| 2022 | A Bandit Model for Human-Machine Decision Making with Private Information and Opacity. Sebastian Bordt, Ulrike von Luxburg |
| 2022 | A Bayesian Approach for Stochastic Continuum-armed Bandit with Long-term Constraints. Zai Shi, Atilla Eryilmaz |
| 2022 | A Bayesian Model for Online Activity Sample Sizes. Thomas S. Richardson, Yu Liu, James McQueen, Doug Hains |
| 2022 | A Class of Geometric Structures in Transfer Learning: Minimax Bounds and Optimality. Xuhui Zhang, José H. Blanchet, Soumyadip Ghosh, Mark S. Squillante |
| 2022 | A Complete Characterisation of ReLU-Invariant Distributions. Jan MacDonald, Stephan Wäldchen |
| 2022 | A Contraction Theory Approach to Optimization Algorithms from Acceleration Flows. Pedro Cisneros-Velarde, Francesco Bullo |
| 2022 | A Cramér Distance perspective on Quantile Regression based Distributional Reinforcement Learning. Alix Lheritier, Nicolas Bondoux |
| 2022 | A Dimensionality Reduction Method for Finding Least Favorable Priors with a Focus on Bregman Divergence. Alex R. Dytso, Mario Goldenbaum, H. Vincent Poor, Shlomo Shamai |
| 2022 | A Dual Approach to Constrained Markov Decision Processes with Entropy Regularization. Donghao Ying, Yuhao Ding, Javad Lavaei |
| 2022 | A Globally Convergent Evolutionary Strategy for Stochastic Constrained Optimization with Applications to Reinforcement Learning. Youssef Diouane, Aurélien Lucchi, Vihang Prakash Patil |
| 2022 | A Last Switch Dependent Analysis of Satiation and Seasonality in Bandits. Pierre Laforgue, Giulia Clerici, Nicolò Cesa-Bianchi, Ran Gilad-Bachrach |
| 2022 | A Manifold View of Adversarial Risk. Wenjia Zhang, Yikai Zhang, Xiaoling Hu, Mayank Goswami, Chao Chen, Dimitris N. Metaxas |
| 2022 | A New Notion of Individually Fair Clustering: α-Equitable k-Center. Darshan Chakrabarti, John P. Dickerson, Seyed A. Esmaeili, Aravind Srinivasan, Leonidas Tsepenekas |
| 2022 | A Non-asymptotic Approach to Best-Arm Identification for Gaussian Bandits. Antoine Barrier, Aurélien Garivier, Tomás Kocák |
| 2022 | A Predictive Approach to Bayesian Nonparametric Survival Analysis. Edwin Fong, Brieuc Lehmann |
| 2022 | A Random Matrix Perspective on Mixtures of Nonlinearities in High Dimensions. Ben Adlam, Jake A. Levinson, Jeffrey Pennington |
| 2022 | A Single-Timescale Method for Stochastic Bilevel Optimization. Tianyi Chen, Yuejiao Sun, Quan Xiao, Wotao Yin |
| 2022 | A Spectral Perspective of DNN Robustness to Label Noise. Oshrat Bar, Amnon Drory, Raja Giryes |
| 2022 | A Unified View of SDP-based Neural Network Verification through Completely Positive Programming. Robin A. Brown, Edward Schmerling, Navid Azizan, Marco Pavone |
| 2022 | A View of Exact Inference in Graphs from the Degree-4 Sum-of-Squares Hierarchy. Kevin Bello, Chuyang Ke, Jean Honorio |
| 2022 | A Witness Two-Sample Test. Jonas M. Kübler, Wittawat Jitkrittum, Bernhard Schölkopf, Krikamol Muandet |
| 2022 | A cautionary tale on fitting decision trees to data from additive models: generalization lower bounds. Yan Shuo Tan, Abhineet Agarwal, Bin Yu |
| 2022 | A general class of surrogate functions for stable and efficient reinforcement learning. Sharan Vaswani, Olivier Bachem, Simone Totaro, Robert Müller, Shivam Garg, Matthieu Geist, Marlos C. Machado, Pablo Samuel Castro, Nicolas Le Roux |
| 2022 | A general sample complexity analysis of vanilla policy gradient. Rui Yuan, Robert M. Gower, Alessandro Lazaric |
| 2022 | A prior-based approximate latent Riemannian metric. Georgios Arvanitidis, Bogdan M. Georgiev, Bernhard Schölkopf |
| 2022 | Acceleration in Distributed Optimization under Similarity. Ye Tian, Gesualdo Scutari, Tianyu Cao, Alexander V. Gasnikov |
| 2022 | Accurate Shapley Values for explaining tree-based models. Salim I. Amoukou, Tangi Salaün, Nicolas J.-B. Brunel |
| 2022 | Ada-BKB: Scalable Gaussian Process Optimization on Continuous Domains by Adaptive Discretization. Marco Rando, Luigi Carratino, Silvia Villa, Lorenzo Rosasco |
| 2022 | AdaBlock: SGD with Practical Block Diagonal Matrix Adaptation for Deep Learning. Jihun Yun, Aurélie C. Lozano, Eunho Yang |
| 2022 | Adaptation of the Independent Metropolis-Hastings Sampler with Normalizing Flow Proposals. James A. Brofos, Marylou Gabrié, Marcus A. Brubaker, Roy R. Lederman |
| 2022 | Adaptive A/B Test on Networks with Cluster Structures. Yang Liu, Yifan Zhou, Ping Li, Feifang Hu |
| 2022 | Adaptive Gaussian Processes on Graphs via Spectral Graph Wavelets. Felix L. Opolka, Yin-Cong Zhi, Pietro Liò, Xiaowen Dong |
| 2022 | Adaptive Importance Sampling meets Mirror Descent : a Bias-variance Tradeoff. Anna Korba, François Portier |
| 2022 | Adaptive Multi-Goal Exploration. Jean Tarbouriech, Omar Darwiche Domingues, Pierre Ménard, Matteo Pirotta, Michal Valko, Alessandro Lazaric |
| 2022 | Adaptive Private-K-Selection with Adaptive K and Application to Multi-label PATE. Yuqing Zhu, Yu-Xiang Wang |
| 2022 | Adaptive Sampling for Heterogeneous Rank Aggregation from Noisy Pairwise Comparisons. Yue Wu, Tao Jin, Hao Lou, Pan Xu, Farzad Farnoud, Quanquan Gu |
| 2022 | Adaptively Partitioning Max-Affine Estimators for Convex Regression. Gábor Balázs |
| 2022 | Adversarial Tracking Control via Strongly Adaptive Online Learning with Memory. Zhiyu Zhang, Ashok Cutkosky, Ioannis Ch. Paschalidis |
| 2022 | Adversarially Robust Kernel Smoothing. Jia-Jie Zhu, Christina Kouridi, Yassine Nemmour, Bernhard Schölkopf |
| 2022 | Aligned Multi-Task Gaussian Process. Olga Mikheeva, Ieva Kazlauskaite, Adam Hartshorne, Hedvig Kjellström, Carl Henrik Ek, Neill D. F. Campbell |
| 2022 | Almost Optimal Universal Lower Bound for Learning Causal DAGs with Atomic Interventions. Vibhor Porwal, Piyush Srivastava, Gaurav Sinha |
| 2022 | Amortised Likelihood-free Inference for Expensive Time-series Simulators with Signatured Ratio Estimation. Joel Dyer, Patrick W. Cannon, Sebastian M. Schmon |
| 2022 | Amortized Rejection Sampling in Universal Probabilistic Programming. Saeid Naderiparizi, Adam Scibior, Andreas Munk, Mehrdad Ghadiri, Atilim Gunes Baydin, Bradley J. Gram-Hansen, Christian A. Schröder de Witt, Robert Zinkov, Philip H. S. Torr, Tom Rainforth, Yee Whye Teh, Frank Wood |
| 2022 | An Alternate Policy Gradient Estimator for Softmax Policies. Shivam Garg, Samuele Tosatto, Yangchen Pan, Martha White, Rupam Mahmood |
| 2022 | An Information-Theoretic Justification for Model Pruning. Berivan Isik, Tsachy Weissman, Albert No |
| 2022 | An Information-theoretical Approach to Semi-supervised Learning under Covariate-shift. Gholamali Aminian, Mahed Abroshan, Mohammad Mahdi Khalili, Laura Toni, Miguel R. D. Rodrigues |
| 2022 | An Online Learning Approach to Interpolation and Extrapolation in Domain Generalization. Elan Rosenfeld, Pradeep Ravikumar, Andrej Risteski |
| 2022 | An Optimal Algorithm for Strongly Convex Minimization under Affine Constraints. Adil Salim, Laurent Condat, Dmitry Kovalev, Peter Richtárik |
| 2022 | An Unsupervised Hunt for Gravitational Lenses. Stephen Sheng, Keerthi Vasan G. C, Chi Po P. Choi, James Sharpnack, Tucker Jones |
| 2022 | Analysis of a Target-Based Actor-Critic Algorithm with Linear Function Approximation. Anas Barakat, Pascal Bianchi, Julien Lehmann |
| 2022 | Approximate Function Evaluation via Multi-Armed Bandits. Tavor Z. Baharav, Gary Cheng, Mert Pilanci, David Tse |
| 2022 | Approximate Top-m Arm Identification with Heterogeneous Reward Variances. Ruida Zhou, Chao Tian |
| 2022 | Are All Linear Regions Created Equal? Matteo Gamba, Adrian Chmielewski-Anders, Josephine Sullivan, Hossein Azizpour, Mårten Björkman |
| 2022 | Asymptotically Optimal Locally Private Heavy Hitters via Parameterized Sketches. Hao Wu, Anthony Wirth |
| 2022 | Asynchronous Distributed Optimization with Stochastic Delays. Margalit R. Glasgow, Mary Wootters |
| 2022 | Asynchronous Upper Confidence Bound Algorithms for Federated Linear Bandits. Chuanhao Li, Hongning Wang |
| 2022 | Basis Matters: Better Communication-Efficient Second Order Methods for Federated Learning. Xun Qian, Rustem Islamov, Mher Safaryan, Peter Richtárik |
| 2022 | Bayesian Classifier Fusion with an Explicit Model of Correlation. Susanne Trick, Constantin A. Rothkopf |
| 2022 | Bayesian Inference and Partial Identification in Multi-Treatment Causal Inference with Unobserved Confounding. Jiajing Zheng, Alexander D'Amour, Alexander Franks |
