General Keywords

[ Algorithms ] [ Algorithms; Optimization ] [ Applications ] [ Data, Challenges, Implementations, and Software ] [ Deep Learning ] [ Deep Learning; Deep Learning ] [ Neuroscience and Cognitive Science ] [ Optimization ] [ Optimization; Optimization ] [ Probabilistic Methods ] [ Probabilistic Methods; Probabilistic Methods ] [ Reinforcement Learning and Planning ] [ Social Aspects of Machine Learning ] [ Theory ] [ Theory; Theory ]

Topic Keywords

[ Active Learning ] [ Active Learning; Algorithms ] [ Activity and Event Recognition ] [ Adaptive Data Analysis; Optimization ] [ Adversarial Examples ] [ Adversarial Learning ] [ Adversarial Learning; Algorithms ] [ Adversarial Networks ] [ Adversarial Networks ] [ Adversarial Networks; Deep Learning ] [ Adversarial Networks; Deep Learning ] [ AI Safety ] [ Algorithms Evaluation ] [ Approximate Inference ] [ Architectures ] [ Attention Models ] [ Audio and Speech Processing ] [ AutoML ] [ Bandit Algorithms ] [ Bandit Algorithms; Algorithms ] [ Bandit Algorithms; Reinforcement Learning and Planning ] [ Bandit Algorithms; Reinforcement Learning and Planning ] [ Bandits ] [ Bayesian Deep Learning ] [ Bayesian Methods ] [ Bayesian Nonparametrics ] [ Bayesian Theory ] [ Bayesian Theory ] [ Benchmarks ] [ Biologically Plausible Deep Networks ] [ Biologically Plausible Deep Networks; Deep Learning ] [ Biologically Plausible Deep Networks; Neuroscience and Cognitive Science ] [ Body Pose, Face, and Gesture Analysis ] [ Body Pose, Face, and Gesture Analysis; Applications ] [ Boosting and Ensemble Methods ] [ Boosting and Ensemble Methods; Algorithms ] [ Boosting and Ensemble Methods; Probabilistic Methods; Probabilistic Methods ] [ Causal Inference ] [ Classification ] [ Classification; Algorithms ] [ Classification; Algorithms ] [ Classification; Applications ] [ Classification; Deep Learning; Deep Learning ] [ Classification; Deep Learning; Deep Learning ] [ Clustering ] [ Clustering; Applications ] [ Clustering; Theory ] [ CNN Architectures; Deep Learning ] [ CNN Architectures; Deep Learning ] [ CNN Architectures; Theory ] [ Cognitive Science; Neuroscience and Cognitive Science ] [ Collaborative Filtering ] [ Collaborative Filtering; Algorithms ] [ Collaborative Filtering; Applications ] [ Combinatorial Optimization ] [ Components Analysis (e.g., CCA, ICA, LDA, PCA) ] [ Computational Biology and Bioinformatics ] [ Computational Biology and Bioinformatics; Applications ] [ Computational Complexity ] [ Computational Learning Theory ] [ Computational Photography ] [ Computational Social Science ] [ Computer Vision ] [ Computer Vision; Applications ] [ Computer Vision; Applications ] [ Computer Vision; Deep Learning ] [ Computer Vision; Deep Learning ] [ Computer Vision; Deep Learning ] [ Computer Vision; Deep Learning ] [ Continual Learning ] [ Convex Optimization ] [ Convex Optimization; Optimization ] [ Convex Optimization; Probabilistic Methods; Theory; Theory ] [ Convex Optimization; Theory ] [ Crowdsourcing ] [ Decision and Control ] [ Deep Autoencoders; Deep Learning ] [ Deep learning Theory ] [ Deep RL ] [ Density Estimation ] [ Density Estimation; Deep Learning ] [ Derivative Free Optimization ] [ Dialog- or Communication-Based Learning ] [ Dimensionality Reduction ] [ Distributed and Parallel Optimization ] [ Distributed Inference ] [ Efficient Inference Methods ] [ Efficient Training Methods; Deep Learning ] [ Embedding and Representation learning ] [ Embedding Approaches ] [ Exploration ] [ Fairness, Accountability, and Transparency ] [ Fairness, Accountability, and Transparency ] [ Few-Shot Learning ] [ Few-Shot Learning; Algorithms ] [ Frequentist Statistics ] [ Game Theory and Computational Economics ] [ Gaussian Processes ] [ Gaussian Processes and Bayesian non-parametrics ] [ Generative Models ] [ Generative Models ] [ Graphical Models ] [ Graphical Models ] [ Hardware and Systems ] [ Healthcare ] [ Human or Animal Learning ] [ Human or Animal Learning; Probabilistic Methods ] [ Image Segmentation ] [ Image Segmentation; Algorithms ] [ Image Segmentation; Applications ] [ Information Theory ] [ Kernel Methods ] [ Kernel Methods; Optimization ] [ Large Deviations and Asymptotic Analysis ] [ Large Scale Learning ] [ Large Scale Learning; Algorithms ] [ Large Scale Learning; Algorithms ] [ Large Scale Learning; Applications ] [ Large Scale Learning; Deep Learning ] [ Large Scale Learning; Probabilistic Methods ] [ Latent Variable Models ] [ Learning Theory ] [ Markov Decision Processes ] [ Markov Decision Processes; Reinforcement Learning and Planning ] [ Markov Decision Processes; Reinforcement Learning and Planning ] [ Matrix and Tensor Factorization ] [ MCMC ] [ Memory ] [ Memory; Optimization ] [ Meta-Learning ] [ Meta-Learning; Applications ] [ Metric Learning ] [ Missing Data; Algorithms ] [ Missing Data; Algorithms ] [ Missing Data; Theory ] [ Model Selection and Structure Learning ] [ Models of Learning and Generalization ] [ Monte Carlo Methods ] [ Multi-Agent RL ] [ Multimodal Learning ] [ Multitask and Transfer Learning ] [ Multitask and Transfer Learning; Algorithms ] [ Multitask and Transfer Learning; Probabilistic Methods ] [ Multitask, Transfer, and Meta Learning ] [ Natural Language Processing ] [ Network Analysis ] [ Networks and Relational Learning ] [ Neural Coding; Neuroscience and Cognitive Science ] [ Neuroscience ] [ Neuroscience and Cognitive Science ] [ Non-Convex Optimization ] [ Non-Convex Optimization ] [ Non-Convex Optimization; Theory ] [ Non-parametric models ] [ Object Detection; Deep Learning ] [ Object Detection; Neuroscience and Cognitive Science ] [ Online Learning ] [ Online Learning Algorithms ] [ Online Learning Theory ] [ Online Learning; Theory ] [ Optimal Transport ] [ Optimization for Deep Networks ] [ Others ] [ Others ] [ Others ] [ Others ] [ Others ] [ Planning and Control ] [ Plasticity and Adaptation ] [ Predictive Models ] [ Predictive Models; Deep Learning ] [ Predictive Models; Deep Learning ] [ Privacy, Anonymity, and Security ] [ Privacy, Anonymity, and Security ] [ Probabilistic Methods ] [ Probabilistic Programming ] [ Program Understanding and Generation ] [ Quantitative Finance and Econometrics ] [ Ranking and Preference Learning ] [ Ranking and Preference Learning; Theory ] [ Reasoning; Optimization ] [ Recommender Systems ] [ Recurrent Networks ] [ Recurrent Networks; Theory ] [ Regression ] [ Regression; Algorithms ] [ Regression; Applications ] [ Regression; Optimization ] [ Regression; Probabilistic Methods; Probabilistic Methods ] [ Regularization ] [ Regularization ] [ Reinforcement Learning ] [ Reinforcement Learning and Planning ] [ Relational Learning ] [ Representation Learning ] [ Representation Learning; Algorithms ] [ Representation Learning; Algorithms ] [ Representation Learning; Neuroscience and Cognitive Science ] [ Representation Learning; Neuroscience and Cognitive Science; Neuroscience and Cognitive Science ] [ Representation Learning; Optimization ] [ RL, Decisions and Control Theory ] [ Robotics ] [ Robust statistics ] [ Semi-Supervised Learning ] [ Social Aspects of Machine Learning ] [ Software Toolkits ] [ Spaces of Functions and Kernels ] [ Sparse Coding and Dimensionality Expansion; Applications ] [ Sparsity and Compressed Sensing ] [ Sparsity and Compressed Sensing; Applications ] [ Sparsity and Compressed Sensing; Optimization; Theory ] [ Speech Recognition ] [ Statistical Learning Theory ] [ Statistical Physics of Learning ] [ Stochastic Optimization ] [ Structured Prediction ] [ Submodular Optimization ] [ Supervised Learning ] [ Sustainability and Environment ] [ Theory ] [ Time Series Analysis ] [ Time Series Analysis; Deep Learning ] [ Time Series Analysis; Probabilistic Methods; Probabilistic Methods ] [ Time Series and Sequences ] [ Topic Models ] [ Uncertainty Estimation ] [ Uncertainty Estimation; Applications; Probabilistic Methods ] [ Unsupervised Learning ] [ Unsupervised Learning; Applications ] [ Unsupervised Learning; Deep Learning ] [ Variational Inference ] [ Visualization or Exposition Techniques for Deep Networks ] [ Visual Question Answering ] [ Visual Scene Analysis and Interpretation ]

93 Results

Affinity Workshop
Mon 15:15 A Tree-Adaptation Mechanism for Covariate and Concept Drift
Leno Silva, Renato Vicente
Spotlight
Tue 6:25 A Probabilistic Approach to Neural Network Pruning
Xin Qian, Diego Klabjan
Spotlight
Tue 6:35 A Functional Perspective on Learning Symmetric Functions with Neural Networks
Aaron Zweig, Joan Bruna
Poster
Tue 9:00 A Functional Perspective on Learning Symmetric Functions with Neural Networks
Aaron Zweig, Joan Bruna
Poster
Tue 9:00 A Probabilistic Approach to Neural Network Pruning
Xin Qian, Diego Klabjan
Spotlight
Tue 18:30 LAMDA: Label Matching Deep Domain Adaptation
Trung Le, Tuan Nguyen, Nhat Ho, Hung Bui, Dinh Phung
Poster
Tue 21:00 LAMDA: Label Matching Deep Domain Adaptation
Trung Le, Tuan Nguyen, Nhat Ho, Hung Bui, Dinh Phung
Oral
Wed 5:00 On Energy-Based Models with Overparametrized Shallow Neural Networks
Carles Domingo-Enrich, Alberto Bietti, Eric Vanden-Eijnden, Joan Bruna
Spotlight
Wed 5:20 Uncertainty Principles of Encoding GANs
TaiGe Feng, Zhouchen Lin, jiapeng zhu, Deli Zhao, Jingren Zhou, Zheng-Jun Zha
Spotlight
Wed 5:25 On the Proof of Global Convergence of Gradient Descent for Deep ReLU Networks with Linear Widths
Quynh Nguyen
Spotlight
Wed 5:30 Tight Bounds on the Smallest Eigenvalue of the Neural Tangent Kernel for Deep ReLU Networks
Quynh Nguyen, Marco Mondelli, Guido Montufar
Spotlight
Wed 5:35 Functional Space Analysis of Local GAN Convergence
Valentin Khrulkov, Artem Babenko, Ivan Oseledets
Spotlight
Wed 5:40 On Learnability via Gradient Method for Two-Layer ReLU Neural Networks in Teacher-Student Setting
Shunta Akiyama, Taiji Suzuki
Spotlight
Wed 5:40 Exact Gap between Generalization Error and Uniform Convergence in Random Feature Models
Zitong Yang, Yu Bai, Song Mei
Spotlight
Wed 5:45 Provable Generalization of SGD-trained Neural Networks of Any Width in the Presence of Adversarial Label Noise
Spencer Frei, Yuan Cao, Quanquan Gu
Oral
Wed 7:00 On the Implicit Bias of Initialization Shape: Beyond Infinitesimal Mirror Descent
Shahar Azulay, Edward Moroshko, Mor Shpigel Nacson, Blake Woodworth, Nati Srebro, Amir Globerson, Daniel Soudry
Spotlight
Wed 7:20 A statistical perspective on distillation
Aditya Menon, Ankit Singh Rawat, Sashank Jakkam Reddi, Seungyeon Kim, Sanjiv Kumar
Spotlight
Wed 7:25 The Lipschitz Constant of Self-Attention
