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 ]

54 Results

Oral
Tue 5:00 BORE: Bayesian Optimization by Density-Ratio Estimation
Louis Tiao, Aaron Klein, Matthias W Seeger, Edwin V Bonilla, Cedric Archambeau, Fabio Ramos
Spotlight
Tue 5:40 Sparsifying Networks via Subdifferential Inclusion
Sagar Verma, Jean-Christophe Pesquet
Oral
Tue 6:00 Variance Reduction via Primal-Dual Accelerated Dual Averaging for Nonsmooth Convex Finite-Sums
Chaobing Song, Stephen Wright, Jelena Diakonikolas
Spotlight
Tue 6:25 Global Optimality Beyond Two Layers: Training Deep ReLU Networks via Convex Programs
Tolga Ergen, Mert Pilanci
Spotlight
Tue 6:30 Parameter-free Locally Accelerated Conditional Gradients
Alejandro Carderera, Jelena Diakonikolas, Cheuk Yin Lin, Sebastian Pokutta
Spotlight
Tue 6:35 Provably Efficient Learning of Transferable Rewards
Alberto Maria Metelli, Giorgia Ramponi, Alessandro Concetti, Marcello Restelli
Spotlight
Tue 6:35 Principal Component Hierarchy for Sparse Quadratic Programs
Robbie Vreugdenhil, Viet Anh Nguyen, Armin Eftekhari, Peyman Mohajerin Esfahani
Spotlight
Tue 6:40 One-sided Frank-Wolfe algorithms for saddle problems
Vladimir Kolmogorov, Thomas Pock
Spotlight
Tue 6:45 ConvexVST: A Convex Optimization Approach to Variance-stabilizing Transformation
Mengfan Wang, Boyu Lyu, Guoqiang Yu
Poster
Tue 9:00 One-sided Frank-Wolfe algorithms for saddle problems
Vladimir Kolmogorov, Thomas Pock
Poster
Tue 9:00 Sparsifying Networks via Subdifferential Inclusion
Sagar Verma, Jean-Christophe Pesquet
Poster
Tue 9:00 Provably Efficient Learning of Transferable Rewards
Alberto Maria Metelli, Giorgia Ramponi, Alessandro Concetti, Marcello Restelli
Poster
Tue 9:00 Variance Reduction via Primal-Dual Accelerated Dual Averaging for Nonsmooth Convex Finite-Sums
Chaobing Song, Stephen Wright, Jelena Diakonikolas
Poster
Tue 9:00 Principal Component Hierarchy for Sparse Quadratic Programs
Robbie Vreugdenhil, Viet Anh Nguyen, Armin Eftekhari, Peyman Mohajerin Esfahani
Poster
Tue 9:00 ConvexVST: A Convex Optimization Approach to Variance-stabilizing Transformation
Mengfan Wang, Boyu Lyu, Guoqiang Yu
Poster
Tue 9:00 BORE: Bayesian Optimization by Density-Ratio Estimation
Louis Tiao, Aaron Klein, Matthias W Seeger, Edwin V Bonilla, Cedric Archambeau, Fabio Ramos
Poster
Tue 9:00 Parameter-free Locally Accelerated Conditional Gradients
Alejandro Carderera, Jelena Diakonikolas, Cheuk Yin Lin, Sebastian Pokutta
Poster
Tue 9:00 Global Optimality Beyond Two Layers: Training Deep ReLU Networks via Convex Programs
Tolga Ergen, Mert Pilanci
Oral
Tue 17:00 Randomized Entity-wise Factorization for Multi-Agent Reinforcement Learning
Shariq Iqbal, Christian Schroeder, Bei Peng, Wendelin Boehmer, Shimon Whiteson, Fei Sha
Spotlight
Tue 18:40 Parallel and Flexible Sampling from Autoregressive Models via Langevin Dynamics
Vivek Jayaram, John Thickstun
Oral
Tue 19:00 Sequential Domain Adaptation by Synthesizing Distributionally Robust Experts
Bahar Taskesen, Man Chung Yue, Jose Blanchet, Daniel Kuhn, Viet Anh Nguyen
Oral
Tue 19:00 Accelerated Algorithms for Smooth Convex-Concave Minimax Problems with O(1/k^2) Rate on Squared Gradient Norm
TaeHo Yoon, Ernest Ryu
Spotlight
Tue 19:25 Optimal regret algorithm for Pseudo-1d Bandit Convex Optimization
Aadirupa Saha, Nagarajan Natarajan, Praneeth Netrapalli, Prateek Jain
Spotlight
Tue 19:30 Fast Stochastic Bregman Gradient Methods: Sharp Analysis and Variance Reduction
Radu Alexandru Dragomir, Mathieu Even, Hadrien Hendrikx
Spotlight
Tue 19:35 Moreau-Yosida $f$-divergences
Dávid Terjék
Spotlight
Tue 19:40 Affine Invariant Analysis of Frank-Wolfe on Strongly Convex Sets
Thomas Kerdreux, Lewis Liu, Simon Lacoste-Julien, Damien Scieur
Spotlight
Tue 19:45 On a Combination of Alternating Minimization and Nesterov's Momentum