| 2022 | Bayesian Link Prediction with Deep Graph Convolutional Gaussian Processes. Felix L. Opolka, Pietro Liò |
| 2022 | Being a Bit Frequentist Improves Bayesian Neural Networks. Agustinus Kristiadi, Matthias Hein, Philipp Hennig |
| 2022 | Best Arm Identification with Safety Constraints. Zhenlin Wang, Andrew J. Wagenmaker, Kevin Jamieson |
| 2022 | Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learning. Yongchan Kwon, James Zou |
| 2022 | Beyond Data Samples: Aligning Differential Networks Estimation with Scientific Knowledge. Arshdeep Sekhon, Zhe Wang, Yanjun Qi |
| 2022 | Beyond the Policy Gradient Theorem for Efficient Policy Updates in Actor-Critic Algorithms. Romain Laroche, Remi Tachet des Combes |
| 2022 | Bias-Variance Decompositions for Margin Losses. Danny Wood, Tingting Mu, Gavin Brown |
| 2022 | CATVI: Conditional and Adaptively Truncated Variational Inference for Hierarchical Bayesian Nonparametric Models. Jones Yirui Liu, Xinghao Qiao, Jessica Lam |
| 2022 | CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networks. Ana Lucic, Maartje A. ter Hoeve, Gabriele Tolomei, Maarten de Rijke, Fabrizio Silvestri |
| 2022 | Calibration Error for Heterogeneous Treatment Effects. Yizhe Xu, Steve Yadlowsky |
| 2022 | Can Functional Transfer Methods Capture Simple Inductive Biases? Arne Nix, Suhas Shrinivasan, Edgar Y. Walker, Fabian H. Sinz |
| 2022 | Can Pretext-Based Self-Supervised Learning Be Boosted by Downstream Data? A Theoretical Analysis. Jiaye Teng, Weiran Huang, Haowei He |
| 2022 | Can we Generalize and Distribute Private Representation Learning? Sheikh Shams Azam, Taejin Kim, Seyyedali Hosseinalipour, Carlee Joe-Wong, Saurabh Bagchi, Christopher G. Brinton |
| 2022 | Causal Effect Identification with Context-specific Independence Relations of Control Variables. Ehsan Mokhtarian, Fateme Jamshidi, Jalal Etesami, Negar Kiyavash |
| 2022 | Causally motivated shortcut removal using auxiliary labels. Maggie Makar, Ben Packer, Dan Moldovan, Davis W. Blalock, Yoni Halpern, Alexander D'Amour |
| 2022 | Certifiably Robust Variational Autoencoders. Ben Barrett, Alexander Camuto, Matthew Willetts, Tom Rainforth |
| 2022 | Characterizing and Understanding the Generalization Error of Transfer Learning with Gibbs Algorithm. Yuheng Bu, Gholamali Aminian, Laura Toni, Gregory W. Wornell, Miguel R. D. Rodrigues |
| 2022 | Chernoff Sampling for Active Testing and Extension to Active Regression. Subhojyoti Mukherjee, Ardhendu S. Tripathy, Robert D. Nowak |
| 2022 | Co-Regularized Adversarial Learning for Multi-Domain Text Classification. Yuan Wu, Diana Inkpen, Ahmed El-Roby |
| 2022 | Common Failure Modes of Subcluster-based Sampling in Dirichlet Process Gaussian Mixture Models - and a Deep-learning Solution. Vlad Winter, Or Dinari, Oren Freifeld |
| 2022 | Common Information based Approximate State Representations in Multi-Agent Reinforcement Learning. Hsu Kao, Vijay G. Subramanian |
| 2022 | Communication-Compressed Adaptive Gradient Method for Distributed Nonconvex Optimization. Yujia Wang, Lu Lin, Jinghui Chen |
| 2022 | Complex Momentum for Optimization in Games. Jonathan P. Lorraine, David Acuna, Paul Vicol, David Duvenaud |
| 2022 | Compressed Rule Ensemble Learning. Malte Nalenz, Thomas Augustin |
| 2022 | Computing D-Stationary Points of ρ-Margin Loss SVM. Lai Tian, Anthony Man-Cho So |
| 2022 | Conditional Gradients for the Approximately Vanishing Ideal. Elias Samuel Wirth, Sebastian Pokutta |
| 2022 | Conditional Linear Regression for Heterogeneous Covariances. Leda Liang, Brendan Juba |
| 2022 | Conditionally Gaussian PAC-Bayes. Eugenio Clerico, George Deligiannidis, Arnaud Doucet |
| 2022 | Conditionally Tractable Density Estimation using Neural Networks. Hailiang Dong, Chiradeep Roy, Tahrima Rahman, Vibhav Gogate, Nicholas Ruozzi |
| 2022 | Confident Least Square Value Iteration with Local Access to a Simulator. Botao Hao, Nevena Lazic, Dong Yin, Yasin Abbasi-Yadkori, Csaba Szepesvári |
| 2022 | ContextGen: Targeted Data Generation for Low Resource Domain Specific Text Classification. Lukas Fromme, Jasmina Bogojeska, Jonas Kuhn |
| 2022 | Contrasting the landscape of contrastive and non-contrastive learning. Ashwini Pokle, Jinjin Tian, Yuchen Li, Andrej Risteski |
| 2022 | Controlling Epidemic Spread using Probabilistic Diffusion Models on Networks. Amy E. Babay, Michael Dinitz, Aravind Srinivasan, Leonidas Tsepenekas, Anil Vullikanti |
| 2022 | Convergence of Langevin Monte Carlo in Chi-Squared and Rényi Divergence. Murat A. Erdogdu, Rasa Hosseinzadeh, Shunshi Zhang |
| 2022 | Convergence of online k-means. Geelon So, Gaurav Mahajan, Sanjoy Dasgupta |
| 2022 | Convergent Working Set Algorithm for Lasso with Non-Convex Sparse Regularizers. Alain Rakotomamonjy, Rémi Flamary, Joseph Salmon, Gilles Gasso |
| 2022 | Convex Analysis of the Mean Field Langevin Dynamics. Atsushi Nitanda, Denny Wu, Taiji Suzuki |
| 2022 | Coresets for Data Discretization and Sine Wave Fitting. Alaa Maalouf, Murad Tukan, Eric Price, Daniel M. Kane, Dan Feldman |
| 2022 | Corruption-robust Offline Reinforcement Learning. Xuezhou Zhang, Yiding Chen, Xiaojin Zhu, Wen Sun |
| 2022 | Counterfactual Explanation Trees: Transparent and Consistent Actionable Recourse with Decision Trees. Kentaro Kanamori, Takuya Takagi, Ken Kobayashi, Yuichi Ike |
| 2022 | Cross-Loss Influence Functions to Explain Deep Network Representations. Andrew Silva, Rohit Chopra, Matthew C. Gombolay |
| 2022 | Crowdsourcing Regression: A Spectral Approach. Yaniv Tenzer, Omer Dror, Boaz Nadler, Erhan Bilal, Yuval Kluger |
| 2022 | Cycle Consistent Probability Divergences Across Different Spaces. Zhengxin Zhang, Youssef Mroueh, Ziv Goldfeld, Bharath K. Sriperumbudur |
| 2022 | DEANN: Speeding up Kernel-Density Estimation using Approximate Nearest Neighbor Search. Matti Karppa, Martin Aumüller, Rasmus Pagh |
| 2022 | Data Appraisal Without Data Sharing. Xinlei Xu, Awni Y. Hannun, Laurens van der Maaten |
| 2022 | Data-splitting improves statistical performance in overparameterized regimes. Nicole Mücke, Enrico Reiss, Jonas Rungenhagen, Markus Klein |
| 2022 | Debiasing Samples from Online Learning Using Bootstrap. Ningyuan Chen, Xuefeng Gao, Yi Xiong |
| 2022 | Decoupling Local and Global Representations of Time Series. Sana Tonekaboni, Chun-Liang Li, Sercan Ö. Arik, Anna Goldenberg, Tomas Pfister |
| 2022 | Deep Generative model with Hierarchical Latent Factors for Time Series Anomaly Detection. Cristian I. Challu, Peihong Jiang, Ying Nian Wu, Laurent Callot |
| 2022 | Deep Layer-wise Networks Have Closed-Form Weights. Chieh Tzu Wu, Aria Masoomi, Arthur Gretton, Jennifer G. Dy |
| 2022 | Deep Multi-Fidelity Active Learning of High-Dimensional Outputs. Shibo Li, Zheng Wang, Robert M. Kirby, Shandian Zhe |
| 2022 | Deep Neyman-Scott Processes. Chengkuan Hong, Christian R. Shelton |
| 2022 | Deep Non-crossing Quantiles through the Partial Derivative. Axel Brando, Joan Gimeno, José A. Rodríguez-Serrano, Jordi Vitrià |
| 2022 | Denoising and change point localisation in piecewise-constant high-dimensional regression coefficients. Fan Wang, Oscar Hernan Madrid Padilla, Yi Yu, Alessandro Rinaldo |
| 2022 | Density Ratio Estimation via Infinitesimal Classification. Kristy Choi, Chenlin Meng, Yang Song, Stefano Ermon |
| 2022 | Derivative-Based Neural Modelling of Cumulative Distribution Functions for Survival Analysis. Dominic Danks, Christopher Yau |
| 2022 | Differentiable Bayesian inference of SDE parameters using a pathwise series expansion of Brownian motion. Sanmitra Ghosh, Paul J. Birrell, Daniela De Angelis |
| 2022 | Differential privacy for symmetric log-concave mechanisms. Staal Amund Vinterbo |
| 2022 | Differentially Private Densest Subgraph. Alireza Farhadi, MohammadTaghi Hajiaghayi, Elaine Shi |
| 2022 | Differentially Private Federated Learning on Heterogeneous Data. Maxence Noble, Aurélien Bellet, Aymeric Dieuleveut |
| 2022 | Differentially Private Histograms under Continual Observation: Streaming Selection into the Unknown. Adrian Rivera Cardoso, Ryan Rogers |
| 2022 | Differentially Private Regression with Unbounded Covariates. Jason Milionis, Alkis Kalavasis, Dimitris A. Fotakis, Stratis Ioannidis |