Hyunjik Kim, George Papamakarios, Andriy Mnih
Spotlight
Wed 7:30 Revealing the Structure of Deep Neural Networks via Convex Duality
Tolga Ergen, Mert Pilanci
Spotlight
Wed 7:35 Representational aspects of depth and conditioning in normalizing flows
Frederic Koehler, Viraj Mehta, Andrej Risteski
Spotlight
Wed 7:40 Toward Understanding the Feature Learning Process of Self-supervised Contrastive Learning
Zixin Wen, Yuanzhi Li
Spotlight
Wed 7:45 The Hintons in your Neural Network: a Quantum Field Theory View of Deep Learning
Roberto Bondesan, Max Welling
Poster
Wed 9:00 Toward Understanding the Feature Learning Process of Self-supervised Contrastive Learning
Zixin Wen, Yuanzhi Li
Poster
Wed 9:00 The Lipschitz Constant of Self-Attention
Hyunjik Kim, George Papamakarios, Andriy Mnih
Poster
Wed 9:00 The Hintons in your Neural Network: a Quantum Field Theory View of Deep Learning
Roberto Bondesan, Max Welling
Poster
Wed 9:00 On the Proof of Global Convergence of Gradient Descent for Deep ReLU Networks with Linear Widths
Quynh Nguyen
Poster
Wed 9:00 Tight Bounds on the Smallest Eigenvalue of the Neural Tangent Kernel for Deep ReLU Networks
Quynh Nguyen, Marco Mondelli, Guido Montufar
Poster
Wed 9:00 Uncertainty Principles of Encoding GANs
TaiGe Feng, Zhouchen Lin, jiapeng zhu, Deli Zhao, Jingren Zhou, Zheng-Jun Zha
Poster
Wed 9:00 A statistical perspective on distillation
Aditya Menon, Ankit Singh Rawat, Sashank Jakkam Reddi, Seungyeon Kim, Sanjiv Kumar
Poster
Wed 9:00 Provable Generalization of SGD-trained Neural Networks of Any Width in the Presence of Adversarial Label Noise
Spencer Frei, Yuan Cao, Quanquan Gu
Poster
Wed 9:00 On Learnability via Gradient Method for Two-Layer ReLU Neural Networks in Teacher-Student Setting
Shunta Akiyama, Taiji Suzuki
Poster
Wed 9:00 Representational aspects of depth and conditioning in normalizing flows
Frederic Koehler, Viraj Mehta, Andrej Risteski
Poster
Wed 9:00 On the Implicit Bias of Initialization Shape: Beyond Infinitesimal Mirror Descent
Shahar Azulay, Edward Moroshko, Mor Shpigel Nacson, Blake Woodworth, Nati Srebro, Amir Globerson, Daniel Soudry
Poster
Wed 9:00 On Energy-Based Models with Overparametrized Shallow Neural Networks
Carles Domingo-Enrich, Alberto Bietti, Eric Vanden-Eijnden, Joan Bruna
Poster
Wed 9:00 Revealing the Structure of Deep Neural Networks via Convex Duality
Tolga Ergen, Mert Pilanci
Poster
Wed 9:00 Exact Gap between Generalization Error and Uniform Convergence in Random Feature Models
Zitong Yang, Yu Bai, Song Mei
Poster
Wed 9:00 Functional Space Analysis of Local GAN Convergence
Valentin Khrulkov, Artem Babenko, Ivan Oseledets
Oral
Wed 17:00 The Implicit Bias for Adaptive Optimization Algorithms on Homogeneous Neural Networks
Bohan Wang, Qi Meng, Wei Chen, Tie-Yan Liu
Spotlight
Wed 17:20 Asymmetric Heavy Tails and Implicit Bias in Gaussian Noise Injections
Alexander D Camuto, Xiaoyu Wang, Lingjiong Zhu, Christopher Holmes, Mert Gurbuzbalaban, Umut Simsekli
Spotlight
Wed 17:25 Understanding Noise Injection in GANs
TaiGe Feng, Deli Zhao, Zheng-Jun Zha
Spotlight
Wed 17:30 FL-NTK: A Neural Tangent Kernel-based Framework for Federated Learning Analysis
Baihe Huang, Xiaoxiao Li, Zhao Song, Xin Yang
Spotlight
Wed 17:35 Improved OOD Generalization via Adversarial Training and Pretraing
Mingyang Yi, Lu Hou, Jiacheng Sun, Lifeng Shang, Xin Jiang, Qun Liu, Zhiming Ma
Spotlight
Wed 17:40 WGAN with an Infinitely Wide Generator Has No Spurious Stationary Points
Albert No, TaeHo Yoon, Sehyun Kwon, Ernest Ryu
