7.1k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- SupélecFR486 papers
- Shandong University of Political Science and LawCN485 papers
- Dalle Molle Institute for Artificial Intelligence ResearchCH65 papers
- New York UniversityUS52 papers
- Università della Svizzera italianaCH21 papers
- Google (United States)US16 papers
- Princeton UniversityUS15 papers
- Courant Institute of Mathematical SciencesUS13 papers
- Politecnico di TorinoIT12 papers
- Meta (Israel)IL11 papers
- Massachusetts Institute of TechnologyUS10 papers
- ETH ZurichCH9 papers
29 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…
Recurrent Neural-Linear Posterior Sampling for Nonstationary Contextual Bandits
Aditya Ramesh, Paulo Rauber, Michelangelo Conserva +1
An agent in a nonstationary contextual bandit problem should balance between exploration and the exploitation of (periodic or structured) patterns present in its previous experienc…
Hedging using reinforcement learning: Contextual -Armed Bandit versus -learning
Loris Cannelli, Giuseppe Nuti, Marzio Sala +1
The construction of replication strategies for contingent claims in the presence of risk and market friction is a key problem of financial engineering. In real markets, continuous…
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…
dynoNet: a neural network architecture for learning dynamical systems
Marco Forgione, Dario Piga
This paper introduces a network architecture, called dynoNet, utilizing linear dynamical operators as elementary building blocks. Owing to the dynamical nature of these blocks, dyn…
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…