research
∙
08/22/2023
Expecting The Unexpected: Towards Broad Out-Of-Distribution Detection
Improving the reliability of deployed machine learning systems often inv...
share
research
∙
07/05/2023
Additive Decoders for Latent Variables Identification and Cartesian-Product Extrapolation
We tackle the problems of latent variables identification and "out-of-su...
research
∙
06/20/2023
No Wrong Turns: The Simple Geometry Of Neural Networks Optimization Paths
Understanding the optimization dynamics of neural networks is necessary ...
research
∙
04/14/2023
Performative Prediction with Neural Networks
Performative prediction is a framework for learning models that influenc...
research
∙
11/26/2022
Synergies Between Disentanglement and Sparsity: a Multi-Task Learning Perspective
Although disentangled representations are often said to be beneficial fo...
research
∙
11/15/2022
Empirical Study on Optimizer Selection for Out-of-Distribution Generalization
Modern deep learning systems are fragile and do not generalize well unde...
research
∙
11/03/2022
Empirical Analysis of Model Selection for Heterogenous Causal Effect Estimation
We study the problem of model selection in causal inference, specificall...
research
∙
10/06/2022
Towards Out-of-Distribution Adversarial Robustness
Adversarial robustness continues to be a major challenge for deep learni...
research
∙
10/04/2022
CADet: Fully Self-Supervised Anomaly Detection With Contrastive Learning
Handling out-of-distribution (OOD) samples has become a major stake in t...
research
∙
10/03/2022
A Reproducible and Realistic Evaluation of Partial Domain Adaptation Methods
Unsupervised Domain Adaptation (UDA) aims at classifying unlabeled targe...
research
∙
09/29/2022
Neural Networks Efficiently Learn Low-Dimensional Representations with SGD
We study the problem of training a two-layer neural network (NN) of arbi...
research
∙
06/22/2022
Optimal transport meets noisy label robust loss and MixUp regularization for domain adaptation
It is common in computer vision to be confronted with domain shift: imag...
research
∙
06/12/2022
A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum Games
Algorithms designed for single-agent reinforcement learning (RL) general...
research
∙
04/10/2022
Towards efficient representation identification in supervised learning
Humans have a remarkable ability to disentangle complex sensory inputs (...
research
∙
10/28/2021
Stochastic Mirror Descent: Convergence Analysis and Adaptive Variants via the Mirror Stochastic Polyak Stepsize
We investigate the convergence of stochastic mirror descent (SMD) in rel...
research
∙
10/20/2021
Convergence Analysis and Implicit Regularization of Feedback Alignment for Deep Linear Networks
We theoretically analyze the Feedback Alignment (FA) algorithm, an effic...
research
∙
06/30/2021
Stochastic Gradient Descent-Ascent and Consensus Optimization for Smooth Games: Convergence Analysis under Expected Co-coercivity
Two of the most prominent algorithms for solving unconstrained smooth ga...
research
∙
06/11/2021
Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalization
The invariance principle from causality is at the heart of notable appro...
research
∙
05/28/2021
Gotta Go Fast When Generating Data with Score-Based Models
Score-based (denoising diffusion) generative models have recently gained...
research
∙
12/10/2020
A Study of Condition Numbers for First-Order Optimization
The study of first-order optimization algorithms (FOA) typically starts ...
research
∙
10/26/2020
LEAD: Least-Action Dynamics for Min-Max Optimization
Adversarial formulations such as generative adversarial networks (GANs) ...
research
∙
10/22/2020
In Search of Robust Measures of Generalization
One of the principal scientific challenges in deep learning is explainin...
research
∙
09/11/2020
Adversarial score matching and improved sampling for image generation
Denoising score matching with Annealed Langevin Sampling (DSM-ALS) is a ...
research
∙
07/08/2020
Stochastic Hamiltonian Gradient Methods for Smooth Games
The success of adversarial formulations in machine learning has brought ...
research
∙
01/02/2020
Accelerating Smooth Games by Manipulating Spectral Shapes
We use matrix iteration theory to characterize acceleration in smooth ga...
research
∙
11/03/2019
Adversarial target-invariant representation learning for domain generalization
In many applications of machine learning, the training and test set data...
research
∙
10/15/2019
Connections between Support Vector Machines, Wasserstein distance and gradient-penalty GANs
We generalize the concept of maximum-margin classifiers (MMCs) to arbitr...
research
∙
06/17/2019
Lower Bounds and Conditioning of Differentiable Games
Many recent machine learning tools rely on differentiable game formulati...
research
∙
06/13/2019
A Tight and Unified Analysis of Extragradient for a Whole Spectrum of Differentiable Games
We consider differentiable games: multi-objective minimization problems,...
research
∙
06/08/2019
Reducing the variance in online optimization by transporting past gradients
Most stochastic optimization methods use gradients once before discardin...
research
∙
05/26/2019
State-Reification Networks: Improving Generalization by Modeling the Distribution of Hidden Representations
Machine learning promises methods that generalize well from finite label...
research
∙
03/29/2019
SysML: The New Frontier of Machine Learning Systems
Machine learning (ML) techniques are enjoying rapidly increasing adoptio...
research
∙
01/24/2019
Multi-objective training of Generative Adversarial Networks with multiple discriminators
Recent literature has demonstrated promising results for training Genera...
research
∙
10/19/2018
A Modern Take on the Bias-Variance Tradeoff in Neural Networks
We revisit the bias-variance tradeoff for neural networks in light of mo...
research
∙
10/06/2018
h-detach: Modifying the LSTM Gradient Towards Better Optimization
Recurrent neural networks are known for their notorious exploding and va...
research
∙
07/12/2018
Negative Momentum for Improved Game Dynamics
Games generalize the optimization paradigm by introducing different obje...
research
∙
04/07/2018
Fortified Networks: Improving the Robustness of Deep Networks by Modeling the Manifold of Hidden Representations
Deep networks have achieved impressive results across a variety of impor...
research
∙
08/17/2017
Deep Learning at 15PF: Supervised and Semi-Supervised Classification for Scientific Data
This paper presents the first, 15-PetaFLOP Deep Learning system for solv...
research
∙
07/18/2017
Improving Gibbs Sampler Scan Quality with DoGS
The pairwise influence matrix of Dobrushin has long been used as an anal...
research
∙
07/10/2017
Accelerated Stochastic Power Iteration
Principal component analysis (PCA) is one of the most powerful tools in ...
research
∙
07/08/2017
Learning Representations and Generative Models for 3D Point Clouds
Three-dimensional geometric data offer an excellent domain for studying ...
research
∙
06/12/2017
YellowFin and the Art of Momentum Tuning
Hyperparameter tuning is one of the big costs of deep learning. State-of...
research
∙
06/23/2016
Parallel SGD: When does averaging help?
Consider a number of workers running SGD independently on the same pool ...
research
∙
06/10/2016
Scan Order in Gibbs Sampling: Models in Which it Matters and Bounds on How Much
Gibbs sampling is a Markov Chain Monte Carlo sampling technique that ite...
research
∙
05/31/2016
Asynchrony begets Momentum, with an Application to Deep Learning
Asynchronous methods are widely used in deep learning, but have limited ...
research
∙
06/28/2013