| 2023 | "Plus/minus the learning rate": Easy and Scalable Statistical Inference with SGD. Jerry Chee, Hwanwoo Kim, Panos Toulis |
| 2023 | A Blessing of Dimensionality in Membership Inference through Regularization. Jasper Tan, Daniel LeJeune, Blake Mason, Hamid Javadi, Richard G. Baraniuk |
| 2023 | A Bregman Divergence View on the Difference-of-Convex Algorithm. Oisin Faust, Hamza Fawzi, James Saunderson |
| 2023 | A Case of Exponential Convergence Rates for SVM. Vivien Cabannes, Stefano Vigogna |
| 2023 | A Conditional Gradient-based Method for Simple Bilevel Optimization with Convex Lower-level Problem. Ruichen Jiang, Nazanin Abolfazli, Aryan Mokhtari, Erfan Yazdandoost Hamedani |
| 2023 | A Constant-Factor Approximation Algorithm for Reconciliation k-Median. Joachim Spoerhase, Kamyar Khodamoradi, Benedikt Riegel, Bruno Ordozgoiti, Aristides Gionis |
| 2023 | A Contrastive Approach to Online Change Point Detection. Nikita Puchkin, Valeriia Shcherbakova |
| 2023 | A Faster Sampler for Discrete Determinantal Point Processes. Simon Barthelmé, Nicolas Tremblay, Pierre-Olivier Amblard |
| 2023 | A Finite Sample Complexity Bound for Distributionally Robust Q-learning. Shengbo Wang, Nian Si, José H. Blanchet, Zhengyuan Zhou |
| 2023 | A Mini-Block Fisher Method for Deep Neural Networks. Achraf Bahamou, Donald Goldfarb, Yi Ren |
| 2023 | A Multi-Task Gaussian Process Model for Inferring Time-Varying Treatment Effects in Panel Data. Yehu Chen, Annamaria Prati, Jacob M. Montgomery, Roman Garnett |
| 2023 | A New Causal Decomposition Paradigm towards Health Equity. Xinwei Sun, Xiangyu Zheng, Jim Weinstein |
| 2023 | A New Modeling Framework for Continuous, Sequential Domains. Hailiang Dong, James Amato, Vibhav Gogate, Nicholas Ruozzi |
| 2023 | A Novel Stochastic Gradient Descent Algorithm for Learning Principal Subspaces. Charline Le Lan, Joshua Greaves, Jesse Farebrother, Mark Rowland, Fabian Pedregosa, Rishabh Agarwal, Marc G. Bellemare |
| 2023 | A Sea of Words: An In-Depth Analysis of Anchors for Text Data. Gianluigi Lopardo, Frédéric Precioso, Damien Garreau |
| 2023 | A Statistical Analysis of Polyak-Ruppert Averaged Q-Learning. Xiang Li, Wenhao Yang, Jiadong Liang, Zhihua Zhang, Michael I. Jordan |
| 2023 | A Statistical Learning Take on the Concordance Index for Survival Analysis. Kevin Elgui, Alex Nowak, Geneviève Robin |
| 2023 | A Tale of Sampling and Estimation in Discounted Reinforcement Learning. Alberto Maria Metelli, Mirco Mutti, Marcello Restelli |
| 2023 | A Tale of Two Efficient Value Iteration Algorithms for Solving Linear MDPs with Large Action Space. Zhaozhuo Xu, Zhao Song, Anshumali Shrivastava |
| 2023 | A Targeted Accuracy Diagnostic for Variational Approximations. Yu Wang, Mikolaj J. Kasprzak, Jonathan H. Huggins |
| 2023 | A Tighter Problem-Dependent Regret Bound for Risk-Sensitive Reinforcement Learning. Xiaoyan Hu, Ho-fung Leung |
| 2023 | A Unified Perspective on Regularization and Perturbation in Differentiable Subset Selection. Xiangqian Sun, Cheuk Hang Leung, Yijun Li, Qi Wu |
| 2023 | A Variance-Reduced and Stabilized Proximal Stochastic Gradient Method with Support Identification Guarantees for Structured Optimization. Yutong Dai, Guanyi Wang, Frank E. Curtis, Daniel P. Robinson |
| 2023 | A principled framework for the design and analysis of token algorithms. Hadrien Hendrikx |