| 2022 | Dimensionality Reduction and Prioritized Exploration for Policy Search. Marius Memmel, Puze Liu, Davide Tateo, Jan Peters |
| 2022 | Discovering Inductive Bias with Gibbs Priors: A Diagnostic Tool for Approximate Bayesian Inference. Luca Rendsburg, Agustinus Kristiadi, Philipp Hennig, Ulrike von Luxburg |
| 2022 | Disentangling Whether from When in a Neural Mixture Cure Model for Failure Time Data. Matthew Engelhard, Ricardo Henao |
| 2022 | Distributed Sparse Multicategory Discriminant Analysis. Hengchao Chen, Qiang Sun |
| 2022 | Distributionally Robust Structure Learning for Discrete Pairwise Markov Networks. Yeshu Li, Zhan Shi, Xinhua Zhang, Brian D. Ziebart |
| 2022 | Diversified Sampling for Batched Bayesian Optimization with Determinantal Point Processes. Elvis Nava, Mojmir Mutny, Andreas Krause |
| 2022 | Diversity and Generalization in Neural Network Ensembles. Luis A. Ortega, Rafael Cabañas, Andrés R. Masegosa |
| 2022 | Double Control Variates for Gradient Estimation in Discrete Latent Variable Models. Michalis K. Titsias, Jiaxin Shi |
| 2022 | Doubly Mixed-Effects Gaussian Process Regression. Jun Ho Yoon, Daniel P. Jeong, Seyoung Kim |
| 2022 | Dropout as a Regularizer of Interaction Effects. Benjamin J. Lengerich, Eric P. Xing, Rich Caruana |
| 2022 | Dual-Level Adaptive Information Filtering for Interactive Image Segmentation. Ervine Zheng, Qi Yu, Rui Li, Pengcheng Shi, Anne R. Haake |
| 2022 | Duel-based Deep Learning system for solving IQ tests. Paulina Tomaszewska, Adam Zychowski, Jacek Mandziuk |
| 2022 | Effective Nonlinear Feature Selection Method based on HSIC Lasso and with Variational Inference. Kazuki Koyama, Keisuke Kiritoshi, Tomomi Okawachi, Tomonori Izumitani |
| 2022 | Efficient Algorithms for Extreme Bandits. Dorian Baudry, Yoan Russac, Emilie Kaufmann |
| 2022 | Efficient Hyperparameter Tuning for Large Scale Kernel Ridge Regression. Giacomo Meanti, Luigi Carratino, Ernesto De Vito, Lorenzo Rosasco |
| 2022 | Efficient Kernelized UCB for Contextual Bandits. Houssam Zenati, Alberto Bietti, Eustache Diemert, Julien Mairal, Matthieu Martin, Pierre Gaillard |
| 2022 | Efficient Online Bayesian Inference for Neural Bandits. Gerardo Duran-Martin, Aleyna Kara, Kevin Murphy |
| 2022 | Efficient and passive learning of networked dynamical systems driven by non-white exogenous inputs. Harish Doddi, Deepjyoti Deka, Saurav Talukdar, Murti V. Salapaka |
| 2022 | Efficient computation of the the volume of a polytope in high-dimensions using Piecewise Deterministic Markov Processes. Augustin Chevallier, Frédéric Cazals, Paul Fearnhead |
| 2022 | Efficient interventional distribution learning in the PAC framework. Arnab Bhattacharyya, Sutanu Gayen, Saravanan Kandasamy, Vedant Raval, N. Variyam Vinodchandran |
| 2022 | Embedded Ensembles: infinite width limit and operating regimes. Maksim Velikanov, Roman V. Kail, Ivan Anokhin, Roman Vashurin, Maxim Panov, Alexey Zaytsev, Dmitry Yarotsky |
| 2022 | Encrypted Linear Contextual Bandit. Evrard Garcelon, Matteo Pirotta, Vianney Perchet |
| 2022 | Entropy Regularized Optimal Transport Independence Criterion. Lang Liu, Soumik Pal, Zaïd Harchaoui |
| 2022 | Entrywise Recovery Guarantees for Sparse PCA via Sparsistent Algorithms. Joshua Agterberg, Jeremias Sulam |
| 2022 | Equivariance Discovery by Learned Parameter-Sharing. Raymond A. Yeh, Yuan-Ting Hu, Mark Hasegawa-Johnson, Alexander G. Schwing |
| 2022 | Equivariant Deep Dynamical Model for Motion Prediction. Bahar Azari, Deniz Erdogmus |
| 2022 | Estimating Functionals of the Out-of-Sample Error Distribution in High-Dimensional Ridge Regression. Pratik Patil, Alessandro Rinaldo, Ryan J. Tibshirani |
| 2022 | Estimators of Entropy and Information via Inference in Probabilistic Models. Feras Saad, Marco F. Cusumano-Towner, Vikash Mansinghka |
| 2022 | Exact Community Recovery over Signed Graphs. Xiaolu Wang, Peng Wang, Anthony Man-Cho So |
| 2022 | ExactBoost: Directly Boosting the Margin in Combinatorial and Non-decomposable Metrics. Daniel Csillag, Carolina Piazza, Thiago Ramos, João Vitor Romano, Roberto I. Oliveira, Paulo Orenstein |
| 2022 | Exploiting Correlation to Achieve Faster Learning Rates in Low-Rank Preference Bandits. Aadirupa Saha, Suprovat Ghoshal |
| 2022 | Exploring Counterfactual Explanations Through the Lens of Adversarial Examples: A Theoretical and Empirical Analysis. Martin Pawelczyk, Chirag Agarwal, Shalmali Joshi, Sohini Upadhyay, Himabindu Lakkaraju |
| 2022 | Exploring Image Regions Not Well Encoded by an INN. Zenan Ling, Fan Zhou, Meng Wei, Quanshi Zhang |
| 2022 | Expressivity of Neural Networks via Chaotic Itineraries beyond Sharkovsky's Theorem. Clayton Hendrick Sanford, Vaggos Chatziafratis |
| 2022 | Extragradient Method: O(1/K) Last-Iterate Convergence for Monotone Variational Inequalities and Connections With Cocoercivity. Eduard Gorbunov, Nicolas Loizou, Gauthier Gidel |
| 2022 | FLIX: A Simple and Communication-Efficient Alternative to Local Methods in Federated Learning. Elnur Gasanov, Ahmed Khaled, Samuel Horváth, Peter Richtárik |
| 2022 | Factorization Approach for Low-complexity Matrix Completion Problems: Exponential Number of Spurious Solutions and Failure of Gradient Methods. Baturalp Yalcin, Haixiang Zhang, Javad Lavaei, Somayeh Sojoudi |
| 2022 | Fair Disaster Containment via Graph-Cut Problems. Michael Dinitz, Aravind Srinivasan, Leonidas Tsepenekas, Anil Vullikanti |
| 2022 | Fast Distributionally Robust Learning with Variance-Reduced Min-Max Optimization. Yaodong Yu, Tianyi Lin, Eric V. Mazumdar, Michael I. Jordan |
| 2022 | Fast Fourier Transform Reductions for Bayesian Network Inference. Vincent Hsiao, Dana S. Nau, Rina Dechter |
| 2022 | Fast Rank-1 NMF for Missing Data with KL Divergence. Kazu Ghalamkari, Mahito Sugiyama |
| 2022 | Fast Sparse Classification for Generalized Linear and Additive Models. Jiachang Liu, Chudi Zhong, Margo I. Seltzer, Cynthia Rudin |
| 2022 | Fast and Scalable Spike and Slab Variable Selection in High-Dimensional Gaussian Processes. Hugh Dance, Brooks Paige |
| 2022 | Fast and accurate optimization on the orthogonal manifold without retraction. Pierre Ablin, Gabriel Peyré |
| 2022 | Faster One-Sample Stochastic Conditional Gradient Method for Composite Convex Minimization. Gideon Dresdner, Maria-Luiza Vladarean, Gunnar Rätsch, Francesco Locatello, Volkan Cevher, Alp Yurtsever |
| 2022 | Faster Rates, Adaptive Algorithms, and Finite-Time Bounds for Linear Composition Optimization and Gradient TD Learning. Anant Raj, Pooria Joulani, András György, Csaba Szepesvári |
| 2022 | Faster Single-loop Algorithms for Minimax Optimization without Strong Concavity. Junchi Yang, Antonio Orvieto, Aurélien Lucchi, Niao He |
| 2022 | Faster Unbalanced Optimal Transport: Translation invariant Sinkhorn and 1-D Frank-Wolfe. Thibault Séjourné, François-Xavier Vialard, Gabriel Peyré |
| 2022 | Feature Collapsing for Gaussian Process Variable Ranking. Isaac Sebenius, Topi Paananen, Aki Vehtari |
| 2022 | Feature screening with kernel knockoffs. Benjamin Poignard, Peter J. Naylor, Héctor Climente-González, Makoto Yamada |
| 2022 | Federated Functional Gradient Boosting. Zebang Shen, Hamed Hassani, Satyen Kale, Amin Karbasi |
| 2022 | Federated Learning with Buffered Asynchronous Aggregation. John Nguyen, Kshitiz Malik, Hongyuan Zhan, Ashkan Yousefpour, Mike Rabbat, Mani Malek, Dzmitry Huba |
| 2022 | Federated Myopic Community Detection with One-shot Communication. Chuyang Ke, Jean Honorio |
| 2022 | Federated Reinforcement Learning with Environment Heterogeneity. Hao Jin, Yang Peng, Wenhao Yang, Shusen Wang, Zhihua Zhang |
| 2022 | Finding Dynamics Preserving Adversarial Winning Tickets. Xupeng Shi, Pengfei Zheng, A. Adam Ding, Yuan Gao, Weizhong Zhang |
| 2022 | Finding Nearly Everything within Random Binary Networks. Kartik Sreenivasan, Shashank Rajput, Jy-yong Sohn, Dimitris S. Papailiopoulos |
| 2022 | Finding Valid Adjustments under Non-ignorability with Minimal DAG Knowledge. Abhin Shah, Karthikeyan Shanmugam, Kartik Ahuja |
| 2022 | Finite Sample Analysis of Mean-Volatility Actor-Critic for Risk-Averse Reinforcement Learning. Khaled Eldowa, Lorenzo Bisi, Marcello Restelli |
| 2022 | Firebolt: Weak Supervision Under Weaker Assumptions. Zhaobin Kuang, Chidubem G. Arachie, Bangyong Liang, Pradyumna Narayana, Giulia DeSalvo, Michael S. Quinn, Bert Huang, Geoffrey Downs, Yang Yang |