Spotlight
Wed 17:45 Besov Function Approximation and Binary Classification on Low-Dimensional Manifolds Using Convolutional Residual Networks
Hao Liu, Minshuo Chen, Tuo Zhao, Wenjing Liao
Oral
Wed 18:00 Dissecting Supervised Constrastive Learning
Florian Graf, Christoph Hofer, Marc Niethammer, Roland Kwitt
Spotlight
Wed 18:20 Noise and Fluctuation of Finite Learning Rate Stochastic Gradient Descent
Kangqiao Liu, Liu Ziyin, Masahito Ueda
Spotlight
Wed 18:25 Tensor Programs IV: Feature Learning in Infinite-Width Neural Networks
Greg Yang, Edward Hu
Spotlight
Wed 18:30 Scaling Properties of Deep Residual Networks
Alain-Sam Cohen, Rama Cont, Alain Rossier, Renyuan Xu
Spotlight
Wed 18:35 Contrastive Learning Inverts the Data Generating Process
Roland S. Zimmermann, Yash Sharma, Steffen Schneider, Matthias Bethge, Wieland Brendel
Spotlight
Wed 18:40 Tensor Programs IIb: Architectural Universality Of Neural Tangent Kernel Training Dynamics
Greg Yang, Etai Littwin
Spotlight
Wed 18:45 Do We Actually Need Dense Over-Parameterization? In-Time Over-Parameterization in Sparse Training
Shiwei Liu, Lu Yin, Decebal Constantin Mocanu, Mykola Pechenizkiy
Spotlight
Wed 19:45 Interpreting and Disentangling Feature Components of Various Complexity from DNNs
Jie Ren, Mingjie Li, Zexu Liu, Quanshi Zhang
Poster
Wed 21:00 Scaling Properties of Deep Residual Networks
Alain-Sam Cohen, Rama Cont, Alain Rossier, Renyuan Xu
Poster
Wed 21:00 Do We Actually Need Dense Over-Parameterization? In-Time Over-Parameterization in Sparse Training
Shiwei Liu, Lu Yin, Decebal Constantin Mocanu, Mykola Pechenizkiy
Poster
Wed 21:00 The Implicit Bias for Adaptive Optimization Algorithms on Homogeneous Neural Networks
Bohan Wang, Qi Meng, Wei Chen, Tie-Yan Liu
Poster
Wed 21:00 Interpreting and Disentangling Feature Components of Various Complexity from DNNs
Jie Ren, Mingjie Li, Zexu Liu, Quanshi Zhang
Poster
Wed 21:00 WGAN with an Infinitely Wide Generator Has No Spurious Stationary Points
Albert No, TaeHo Yoon, Sehyun Kwon, Ernest Ryu
Poster
Wed 21:00 Dissecting Supervised Constrastive Learning
Florian Graf, Christoph Hofer, Marc Niethammer, Roland Kwitt
Poster
Wed 21:00 Contrastive Learning Inverts the Data Generating Process
Roland S. Zimmermann, Yash Sharma, Steffen Schneider, Matthias Bethge, Wieland Brendel
Poster
Wed 21:00 Besov Function Approximation and Binary Classification on Low-Dimensional Manifolds Using Convolutional Residual Networks
Hao Liu, Minshuo Chen, Tuo Zhao, Wenjing Liao
Poster
Wed 21:00 Asymmetric Heavy Tails and Implicit Bias in Gaussian Noise Injections
Alexander D Camuto, Xiaoyu Wang, Lingjiong Zhu, Christopher Holmes, Mert Gurbuzbalaban, Umut Simsekli
Poster
Wed 21:00 Improved OOD Generalization via Adversarial Training and Pretraing
Mingyang Yi, Lu Hou, Jiacheng Sun, Lifeng Shang, Xin Jiang, Qun Liu, Zhiming Ma
Poster
Wed 21:00 Understanding Noise Injection in GANs
TaiGe Feng, Deli Zhao, Zheng-Jun Zha
Poster
Wed 21:00 FL-NTK: A Neural Tangent Kernel-based Framework for Federated Learning Analysis
Baihe Huang, Xiaoxiao Li, Zhao Song, Xin Yang
Poster
Wed 21:00 Noise and Fluctuation of Finite Learning Rate Stochastic Gradient Descent
Kangqiao Liu, Liu Ziyin, Masahito Ueda
Poster
Wed 21:00 Tensor Programs IV: Feature Learning in Infinite-Width Neural Networks
Greg Yang, Edward Hu
Poster
Wed 21:00 Tensor Programs IIb: Architectural Universality Of Neural Tangent Kernel Training Dynamics
Greg Yang, Etai Littwin
Oral
