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
26 papers · 2 filters
Blackwell Online Learning for Markov Decision Processes
Tao Li, Guanze Peng, Quanyan Zhu
This work provides a novel interpretation of Markov Decision Processes (MDP) from the online optimization viewpoint. In such an online optimization context, the policy of the MDP i…
Bait and Switch: Online Training Data Poisoning of Autonomous Driving Systems
Naman Patel, Prashanth Krishnamurthy, Siddharth Garg +1
We show that by controlling parts of a physical environment in which a pre-trained deep neural network (DNN) is being fine-tuned online, an adversary can launch subtle data poisoni…
A Study on the Autoregressive and non-Autoregressive Multi-label Learning
Elham J. Barezi, Iacer Calixto, Kyunghyun Cho +1
Extreme classification tasks are multi-label tasks with an extremely large number of labels (tags). These tasks are hard because the label space is usually (i) very large, e.g. tho…
Catastrophic Fisher Explosion: Early Phase Fisher Matrix Impacts Generalization
Stanislaw Jastrzebski, Devansh Arpit, Oliver Astrand +6
The early phase of training a deep neural network has a dramatic effect on the local curvature of the loss function. For instance, using a small learning rate does not guarantee st…
Learning Invariances in Neural Networks
Gregory Benton, Marc Finzi, Pavel Izmailov +1
Invariances to translations have imbued convolutional neural networks with powerful generalization properties. However, we often do not know a priori what invariances are present i…
Learning Principle of Least Action with Reinforcement Learning
Zehao Jin, Joshua Yao-Yu Lin, Siao-Fong Li
Nature provides a way to understand physics with reinforcement learning since nature favors the economical way for an object to propagate. In the case of classical mechanics, natur…
Simplifying Hamiltonian and Lagrangian Neural Networks via Explicit Constraints
Marc Finzi, Ke Alexander Wang, Andrew Gordon Wilson
Reasoning about the physical world requires models that are endowed with the right inductive biases to learn the underlying dynamics. Recent works improve generalization for predic…
On Evaluating Neural Network Backdoor Defenses
Akshaj Veldanda, Siddharth Garg
Deep neural networks (DNNs) demonstrate superior performance in various fields, including scrutiny and security. However, recent studies have shown that DNNs are vulnerable to back…
Probabilistic Numeric Convolutional Neural Networks
Marc Finzi, Roberto Bondesan, Max Welling
Continuous input signals like images and time series that are irregularly sampled or have missing values are challenging for existing deep learning methods. Coherently defined feat…
DoubleEnsemble: A New Ensemble Method Based on Sample Reweighting and Feature Selection for Financial Data Analysis
Chuheng Zhang, Yuanqi Li, Xi Chen +3
Modern machine learning models (such as deep neural networks and boosting decision tree models) have become increasingly popular in financial market prediction, due to their superi…
Kernel-Based Smoothness Analysis of Residual Networks
Tom Tirer, Joan Bruna, Raja Giryes
A major factor in the success of deep neural networks is the use of sophisticated architectures rather than the classical multilayer perceptron (MLP). Residual networks (ResNets) s…
Evaluating representations by the complexity of learning low-loss predictors
William F. Whitney, Min Jae Song, David Brandfonbrener +2
We consider the problem of evaluating representations of data for use in solving a downstream task. We propose to measure the quality of a representation by the complexity of learn…
A Discriminative Technique for Multiple-Source Adaptation
Corinna Cortes, Mehryar Mohri, Ananda Theertha Suresh +1
We present a new discriminative technique for the multiple-source adaptation, MSA, problem. Unlike previous work, which relies on density estimation for each source domain, our sol…
A Functional Perspective on Learning Symmetric Functions with Neural Networks
Aaron Zweig, Joan Bruna
Symmetric functions, which take as input an unordered, fixed-size set, are known to be universally representable by neural networks that enforce permutation invariance. These archi…
On the Rademacher Complexity of Linear Hypothesis Sets
Pranjal Awasthi, Natalie Frank, Mehryar Mohri
Linear predictors form a rich class of hypotheses used in a variety of learning algorithms. We present a tight analysis of the empirical Rademacher complexity of the family of line…
Implementing the ICE Estimator in Multilayer Perceptron Classifiers
Tyler Ward
This paper describes the techniques used to implement the ICE estimator for a multilayer perceptron model, and reviews the performance of the resulting models. The ICE estimator is…
Second-Order Information in Non-Convex Stochastic Optimization: Power and Limitations
Yossi Arjevani, Yair Carmon, John C. Duchi +3
We design an algorithm which finds an -approximate stationary point (with ) using stochastic gradient and Hessian-vector products, matching gua…
Causality and Batch Reinforcement Learning: Complementary Approaches To Planning In Unknown Domains
James Bannon, Brad Windsor, Wenbo Song +1
Reinforcement learning algorithms have had tremendous successes in online learning settings. However, these successes have relied on low-stakes interactions between the algorithmic…
Multi-Stage Transfer Learning with an Application to Selection Process
Andre Mendes, Julian Togelius, Leandro dos Santos Coelho
In multi-stage processes, decisions happen in an ordered sequence of stages. Many of them have the structure of dual funnel problem: as the sample size decreases from one stage to…
Generalization Error of Generalized Linear Models in High Dimensions
Melikasadat Emami, Mojtaba Sahraee-Ardakan, Parthe Pandit +2
At the heart of machine learning lies the question of generalizability of learned rules over previously unseen data. While over-parameterized models based on neural networks are no…
Adversarial Learning Guarantees for Linear Hypotheses and Neural Networks
Pranjal Awasthi, Natalie Frank, Mehryar Mohri
Adversarial or test time robustness measures the susceptibility of a classifier to perturbations to the test input. While there has been a flurry of recent work on designing defens…
Provably Efficient Third-Person Imitation from Offline Observation
Aaron Zweig, Joan Bruna
Domain adaptation in imitation learning represents an essential step towards improving generalizability. However, even in the restricted setting of third-person imitation where tra…
Adaptive Region-Based Active Learning
Corinna Cortes, Giulia DeSalvo, Claudio Gentile +2
We present a new active learning algorithm that adaptively partitions the input space into a finite number of regions, and subsequently seeks a distinct predictor for each region,…
On the Discrepancy between Density Estimation and Sequence Generation
Jason Lee, Dustin Tran, Orhan Firat +1
Many sequence-to-sequence generation tasks, including machine translation and text-to-speech, can be posed as estimating the density of the output y given the input x: p(y|x). Give…