vitaly Kurin, Alessandro De Palma, Ilya Kostrikov +2
Recent multi-task learning research argues against unitary scalarization, where training simply minimizes the sum of the task losses. Several ad-hoc multi-task optimization algorit…
ANCER: Anisotropic Certification via Sample-wise Volume Maximization
Francisco Eiras, Motasem Alfarra, M. Pawan Kumar +4
Randomized smoothing has recently emerged as an effective tool that enables certification of deep neural network classifiers at scale. All prior art on randomized smoothing has foc…
Make Sure You're Unsure: A Framework for Verifying Probabilistic Specifications
Leonard Berrada, Sumanth Dathathri, Krishnamurthy Dvijotham +5
Most real world applications require dealing with stochasticity like sensor noise or predictive uncertainty, where formal specifications of desired behavior are inherently probabil…
Scaling the Convex Barrier with Sparse Dual Algorithms
Alessandro De Palma, Harkirat Singh Behl, Rudy Bunel +2
Tight and efficient neural network bounding is crucial to the scaling of neural network verification systems. Many efficient bounding algorithms have been presented recently, but t…
Branch and Bound for Piecewise Linear Neural Network Verification
Rudy Bunel, Jingyue Lu, Ilker Turkaslan +3
The success of Deep Learning and its potential use in many safety-critical applications has motivated research on formal verification of Neural Network (NN) models. In this context…
A Unified View of Piecewise Linear Neural Network Verification
Rudy Bunel, Ilker Turkaslan, Philip H. S. Torr +2
The success of Deep Learning and its potential use in many safety-critical applications has motivated research on formal verification of Neural Network (NN) models. Despite the rep…
Learning to superoptimize programs - Workshop Version
Rudy Bunel, Alban Desmaison, M. Pawan Kumar +2
Superoptimization requires the estimation of the best program for a given computational task. In order to deal with large programs, superoptimization techniques perform a stochasti…
Efficient Linear Programming for Dense CRFs
Thalaiyasingam Ajanthan, Alban Desmaison, Rudy Bunel +3
The fully connected conditional random field (CRF) with Gaussian pairwise potentials has proven popular and effective for multi-class semantic segmentation. While the energy of a d…
Trusting SVM for Piecewise Linear CNNs
Leonard Berrada, Andrew Zisserman, M. Pawan Kumar
We present a novel layerwise optimization algorithm for the learning objective of Piecewise-Linear Convolutional Neural Networks (PL-CNNs), a large class of convolutional neural ne…
Learning to superoptimize programs
Rudy Bunel, Alban Desmaison, M. Pawan Kumar +2
Code super-optimization is the task of transforming any given program to a more efficient version while preserving its input-output behaviour. In some sense, it is similar to the p…
Efficient Optimization for Rank-based Loss Functions
Pritish Mohapatra, Michal Rolinek, C. V. Jawahar +2
The accuracy of information retrieval systems is often measured using complex loss functions such as the average precision (AP) or the normalized discounted cumulative gain (NDCG).…
Truncated Max-of-Convex Models
Pankaj Pansari, M. Pawan Kumar
Truncated convex models (TCM) are a special case of pairwise random fields that have been widely used in computer vision. However, by restricting the order of the potentials to be…
Modeling Latent Variable Uncertainty for Loss-based Learning
M. Pawan Kumar, Ben Packer, Daphne Koller
We consider the problem of parameter estimation using weakly supervised datasets, where a training sample consists of the input and a partially specified annotation, which we refer…
MAP Estimation of Semi-Metric MRFs via Hierarchical Graph Cuts
M. Pawan Kumar, Daphne Koller
We consider the task of obtaining the maximum a posteriori estimate of discrete pairwise random fields with arbitrary unary potentials and semimetric pairwise potentials. For this…