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 ]

90 Results

Affinity Workshop
Mon 9:15 OCDE: Odds Conditional Density Estimator
Alex Aki Okuno, Felipe Polo
Spotlight
Tue 7:20 Learning Bounds for Open-Set Learning
Zhen Fang, Jie Lu, Anjin Liu, Feng Liu, Guangquan Zhang
Poster
Tue 9:00 Learning Bounds for Open-Set Learning
Zhen Fang, Jie Lu, Anjin Liu, Feng Liu, Guangquan Zhang
Oral
Wed 5:00 Robust Density Estimation from Batches: The Best Things in Life are (Nearly) Free
Ayush Jain, Alon Orlitsky
Spotlight
Wed 5:20 Generalization Bounds in the Presence of Outliers: a Median-of-Means Study
Pierre Laforgue, Guillaume Staerman, Stephan Clémençon
Spotlight
Wed 5:20 Theory of Spectral Method for Union of Subspaces-Based Random Geometry Graph
Gen Li, Yuantao Gu
Spotlight
Wed 5:25 Meta Learning for Support Recovery in High-dimensional Precision Matrix Estimation
Qian Zhang, Yilin Zheng, Jean Honorio
Spotlight
Wed 5:25 Approximating a Distribution Using Weight Queries
Nadav Barak, Sivan Sabato
Spotlight
Wed 5:30 Robust Inference for High-Dimensional Linear Models via Residual Randomization
Y. Samuel Wang, Si Kai Lee, Panos Toulis, Mladen Kolar
Spotlight
Wed 5:35 Don’t Just Blame Over-parametrization for Over-confidence: Theoretical Analysis of Calibration in Binary Classification
Yu Bai, Song Mei, Huan Wang, Caiming Xiong
Spotlight
Wed 5:40 Generalization Guarantees for Neural Architecture Search with Train-Validation Split
Samet Oymak, Mingchen Li, Mahdi Soltanolkotabi
Spotlight
Wed 5:45 Optimal Estimation of High Dimensional Smooth Additive Function Based on Noisy Observations
Fan Zhou, Ping Li
Oral
Wed 6:00 Agnostic Learning of Halfspaces with Gradient Descent via Soft Margins
Spencer Frei, Yuan Cao, Quanquan Gu
Spotlight
Wed 6:20 Two-way kernel matrix puncturing: towards resource-efficient PCA and spectral clustering
Romain COUILLET, Florent Chatelain, Nicolas Le Bihan
Spotlight
Wed 6:25 A Lower Bound for the Sample Complexity of Inverse Reinforcement Learning
Abi Komanduru, Jean Honorio
Spotlight
Wed 6:30 Estimation and Quantization of Expected Persistence Diagrams
Vincent Divol, Theo Lacombe
Spotlight
Wed 6:35 Post-selection inference with HSIC-Lasso
Tobias Freidling, Benjamin Poignard, Héctor Climente-González, Makoto Yamada
Spotlight
Wed 6:40 Provable Robustness of Adversarial Training for Learning Halfspaces with Noise
Difan Zou, Spencer Frei, Quanquan Gu
Spotlight
Wed 6:45 Distribution-Free Calibration Guarantees for Histogram Binning without Sample Splitting
Chirag Gupta, Aaditya Ramdas
Poster
Wed 9:00 Estimation and Quantization of Expected Persistence Diagrams
Vincent Divol, Theo Lacombe
Poster
Wed 9:00 Theory of Spectral Method for Union of Subspaces-Based Random Geometry Graph
