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 ]

55 Results

Spotlight
Tue 5:20 Offline Contextual Bandits with Overparameterized Models
David Brandfonbrener, Will Whitney, Rajesh Ranganath, Joan Bruna
Spotlight
Tue 5:35 Zeroth-Order Non-Convex Learning via Hierarchical Dual Averaging
Amélie Héliou, Matthieu Martin, Panayotis Mertikopoulos, Thibaud J Rahier
Poster
Tue 9:00 Zeroth-Order Non-Convex Learning via Hierarchical Dual Averaging
Amélie Héliou, Matthieu Martin, Panayotis Mertikopoulos, Thibaud J Rahier
Poster
Tue 9:00 Offline Contextual Bandits with Overparameterized Models
David Brandfonbrener, Will Whitney, Rajesh Ranganath, Joan Bruna
Spotlight
Tue 17:30 Joining datasets via data augmentation in the label space for neural networks
Jake Zhao Zhao, Mingfeng Ou, linji Xue, Yunkai Cui, Sai Wu, Gang Chen
Spotlight
Tue 19:40 Neural Pharmacodynamic State Space Modeling
Zeshan Hussain, Rahul G. Krishnan, David Sontag
Spotlight
Tue 19:45 Three Operator Splitting with a Nonconvex Loss Function
Alp Yurtsever, Varun Mangalick, Suvrit Sra
Poster
Tue 21:00 Joining datasets via data augmentation in the label space for neural networks
Jake Zhao Zhao, Mingfeng Ou, linji Xue, Yunkai Cui, Sai Wu, Gang Chen
Poster
Tue 21:00 Three Operator Splitting with a Nonconvex Loss Function
Alp Yurtsever, Varun Mangalick, Suvrit Sra
Oral
Wed 5:00 Optimizing persistent homology based functions
Mathieu Carrière, Frederic Chazal, Marc Glisse, Yuichi Ike, Hariprasad Kannan, Yuhei Umeda
Spotlight
Wed 5:20 Debiasing a First-order Heuristic for Approximate Bi-level Optimization
Valerii Likhosherstov, Xingyou Song, Krzysztof Choromanski, Jared Quincy Davis, Adrian Weller
Spotlight
Wed 5:25 SMG: A Shuffling Gradient-Based Method with Momentum
Trang Tran, Lam Nguyen, Quoc Tran-Dinh
Spotlight
Wed 5:30 Regret Minimization in Stochastic Non-Convex Learning via a Proximal-Gradient Approach
Nadav Hallak, Panayotis Mertikopoulos, Volkan Cevher
Spotlight
Wed 5:35 MARINA: Faster Non-Convex Distributed Learning with Compression
Eduard Gorbunov, Konstantin Burlachenko, Zhize Li, Peter Richtarik
Spotlight
Wed 5:40 Bilevel Optimization: Convergence Analysis and Enhanced Design
Kaiyi Ji, Junjie Yang, Yingbin LIANG
Spotlight
Wed 5:45 Learning from History for Byzantine Robust Optimization
Praneeth Karimireddy, Lie He, Martin Jaggi
Spotlight
Wed 6:25 A Modular Analysis of Provable Acceleration via Polyak's Momentum: Training a Wide ReLU Network and a Deep Linear Network
Jun-Kun Wang, Chi-Heng Lin, Jake Abernethy
Spotlight
Wed 6:35 Fast Algorithms for Stackelberg Prediction Game with Least Squares Loss
jiali wang, He Chen, Rujun Jiang, Xudong Li, Zihao Li
Spotlight
Wed 6:40 TempoRL: Learning When to Act
André Biedenkapp, Raghu Rajan, Frank Hutter, Marius Lindauer
Poster
Wed 9:00 SMG: A Shuffling Gradient-Based Method with Momentum
Trang Tran, Lam Nguyen, Quoc Tran-Dinh
Poster
Wed 9:00 Fast Algorithms for Stackelberg Prediction Game with Least Squares Loss
jiali wang, He Chen, Rujun Jiang, Xudong Li, Zihao Li
Poster
Wed 9:00 Debiasing a First-order Heuristic for Approximate Bi-level Optimization
Valerii Likhosherstov, Xingyou Song, Krzysztof Choromanski, Jared Quincy Davis, Adrian Weller
Poster
Wed 9:00 Bilevel Optimization: Convergence Analysis and Enhanced Design
Kaiyi Ji, Junjie Yang, Yingbin LIANG
Poster
Wed 9:00 Regret Minimization in Stochastic Non-Convex Learning via a Proximal-Gradient Approach
Nadav Hallak, Panayotis Mertikopoulos, Volkan Cevher
Poster
Wed 9:00 MARINA: Faster Non-Convex Distributed Learning with Compression
Eduard Gorbunov, Konstantin Burlachenko, Zhize Li, Peter Richtarik
Poster
Wed 9:00 TempoRL: Learning When to Act
André Biedenkapp, Raghu Rajan, Frank Hutter, Marius Lindauer
Poster
Wed 9:00 Learning from History for Byzantine Robust Optimization
Praneeth Karimireddy, Lie He, Martin Jaggi
Poster