| 2023 | A stopping criterion for Bayesian optimization by the gap of expected minimum simple regrets. Hideaki Ishibashi, Masayuki Karasuyama, Ichiro Takeuchi, Hideitsu Hino |
| 2023 | ANACONDA: An Improved Dynamic Regret Algorithm for Adaptive Non-Stationary Dueling Bandits. Thomas Kleine Buening, Aadirupa Saha |
| 2023 | ASkewSGD : An Annealed interval-constrained Optimisation method to train Quantized Neural Networks. Louis Leconte, Sholom Schechtman, Eric Moulines |
| 2023 | AUC-based Selective Classification. Andrea Pugnana, Salvatore Ruggieri |
| 2023 | Acceleration of Frank-Wolfe Algorithms with Open-Loop Step-Sizes. Elias Samuel Wirth, Thomas Kerdreux, Sebastian Pokutta |
| 2023 | Active Cost-aware Labeling of Streaming Data. Ting Cai, Kirthevasan Kandasamy |
| 2023 | Active Exploration via Experiment Design in Markov Chains. Mojmir Mutny, Tadeusz Janik, Andreas Krause |
| 2023 | Active Learning for Single Neuron Models with Lipschitz Non-Linearities. Aarshvi Gajjar, Christopher Musco, Chinmay Hegde |
| 2023 | Active Membership Inference Attack under Local Differential Privacy in Federated Learning. Truc D. T. Nguyen, Phung Lai, Khang Tran, NhatHai Phan, My T. Thai |
| 2023 | Actually Sparse Variational Gaussian Processes. Harry Jake Cunningham, Daniel Augusto de Souza, So Takao, Mark van der Wilk, Marc Peter Deisenroth |
| 2023 | AdaGDA: Faster Adaptive Gradient Descent Ascent Methods for Minimax Optimization. Feihu Huang, Xidong Wu, Zhengmian Hu |
| 2023 | Adaptation to Misspecified Kernel Regularity in Kernelised Bandits. Yusha Liu, Aarti Singh |
| 2023 | Adapting to Latent Subgroup Shifts via Concepts and Proxies. Ibrahim Alabdulmohsin, Nicole Chiou, Alexander D'Amour, Arthur Gretton, Sanmi Koyejo, Matt J. Kusner, Stephen R. Pfohl, Olawale Salaudeen, Jessica Schrouff, Katherine Tsai |
| 2023 | Adaptive Cholesky Gaussian Processes. Simon Bartels, Kristoffer Stensbo-Smidt, Pablo Moreno-Muñoz, Wouter Boomsma, Jes Frellsen, Søren Hauberg |
| 2023 | Adaptive Dimension Reduction and Variational Inference for Transductive Few-Shot Classification. Yuqing Hu, Stéphane Pateux, Vincent Gripon |
| 2023 | Adaptive Tuning for Metropolis Adjusted Langevin Trajectories. Lionel Riou-Durand, Pavel Sountsov, Jure Vogrinc, Charles Margossian, Sam Power |
| 2023 | Adversarial De-confounding in Individualised Treatment Effects Estimation. Vinod Kumar Chauhan, Soheila Molaei, Marzia Hoque Tania, Anshul Thakur, Tingting Zhu, David A. Clifton |
| 2023 | Adversarial Noises Are Linearly Separable for (Nearly) Random Neural Networks. Huishuai Zhang, Da Yu, Yiping Lu, Di He |
| 2023 | Adversarial Random Forests for Density Estimation and Generative Modeling. David S. Watson, Kristin Blesch, Jan Kapar, Marvin N. Wright |
| 2023 | Adversarial robustness of VAEs through the lens of local geometry. Asif Khan, Amos Storkey |
| 2023 | Agnostic PAC Learning of k-juntas Using L Mohsen Heidari, Wojciech Szpankowski |
| 2023 | Algorithm for Constrained Markov Decision Process with Linear Convergence. Egor Gladin, Maksim Lavrik-Karmazin, Karina Zainullina, Varvara Rudenko, Alexander V. Gasnikov, Martin Takác |
| 2023 | Algorithm-Dependent Bounds for Representation Learning of Multi-Source Domain Adaptation. Qi Chen, Mario Marchand |
| 2023 | Alternating Projected SGD for Equality-constrained Bilevel Optimization. Quan Xiao, Han Shen, Wotao Yin, Tianyi Chen |
| 2023 | An Efficient and Continuous Voronoi Density Estimator. Giovanni Luca Marchetti, Vladislav Polianskii, Anastasiia Varava, Florian T. Pokorny, Danica Kragic |
