5.2k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- SupélecFR485 papers
- University of Applied Sciences and Arts of Southern SwitzerlandCH485 papers
- New York UniversityUS52 papers
- Google (United States)US16 papers
- Princeton UniversityUS15 papers
- Courant Institute of Mathematical SciencesUS13 papers
- Meta (Israel)IL11 papers
- Massachusetts Institute of TechnologyUS10 papers
- Stanford UniversityUS8 papers
- University of California, BerkeleyUS8 papers
- Cornell UniversityUS7 papers
- Carnegie Mellon UniversityUS6 papers
44 papers · 2 filters
AutoDrop: Training Deep Learning Models with Automatic Learning Rate Drop
Yunfei Teng, Jing Wang, Anna Choromanska
Modern deep learning (DL) architectures are trained using variants of the SGD algorithm that is run with a defined learning rate schedule, i.e., the learning ra…
Quantile Filtered Imitation Learning
David Brandfonbrener, William F. Whitney, Rajesh Ranganath +1
We introduce quantile filtered imitation learning (QFIL), a novel policy improvement operator designed for offline reinforcement learning. QFIL performs policy improvement by runni…
Optimizing High-Dimensional Physics Simulations via Composite Bayesian Optimization
Wesley Maddox, Qing Feng, Max Balandat
Physical simulation-based optimization is a common task in science and engineering. Many such simulations produce image- or tensor-based outputs where the desired objective is a fu…
A Simple and Fast Baseline for Tuning Large XGBoost Models
Sanyam Kapoor, Valerio Perrone
XGBoost, a scalable tree boosting algorithm, has proven effective for many prediction tasks of practical interest, especially using tabular datasets. Hyperparameter tuning can furt…
AlphaD3M: Machine Learning Pipeline Synthesis
Iddo Drori, Yamuna Krishnamurthy, Remi Rampin +5
We introduce AlphaD3M, an automatic machine learning (AutoML) system based on meta reinforcement learning using sequence models with self play. AlphaD3M is based on edit operations…
Fairness-aware Online Price Discrimination with Nonparametric Demand Models
Xi Chen, Jiameng Lyu, Xuan Zhang +1
Price discrimination, which refers to the strategy of setting different prices for different customer groups, has been widely used in online retailing. Although it helps boost the…
Inverse-Weighted Survival Games
Xintian Han, Mark Goldstein, Aahlad Puli +3
Deep models trained through maximum likelihood have achieved state-of-the-art results for survival analysis. Despite this training scheme, practitioners evaluate models under other…
Dynamic population-based meta-learning for multi-agent communication with natural language
Abhinav Gupta, Marc Lanctot, Angeliki Lazaridou
In this work, our goal is to train agents that can coordinate with seen, unseen as well as human partners in a multi-agent communication environment involving natural language. Pre…
Cramér-Rao bound-informed training of neural networks for quantitative MRI
Xiaoxia Zhang, Quentin Duchemin, Kangning Liu +4
Neural networks are increasingly used to estimate parameters in quantitative MRI, in particular in magnetic resonance fingerprinting. Their advantages over the gold standard non-li…
TEMGNet: Deep Transformer-based Decoding of Upperlimb sEMG for Hand Gestures Recognition
Elahe Rahimian, Soheil Zabihi, Amir Asif +3
There has been a surge of recent interest in Machine Learning (ML), particularly Deep Neural Network (DNN)-based models, to decode muscle activities from surface Electromyography (…
Vitruvion: A Generative Model of Parametric CAD Sketches
Ari Seff, Wenda Zhou, Nick Richardson +1
Parametric computer-aided design (CAD) tools are the predominant way that engineers specify physical structures, from bicycle pedals to airplanes to printed circuit boards. The key…
Understanding Failures in Out-of-Distribution Detection with Deep Generative Models
Lily H. Zhang, Mark Goldstein, Rajesh Ranganath
Deep generative models (DGMs) seem a natural fit for detecting out-of-distribution (OOD) inputs, but such models have been shown to assign higher probabilities or densities to OOD…
Sisyphus: A Cautionary Tale of Using Low-Degree Polynomial Activations in Privacy-Preserving Deep Learning
Karthik Garimella, Nandan Kumar Jha, Brandon Reagen
Privacy concerns in client-server machine learning have given rise to private inference (PI), where neural inference occurs directly on encrypted inputs. PI protects clients' perso…
Connectivity Matters: Neural Network Pruning Through the Lens of Effective Sparsity
Artem Vysogorets, Julia Kempe
Neural network pruning is a fruitful area of research with surging interest in high sparsity regimes. Benchmarking in this domain heavily relies on faithful representation of the s…
Disaggregated Interventions to Reduce Inequality
Lucius E. J. Bynum, Joshua R. Loftus, Julia Stoyanovich
A significant body of research in the data sciences considers unfair discrimination against social categories such as race or gender that could occur or be amplified as a result of…
Joining datasets via data augmentation in the label space for neural networks
Jake Zhao, Mingfeng Ou, Linji Xue +3
Most, if not all, modern deep learning systems restrict themselves to a single dataset for neural network training and inference. In this article, we are interested in systematic w…
Bandit Modeling of Map Selection in Counter-Strike: Global Offensive
Guido Petri, Michael H. Stanley, Alec B. Hon +3
Many esports use a pick and ban process to define the parameters of a match before it starts. In Counter-Strike: Global Offensive (CSGO) matches, two teams first pick and ban maps,…
On-Policy Deep Reinforcement Learning for the Average-Reward Criterion
Yiming Zhang, Keith W. Ross
We develop theory and algorithms for average-reward on-policy Reinforcement Learning (RL). We first consider bounding the difference of the long-term average reward for two policie…
SKIing on Simplices: Kernel Interpolation on the Permutohedral Lattice for Scalable Gaussian Processes
Sanyam Kapoor, Marc Finzi, Ke Alexander Wang +1
State-of-the-art methods for scalable Gaussian processes use iterative algorithms, requiring fast matrix vector multiplies (MVMs) with the covariance kernel. The Structured Kernel…
Scalable Variational Gaussian Processes via Harmonic Kernel Decomposition
Shengyang Sun, Jiaxin Shi, Andrew Gordon Wilson +1
We introduce a new scalable variational Gaussian process approximation which provides a high fidelity approximation while retaining general applicability. We propose the harmonic k…
Mode recovery in neural autoregressive sequence modeling
Ilia Kulikov, Sean Welleck, Kyunghyun Cho
Despite its wide use, recent studies have revealed unexpected and undesirable properties of neural autoregressive sequence models trained with maximum likelihood, such as an unreas…
Bayesian Optimization with High-Dimensional Outputs
Wesley J. Maddox, Maximilian Balandat, Andrew Gordon Wilson +1
Bayesian Optimization is a sample-efficient black-box optimization procedure that is typically applied to problems with a small number of independent objectives. However, in practi…
Offline RL Without Off-Policy Evaluation
David Brandfonbrener, William F. Whitney, Rajesh Ranganath +1
Most prior approaches to offline reinforcement learning (RL) have taken an iterative actor-critic approach involving off-policy evaluation. In this paper we show that simply doing…
Compressing Heavy-Tailed Weight Matrices for Non-Vacuous Generalization Bounds
John Y. Shin
Heavy-tailed distributions have been studied in statistics, random matrix theory, physics, and econometrics as models of correlated systems, among other domains. Further, heavy-tai…