Sergey Guminov, Pavel Dvurechenskii, Nazarii Tupitsa, Alexander Gasnikov
Poster
Tue 21:00 Parallel and Flexible Sampling from Autoregressive Models via Langevin Dynamics
Vivek Jayaram, John Thickstun
Poster
Tue 21:00 On a Combination of Alternating Minimization and Nesterov's Momentum
Sergey Guminov, Pavel Dvurechenskii, Nazarii Tupitsa, Alexander Gasnikov
Poster
Tue 21:00 Affine Invariant Analysis of Frank-Wolfe on Strongly Convex Sets
Thomas Kerdreux, Lewis Liu, Simon Lacoste-Julien, Damien Scieur
Poster
Tue 21:00 Fast Stochastic Bregman Gradient Methods: Sharp Analysis and Variance Reduction
Radu Alexandru Dragomir, Mathieu Even, Hadrien Hendrikx
Poster
Tue 21:00 Accelerated Algorithms for Smooth Convex-Concave Minimax Problems with O(1/k^2) Rate on Squared Gradient Norm
TaeHo Yoon, Ernest Ryu
Poster
Tue 21:00 Moreau-Yosida $f$-divergences
Dávid Terjék
Poster
Tue 21:00 Optimal regret algorithm for Pseudo-1d Bandit Convex Optimization
Aadirupa Saha, Nagarajan Natarajan, Praneeth Netrapalli, Prateek Jain
Poster
Tue 21:00 Randomized Entity-wise Factorization for Multi-Agent Reinforcement Learning
Shariq Iqbal, Christian Schroeder, Bei Peng, Wendelin Boehmer, Shimon Whiteson, Fei Sha
Poster
Tue 21:00 Sequential Domain Adaptation by Synthesizing Distributionally Robust Experts
Bahar Taskesen, Man Chung Yue, Jose Blanchet, Daniel Kuhn, Viet Anh Nguyen
Oral
Wed 7:00 Inferring serial correlation with dynamic backgrounds
Song Wei, Yao Xie, Dobromir Rahnev
Spotlight
Wed 7:30 Multiplying Matrices Without Multiplying
Davis Blalock, John Guttag
Poster
Wed 9:00 Multiplying Matrices Without Multiplying
Davis Blalock, John Guttag
Poster
Wed 9:00 Inferring serial correlation with dynamic backgrounds
Song Wei, Yao Xie, Dobromir Rahnev
Oral
Wed 19:00 Multi-layered Network Exploration via Random Walks: From Offline Optimization to Online Learning
Xutong Liu, Jinhang Zuo, Xiaowei Chen, Wei Chen, John C. S. Lui
Poster
Wed 21:00 Multi-layered Network Exploration via Random Walks: From Offline Optimization to Online Learning
Xutong Liu, Jinhang Zuo, Xiaowei Chen, Wei Chen, John C. S. Lui
Oral
Thu 5:00 Differentiable Particle Filtering via Entropy-Regularized Optimal Transport
Adrien Corenflos, James Thornton, George Deligiannidis, Arnaud Doucet
Spotlight
Thu 5:45 Aggregating From Multiple Target-Shifted Sources
Changjian Shui, Zijian Li, Jiaqi Li, Christian Gagne, Charles X. Ling, Boyu Wang
Oral
Thu 6:00 Improved, Deterministic Smoothing for L_1 Certified Robustness
Alexander Levine, Soheil Feizi
Oral
Thu 6:00 Differentially Private Query Release Through Adaptive Projection
Sergul Aydore, William Brown, Michael Kearns, Krishnaram Kenthapadi, Luca Melis, Aaron Roth, Ankit Siva
Spotlight
Thu 7:40 Bayesian Quadrature on Riemannian Data Manifolds
Christian Fröhlich, Alexandra Gessner, Philipp Hennig, Bernhard Schölkopf, Georgios Arvanitidis
Poster
Thu 9:00 Improved, Deterministic Smoothing for L_1 Certified Robustness
Alexander Levine, Soheil Feizi
Poster
Thu 9:00 Differentially Private Query Release Through Adaptive Projection
Sergul Aydore, William Brown, Michael Kearns, Krishnaram Kenthapadi, Luca Melis, Aaron Roth, Ankit Siva
Poster
Thu 9:00 Bayesian Quadrature on Riemannian Data Manifolds
Christian Fröhlich, Alexandra Gessner, Philipp Hennig, Bernhard Schölkopf, Georgios Arvanitidis
Poster
Thu 9:00 Aggregating From Multiple Target-Shifted Sources
Changjian Shui, Zijian Li, Jiaqi Li, Christian Gagne, Charles X. Ling, Boyu Wang
Poster
Thu 9:00 Differentiable Particle Filtering via Entropy-Regularized Optimal Transport
Adrien Corenflos, James Thornton, George Deligiannidis, Arnaud Doucet
Spotlight
Thu 20:35 Fast margin maximization via dual acceleration
Ziwei Ji, Nati Srebro, Matus Telgarsky
Poster
Thu 21:00 Fast margin maximization via dual acceleration
Ziwei Ji, Nati Srebro, Matus Telgarsky