Thu 5:00 Tilting the playing field: Dynamical loss functions for machine learning
Miguel Ruiz Garcia, Ge Zhang, Samuel Schoenholz, Andrea Liu
Spotlight
Thu 5:20 Adversarial Robustness Guarantees for Random Deep Neural Networks
Giacomo De Palma, Bobak T Kiani, Seth Lloyd
Spotlight
Thu 5:25 Implicit Bias of Linear RNNs
Melika Emami, Moji Sahraee-Ardakan, Parthe Pandit, Sundeep Rangan, Alyson Fletcher
Spotlight
Thu 5:30 Analyzing the tree-layer structure of Deep Forests
Ludovic Arnould, Claire Boyer, Erwan Scornet
Spotlight
Thu 5:35 Quantifying the Benefit of Using Differentiable Learning over Tangent Kernels
Eran Malach, Pritish Kamath, Emmanuel Abbe, Nati Srebro
Spotlight
Thu 5:40 Implicit Regularization in Tensor Factorization
Noam Razin, Asaf Maman, Nadav Cohen
Spotlight
Thu 5:45 Uniform Convergence, Adversarial Spheres and a Simple Remedy
Gregor Bachmann, Seyed Moosavi, Thomas Hofmann
Poster
Thu 9:00 Uniform Convergence, Adversarial Spheres and a Simple Remedy
Gregor Bachmann, Seyed Moosavi, Thomas Hofmann
Poster
Thu 9:00 Adversarial Robustness Guarantees for Random Deep Neural Networks
Giacomo De Palma, Bobak T Kiani, Seth Lloyd
Poster
Thu 9:00 Tilting the playing field: Dynamical loss functions for machine learning
Miguel Ruiz Garcia, Ge Zhang, Samuel Schoenholz, Andrea Liu
Poster
Thu 9:00 Implicit Regularization in Tensor Factorization
Noam Razin, Asaf Maman, Nadav Cohen
Poster
Thu 9:00 Analyzing the tree-layer structure of Deep Forests
Ludovic Arnould, Claire Boyer, Erwan Scornet
Poster
Thu 9:00 Implicit Bias of Linear RNNs
Melika Emami, Moji Sahraee-Ardakan, Parthe Pandit, Sundeep Rangan, Alyson Fletcher
Poster
Thu 9:00 Quantifying the Benefit of Using Differentiable Learning over Tangent Kernels
Eran Malach, Pritish Kamath, Emmanuel Abbe, Nati Srebro
Spotlight
Thu 18:45 Which transformer architecture fits my data? A vocabulary bottleneck in self-attention
Noam Wies, Yoav Levine, Daniel Jannai, Amnon Shashua
Spotlight
Thu 20:30 Discretization Drift in Two-Player Games
Mihaela Rosca, Yan Wu, Benoit Dherin, David GT Barrett
Spotlight
Thu 20:35 Elementary superexpressive activations
Dmitry Yarotsky
Poster
Thu 21:00 Which transformer architecture fits my data? A vocabulary bottleneck in self-attention
Noam Wies, Yoav Levine, Daniel Jannai, Amnon Shashua
Poster
Thu 21:00 Elementary superexpressive activations
Dmitry Yarotsky
Poster
Thu 21:00 Discretization Drift in Two-Player Games
Mihaela Rosca, Yan Wu, Benoit Dherin, David GT Barrett
Workshop
Sat 12:04 Unconstrained Submodular Maximization with Modular Costs: Tight Approximation and Application to Profit Maximization
Tianyuan Jin, Yu Yang, Renchi Yang, Jieming Shi, Keke Huang, Xiaokui Xiao
Workshop
Unconstrained Submodular Maximization with Modular Costs: Tight Approximation and Application to Profit Maximization
Tianyuan Jin, Yu Yang, Renchi Yang, Jieming Shi, Keke Huang, Xiaokui Xiao
Workshop
On the interplay between data structure and loss function: an analytical study of generalization for classification
Stéphane d'Ascoli, Marylou Gabrié, Levent Sagun, Giulio Biroli
Workshop
MetaDataset: A Dataset of Datasets for Evaluating Distribution Shifts and Training Conflicts
Weixin Liang, James Zou, Weixin Liang
Workshop
Strategic Instrumental Variable Regression: Recovering Causal Relationships From Strategic Responses
Keegan Harris, Daniel Ngo, Logan Stapleton, Hoda Heidari, Steven Wu
Workshop
Improve Generalization and Robustness of Neural Networks via Weight Scale Shifting Invariant Regularizations
Ziquan Liu, Yufei Cui, Antoni Chan