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Tutorial
Mon 12:00 Online and non-stochastic control
Elad Hazan, Karan Singh
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 5:20 Stochastic Sign Descent Methods: New Algorithms and Better Theory
Mher Safaryan, Peter Richtarik
Spotlight
Tue 5:20 Offline Contextual Bandits with Overparameterized Models
David Brandfonbrener, Will Whitney, Rajesh Ranganath, Joan Bruna
Spotlight
Tue 5:30 A Hybrid Variance-Reduced Method for Decentralized Stochastic Non-Convex Optimization
Ran Xin, Usman Khan, Soummya Kar
Spotlight
Tue 5:35 Zeroth-Order Non-Convex Learning via Hierarchical Dual Averaging
Amélie Héliou, Matthieu Martin, Panayotis Mertikopoulos, Thibaud J Rahier
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
Oral
Tue 6:00 PAGE: A Simple and Optimal Probabilistic Gradient Estimator for Nonconvex Optimization
Zhize Li, Hongyan Bao, Xiangliang Zhang, Peter Richtarik
Spotlight
Tue 6:20 Dueling Convex Optimization
Aadirupa Saha, Tomer Koren, Yishay Mansour
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 Principal Component Hierarchy for Sparse Quadratic Programs
Robbie Vreugdenhil, Viet Anh Nguyen, Armin Eftekhari, Peyman Mohajerin Esfahani
Spotlight
Tue 6:35 Provably Efficient Learning of Transferable Rewards
Alberto Maria Metelli, Giorgia Ramponi, Alessandro Concetti, Marcello Restelli
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
Spotlight
Tue 7:25 Variational Data Assimilation with a Learned Inverse Observation Operator
Thomas Frerix, Dmitrii Kochkov, Jamie Smith, Daniel Cremers, Michael Brenner, Stephan Hoyer
Poster
Tue 9:00 ConvexVST: A Convex Optimization Approach to Variance-stabilizing Transformation
Mengfan Wang, Boyu Lyu, Guoqiang Yu
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 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 A Hybrid Variance-Reduced Method for Decentralized Stochastic Non-Convex Optimization
Ran Xin, Usman Khan, Soummya Kar
Poster
Tue 9:00 Offline Contextual Bandits with Overparameterized Models
David Brandfonbrener, Will Whitney, Rajesh Ranganath, Joan Bruna
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 Variational Data Assimilation with a Learned Inverse Observation Operator
Thomas Frerix, Dmitrii Kochkov, Jamie Smith, Daniel Cremers, Michael Brenner, Stephan Hoyer
Poster
Tue 9:00 One-sided Frank-Wolfe algorithms for saddle problems
Vladimir Kolmogorov, Thomas Pock
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 PAGE: A Simple and Optimal Probabilistic Gradient Estimator for Nonconvex Optimization
Zhize Li, Hongyan Bao, Xiangliang Zhang, Peter Richtarik
Poster
Tue 9:00 Sparsifying Networks via Subdifferential Inclusion
Sagar Verma, Jean-Christophe Pesquet
Poster
Tue 9:00 Decentralized Riemannian Gradient Descent on the Stiefel Manifold
Shixiang Chen, Alfredo Garcia, Mingyi Hong, Shahin Shahrampour
Poster
Tue 9:00 Global Optimality Beyond Two Layers: Training Deep ReLU Networks via Convex Programs
Tolga Ergen, Mert Pilanci
Poster
Tue 9:00 Provably Efficient Learning of Transferable Rewards
Alberto Maria Metelli, Giorgia Ramponi, Alessandro Concetti, Marcello Restelli
Poster
Tue 9:00 Dueling Convex Optimization
Aadirupa Saha, Tomer Koren, Yishay Mansour
Poster
Tue 9:00 Stochastic Sign Descent Methods: New Algorithms and Better Theory
Mher Safaryan, Peter Richtarik
Poster
Tue 9:00 Parameter-free Locally Accelerated Conditional Gradients
Alejandro Carderera, Jelena Diakonikolas, Cheuk Yin Lin, Sebastian Pokutta
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 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 17:45 Training Quantized Neural Networks to Global Optimality via Semidefinite Programming
Burak Bartan, Mert Pilanci
Spotlight
Tue 18:35 Asynchronous Distributed Learning : Adapting to Gradient Delays without Prior Knowledge