Gen Li, Yuantao Gu
Poster
Wed 9:00 Agnostic Learning of Halfspaces with Gradient Descent via Soft Margins
Spencer Frei, Yuan Cao, Quanquan Gu
Poster
Wed 9:00 Approximating a Distribution Using Weight Queries
Nadav Barak, Sivan Sabato
Poster
Wed 9:00 Don’t Just Blame Over-parametrization for Over-confidence: Theoretical Analysis of Calibration in Binary Classification
Yu Bai, Song Mei, Huan Wang, Caiming Xiong
Poster
Wed 9:00 Two-way kernel matrix puncturing: towards resource-efficient PCA and spectral clustering
Romain COUILLET, Florent Chatelain, Nicolas Le Bihan
Poster
Wed 9:00 Distribution-Free Calibration Guarantees for Histogram Binning without Sample Splitting
Chirag Gupta, Aaditya Ramdas
Poster
Wed 9:00 Generalization Bounds in the Presence of Outliers: a Median-of-Means Study
Pierre Laforgue, Guillaume Staerman, Stephan Clémençon
Poster
Wed 9:00 Provable Robustness of Adversarial Training for Learning Halfspaces with Noise
Difan Zou, Spencer Frei, Quanquan Gu
Poster
Wed 9:00 Robust Inference for High-Dimensional Linear Models via Residual Randomization
Y. Samuel Wang, Si Kai Lee, Panos Toulis, Mladen Kolar
Poster
Wed 9:00 Meta Learning for Support Recovery in High-dimensional Precision Matrix Estimation
Qian Zhang, Yilin Zheng, Jean Honorio
Poster
Wed 9:00 Generalization Guarantees for Neural Architecture Search with Train-Validation Split
Samet Oymak, Mingchen Li, Mahdi Soltanolkotabi
Poster
Wed 9:00 A Lower Bound for the Sample Complexity of Inverse Reinforcement Learning
Abi Komanduru, Jean Honorio
Poster
Wed 9:00 Robust Density Estimation from Batches: The Best Things in Life are (Nearly) Free
Ayush Jain, Alon Orlitsky
Poster
Wed 9:00 Post-selection inference with HSIC-Lasso
Tobias Freidling, Benjamin Poignard, Héctor Climente-González, Makoto Yamada
Poster
Wed 9:00 Optimal Estimation of High Dimensional Smooth Additive Function Based on Noisy Observations
Fan Zhou, Ping Li
Oral
Wed 17:00 Rate-Distortion Analysis of Minimum Excess Risk in Bayesian Learning
Hassan Hafez-Kolahi, Behrad Moniri, Shohreh Kasaei, Mahdieh Soleymani Baghshah
Spotlight
Wed 17:20 Near-Optimal Linear Regression under Distribution Shift
Qi Lei, Wei Hu, Jason Lee
Spotlight
Wed 17:25 Detection of Signal in the Spiked Rectangular Models
Ji Hyung Jung, Hye Won Chung, Ji Oon Lee
Spotlight
Wed 17:30 A Distribution-dependent Analysis of Meta Learning
Mikhail Konobeev, Ilja Kuzborskij, Csaba Szepesvari
Spotlight
Wed 17:35 Sparse Feature Selection Makes Batch Reinforcement Learning More Sample Efficient
Botao Hao, Yaqi Duan, Tor Lattimore, Csaba Szepesvari, Mengdi Wang
Spotlight
Wed 17:35 How Important is the Train-Validation Split in Meta-Learning?