| 2022 | Fixed Support Tree-Sliced Wasserstein Barycenter. Yuki Takezawa, Ryoma Sato, Zornitsa Kozareva, Sujith Ravi, Makoto Yamada |
| 2022 | Flexible Accuracy for Differential Privacy. Aman Bansal, Rahul Chunduru, Deepesh Data, Manoj Prabhakaran |
| 2022 | Forward Looking Best-Response Multiplicative Weights Update Methods for Bilinear Zero-sum Games. Michail Fasoulakis, Evangelos Markakis, Yannis Pantazis, Constantinos Varsos |
| 2022 | Fundamental limits for rank-one matrix estimation with groupwise heteroskedasticity. Joshua K. Behne, Galen Reeves |
| 2022 | GalilAI: Out-of-Task Distribution Detection using Causal Active Experimentation for Safe Transfer RL. Sumedh A. Sontakke, Stephen Iota, Zizhao Hu, Arash Mehrjou, Laurent Itti, Bernhard Schölkopf |
| 2022 | Gap-Dependent Bounds for Two-Player Markov Games. Zehao Dou, Zhuoran Yang, Zhaoran Wang, Simon S. Du |
| 2022 | Gap-Dependent Unsupervised Exploration for Reinforcement Learning. Jingfeng Wu, Vladimir Braverman, Lin Yang |
| 2022 | Gaussian Process Bandit Optimization with Few Batches. Zihan Li, Jonathan Scarlett |
| 2022 | Generalised GPLVM with Stochastic Variational Inference. Vidhi Lalchand, Aditya Ravuri, Neil D. Lawrence |
| 2022 | Generalized Group Testing. Xiwei Cheng, Sidharth Jaggi, Qiaoqiao Zhou |
| 2022 | Generative Models as Distributions of Functions. Emilien Dupont, Yee Whye Teh, Arnaud Doucet |
| 2022 | GraphAdaMix: Enhancing Node Representations with Graph Adaptive Mixtures. Da Sun Handason Tam, Siyue Xie, Wing Cheong Lau |
| 2022 | Grassmann Stein Variational Gradient Descent. Xing Liu, Harrison Zhu, Jean-Francois Ton, George Wynne, Andrew B. Duncan |
| 2022 | Hardness of Learning a Single Neuron with Adversarial Label Noise. Ilias Diakonikolas, Daniel Kane, Pasin Manurangsi, Lisheng Ren |
| 2022 | Harmless interpolation in regression and classification with structured features. Andrew D. McRae, Santhosh Karnik, Mark A. Davenport, Vidya K. Muthukumar |
| 2022 | Heavy-tailed Streaming Statistical Estimation. Che-Ping Tsai, Adarsh Prasad, Sivaraman Balakrishnan, Pradeep Ravikumar |
| 2022 | Hierarchical Bayesian Bandits. Joey Hong, Branislav Kveton, Manzil Zaheer, Mohammad Ghavamzadeh |
| 2022 | How and When Random Feedback Works: A Case Study of Low-Rank Matrix Factorization. Shivam Garg, Santosh S. Vempala |
| 2022 | How to Learn when Data Gradually Reacts to Your Model. Zachary Izzo, James Zou, Lexing Ying |
| 2022 | How to scale hyperparameters for quickshift image segmentation. Damien Garreau |
| 2022 | Hypergraph Simultaneous Generators. Bahman Pedrood, Carlotta Domeniconi, Kathryn B. Laskey |
| 2022 | Identifiable Energy-based Representations: An Application to Estimating Heterogeneous Causal Effects. Yao Zhang, Jeroen Berrevoets, Mihaela van der Schaar |
| 2022 | Identification in Tree-shaped Linear Structural Causal Models. Benito van der Zander, Marcel Wienöbst, Markus Bläser, Maciej Liskiewicz |
| 2022 | Identity Testing of Reversible Markov Chains. Sela Fried, Geoffrey Wolfer |
| 2022 | Implicitly Regularized RL with Implicit Q-values. Nino Vieillard, Marcin Andrychowicz, Anton Raichuk, Olivier Pietquin, Matthieu Geist |
| 2022 | Improved Algorithms for Misspecified Linear Markov Decision Processes. Daniel Vial, Advait Parulekar, Sanjay Shakkottai, R. Srikant |
| 2022 | Improved Approximation Algorithms for Individually Fair Clustering. Ali Vakilian, Mustafa Yalçiner |
| 2022 | Improved analysis of randomized SVD for top-eigenvector approximation. Ruo-Chun Tzeng, Po-An Wang, Florian Adriaens, Aristides Gionis, Chi-Jen Lu |
| 2022 | Improving Attribution Methods by Learning Submodular Functions. Piyushi Manupriya, Tarun Ram Menta, Saketha Nath Jagarlapudi, Vineeth N. Balasubramanian |
| 2022 | Increasing the accuracy and resolution of precipitation forecasts using deep generative models. Ilan Price, Stephan Rasp |
| 2022 | Independent Natural Policy Gradient always converges in Markov Potential Games. Roy Fox, Stephen M. McAleer, Will Overman, Ioannis Panageas |
| 2022 | Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations. Winnie Xu, Ricky T. Q. Chen, Xuechen Li, David Duvenaud |
| 2022 | Information-Theoretic Analysis of Epistemic Uncertainty in Bayesian Meta-learning. Sharu Theresa Jose, Sangwoo Park, Osvaldo Simeone |
| 2022 | International Conference on Artificial Intelligence and Statistics, AISTATS 2022, 28-30 March 2022, Virtual Event. Gustau Camps-Valls, Francisco J. R. Ruiz, Isabel Valera |
| 2022 | Investigating the Role of Negatives in Contrastive Representation Learning. Jordan T. Ash, Surbhi Goel, Akshay Krishnamurthy, Dipendra Misra |
| 2022 | Is Bayesian Model-Agnostic Meta Learning Better than Model-Agnostic Meta Learning, Provably? Lisha Chen, Tianyi Chen |
| 2022 | Iterative Alignment Flows. Zeyu Zhou, Ziyu Gong, Pradeep Ravikumar, David I. Inouye |
| 2022 | Jointly Efficient and Optimal Algorithms for Logistic Bandits. Louis Faury, Marc Abeille, Kwang-Sung Jun, Clément Calauzènes |
| 2022 | Kantorovich Mechanism for Pufferfish Privacy. Ni Ding |
| 2022 | LIMESegment: Meaningful, Realistic Time Series Explanations. Torty Sivill, Peter A. Flach |
| 2022 | Label differential privacy via clustering. Hossein Esfandiari, Vahab S. Mirrokni, Umar Syed, Sergei Vassilvitskii |
| 2022 | Lagrangian manifold Monte Carlo on Monge patches. Marcelo Hartmann, Mark Girolami, Arto Klami |
| 2022 | Laplacian Constrained Precision Matrix Estimation: Existence and High Dimensional Consistency. Eduardo Pavez |
| 2022 | Last Layer Marginal Likelihood for Invariance Learning. Pola Schwöbel, Martin Jørgensen, Sebastian W. Ober, Mark van der Wilk |
| 2022 | Learning Competitive Equilibria in Exchange Economies with Bandit Feedback. Wenshuo Guo, Kirthevasan Kandasamy, Joseph Gonzalez, Michael I. Jordan, Ion Stoica |
| 2022 | Learning Inconsistent Preferences with Gaussian Processes. Siu Lun Chau, Javier González, Dino Sejdinovic |
| 2022 | Learning Interpretable, Tree-Based Projection Mappings for Nonlinear Embeddings. Arman Serikuly Zharmagambetov, Miguel Á. Carreira-Perpiñán |
| 2022 | Learning Pareto-Efficient Decisions with Confidence. Sofia Ek, Dave Zachariah, Peter Stoica |
| 2022 | Learning Personalized Item-to-Item Recommendation Metric via Implicit Feedback. Trong Nghia Hoang, Anoop Deoras, Tong Zhao, Jin Li, George Karypis |
| 2022 | Learning Proposals for Practical Energy-Based Regression. Fredrik K. Gustafsson, Martin Danelljan, Thomas B. Schön |
| 2022 | Learning Quantile Functions for Temporal Point Processes with Recurrent Neural Splines. Souhaib Ben Taieb |
| 2022 | Learning Quantile Functions without Quantile Crossing for Distribution-free Time Series Forecasting. Youngsuk Park, Danielle C. Maddix, François-Xavier Aubet, Kelvin Kan, Jan Gasthaus, Yuyang Wang |
| 2022 | Learning Revenue-Maximizing Auctions With Differentiable Matching. Michael J. Curry, Uro Lyi, Tom Goldstein, John P. Dickerson |
| 2022 | Learning Sparse Fixed-Structure Gaussian Bayesian Networks. Arnab Bhattacharyya, Davin Choo, Rishikesh Gajjala, Sutanu Gayen, Yuhao Wang |
| 2022 | Learning Tensor Representations for Meta-Learning. Samuel Deng, Yilin Guo, Daniel Hsu, Debmalya Mandal |
| 2022 | Learning a Single Neuron for Non-monotonic Activation Functions. Lei Wu |
| 2022 | Learning and Generalization in Overparameterized Normalizing Flows. Kulin Shah, Amit Deshpande, Navin Goyal |
| 2022 | Learning from Multiple Noisy Partial Labelers. Peilin Yu, Tiffany Ding, Stephen H. Bach |
| 2022 | Learning from an Exploring Demonstrator: Optimal Reward Estimation for Bandits. Wenshuo Guo, Kumar Krishna Agrawal, Aditya Grover, Vidya K. Muthukumar, Ashwin Pananjady |
| 2022 | Learning in Stochastic Monotone Games with Decision-Dependent Data. Adhyyan Narang, Evan Faulkner, Dmitriy Drusvyatskiy, Maryam Fazel, Lillian J. Ratliff |
| 2022 | Learning to Plan Variable Length Sequences of Actions with a Cascading Bandit Click Model of User Feedback. Anirban Santara, Gaurav Aggarwal, Shuai Li, Claudio Gentile |
| 2022 | Leveraging Time Irreversibility with Order-Contrastive Pre-training. Monica N. Agrawal, Hunter Lang, Michael Offin, Lior Gazit, David A. Sontag |
| 2022 | Lifted Division for Lifted Hugin Belief Propagation. Moritz P. Hoffmann, Tanya Braun, Ralf Möller |