| 2023 | An Homogeneous Unbalanced Regularized Optimal Transport Model with Applications to Optimal Transport with Boundary. Théo Lacombe |
| 2023 | An Online and Unified Algorithm for Projection Matrix Vector Multiplication with Application to Empirical Risk Minimization. Lianke Qin, Zhao Song, Lichen Zhang, Danyang Zhuo |
| 2023 | An Optimization-based Algorithm for Non-stationary Kernel Bandits without Prior Knowledge. Kihyuk Hong, Yuhang Li, Ambuj Tewari |
| 2023 | An Unpooling Layer for Graph Generation. Yinglong Guo, Dongmian Zou, Gilad Lerman |
| 2023 | Analysis of Catastrophic Forgetting for Random Orthogonal Transformation Tasks in the Overparameterized Regime. Daniel Goldfarb, Paul Hand |
| 2023 | Approximate Regions of Attraction in Learning with Decision-Dependent Distributions. Roy Dong, Heling Zhang, Lillian J. Ratliff |
| 2023 | Approximating a RUM from Distributions on k-Slates. Flavio Chierichetti, Mirko Giacchini, Ravi Kumar, Alessandro Panconesi, Andrew Tomkins |
| 2023 | Asymptotic Bayes risk of semi-supervised multitask learning on Gaussian mixture. Minh-Toan Nguyen, Romain Couillet |
| 2023 | Asymptotically Unbiased Off-Policy Policy Evaluation when Reusing Old Data in Nonstationary Environments. Vincent Liu, Yash Chandak, Philip S. Thomas, Martha White |
| 2023 | Autoencoded sparse Bayesian in-IRT factorization, calibration, and amortized inference for the Work Disability Functional Assessment Battery. Joshua C. Chang, Carson C. Chow, Julia Porcino |
| 2023 | Automatic Attention Pruning: Improving and Automating Model Pruning using Attentions. Kaiqi Zhao, Animesh Jain, Ming Zhao |
| 2023 | Average Adjusted Association: Efficient Estimation with High Dimensional Confounders. Sung Jae Jun, Sokbae Lee |
| 2023 | Average case analysis of Lasso under ultra sparse conditions. Koki Okajima, Xiangming Meng, Takashi Takahashi, Yoshiyuki Kabashima |
| 2023 | BaCaDI: Bayesian Causal Discovery with Unknown Interventions. Alexander Hägele, Jonas Rothfuss, Lars Lorch, Vignesh Ram Somnath, Bernhard Schölkopf, Andreas Krause |
| 2023 | Balanced Off-Policy Evaluation for Personalized Pricing. Adam N. Elmachtoub, Vishal Gupta, Yunfan Zhao |
| 2023 | Barlow Graph Auto-Encoder for Unsupervised Network Embedding. Rayyan Ahmad Khan, Martin Kleinsteuber |
| 2023 | Bayesian Convolutional Deep Sets with Task-Dependent Stationary Prior. Yohan Jung, Jinkyoo Park |
| 2023 | Bayesian Hierarchical Models for Counterfactual Estimation. Natraj Raman, Daniele Magazzeni, Sameena Shah |
| 2023 | Bayesian Optimization Over Iterative Learners with Structured Responses: A Budget-aware Planning Approach. Syrine Belakaria, Janardhan Rao Doppa, Nicolò Fusi, Rishit Sheth |
| 2023 | Bayesian Optimization over High-Dimensional Combinatorial Spaces via Dictionary-based Embeddings. Aryan Deshwal, Sebastian Ament, Maximilian Balandat, Eytan Bakshy, Janardhan Rao Doppa, David Eriksson |
| 2023 | Bayesian Optimization with Conformal Prediction Sets. Samuel Stanton, Wesley J. Maddox, Andrew Gordon Wilson |
| 2023 | Bayesian Strategy-Proof Facility Location via Robust Estimation. Emmanouil Zampetakis, Fred Zhang |
| 2023 | Bayesian Structure Scores for Probabilistic Circuits. Yang Yang, Gennaro Gala, Robert Peharz |
| 2023 | Bayesian Variable Selection in a Million Dimensions. Martin Jankowiak |
| 2023 | Benign overfitting of non-smooth neural networks beyond lazy training. Xingyu Xu, Yuantao Gu |