Yu Bai, Minshuo Chen, Pan Zhou, Tuo Zhao, Jason Lee, Sham Kakade, Huan Wang, Caiming Xiong
Spotlight
Wed 17:40 Robust Unsupervised Learning via L-statistic Minimization
Andreas Maurer, Daniela Angela Parletta, Andrea Paudice, Massimiliano Pontil
Spotlight
Wed 17:45 A Theory of Label Propagation for Subpopulation Shift
Tianle Cai, Ruiqi Gao, Jason Lee, Qi Lei
Oral
Wed 18:00 Stability and Generalization of Stochastic Gradient Methods for Minimax Problems
Yunwen Lei, Zhenhuan Yang, Tianbao Yang, Yiming Ying
Spotlight
Wed 18:20 Outside the Echo Chamber: Optimizing the Performative Risk
John Miller, Juan Perdomo, Tijana Zrnic
Spotlight
Wed 18:25 Asymptotic Normality and Confidence Intervals for Prediction Risk of the Min-Norm Least Squares Estimator
Zeng Li, Chuanlong Xie, Qinwen Wang
Spotlight
Wed 18:30 Provable Meta-Learning of Linear Representations
Nilesh Tripuraneni, Chi Jin, Michael Jordan
Spotlight
Wed 18:35 Sample Complexity of Robust Linear Classification on Separated Data
Robi Bhattacharjee, Somesh Jha, Kamalika Chaudhuri
Spotlight
Wed 18:40 The Impact of Record Linkage on Learning from Feature Partitioned Data
Richard Nock, Stephen J Hardy, Wilko Henecka, Hamish Ivey-Law, Jakub Nabaglo, Giorgio Patrini, Guillaume Smith, Brian Thorne
Spotlight
Wed 18:45 Train simultaneously, generalize better: Stability of gradient-based minimax learners
Farzan Farnia, Asuman Ozdaglar
Oral
Wed 19:00 A Precise Performance Analysis of Support Vector Regression
Houssem Sifaou, Abla Kammoun, Mohamed-Slim Alouini
Spotlight
Wed 19:20 Lower-Bounded Proper Losses for Weakly Supervised Classification
Shuhei M Yoshida, Takashi Takenouchi, Masashi Sugiyama
Spotlight
Wed 19:25 On Variational Inference in Biclustering Models
Guanhua Fang, Ping Li
Spotlight
Wed 19:30 Infinite-Dimensional Optimization for Zero-Sum Games via Variational Transport
Lewis Liu, Yufeng Zhang, Zhuoran Yang, Reza Babanezhad, Zhaoran Wang
Spotlight
Wed 19:35 Dropout: Explicit Forms and Capacity Control
Raman Arora, Peter Bartlett, Poorya Mianjy, Nati Srebro
Spotlight
Wed 19:40 Finding Relevant Information via a Discrete Fourier Expansion
Mohsen Heidari, Jithin Sreedharan, Gil Shamir, Wojciech Szpankowski
Spotlight
Wed 19:45 On the Inherent Regularization Effects of Noise Injection During Training
Oussama Dhifallah, Yue Lu
Poster
Wed 21:00 Sample Complexity of Robust Linear Classification on Separated Data
Robi Bhattacharjee, Somesh Jha, Kamalika Chaudhuri
Poster
Wed 21:00 Provable Meta-Learning of Linear Representations
Nilesh Tripuraneni, Chi Jin, Michael Jordan
Poster
Wed 21:00 Robust Unsupervised Learning via L-statistic Minimization
Andreas Maurer, Daniela Angela Parletta, Andrea Paudice, Massimiliano Pontil
Poster
Wed 21:00 Stability and Generalization of Stochastic Gradient Methods for Minimax Problems
Yunwen Lei, Zhenhuan Yang, Tianbao Yang, Yiming Ying
Poster
Wed 21:00 Rate-Distortion Analysis of Minimum Excess Risk in Bayesian Learning
Hassan Hafez-Kolahi, Behrad Moniri, Shohreh Kasaei, Mahdieh Soleymani Baghshah
Poster
Wed 21:00 A Distribution-dependent Analysis of Meta Learning
Mikhail Konobeev, Ilja Kuzborskij, Csaba Szepesvari
Poster
Wed 21:00 The Impact of Record Linkage on Learning from Feature Partitioned Data
Richard Nock, Stephen J Hardy, Wilko Henecka, Hamish Ivey-Law, Jakub Nabaglo, Giorgio Patrini, Guillaume Smith, Brian Thorne
Poster
Wed 21:00 On Variational Inference in Biclustering Models
Guanhua Fang, Ping Li
Poster
Wed 21:00 Train simultaneously, generalize better: Stability of gradient-based minimax learners
Farzan Farnia, Asuman Ozdaglar
Poster
Wed 21:00 Outside the Echo Chamber: Optimizing the Performative Risk
John Miller, Juan Perdomo, Tijana Zrnic
Poster
Wed 21:00 Finding Relevant Information via a Discrete Fourier Expansion
Mohsen Heidari, Jithin Sreedharan, Gil Shamir, Wojciech Szpankowski
Poster
Wed 21:00 A Theory of Label Propagation for Subpopulation Shift
Tianle Cai, Ruiqi Gao, Jason Lee, Qi Lei
Poster
Wed 21:00 How Important is the Train-Validation Split in Meta-Learning?