Wed 9:00 A Modular Analysis of Provable Acceleration via Polyak's Momentum: Training a Wide ReLU Network and a Deep Linear Network
Jun-Kun Wang, Chi-Heng Lin, Jake Abernethy
Poster
Wed 9:00 Optimizing persistent homology based functions
Mathieu Carrière, Frederic Chazal, Marc Glisse, Yuichi Ike, Hariprasad Kannan, Yuhei Umeda
Spotlight
Wed 17:35 Improved Corruption Robust Algorithms for Episodic Reinforcement Learning
Yifang Chen, Simon Du, Kevin Jamieson
Poster
Wed 21:00 Improved Corruption Robust Algorithms for Episodic Reinforcement Learning
Yifang Chen, Simon Du, Kevin Jamieson
Spotlight
Thu 5:30 Learning disentangled representations via product manifold projection
Marco Fumero, Luca Cosmo, Simone Melzi, Emanuele Rodola
Spotlight
Thu 5:45 A Scalable Second Order Method for Ill-Conditioned Matrix Completion from Few Samples
Christian Kümmerle, Claudio Mayrink Verdun
Spotlight
Thu 6:30 Phase Transitions, Distance Functions, and Implicit Neural Representations
Yaron Lipman
Spotlight
Thu 6:35 Quantum algorithms for reinforcement learning with a generative model
Daochen Wang, Aarthi Sundaram, Robin Kothari, Ashish Kapoor, Martin Roetteler
Poster
Thu 9:00 Learning disentangled representations via product manifold projection
Marco Fumero, Luca Cosmo, Simone Melzi, Emanuele Rodola
Poster
Thu 9:00 Phase Transitions, Distance Functions, and Implicit Neural Representations
Yaron Lipman
Poster
Thu 9:00 Quantum algorithms for reinforcement learning with a generative model
Daochen Wang, Aarthi Sundaram, Robin Kothari, Ashish Kapoor, Martin Roetteler
Poster
Thu 9:00 A Scalable Second Order Method for Ill-Conditioned Matrix Completion from Few Samples
Christian Kümmerle, Claudio Mayrink Verdun
Spotlight
Thu 18:25 Watermarking Deep Neural Networks with Greedy Residuals
Hanwen Liu, Zhenyu Weng, Yuesheng Zhu
Spotlight
Thu 18:40 Optimal Non-Convex Exact Recovery in Stochastic Block Model via Projected Power Method
Peng Wang, Huikang Liu, Zirui Zhou, Anthony Man-Cho So
Spotlight
Thu 20:40 A Second look at Exponential and Cosine Step Sizes: Simplicity, Adaptivity, and Performance
Xiaoyu Li, Zhenxun Zhuang, Francesco Orabona
Spotlight
Thu 20:45 A Value-Function-based Interior-point Method for Non-convex Bi-level Optimization
Risheng Liu, Xuan Liu, Xiaoming Yuan, Shangzhi Zeng, Jin Zhang
Poster
Thu 21:00 Optimal Non-Convex Exact Recovery in Stochastic Block Model via Projected Power Method
Peng Wang, Huikang Liu, Zirui Zhou, Anthony Man-Cho So
Poster
Thu 21:00 A Value-Function-based Interior-point Method for Non-convex Bi-level Optimization
Risheng Liu, Xuan Liu, Xiaoming Yuan, Shangzhi Zeng, Jin Zhang
Poster
Thu 21:00 Neural Pharmacodynamic State Space Modeling
Zeshan Hussain, Rahul G. Krishnan, David Sontag
Poster
Thu 21:00 A Second look at Exponential and Cosine Step Sizes: Simplicity, Adaptivity, and Performance
Xiaoyu Li, Zhenxun Zhuang, Francesco Orabona
Poster
Thu 21:00 Watermarking Deep Neural Networks with Greedy Residuals
Hanwen Liu, Zhenyu Weng, Yuesheng Zhu
Workshop
Sat 13:02 Sparse Bayesian Learning via Stepwise Regression
Sebastian Ament, Carla Gomes
Workshop
On Frank-Wolfe Adversarial Training
Theodoros Tsiligkaridis, Jay Roberts
Workshop
Sparse Bayesian Learning via Stepwise Regression
Sebastian Ament, Carla Gomes
Workshop
Algorithmic Recourse in Partially and Fully Confounded Settings Through Bounding Counterfactual Effects
Julius von Kügelgen, Nikita Agarwal, Jakob Zeitler, Afsaneh Mastouri, Bernhard Schölkopf
Workshop
Benchmarking Differential Privacy and Federated Learning for BERT Models
Priyam Basu, Rakshit Naidu, Zumrut Muftuoglu, Sahib Singh, FatemehSadat Mireshghallah
Workshop
On Low Rank Training of Deep Neural Networks
Sid Kamalakara, Acyr Locatelli, Bharat Venkitesh, Jimmy Ba, Yarin Gal, Aidan Gomez
Workshop
Unsupervised Information Obfuscation for Split Inference of Neural Networks
Mohammad Samragh, Hossein Hosseini, Aleksei Triastcyn, Kambiz Azarian, Joseph B Soriaga, Farinaz Koushanfar