| 2023 | Beyond Performative Prediction: Open-environment Learning with Presence of Corruptions. Jia-Wei Shan, Peng Zhao, Zhi-Hua Zhou |
| 2023 | Blessing of Class Diversity in Pre-training. Yulai Zhao, Jianshu Chen, Simon S. Du |
| 2023 | BlitzMask: Real-Time Instance Segmentation Approach for Mobile Devices. Vitalii Bulygin, Dmytro Mykheievskyi, Kyrylo Kuchynskyi |
| 2023 | Boosted Off-Policy Learning. Ben London, Levi Lu, Ted Sandler, Thorsten Joachims |
| 2023 | Bounding Evidence and Estimating Log-Likelihood in VAE. Lukasz Struski, Marcin Mazur, Pawel Batorski, Przemyslaw Spurek, Jacek Tabor |
| 2023 | Breaking a Classical Barrier for Classifying Arbitrary Test Examples in the Quantum Model. Grzegorz Gluch, Khashayar Barooti, Rüdiger L. Urbanke |
| 2023 | Bures-Wasserstein Barycenters and Low-Rank Matrix Recovery. Tyler Maunu, Thibaut Le Gouic, Philippe Rigollet |
| 2023 | But Are You Sure? An Uncertainty-Aware Perspective on Explainable AI. Charles Marx, Youngsuk Park, Hilaf Hasson, Yuyang Wang, Stefano Ermon, Luke Huan |
| 2023 | Byzantine-Robust Federated Learning with Optimal Statistical Rates. Banghua Zhu, Lun Wang, Qi Pang, Shuai Wang, Jiantao Jiao, Dawn Song, Michael I. Jordan |
| 2023 | Byzantine-Robust Online and Offline Distributed Reinforcement Learning. Yiding Chen, Xuezhou Zhang, Kaiqing Zhang, Mengdi Wang, Xiaojin Zhu |
| 2023 | CLIP-Lite: Information Efficient Visual Representation Learning with Language Supervision. Aman Shrivastava, Ramprasaath R. Selvaraju, Nikhil Naik, Vicente Ordonez |
| 2023 | Can 5th Generation Local Training Methods Support Client Sampling? Yes! Michal Grudzien, Grigory Malinovsky, Peter Richtárik |
| 2023 | Catalyst Acceleration of Error Compensated Methods Leads to Better Communication Complexity. Xun Qian, Hanze Dong, Tong Zhang, Peter Richtárik |
| 2023 | Causal Entropy Optimization. Nicola Branchini, Virginia Aglietti, Neil Dhir, Theodoros Damoulas |
| 2023 | Characterizing Internal Evasion Attacks in Federated Learning. Taejin Kim, Shubhranshu Singh, Nikhil Madaan, Carlee Joe-Wong |
| 2023 | Characterizing Polarization in Social Networks using the Signed Relational Latent Distance Model. Nikolaos Nakis, Abdulkadir Çelikkanat, Louis Boucherie, Christian Djurhuus, Felix Burmester, Daniel Mathias Holmelund, Monika Frolcová, Morten Mørup |
| 2023 | Classification of Adolescents' Risky Behavior in Instant Messaging Conversations. Jaromír Plhák, Ondrej Sotolár, Michaela Lebedíková, David Smahel |
| 2023 | Clustering High-dimensional Data with Ordered Weighted ℓ Chandramauli Chakraborty, Sayan Paul, Saptarshi Chakraborty, Swagatam Das |
| 2023 | Clustering above Exponential Families with Tempered Exponential Measures. Ehsan Amid, Richard Nock, Manfred K. Warmuth |
| 2023 | Coarse-Grained Smoothness for Reinforcement Learning in Metric Spaces. Omer Gottesman, Kavosh Asadi, Cameron S. Allen, Samuel Lobel, George Konidaris, Michael Littman |
| 2023 | Coherent Probabilistic Forecasting of Temporal Hierarchies. Syama Sundar Rangapuram, Shubham Kapoor, Rajbir-Singh Nirwan, Pedro Mercado, Tim Januschowski, Yuyang Wang, Michael Bohlke-Schneider |
| 2023 | Collision Probability Matching Loss for Disentangling Epistemic Uncertainty from Aleatoric Uncertainty. Hiromi Narimatsu, Mayuko Ozawa, Shiro Kumano |
| 2023 | Combining Graphical and Algebraic Approaches for Parameter Identification in Latent Variable Structural Equation Models. Ankur Ankan, Inge M. N. Wortel, Kenneth Bollen, Johannes Textor |