| 2022 | Lifted Primal-Dual Method for Bilinearly Coupled Smooth Minimax Optimization. Kiran Koshy Thekumparampil, Niao He, Sewoong Oh |
| 2022 | Local SGD Optimizes Overparameterized Neural Networks in Polynomial Time. Yuyang Deng, Mohammad Mahdi Kamani, Mehrdad Mahdavi |
| 2022 | LocoProp: Enhancing BackProp via Local Loss Optimization. Ehsan Amid, Rohan Anil, Manfred K. Warmuth |
| 2022 | Look-Ahead Acquisition Functions for Bernoulli Level Set Estimation. Benjamin Letham, Phillip Guan, Chase Tymms, Eytan Bakshy, Michael Shvartsman |
| 2022 | Loss as the Inconsistency of a Probabilistic Dependency Graph: Choose Your Model, Not Your Loss Function. Oliver E. Richardson |
| 2022 | Low-Pass Filtering SGD for Recovering Flat Optima in the Deep Learning Optimization Landscape. Devansh Bisla, Jing Wang, Anna Choromanska |
| 2022 | MLDemon: Deployment Monitoring for Machine Learning Systems. Tony Ginart, Martin Jinye Zhang, James Zou |
| 2022 | MT3: Meta Test-Time Training for Self-Supervised Test-Time Adaption. Alexander Bartler, Andre Bühler, Felix Wiewel, Mario Döbler, Bin Yang |
| 2022 | Maillard Sampling: Boltzmann Exploration Done Optimally. Jie Bian, Kwang-Sung Jun |
| 2022 | Many processors, little time: MCMC for partitions via optimal transport couplings. Tin D. Nguyen, Brian L. Trippe, Tamara Broderick |
| 2022 | Margin-distancing for safe model explanation. Tom Yan, Chicheng Zhang |
| 2022 | Marginalising over Stationary Kernels with Bayesian Quadrature. Saad Hamid, Sebastian Schulze, Michael A. Osborne, Stephen J. Roberts |
| 2022 | Marginalized Operators for Off-policy Reinforcement Learning. Yunhao Tang, Mark Rowland, Rémi Munos, Michal Valko |
| 2022 | Masked Training of Neural Networks with Partial Gradients. Amirkeivan Mohtashami, Martin Jaggi, Sebastian U. Stich |
| 2022 | Mean Nyström Embeddings for Adaptive Compressive Learning. Antoine Chatalic, Luigi Carratino, Ernesto De Vito, Lorenzo Rosasco |
| 2022 | Measuring the robustness of Gaussian processes to kernel choice. William T. Stephenson, Soumya Ghosh, Tin D. Nguyen, Mikhail Yurochkin, Sameer K. Deshpande, Tamara Broderick |
| 2022 | Meta Learning MDPs with linear transition models. Robert Müller, Aldo Pacchiano |
| 2022 | Metalearning Linear Bandits by Prior Update. Amit Peleg, Naama Pearl, Ron Meir |
| 2022 | Minimal Expected Regret in Linear Quadratic Control. Yassir Jedra, Alexandre Proutière |
| 2022 | Minimax Kernel Machine Learning for a Class of Doubly Robust Functionals with Application to Proximal Causal Inference. AmirEmad Ghassami, Andrew Ying, Ilya Shpitser, Eric Tchetgen Tchetgen |
| 2022 | Minimax Optimization: The Case of Convex-Submodular. Arman Adibi, Aryan Mokhtari, Hamed Hassani |
| 2022 | Mitigating Bias in Calibration Error Estimation. Rebecca Roelofs, Nicholas Cain, Jonathon Shlens, Michael C. Mozer |
| 2022 | Mode estimation on matrix manifolds: Convergence and robustness. Hiroaki Sasaki, Jun-ichiro Hirayama, Takafumi Kanamori |
| 2022 | Model-agnostic out-of-distribution detection using combined statistical tests. Federico Bergamin, Pierre-Alexandre Mattei, Jakob Drachmann Havtorn, Hugo Sénétaire, Hugo Schmutz, Lars Maaløe, Søren Hauberg, Jes Frellsen |
| 2022 | Model-free Policy Learning with Reward Gradients. Qingfeng Lan, Samuele Tosatto, Homayoon Farrahi, Rupam Mahmood |
| 2022 | Modeling Conditional Dependencies in Multiagent Trajectories. Yannick Rudolph, Ulf Brefeld |
| 2022 | Modelling Non-Smooth Signals with Complex Spectral Structure. Wessel P. Bruinsma, Martin Tegner, Richard E. Turner |
| 2022 | Moment Matching Deep Contrastive Latent Variable Models. Ethan Weinberger, Nicasia Beebe-Wang, Su-In Lee |
| 2022 | Momentum Accelerates the Convergence of Stochastic AUPRC Maximization. Guanghui Wang, Ming Yang, Lijun Zhang, Tianbao Yang |
| 2022 | Multi-armed Bandit Algorithm against Strategic Replication. Suho Shin, Seungjoon Lee, Jungseul Ok |
| 2022 | Multi-class classification in nonparametric active learning. Boris Ndjia Njike, Xavier Siebert |
| 2022 | Multiple Importance Sampling ELBO and Deep Ensembles of Variational Approximations. Oskar Kviman, Harald Melin, Hazal Koptagel, Victor Elvira, Jens Lagergren |
| 2022 | Multivariate Quantile Function Forecaster. Kelvin Kan, François-Xavier Aubet, Tim Januschowski, Youngsuk Park, Konstantinos Benidis, Lars Ruthotto, Jan Gasthaus |
| 2022 | Multiway Spherical Clustering via Degree-Corrected Tensor Block Models. Jiaxin Hu, Miaoyan Wang |
| 2022 | Near Instance Optimal Model Selection for Pure Exploration Linear Bandits. Yinglun Zhu, Julian Katz-Samuels, Robert D. Nowak |
| 2022 | Near-Optimal Task Selection for Meta-Learning with Mutual Information and Online Variational Bayesian Unlearning. Yizhou Chen, Shizhuo Zhang, Bryan Kian Hsiang Low |
| 2022 | Near-optimal Local Convergence of Alternating Gradient Descent-Ascent for Minimax Optimization. Guodong Zhang, Yuanhao Wang, Laurent Lessard, Roger B. Grosse |
| 2022 | Near-optimal Policy Optimization Algorithms for Learning Adversarial Linear Mixture MDPs. Jiafan He, Dongruo Zhou, Quanquan Gu |
| 2022 | Nearly Minimax Optimal Regret for Learning Infinite-horizon Average-reward MDPs with Linear Function Approximation. Yue Wu, Dongruo Zhou, Quanquan Gu |
| 2022 | Nearly Optimal Algorithms for Level Set Estimation. Blake Mason, Lalit K. Jain, Subhojyoti Mukherjee, Romain Camilleri, Kevin Jamieson, Robert D. Nowak |
| 2022 | Nearly Tight Convergence Bounds for Semi-discrete Entropic Optimal Transport. Alex Delalande |
| 2022 | Neural Contextual Bandits without Regret. Parnian Kassraie, Andreas Krause |
| 2022 | Neural Enhanced Dynamic Message Passing. Fei Gao, Jiang Zhang, Yan Zhang |
| 2022 | Neural score matching for high-dimensional causal inference. Oscar Clivio, Fabian Falck, Brieuc Lehmann, George Deligiannidis, Chris C. Holmes |
| 2022 | New Coresets for Projective Clustering and Applications. Murad Tukan, Xuan Wu, Samson Zhou, Vladimir Braverman, Dan Feldman |
| 2022 | Node Feature Kernels Increase Graph Convolutional Network Robustness. Mohamed El Amine Seddik, Changmin Wu, Johannes F. Lutzeyer, Michalis Vazirgiannis |
| 2022 | Noise Regularizes Over-parameterized Rank One Matrix Recovery, Provably. Tianyi Liu, Yan Li, Enlu Zhou, Tuo Zhao |
| 2022 | Non-separable Spatio-temporal Graph Kernels via SPDEs. Alexander Nikitin, S. T. John, Arno Solin, Samuel Kaski |
| 2022 | Non-stationary Online Learning with Memory and Non-stochastic Control. Peng Zhao, Yu-Xiang Wang, Zhi-Hua Zhou |
| 2022 | Nonparametric Relational Models with Superrectangulation. Masahiro Nakano, Ryo Nishikimi, Yasuhiro Fujiwara, Akisato Kimura, Takeshi Yamada, Naonori Ueda |
| 2022 | Nonstationary multi-output Gaussian processes via harmonizable spectral mixtures. Matías Altamirano, Felipe A. Tobar |
| 2022 | Nonstochastic Bandits and Experts with Arm-Dependent Delays. Dirk van der Hoeven, Nicolò Cesa-Bianchi |
| 2022 | Norm-Agnostic Linear Bandits. Spencer B. Gales, Sunder Sethuraman, Kwang-Sung Jun |
| 2022 | Nuances in Margin Conditions Determine Gains in Active Learning. Samory Kpotufe, Gan Yuan, Yunfan Zhao |
| 2022 | Obtaining Causal Information by Merging Datasets with MAXENT. Sergio Hernan Garrido Mejia, Elke Kirschbaum, Dominik Janzing |
| 2022 | Off-Policy Risk Assessment for Markov Decision Processes. Audrey Huang, Liu Leqi, Zachary C. Lipton, Kamyar Azizzadenesheli |
| 2022 | Offline Policy Selection under Uncertainty. Mengjiao Yang, Bo Dai, Ofir Nachum, George Tucker, Dale Schuurmans |
| 2022 | On Combining Bags to Better Learn from Label Proportions. Rishi Saket, Aravindan Raghuveer, Balaraman Ravindran |
| 2022 | On Convergence of Lookahead in Smooth Games. Junsoo Ha, Gunhee Kim |
| 2022 | On Coresets for Fair Regression and Individually Fair Clustering. Rachit Chhaya, Anirban Dasgupta, Jayesh Choudhari, Supratim Shit |
| 2022 | On Distributionally Robust Optimization and Data Rebalancing. Agnieszka Slowik, Léon Bottou |
| 2022 | On Facility Location Problem in the Local Differential Privacy Model. Vincent Cohen-Addad, Yunus Esencayi, Chenglin Fan, Marco Gaboardi, Shi Li, Di Wang |
| 2022 | On Global-view Based Defense via Adversarial Attack and Defense Risk Guaranteed Bounds. Trung Le, Anh Tuan Bui, Le Minh Tri Tue, He Zhao, Paul Montague, Quan Hung Tran, Dinh Q. Phung |