Rotem Zamir Aviv, Ido Hakimi, Assaf Schuster, Kfir Levy
Spotlight
Tue 18:40 Parallel and Flexible Sampling from Autoregressive Models via Langevin Dynamics
Vivek Jayaram, John Thickstun
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
Oral
Tue 19:00 Sequential Domain Adaptation by Synthesizing Distributionally Robust Experts
Bahar Taskesen, Man Chung Yue, Jose Blanchet, Daniel Kuhn, Viet Anh Nguyen
Spotlight
Tue 19:20 Oblivious Sketching-based Central Path Method for Linear Programming
Zhao Song, Zheng Yu
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 19:40 Neural Pharmacodynamic State Space Modeling
Zeshan Hussain, Rahul G. Krishnan, David Sontag
Spotlight
Tue 19:45 On a Combination of Alternating Minimization and Nesterov's Momentum
Sergey Guminov, Pavel Dvurechenskii, Nazarii Tupitsa, Alexander Gasnikov
Spotlight
Tue 19:45 Three Operator Splitting with a Nonconvex Loss Function
Alp Yurtsever, Varun Mangalick, Suvrit Sra
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 Asynchronous Distributed Learning : Adapting to Gradient Delays without Prior Knowledge
Rotem Zamir Aviv, Ido Hakimi, Assaf Schuster, Kfir Levy
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 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 Training Quantized Neural Networks to Global Optimality via Semidefinite Programming
Burak Bartan, Mert Pilanci
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 Oblivious Sketching-based Central Path Method for Linear Programming
Zhao Song, Zheng Yu
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 Sequential Domain Adaptation by Synthesizing Distributionally Robust Experts
Bahar Taskesen, Man Chung Yue, Jose Blanchet, Daniel Kuhn, Viet Anh Nguyen
Poster
Tue 21:00 Parallel and Flexible Sampling from Autoregressive Models via Langevin Dynamics
Vivek Jayaram, John Thickstun
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
Oral
Wed 6:00 Agnostic Learning of Halfspaces with Gradient Descent via Soft Margins
Spencer Frei, Yuan Cao, Quanquan Gu
Oral
Wed 6:00 Regret and Cumulative Constraint Violation Analysis for Online Convex Optimization with Long Term Constraints
Xinlei Yi, Xiuxian Li, Tao Yang, Lihua Xie, Tianyou Chai, Karl Johansson
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 6:30 Online A-Optimal Design and Active Linear Regression
Xavier Fontaine, Pierre Perrault, Michal Valko, Vianney Perchet
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
Spotlight
Wed 6:40 Efficient Performance Bounds for Primal-Dual Reinforcement Learning from Demonstrations
Angeliki Kamoutsi, Goran Banjac, John Lygeros
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 Online A-Optimal Design and Active Linear Regression
Xavier Fontaine, Pierre Perrault, Michal Valko, Vianney Perchet
Poster
Wed 9:00 Agnostic Learning of Halfspaces with Gradient Descent via Soft Margins
Spencer Frei, Yuan Cao, Quanquan Gu
Poster
Wed 9:00 Efficient Performance Bounds for Primal-Dual Reinforcement Learning from Demonstrations
Angeliki Kamoutsi, Goran Banjac, John Lygeros
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 Learning from History for Byzantine Robust Optimization
Praneeth Karimireddy, Lie He, Martin Jaggi
Poster
Wed 9:00 Inferring serial correlation with dynamic backgrounds
Song Wei, Yao Xie, Dobromir Rahnev
Poster
Wed 9:00 Multiplying Matrices Without Multiplying
Davis Blalock, John Guttag
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 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 Regret and Cumulative Constraint Violation Analysis for Online Convex Optimization with Long Term Constraints
Xinlei Yi, Xiuxian Li, Tao Yang, Lihua Xie, Tianyou Chai, Karl Johansson
Poster
Wed 9:00 MARINA: Faster Non-Convex Distributed Learning with Compression