| 2023 | Competing against Adaptive Strategies in Online Learning via Hints. Aditya Bhaskara, Kamesh Munagala |
| 2023 | Complex-to-Real Sketches for Tensor Products with Applications to the Polynomial Kernel. Jonas Wacker, Ruben Ohana, Maurizio Filippone |
| 2023 | Compositional Probabilistic and Causal Inference using Tractable Circuit Models. Benjie Wang, Marta Kwiatkowska |
| 2023 | Compress Then Test: Powerful Kernel Testing in Near-linear Time. Carles Domingo-Enrich, Raaz Dwivedi, Lester Mackey |
| 2023 | Computing Abductive Explanations for Boosted Trees. Gilles Audemard, Jean-Marie Lagniez, Pierre Marquis, Nicolas Szczepanski |
| 2023 | Conformal Off-Policy Prediction. Yingying Zhang, Chengchun Shi, Shikai Luo |
| 2023 | Conformalized Unconditional Quantile Regression. Ahmed M. Alaa, Zeshan M. Hussain, David A. Sontag |
| 2023 | Conjugate Gradient Method for Generative Adversarial Networks. Hiroki Naganuma, Hideaki Iiduka |
| 2023 | Connectivity-contrastive learning: Combining causal discovery and representation learning for multimodal data. Hiroshi Morioka, Aapo Hyvärinen |
| 2023 | Consistent Complementary-Label Learning via Order-Preserving Losses. Shuqi Liu, Yuzhou Cao, Qiaozhen Zhang, Lei Feng, Bo An |
| 2023 | Context-Specific Causal Discovery for Categorical Data Using Staged Trees. Manuele Leonelli, Gherardo Varando |
| 2023 | Contextual Linear Bandits under Noisy Features: Towards Bayesian Oracles. Jung-Hun Kim, Se-Young Yun, Minchan Jeong, Junhyun Nam, Jinwoo Shin, Richard Combes |
| 2023 | Continuous-Time Decision Transformer for Healthcare Applications. Zhiyue Zhang, Hongyuan Mei, Yanxun Xu |
| 2023 | Convergence of Stein Variational Gradient Descent under a Weaker Smoothness Condition. Lukang Sun, Avetik G. Karagulyan, Peter Richtárik |
| 2023 | Convex Bounds on the Softmax Function with Applications to Robustness Verification. Dennis Wei, Haoze Wu, Min Wu, Pin-Yu Chen, Clark W. Barrett, Eitan Farchi |
| 2023 | Convolutional Persistence as a Remedy to Neural Model Analysis. Ekaterina Khramtsova, Guido Zuccon, Xi Wang, Mahsa Baktashmotlagh |
| 2023 | Cooperative Inverse Decision Theory for Uncertain Preferences. Zachary Robertson, Hantao Zhang, Sanmi Koyejo |
| 2023 | Coordinate Ascent for Off-Policy RL with Global Convergence Guarantees. Hsin-En Su, Yen-Ju Chen, Ping-Chun Hsieh, Xi Liu |
| 2023 | Coordinate Descent for SLOPE. Johan Larsson, Quentin Klopfenstein, Mathurin Massias, Jonas Wallin |
| 2023 | Covariate-informed Representation Learning to Prevent Posterior Collapse of iVAE. Young-geun Kim, Ying Liu, Xuexin Wei |
| 2023 | DIET: Conditional independence testing with marginal dependence measures of residual information. Mukund Sudarshan, Aahlad Manas Puli, Wesley Tansey, Rajesh Ranganath |
| 2023 | Data Augmentation for Imbalanced Regression. Samuel Stocksieker, Denys Pommeret, Arthur Charpentier |
| 2023 | Data Banzhaf: A Robust Data Valuation Framework for Machine Learning. Jiachen T. Wang, Ruoxi Jia |
| 2023 | Deep Grey-Box Modeling With Adaptive Data-Driven Models Toward Trustworthy Estimation of Theory-Driven Models. Naoya Takeishi, Alexandros Kalousis |
| 2023 | Deep Joint Source-Channel Coding with Iterative Source Error Correction. Changwoo Lee, Xiao Hu, Hun-Seok Kim |
| 2023 | Deep Neural Networks with Efficient Guaranteed Invariances. Matthias Rath, Alexandru Paul Condurache |