| 2022 | On Learning Mixture Models with Sparse Parameters. Soumyabrata Pal, Arya Mazumdar |
| 2022 | On Linear Model with Markov Signal Priors. Lan V. Truong |
| 2022 | On Margins and Derandomisation in PAC-Bayes. Felix Biggs, Benjamin Guedj |
| 2022 | On Multimarginal Partial Optimal Transport: Equivalent Forms and Computational Complexity. Khang Le, Huy Nguyen, Khai Nguyen, Tung Pham, Nhat Ho |
| 2022 | On PAC-Bayesian reconstruction guarantees for VAEs. Badr-Eddine Chérief-Abdellatif, Yuyang Shi, Arnaud Doucet, Benjamin Guedj |
| 2022 | On Some Fast And Robust Classifiers For High Dimension, Low Sample Size Data. Sarbojit Roy, Jyotishka Ray Choudhury, Subhajit Dutta |
| 2022 | On Structured Filtering-Clustering: Global Error Bound and Optimal First-Order Algorithms. Nhat Ho, Tianyi Lin, Michael I. Jordan |
| 2022 | On Uncertainty Estimation by Tree-based Surrogate Models in Sequential Model-based Optimization. Jungtaek Kim, Seungjin Choi |
| 2022 | On a Connection Between Fast and Sparse Oblivious Subspace Embeddings. Rui Wang, Wangli Xu |
| 2022 | On perfectness in Gaussian graphical models. Arash A. Amini, Bryon Aragam, Qing Zhou |
| 2022 | On the Assumptions of Synthetic Control Methods. Claudia Shi, Dhanya Sridhar, Vishal Misra, David M. Blei |
| 2022 | On the Consistency of Max-Margin Losses. Alex Nowak, Alessandro Rudi, Francis R. Bach |
| 2022 | On the Convergence Rate of Off-Policy Policy Optimization Methods with Density-Ratio Correction. Jiawei Huang, Nan Jiang |
| 2022 | On the Convergence of Continuous Constrained Optimization for Structure Learning. Ignavier Ng, Sébastien Lachapelle, Nan Rosemary Ke, Simon Lacoste-Julien, Kun Zhang |
| 2022 | On the Convergence of Stochastic Extragradient for Bilinear Games using Restarted Iteration Averaging. Chris Junchi Li, Yaodong Yu, Nicolas Loizou, Gauthier Gidel, Yi Ma, Nicolas Le Roux, Michael I. Jordan |
| 2022 | On the Generalization of Representations in Reinforcement Learning. Charline Le Lan, Stephen Tu, Adam Oberman, Rishabh Agarwal, Marc G. Bellemare |
| 2022 | On the Global Optimum Convergence of Momentum-based Policy Gradient. Yuhao Ding, Junzi Zhang, Javad Lavaei |
| 2022 | On the Implicit Bias of Gradient Descent for Temporal Extrapolation. Edo Cohen-Karlik, Avichai Ben David, Nadav Cohen, Amir Globerson |
| 2022 | On the Interplay between Information Loss and Operation Loss in Representations for Classification. Jorge Silva, Felipe A. Tobar |
| 2022 | On the Oracle Complexity of Higher-Order Smooth Non-Convex Finite-Sum Optimization. Nicolas Emmenegger, Rasmus Kyng, Ahad N. Zehmakan |
| 2022 | On the Value of Prior in Online Learning to Rank. Branislav Kveton, Ofer Meshi, Masrour Zoghi, Zhen Qin |
| 2022 | On the complexity of the optimal transport problem with graph-structured cost. Jiaojiao Fan, Isabel Haasler, Johan Karlsson, Yongxin Chen |
| 2022 | On the equivalence of Oja's algorithm and GROUSE. Laura Balzano |
| 2022 | One-bit Submission for Locally Private Quasi-MLE: Its Asymptotic Normality and Limitation. Hajime Ono, Kazuhiro Minami, Hideitsu Hino |
| 2022 | Online Competitive Influence Maximization. Jinhang Zuo, Xutong Liu, Carlee Joe-Wong, John C. S. Lui, Wei Chen |
| 2022 | Online Continual Adaptation with Active Self-Training. Shiji Zhou, Han Zhao, Shanghang Zhang, Lianzhe Wang, Heng Chang, Zhi Wang, Wenwu Zhu |
| 2022 | Online Control of the False Discovery Rate under "Decision Deadlines". Aaron J. Fisher |
| 2022 | Online Learning for Unknown Partially Observable MDPs. Mehdi Jafarnia-Jahromi, Rahul Jain, Ashutosh Nayyar |
| 2022 | Online Page Migration with ML Advice. Piotr Indyk, Frederik Mallmann-Trenn, Slobodan Mitrovic, Ronitt Rubinfeld |
| 2022 | Optimal Accounting of Differential Privacy via Characteristic Function. Yuqing Zhu, Jinshuo Dong, Yu-Xiang Wang |
| 2022 | Optimal Compression of Locally Differentially Private Mechanisms. Abhin Shah, Wei-Ning Chen, Johannes Ballé, Peter Kairouz, Lucas Theis |
| 2022 | Optimal Design of Stochastic DNA Synthesis Protocols based on Generative Sequence Models. Eli N. Weinstein, Alan Nawzad Amin, Will S. Grathwohl, Daniel Kassler, Jean Disset, Debora S. Marks |
| 2022 | Optimal Dynamic Regret in Proper Online Learning with Strongly Convex Losses and Beyond. Dheeraj Baby, Yu-Xiang Wang |
| 2022 | Optimal Rates of (Locally) Differentially Private Heavy-tailed Multi-Armed Bandits. Youming Tao, Yulian Wu, Peng Zhao, Di Wang |
| 2022 | Optimal channel selection with discrete QCQP. Yeonwoo Jeong, Deokjae Lee, Gaon An, Changyong Son, Hyun Oh Song |
| 2022 | Optimal estimation of Gaussian DAG models. Ming Gao, Wai Ming Tai, Bryon Aragam |
| 2022 | Optimal partition recovery in general graphs. Yi Yu, Oscar Hernan Madrid Padilla, Alessandro Rinaldo |
| 2022 | Optimal transport with f-divergence regularization and generalized Sinkhorn algorithm. Dávid Terjék, Diego González-Sánchez |
| 2022 | Optimizing Early Warning Classifiers to Control False Alarms via a Minimum Precision Constraint. Preetish Rath, Michael C. Hughes |
| 2022 | Orbital MCMC. Kirill Neklyudov, Max Welling |
| 2022 | Orthogonal Multi-Manifold Enriching of Directed Networks. Ramit Sawhney, Shivam Agarwal, Atula Tejaswi Neerkaje, Kapil Jayesh Pathak |
| 2022 | Outcome Assumptions and Duality Theory for Balancing Weights. David A. Bruns-Smith, Avi Feller |
| 2022 | Outlier-Robust Optimal Transport: Duality, Structure, and Statistical Analysis. Sloan Nietert, Ziv Goldfeld, Rachel Cummings |
| 2022 | PAC Learning of Quantum Measurement Classes : Sample Complexity Bounds and Universal Consistency. Arun Padakandla, Abram Magner |
| 2022 | PAC Mode Estimation using PPR Martingale Confidence Sequences. Shubham Anand Jain, Rohan Shah, Sanit Gupta, Denil Mehta, Inderjeet J. Nair, Jian Vora, Sushil Khyalia, Sourav Das, Vinay J. Ribeiro, Shivaram Kalyanakrishnan |
| 2022 | PAC Top-k Identification under SST in Limited Rounds. Arpit Agarwal, Sanjeev Khanna, Prathamesh Patil |
| 2022 | PACm-Bayes: Narrowing the Empirical Risk Gap in the Misspecified Bayesian Regime. Warren R. Morningstar, Alex Alemi, Joshua V. Dillon |
| 2022 | Pairwise Fairness for Ordinal Regression. Matthäus Kleindessner, Samira Samadi, Muhammad Bilal Zafar, Krishnaram Kenthapadi, Chris Russell |
| 2022 | Pairwise Supervision Can Provably Elicit a Decision Boundary. Han Bao, Takuya Shimada, Liyuan Xu, Issei Sato, Masashi Sugiyama |
| 2022 | Parallel MCMC Without Embarrassing Failures. Daniel Augusto de Souza, Diego Mesquita, Samuel Kaski, Luigi Acerbi |
| 2022 | Parameter-Free Online Linear Optimization with Side Information via Universal Coin Betting. Jongha J. Ryu, Alankrita Bhatt, Young-Han Kim |
| 2022 | Parametric Bootstrap for Differentially Private Confidence Intervals. Cecilia Ferrando, Shufan Wang, Daniel Sheldon |
| 2022 | Pareto Optimal Model Selection in Linear Bandits. Yinglun Zhu, Robert D. Nowak |
| 2022 | Particle-based Adversarial Local Distribution Regularization. Thanh Nguyen-Duc, Trung Le, He Zhao, Jianfei Cai, Dinh Q. Phung |
| 2022 | Performative Prediction in a Stateful World. Gavin Brown, Shlomi Hod, Iden Kalemaj |
| 2022 | Permutation Equivariant Layers for Higher Order Interactions. Horace Pan, Risi Kondor |
| 2022 | Physics Informed Deep Kernel Learning. Zheng Wang, Wei W. Xing, Robert M. Kirby, Shandian Zhe |
| 2022 | Pick-and-Mix Information Operators for Probabilistic ODE Solvers. Nathanael Bosch, Filip Tronarp, Philipp Hennig |
| 2022 | Point Cloud Generation with Continuous Conditioning. Larissa T. Triess, Andre Bühler, David Peter, Fabian B. Flohr, Marius Zöllner |
| 2022 | Policy Learning and Evaluation with Randomized Quasi-Monte Carlo. Sébastien M. R. Arnold, Pierre L'Ecuyer, Liyu Chen, Yi-Fan Chen, Fei Sha |
| 2022 | Policy Learning for Optimal Individualized Dose Intervals. Guanhua Chen, Xiaomao Li, Menggang Yu |
| 2022 | Polynomial Time Reinforcement Learning in Factored State MDPs with Linear Value Functions. Zihao Deng, Siddartha Devic, Brendan Juba |
| 2022 | Practical Schemes for Finding Near-Stationary Points of Convex Finite-Sums. Kaiwen Zhou, Lai Tian, Anthony Man-Cho So, James Cheng |
| 2022 | Predicting the impact of treatments over time with uncertainty aware neural differential equations. Edward De Brouwer, Javier González, Stephanie L. Hyland |