Yu Bai, Minshuo Chen, Pan Zhou, Tuo Zhao, Jason Lee, Sham Kakade, Huan Wang, Caiming Xiong
Poster
Wed 21:00 On the Inherent Regularization Effects of Noise Injection During Training
Oussama Dhifallah, Yue Lu
Poster
Wed 21:00 Infinite-Dimensional Optimization for Zero-Sum Games via Variational Transport
Lewis Liu, Yufeng Zhang, Zhuoran Yang, Reza Babanezhad, Zhaoran Wang
Poster
Wed 21:00 A Precise Performance Analysis of Support Vector Regression
Houssem Sifaou, Abla Kammoun, Mohamed-Slim Alouini
Poster
Wed 21:00 Near-Optimal Linear Regression under Distribution Shift
Qi Lei, Wei Hu, Jason Lee
Poster
Wed 21:00 Lower-Bounded Proper Losses for Weakly Supervised Classification
Shuhei M Yoshida, Takashi Takenouchi, Masashi Sugiyama
Poster
Wed 21:00 Dropout: Explicit Forms and Capacity Control
Raman Arora, Peter Bartlett, Poorya Mianjy, Nati Srebro
Poster
Wed 21:00 Asymptotic Normality and Confidence Intervals for Prediction Risk of the Min-Norm Least Squares Estimator
Zeng Li, Chuanlong Xie, Qinwen Wang
Poster
Wed 21:00 Sparse Feature Selection Makes Batch Reinforcement Learning More Sample Efficient
Botao Hao, Yaqi Duan, Tor Lattimore, Csaba Szepesvari, Mengdi Wang
Poster
Wed 21:00 Detection of Signal in the Spiked Rectangular Models
Ji Hyung Jung, Hye Won Chung, Ji Oon Lee
Spotlight
Thu 6:45 Learning Interaction Kernels for Agent Systems on Riemannian Manifolds
Mauro Maggioni, Jason Miller, Hongda Qiu, Ming Zhong
Poster
Thu 9:00 Learning Interaction Kernels for Agent Systems on Riemannian Manifolds
Mauro Maggioni, Jason Miller, Hongda Qiu, Ming Zhong
Spotlight
Thu 19:35 GBHT: Gradient Boosting Histogram Transform for Density Estimation
Jingyi Cui, Hanyuan Hang, Yisen Wang, Zhouchen Lin
Spotlight
Thu 20:45 Smooth $p$-Wasserstein Distance: Structure, Empirical Approximation, and Statistical Applications
Sloan Nietert, Ziv Goldfeld, Kengo Kato
Spotlight
Thu 20:50 How rotational invariance of common kernels prevents generalization in high dimensions
Konstantin Donhauser, Mingqi Wu, Fanny Yang
Poster
Thu 21:00 How rotational invariance of common kernels prevents generalization in high dimensions
Konstantin Donhauser, Mingqi Wu, Fanny Yang
Poster
Thu 21:00 Smooth $p$-Wasserstein Distance: Structure, Empirical Approximation, and Statistical Applications
Sloan Nietert, Ziv Goldfeld, Kengo Kato
Poster
Thu 21:00 GBHT: Gradient Boosting Histogram Transform for Density Estimation
Jingyi Cui, Hanyuan Hang, Yisen Wang, Zhouchen Lin
Workshop
Sat 15:40 An Extreme Point Approach to Subset Selection
Viveck Cadambe, Bill Kay
Workshop
An Extreme Point Approach to Subset Selection
Viveck Cadambe, Bill Kay
Workshop
Adversarial Sample Detection via Channel Pruning
Zuohui Chen, RenXuan Wang, Yao Lu, jingyang Xiang, Qi Xuan