| 2023 | Deep Value Function Networks for Large-Scale Multistage Stochastic Programs. Hyunglip Bae, Jinkyu Lee, Woo Chang Kim, Yongjae Lee |
| 2023 | Deep equilibrium models as estimators for continuous latent variables. Russell Tsuchida, Cheng Soon Ong |
| 2023 | Delayed Feedback in Generalised Linear Bandits Revisited. Benjamin Howson, Ciara Pike-Burke, Sarah Filippi |
| 2023 | Density Ratio Estimation and Neyman Pearson Classification with Missing Data. Josh Givens, Song Liu, Henry W. J. Reeve |
| 2023 | Differentiable Change-point Detection With Temporal Point Processes. Paramita Koley, Harshavardhan Alimi, Shrey Singla, Sourangshu Bhattacharya, Niloy Ganguly, Abir De |
| 2023 | Differentially Private Matrix Completion through Low-rank Matrix Factorization. Lingxiao Wang, Boxin Zhao, Mladen Kolar |
| 2023 | Differentially Private Synthetic Control. Saeyoung Rho, Rachel Cummings, Vishal Misra |
| 2023 | Diffusion Generative Models in Infinite Dimensions. Gavin Kerrigan, Justin Ley, Padhraic Smyth |
| 2023 | Dimensionality Collapse: Optimal Measurement Selection for Low-Error Infinite-Horizon Forecasting. Helmuth J. Naumer, Farzad Kamalabadi |
| 2023 | Direct Inference of Effect of Treatment (DIET) for a Cookieless World. Shiv Shankar, Ritwik Sinha, Saayan Mitra, Moumita Sinha, Madalina Fiterau |
| 2023 | Discovering Many Diverse Solutions with Bayesian Optimization. Natalie Maus, Kaiwen Wu, David Eriksson, Jacob R. Gardner |
| 2023 | Discrete Distribution Estimation under User-level Local Differential Privacy. Jayadev Acharya, Yuhan Liu, Ziteng Sun |
| 2023 | Discrete Langevin Samplers via Wasserstein Gradient Flow. Haoran Sun, Hanjun Dai, Bo Dai, Haomin Zhou, Dale Schuurmans |
| 2023 | Distance-to-Set Priors and Constrained Bayesian Inference. Rick Presman, Jason Xu |
| 2023 | Distill n' Explain: explaining graph neural networks using simple surrogates. Tamara A. Pereira, Erik Nascimento, Lucas E. Resck, Diego Mesquita, Amauri H. Souza |
| 2023 | Distributed Offline Policy Optimization Over Batch Data. Han Shen, Songtao Lu, Xiaodong Cui, Tianyi Chen |
| 2023 | Distributionally Robust Policy Gradient for Offline Contextual Bandits. Zhouhao Yang, Yihong Guo, Pan Xu, Anqi Liu, Animashree Anandkumar |
| 2023 | Do Bayesian Neural Networks Need To Be Fully Stochastic? Mrinank Sharma, Sebastian Farquhar, Eric T. Nalisnick, Tom Rainforth |
| 2023 | Does Label Differential Privacy Prevent Label Inference Attacks? Ruihan Wu, Jin Peng Zhou, Kilian Q. Weinberger, Chuan Guo |
| 2023 | Domain Adaptation under Missingness Shift. Helen Zhou, Sivaraman Balakrishnan, Zachary C. Lipton |
| 2023 | Don't be fooled: label leakage in explanation methods and the importance of their quantitative evaluation. Neil Jethani, Adriel Saporta, Rajesh Ranganath |
| 2023 | Doubly Fair Dynamic Pricing. Jianyu Xu, Dan Qiao, Yu-Xiang Wang |
| 2023 | Dropout-Resilient Secure Multi-Party Collaborative Learning with Linear Communication Complexity. Xingyu Lu, Hasin Us Sami, Basak Güler |
| 2023 | Dueling RL: Reinforcement Learning with Trajectory Preferences. Aadirupa Saha, Aldo Pacchiano, Jonathan Lee |
| 2023 | EEGNN: Edge Enhanced Graph Neural Network with a Bayesian Nonparametric Graph Model. Yirui Liu, Xinghao Qiao, Liying Wang, Jessica Lam |
| 2023 | EGG-GAE: scalable graph neural networks for tabular data imputation. Lev Telyatnikov, Simone Scardapane |