| 2022 | Predicting the utility of search spaces for black-box optimization: a simple, budget-aware approach. Setareh Ariafar, Justin Gilmer, Zachary Nado, Jasper Snoek, Rodolphe Jenatton, George E. Dahl |
| 2022 | Predictive variational Bayesian inference as risk-seeking optimization. Futoshi Futami, Tomoharu Iwata, Naonori Ueda, Issei Sato, Masashi Sugiyama |
| 2022 | Preference Exploration for Efficient Bayesian Optimization with Multiple Outcomes. Zhiyuan (Jerry) Lin, Raul Astudillo, Peter I. Frazier, Eytan Bakshy |
| 2022 | Primal-Dual Stochastic Mirror Descent for MDPs. Daniil Tiapkin, Alexander V. Gasnikov |
| 2022 | Privacy Amplification by Decentralization. Edwige Cyffers, Aurélien Bellet |
| 2022 | Privacy Amplification by Subsampling in Time Domain. Tatsuki Koga, Casey Meehan, Kamalika Chaudhuri |
| 2022 | Private Sequential Hypothesis Testing for Statisticians: Privacy, Error Rates, and Sample Size. Wanrong Zhang, Yajun Mei, Rachel Cummings |
| 2022 | Probabilistic Numerical Method of Lines for Time-Dependent Partial Differential Equations. Nicholas Krämer, Jonathan Schmidt, Philipp Hennig |
| 2022 | Probing GNN Explainers: A Rigorous Theoretical and Empirical Analysis of GNN Explanation Methods. Chirag Agarwal, Marinka Zitnik, Himabindu Lakkaraju |
| 2022 | Projection Predictive Inference for Generalized Linear and Additive Multilevel Models. Alejandro Catalina, Paul-Christian Bürkner, Aki Vehtari |
| 2022 | Provable Adversarial Robustness for Fractional Lp Threat Models. Alexander Levine, Soheil Feizi |
| 2022 | Provable Continual Learning via Sketched Jacobian Approximations. Reinhard Heckel |
| 2022 | Provable Lifelong Learning of Representations. Xinyuan Cao, Weiyang Liu, Santosh S. Vempala |
| 2022 | Provably Efficient Policy Optimization for Two-Player Zero-Sum Markov Games. Yulai Zhao, Yuandong Tian, Jason D. Lee, Simon S. Du |
| 2022 | Proximal Optimal Transport Modeling of Population Dynamics. Charlotte Bunne, Laetitia Papaxanthos, Andreas Krause, Marco Cuturi |
| 2022 | Pulling back information geometry. Georgios Arvanitidis, Miguel González Duque, Alison Pouplin, Dimitrios Kalatzis, Søren Hauberg |
| 2022 | QLSD: Quantised Langevin Stochastic Dynamics for Bayesian Federated Learning. Maxime Vono, Vincent Plassier, Alain Durmus, Aymeric Dieuleveut, Eric Moulines |
| 2022 | Quadric Hypersurface Intersection for Manifold Learning in Feature Space. Fedor Pavutnitskiy, Sergei O. Ivanov, Evgeniy Abramov, Viacheslav Borovitskiy, Artem Klochkov, Viktor Vyalov, Anatolii Zaikovskii, Aleksandr Petiushko |
| 2022 | REPID: Regional Effect Plots with implicit Interaction Detection. Julia Herbinger, Bernd Bischl, Giuseppe Casalicchio |
| 2022 | Random Effect Bandits. Rong Zhu, Branislav Kveton |
| 2022 | Randomized Stochastic Gradient Descent Ascent. Othmane Sebbouh, Marco Cuturi, Gabriel Peyré |
| 2022 | Rapid Convergence of Informed Importance Tempering. Quan Zhou, Aaron Smith |
| 2022 | Reconstructing Test Labels from Noisy Loss Functions. Abhinav Aggarwal, Shiva Prasad Kasiviswanathan, Zekun Xu, Oluwaseyi Feyisetan, Nathanael Teissier |
| 2022 | Recoverability Landscape of Tree Structured Markov Random Fields under Symmetric Noise. Ashish Katiyar, Soumya Basu, Vatsal Shah, Constantine Caramanis |
| 2022 | Regret Bounds for Expected Improvement Algorithms in Gaussian Process Bandit Optimization. Hung Tran-The, Sunil Gupta, Santu Rana, Svetha Venkatesh |
| 2022 | Regret, stability & fairness in matching markets with bandit learners. Sarah Huiyi Cen, Devavrat Shah |
| 2022 | Reinforcement Learning with Fast Stabilization in Linear Dynamical Systems. Sahin Lale, Kamyar Azizzadenesheli, Babak Hassibi, Animashree Anandkumar |
| 2022 | Rejection sampling from shape-constrained distributions in sublinear time. Sinho Chewi, Patrik R. Gerber, Chen Lu, Thibaut Le Gouic, Philippe Rigollet |
| 2022 | Relational Neural Markov Random Fields. Yuqiao Chen, Sriraam Natarajan, Nicholas Ruozzi |
| 2022 | Resampling Base Distributions of Normalizing Flows. Vincent Stimper, Bernhard Schölkopf, José Miguel Hernández-Lobato |
| 2022 | Reward-Free Policy Space Compression for Reinforcement Learning. Mirco Mutti, Stefano Del Col, Marcello Restelli |
| 2022 | Robust Bayesian Inference for Simulator-based Models via the MMD Posterior Bootstrap. Charita Dellaporta, Jeremias Knoblauch, Theodoros Damoulas, François-Xavier Briol |
| 2022 | Robust Deep Learning from Crowds with Belief Propagation. Hoyoung Kim, Seunghyuk Cho, Dongwoo Kim, Jungseul Ok |
| 2022 | Robust Probabilistic Time Series Forecasting. Taeho Yoon, Youngsuk Park, Ernest K. Ryu, Yuyang Wang |
| 2022 | Robust Stochastic Linear Contextual Bandits Under Adversarial Attacks. Qin Ding, Cho-Jui Hsieh, James Sharpnack |
| 2022 | Robust Training in High Dimensions via Block Coordinate Geometric Median Descent. Anish Acharya, Abolfazl Hashemi, Prateek Jain, Sujay Sanghavi, Inderjit S. Dhillon, Ufuk Topcu |
| 2022 | Robustness and Reliability When Training With Noisy Labels. Amanda Olmin, Fredrik Lindsten |
| 2022 | SAN: Stochastic Average Newton Algorithm for Minimizing Finite Sums. Jiabin Chen, Rui Yuan, Guillaume Garrigos, Robert M. Gower |
| 2022 | SHAFF: Fast and consistent SHApley eFfect estimates via random Forests. Clément Bénard, Gérard Biau, Sébastien Da Veiga, Erwan Scornet |
| 2022 | Safe Active Learning for Multi-Output Gaussian Processes. Cen-You Li, Barbara Rakitsch, Christoph Zimmer |
| 2022 | Safe Optimal Design with Applications in Off-Policy Learning. Ruihao Zhu, Branislav Kveton |
| 2022 | Sample Complexity of Policy-Based Methods under Off-Policy Sampling and Linear Function Approximation. Zaiwei Chen, Siva Theja Maguluri |
| 2022 | Sample Complexity of Robust Reinforcement Learning with a Generative Model. Kishan Panaganti, Dileep M. Kalathil |
| 2022 | Sample-and-threshold differential privacy: Histograms and applications. Graham Cormode, Akash Bharadwaj |
| 2022 | Sampling from Arbitrary Functions via PSD Models. Ulysse Marteau-Ferey, Francis R. Bach, Alessandro Rudi |
| 2022 | Sampling in Dirichlet Process Mixture Models for Clustering Streaming Data. Or Dinari, Oren Freifeld |
| 2022 | Scaling and Scalability: Provable Nonconvex Low-Rank Tensor Completion. Tian Tong, Cong Ma, Ashley Prater-Bennette, Erin E. Tripp, Yuejie Chi |
| 2022 | Second-Order Sensitivity Analysis for Bilevel Optimization. Robert Dyro, Edward Schmerling, Nikos Aréchiga, Marco Pavone |
| 2022 | Self-training Converts Weak Learners to Strong Learners in Mixture Models. Spencer Frei, Difan Zou, Zixiang Chen, Quanquan Gu |
| 2022 | Semi-Implicit Hybrid Gradient Methods with Application to Adversarial Robustness. Beomsu Kim, Junghoon Seo |
| 2022 | Sensing Cox Processes via Posterior Sampling and Positive Bases. Mojmir Mutny, Andreas Krause |
| 2022 | Sequential Multivariate Change Detection with Calibrated and Memoryless False Detection Rates. Oliver Cobb, Arnaud Van Looveren, Janis Klaise |
| 2022 | Sharp Bounds for Federated Averaging (Local SGD) and Continuous Perspective. Margalit R. Glasgow, Honglin Yuan, Tengyu Ma |
| 2022 | Sinkformers: Transformers with Doubly Stochastic Attention. Michael E. Sander, Pierre Ablin, Mathieu Blondel, Gabriel Peyré |
| 2022 | Sketch-and-lift: scalable subsampled semidefinite program for K-means clustering. Yubo Zhuang, Xiaohui Chen, Yun Yang |
| 2022 | Sobolev Norm Learning Rates for Conditional Mean Embeddings. Prem Talwai, Ali Shameli, David Simchi-Levi |
| 2022 | Sobolev Transport: A Scalable Metric for Probability Measures with Graph Metrics. Tam Le, Truyen Nguyen, Dinh Phung, Viet Anh Nguyen |
| 2022 | Solving Marginal MAP Exactly by Probabilistic Circuit Transformations. YooJung Choi, Tal Friedman, Guy Van den Broeck |
| 2022 | Solving Multi-Arm Bandit Using a Few Bits of Communication. Osama A. Hanna, Lin Yang, Christina Fragouli |
| 2022 | SparseFed: Mitigating Model Poisoning Attacks in Federated Learning with Sparsification. Ashwinee Panda, Saeed Mahloujifar, Arjun Nitin Bhagoji, Supriyo Chakraborty, Prateek Mittal |
| 2022 | Spectral Pruning for Recurrent Neural Networks. Takashi Furuya, Kazuma Suetake, Koichi Taniguchi, Hiroyuki Kusumoto, Ryuji Saiin, Tomohiro Daimon |
| 2022 | Spectral Robustness for Correlation Clustering Reconstruction in Semi-Adversarial Models. Flavio Chierichetti, Alessandro Panconesi, Giuseppe Re, Luca Trevisan |