Eduard Gorbunov, Konstantin Burlachenko, Zhize Li, Peter Richtarik
Poster
Wed 9:00 Bilevel Optimization: Convergence Analysis and Enhanced Design
Kaiyi Ji, Junjie Yang, Yingbin LIANG
Poster
Wed 9:00 SMG: A Shuffling Gradient-Based Method with Momentum
Trang Tran, Lam Nguyen, Quoc Tran-Dinh
Poster
Wed 9:00 Optimizing persistent homology based functions
Mathieu Carrière, Frederic Chazal, Marc Glisse, Yuichi Ike, Hariprasad Kannan, Yuhei Umeda
Poster
Wed 9:00 TempoRL: Learning When to Act
André Biedenkapp, Raghu Rajan, Frank Hutter, Marius Lindauer
Poster
Wed 9:00 Fast Algorithms for Stackelberg Prediction Game with Least Squares Loss
jiali wang, He Chen, Rujun Jiang, Xudong Li, Zihao Li
Spotlight
Wed 17:35 Leveraging Non-uniformity in First-order Non-convex Optimization
Jincheng Mei, Yue Gao, Bo Dai, Csaba Szepesvari, Dale Schuurmans
Spotlight
Wed 17:35 Improved Corruption Robust Algorithms for Episodic Reinforcement Learning
Yifang Chen, Simon Du, Kevin Jamieson
Spotlight
Wed 18:20 Outside the Echo Chamber: Optimizing the Performative Risk
John Miller, Juan Perdomo, Tijana Zrnic
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
Spotlight
Wed 19:40 Adaptive Newton Sketch: Linear-time Optimization with Quadratic Convergence and Effective Hessian Dimensionality
Jonathan Lacotte, Yifei Wang, Mert Pilanci
Poster
Wed 21:00 Improved Corruption Robust Algorithms for Episodic Reinforcement Learning
Yifang Chen, Simon Du, Kevin Jamieson
Poster
Wed 21:00 Leveraging Non-uniformity in First-order Non-convex Optimization
Jincheng Mei, Yue Gao, Bo Dai, Csaba Szepesvari, Dale Schuurmans
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
Poster
Wed 21:00 Adaptive Newton Sketch: Linear-time Optimization with Quadratic Convergence and Effective Hessian Dimensionality
Jonathan Lacotte, Yifei Wang, Mert Pilanci
Poster
Wed 21:00 Outside the Echo Chamber: Optimizing the Performative Risk
John Miller, Juan Perdomo, Tijana Zrnic
Oral
Thu 5:00 Differentiable Particle Filtering via Entropy-Regularized Optimal Transport
Adrien Corenflos, James Thornton, George Deligiannidis, Arnaud Doucet
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 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 Dash: Semi-Supervised Learning with Dynamic Thresholding
Yi Xu, Lei Shang, Jinxing Ye, Qi Qian, Yufeng Li, Baigui Sun, Hao Li, rong jin
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
Spotlight
Thu 6:45 Principal Bit Analysis: Autoencoding with Schur-Concave Loss
Sourbh Bhadane, Aaron Wagner, Jayadev Acharya
Spotlight
Thu 7:35 Matrix Completion with Model-free Weighting
Jiayi Wang, Raymond K. W. Wong, Xiaojun Mao, Kwun Chuen Gary Chan
Spotlight
Thu 7:40 Bayesian Quadrature on Riemannian Data Manifolds
Christian Fröhlich, Alexandra Gessner, Philipp Hennig, Bernhard Schölkopf, Georgios Arvanitidis
Poster
Thu 9:00 Differentiable Particle Filtering via Entropy-Regularized Optimal Transport
Adrien Corenflos, James Thornton, George Deligiannidis, Arnaud Doucet
Poster
Thu 9:00 Bayesian Quadrature on Riemannian Data Manifolds
Christian Fröhlich, Alexandra Gessner, Philipp Hennig, Bernhard Schölkopf, Georgios Arvanitidis
Poster
Thu 9:00 Learning disentangled representations via product manifold projection
Marco Fumero, Luca Cosmo, Simone Melzi, Emanuele Rodola
Poster
Thu 9:00 Principal Bit Analysis: Autoencoding with Schur-Concave Loss
Sourbh Bhadane, Aaron Wagner, Jayadev Acharya
Poster
Thu 9:00 Dash: Semi-Supervised Learning with Dynamic Thresholding
Yi Xu, Lei Shang, Jinxing Ye, Qi Qian, Yufeng Li, Baigui Sun, Hao Li, rong jin
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 Quantum algorithms for reinforcement learning with a generative model
Daochen Wang, Aarthi Sundaram, Robin Kothari, Ashish Kapoor, Martin Roetteler
Poster