| 2022 | Spectral risk-based learning using unbounded losses. Matthew J. Holland, El Mehdi Haress |
| 2022 | Spiked Covariance Estimation from Modulo-Reduced Measurements. Elad Romanov, Or Ordentlich |
| 2022 | Standardisation-function Kernel Stein Discrepancy: A Unifying View on Kernel Stein Discrepancy Tests for Goodness-of-fit. Wenkai Xu |
| 2022 | State Dependent Performative Prediction with Stochastic Approximation. Qiang Li, Hoi-To Wai |
| 2022 | Stateful Offline Contextual Policy Evaluation and Learning. Nathan Kallus, Angela Zhou |
| 2022 | Statistical Depth Functions for Ranking Distributions: Definitions, Statistical Learning and Applications. Morgane Goibert, Stéphan Clémençon, Ekhine Irurozki, Pavlo Mozharovskyi |
| 2022 | Statistical and computational thresholds for the planted k-densest sub-hypergraph problem. Luca Corinzia, Paolo Penna, Wojciech Szpankowski, Joachim M. Buhmann |
| 2022 | Stochastic Extragradient: General Analysis and Improved Rates. Eduard Gorbunov, Hugo Berard, Gauthier Gidel, Nicolas Loizou |
| 2022 | Strategic ranking. Lydia T. Liu, Nikhil Garg, Christian Borgs |
| 2022 | Structured Multi-task Learning for Molecular Property Prediction. Shengchao Liu, Meng Qu, Zuobai Zhang, Huiyu Cai, Jian Tang |
| 2022 | Structured variational inference in Bayesian state-space models. Honggang Wang, Anirban Bhattacharya, Debdeep Pati, Yun Yang |
| 2022 | Super-Acceleration with Cyclical Step-sizes. Baptiste Goujaud, Damien Scieur, Aymeric Dieuleveut, Adrien B. Taylor, Fabian Pedregosa |
| 2022 | Survival regression with proper scoring rules and monotonic neural networks. David Rindt, Robert Hu, David Steinsaltz, Dino Sejdinovic |
| 2022 | Synthsonic: Fast, Probabilistic modeling and Synthesis of Tabular Data. Max Baak, Simon Brugman, Ilan Fridman Rojas, Lorraine Dalmeida, Ralph E. Q. Urlus, Jean-Baptiste Oger |
| 2022 | System-Agnostic Meta-Learning for MDP-based Dynamic Scheduling via Descriptive Policy. Hyun-Suk Lee |
| 2022 | TD-GEN: Graph Generation Using Tree Decomposition. Hamed Shirzad, Hossein Hajimirsadeghi, Amir H. Abdi, Greg Mori |
| 2022 | Testing Granger Non-Causality in Panels with Cross-Sectional Dependencies. Lenon Minorics, Ali Caner Türkmen, David Kernert, Patrick Blöbaum, Laurent Callot, Dominik Janzing |
| 2022 | The Curse Revisited: When are Distances Informative for the Ground Truth in Noisy High-Dimensional Data? Robin Vandaele, Bo Kang, Tijl De Bie, Yvan Saeys |
| 2022 | The Curse of Passive Data Collection in Batch Reinforcement Learning. Chenjun Xiao, Ilbin Lee, Bo Dai, Dale Schuurmans, Csaba Szepesvári |
| 2022 | The Fast Kernel Transform. John Paul Ryan, Sebastian E. Ament, Carla P. Gomes, Anil Damle |
| 2022 | The Importance of Future Information in Credit Card Fraud Detection. Van Bach Nguyen, Kanishka Ghosh Dastidar, Michael Granitzer, Wissam Siblini |
| 2022 | The Tree Loss: Improving Generalization with Many Classes. Yujie Wang, Mike Izbicki |
| 2022 | The role of optimization geometry in single neuron learning. Nicholas M. Boffi, Stephen Tu, Jean-Jacques E. Slotine |
| 2022 | Thompson Sampling with a Mixture Prior. Joey Hong, Branislav Kveton, Manzil Zaheer, Mohammad Ghavamzadeh, Craig Boutilier |
| 2022 | Threading the Needle of On and Off-Manifold Value Functions for Shapley Explanations. Chih-Kuan Yeh, Kuan-Yun Lee, Frederick Liu, Pradeep Ravikumar |
| 2022 | Tight bounds for minimum ℓ Guillaume Wang, Konstantin Donhauser, Fanny Yang |
| 2022 | Tile Networks: Learning Optimal Geometric Layout for Whole-page Recommendation. Shuai Xiao, Zaifan Jiang, Shuang Yang |
| 2022 | Top K Ranking for Multi-Armed Bandit with Noisy Evaluations. Evrard Garcelon, Vashist Avadhanula, Alessandro Lazaric, Matteo Pirotta |
| 2022 | Towards Agnostic Feature-based Dynamic Pricing: Linear Policies vs Linear Valuation with Unknown Noise. Jianyu Xu, Yu-Xiang Wang |
| 2022 | Towards Federated Bayesian Network Structure Learning with Continuous Optimization. Ignavier Ng, Kun Zhang |
| 2022 | Towards Return Parity in Markov Decision Processes. Jianfeng Chi, Jian Shen, Xinyi Dai, Weinan Zhang, Yuan Tian, Han Zhao |
| 2022 | Towards Statistical and Computational Complexities of Polyak Step Size Gradient Descent. Tongzheng Ren, Fuheng Cui, Alexia Atsidakou, Sujay Sanghavi, Nhat Ho |
| 2022 | Towards Understanding Biased Client Selection in Federated Learning. Yae Jee Cho, Jianyu Wang, Gauri Joshi |
| 2022 | Towards an Understanding of Default Policies in Multitask Policy Optimization. Ted Moskovitz, Michael Arbel, Jack Parker-Holder, Aldo Pacchiano |
| 2022 | Transductive Robust Learning Guarantees. Omar Montasser, Steve Hanneke, Nathan Srebro |
| 2022 | Transfer Learning with Gaussian Processes for Bayesian Optimization. Petru Tighineanu, Kathrin Skubch, Paul Baireuther, Attila Reiss, Felix Berkenkamp, Julia Vinogradska |
| 2022 | Triangular Flows for Generative Modeling: Statistical Consistency, Smoothness Classes, and Fast Rates. Nicholas J. Irons, Meyer Scetbon, Soumik Pal, Zaïd Harchaoui |
| 2022 | Triple-Q: A Model-Free Algorithm for Constrained Reinforcement Learning with Sublinear Regret and Zero Constraint Violation. Honghao Wei, Xin Liu, Lei Ying |
| 2022 | Tuning-Free Generalized Hamiltonian Monte Carlo. Matthew D. Hoffman, Pavel Sountsov |
| 2022 | Two-Sample Test with Kernel Projected Wasserstein Distance. Jie Wang, Rui Gao, Yao Xie |
| 2022 | Two-way Sparse Network Inference for Count Data. Sijia Li, Martín López-García, Neil D. Lawrence, Luisa Cutillo |
| 2022 | Uncertainty Quantification for Bayesian Optimization. Rui Tuo, Wenjia Wang |
| 2022 | Uncertainty Quantification for Low-Rank Matrix Completion with Heterogeneous and Sub-Exponential Noise. Vivek F. Farias, Andrew A. Li, Tianyi Peng |
| 2022 | Unifying Importance Based Regularisation Methods for Continual Learning. Frederik Benzing |
| 2022 | Unlabeled Data Help: Minimax Analysis and Adversarial Robustness. Yue Xing, Qifan Song, Guang Cheng |
| 2022 | Using time-series privileged information for provably efficient learning of prediction models. Rickard K. A. Karlsson, Martin Willbo, Zeshan M. Hussain, Rahul G. Krishnan, David A. Sontag, Fredrik Johansson |
| 2022 | VFDS: Variational Foresight Dynamic Selection in Bayesian Neural Networks for Efficient Human Activity Recognition. Randy Ardywibowo, Shahin Boluki, Zhangyang Wang, Bobak J. Mortazavi, Shuai Huang, Xiaoning Qian |
| 2022 | Vanishing Curvature in Randomly Initialized Deep ReLU Networks. Antonio Orvieto, Jonas Kohler, Dario Pavllo, Thomas Hofmann, Aurélien Lucchi |
| 2022 | Variance Minimization in the Wasserstein Space for Invariant Causal Prediction. Guillaume G. Martinet, Alexander Strzalkowski, Barbara E. Engelhardt |
| 2022 | Variational Autoencoders: A Harmonic Perspective. Alexander Camuto, Matthew Willetts |
| 2022 | Variational Continual Proxy-Anchor for Deep Metric Learning. Minyoung Kim, Ricardo Guerrero, Hai Xuan Pham, Vladimir Pavlovic |
| 2022 | Variational Gaussian Processes: A Functional Analysis View. George Wynne, Veit Wild |
| 2022 | Variational Marginal Particle Filters. Jinlin Lai, Justin Domke, Daniel Sheldon |
| 2022 | Warping Layer: Representation Learning for Label Structures in Weakly Supervised Learning. Yingyi Ma, Xinhua Zhang |
| 2022 | Weak Separation in Mixture Models and Implications for Principal Stratification. Nhat Ho, Avi Feller, Evan Greif, Luke Miratrix, Natesh S. Pillai |
| 2022 | Weighted Gaussian Process Bandits for Non-stationary Environments. Yuntian Deng, Xingyu Zhou, Baekjin Kim, Ambuj Tewari, Abhishek Gupta, Ness B. Shroff |
| 2022 | Wide Mean-Field Bayesian Neural Networks Ignore the Data. Beau Coker, Wessel P. Bruinsma, David R. Burt, Weiwei Pan, Finale Doshi-Velez |
| 2022 | Zeroth-Order Methods for Convex-Concave Min-max Problems: Applications to Decision-Dependent Risk Minimization. Chinmay Maheshwari, Chih-Yuan Chiu, Eric Mazumdar, Shankar Sastry, Lillian J. Ratliff |
| 2022 | k-Pareto Optimality-Based Sorting with Maximization of Choice. Jean Ruppert, Marharyta Aleksandrova, Thomas Engel |
| 2022 | k-experts - Online Policies and Fundamental Limits. Samrat Mukhopadhyay, Sourav Sahoo, Abhishek Sinha |
| 2022 | p-Generalized Probit Regression and Scalable Maximum Likelihood Estimation via Sketching and Coresets. Alexander Munteanu, Simon Omlor, Christian Peters |