Thu 9:00 Improved, Deterministic Smoothing for L_1 Certified Robustness
Alexander Levine, Soheil Feizi
Poster
Thu 9:00 A Scalable Second Order Method for Ill-Conditioned Matrix Completion from Few Samples
Christian Kümmerle, Claudio Mayrink Verdun
Poster
Thu 9:00 Matrix Completion with Model-free Weighting
Jiayi Wang, Raymond K. W. Wong, Xiaojun Mao, Kwun Chuen Gary Chan
Poster
Thu 9:00 Phase Transitions, Distance Functions, and Implicit Neural Representations
Yaron Lipman
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 20:30 Decentralized Riemannian Gradient Descent on the Stiefel Manifold
Shixiang Chen, Alfredo Garcia, Mingyi Hong, Shahin Shahrampour
Spotlight
Thu 20:35 Boosting for Online Convex Optimization
Elad Hazan, Karan Singh
Spotlight
Thu 20:35 Fast margin maximization via dual acceleration
Ziwei Ji, Nati Srebro, Matus Telgarsky
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
Spotlight
Thu 20:45 Learner-Private Convex Optimization
Jiaming Xu, Kuang Xu, Dana Yang
Poster
Thu 21:00 Boosting for Online Convex Optimization
Elad Hazan, Karan Singh
Poster
Thu 21:00 Fast margin maximization via dual acceleration
Ziwei Ji, Nati Srebro, Matus Telgarsky
Poster
Thu 21:00 Learner-Private Convex Optimization
Jiaming Xu, Kuang Xu, Dana Yang
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 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 Neural Pharmacodynamic State Space Modeling
Zeshan Hussain, Rahul G. Krishnan, David Sontag
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
Workshop
Fri 9:49 Neural Fixed-Point Acceleration for Convex Optimization
Shobha Venkataraman
Workshop
Fri 10:40 Contributed Talks Session 2
Saeed Sharifi-Malvajerdi, Audra McMillan, Ryan McKenna
Workshop
Sat 8:05 Conjugate gradient techniques for nonconvex optimization
Clément Royer
Workshop
Sat 11:45 Morning Poster Session: Revisiting Dynamic Regret of Strongly Adaptive Methods
Dheeraj Baby
Workshop
Sat 13:02 Sparse Bayesian Learning via Stepwise Regression
Sebastian Ament, Carla Gomes
Workshop
Sat 15:45 Stochastic Variance-Reduced High-order Optimization for Nonconvex Optimization
Quanquan Gu
Workshop
Unsupervised Information Obfuscation for Split Inference of Neural Networks
Mohammad Samragh, Hossein Hosseini, Aleksei Triastcyn, Kambiz Azarian, Joseph B Soriaga, Farinaz Koushanfar
Workshop
Non-Euclidean Differentially Private Stochastic Convex Optimization
Raef Bassily, Cristobal Guzman, Anupama Nandi
Workshop
Adapting to function difficulty and growth conditions in private optimization
Hilal Asi, Daniel A Levy, John Duchi
Workshop
Learning with User-Level Privacy
Daniel A Levy, Ziteng Sun, Kareem Amin, Satyen Kale, Alex Kulesza, Mehryar Mohri, Ananda Theertha Suresh
Workshop
Improved Rates for Differentially Private Stochastic Convex Optimization with Heavy-Tailed Data
Gautam Kamath, Xingtu Liu, Huanyu Zhang
Workshop
A Practitioners Guide to Differentially Private Convex Optimization
Ryan McKenna, Hristo Paskov, Kunal Talwar
Workshop
Shuffle Private Stochastic Convex Optimization
Albert Cheu, Matthew Joseph, Jieming Mao, Binghui Peng
Workshop
Neural Fixed-Point Acceleration for Convex Optimization
Shobha Venkataraman, Brandon Amos
Workshop
A general sample complexity analysis of vanilla policy gradient
Rui Yuan, Robert Gower, Alessandro Lazaric
Workshop
Sparse Bayesian Learning via Stepwise Regression
Sebastian Ament, Carla Gomes
Workshop
On Frank-Wolfe Adversarial Training
Theodoros Tsiligkaridis, Jay Roberts
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
On Low Rank Training of Deep Neural Networks
Sid Kamalakara, Acyr Locatelli, Bharat Venkitesh, Jimmy Ba, Yarin Gal, Aidan Gomez
Workshop
Benchmarking Differential Privacy and Federated Learning for BERT Models
Priyam Basu, Rakshit Naidu, Zumrut Muftuoglu, Sahib Singh, FatemehSadat Mireshghallah