| 2023 | Efficient Informed Proposals for Discrete Distributions via Newton's Series Approximation. Yue Xiang, Dongyao Zhu, Bowen Lei, Dongkuan Xu, Ruqi Zhang |
| 2023 | Efficient Planning in Combinatorial Action Spaces with Applications to Cooperative Multi-Agent Reinforcement Learning. Volodymyr Tkachuk, Seyed Alireza Bakhtiari, Johannes Kirschner, Matej Jusup, Ilija Bogunovic, Csaba Szepesvári |
| 2023 | Efficient SAGE Estimation via Causal Structure Learning. Christoph Luther, Gunnar König, Moritz Grosse-Wentrup |
| 2023 | Efficient and Light-Weight Federated Learning via Asynchronous Distributed Dropout. Chen Dun, Mirian Hipolito Garcia, Chris Jermaine, Dimitrios Dimitriadis, Anastasios Kyrillidis |
| 2023 | Efficient fair PCA for fair representation learning. Matthäus Kleindessner, Michele Donini, Chris Russell, Muhammad Bilal Zafar |
| 2023 | Efficiently Forgetting What You Have Learned in Graph Representation Learning via Projection. Weilin Cong, Mehrdad Mahdavi |
| 2023 | Encoding Domain Knowledge in Multi-view Latent Variable Models: A Bayesian Approach with Structured Sparsity. Arber Qoku, Florian Buettner |
| 2023 | Energy-Based Models for Functional Data using Path Measure Tilting. Jen Ning Lim, Sebastian J. Vollmer, Lorenz Wolf, Andrew B. Duncan |
| 2023 | Entropic Risk Optimization in Discounted MDPs. Jia Lin Hau, Marek Petrik, Mohammad Ghavamzadeh |
| 2023 | Equivariant Representation Learning via Class-Pose Decomposition. Giovanni Luca Marchetti, Gustaf Tegnér, Anastasiia Varava, Danica Kragic |
| 2023 | Error Estimation for Random Fourier Features. Junwen Yao, N. Benjamin Erichson, Miles E. Lopes |
| 2023 | Estimating Conditional Average Treatment Effects with Missing Treatment Information. Milan Kuzmanovic, Tobias Hatt, Stefan Feuerriegel |
| 2023 | Estimating Total Correlation with Mutual Information Estimators. Ke Bai, Pengyu Cheng, Weituo Hao, Ricardo Henao, Larry Carin |
| 2023 | Exact Gradient Computation for Spiking Neural Networks via Forward Propagation. Jane H. Lee, Saeid Haghighatshoar, Amin Karbasi |
| 2023 | Explicit Regularization in Overparametrized Models via Noise Injection. Antonio Orvieto, Anant Raj, Hans Kersting, Francis R. Bach |
| 2023 | Exploration in Linear Bandits with Rich Action Sets and its Implications for Inference. Debangshu Banerjee, Avishek Ghosh, Sayak Ray Chowdhury, Aditya Gopalan |
| 2023 | Exploration in Reward Machines with Low Regret. Hippolyte Bourel, Anders Jonsson, Odalric-Ambrym Maillard, Mohammad Sadegh Talebi |
| 2023 | FAIR: Fair Collaborative Active Learning with Individual Rationality for Scientific Discovery. Xinyi Xu, Zhaoxuan Wu, Arun Verma, Chuan Sheng Foo, Bryan Kian Hsiang Low |
| 2023 | Factorial SDE for Multi-Output Gaussian Process Regression. Daniel P. Jeong, Seyoung Kim |
| 2023 | Fair Representation Learning with Unreliable Labels. Yixuan Zhang, Feng Zhou, Zhidong Li, Yang Wang, Fang Chen |
| 2023 | Fair learning with Wasserstein barycenters for non-decomposable performance measures. Solenne Gaucher, Nicolas Schreuder, Evgenii Chzhen |
| 2023 | Faithful Heteroscedastic Regression with Neural Networks. Andrew Stirn, Harm Wessels, Megan Schertzer, Laura Pereira, Neville E. Sanjana, David A. Knowles |
| 2023 | Falsification of Internal and External Validity in Observational Studies via Conditional Moment Restrictions. Zeshan M. Hussain, Ming-Chieh Shih, Michael Oberst, Ilker Demirel, David A. Sontag |
| 2023 | Fast Block Coordinate Descent for Non-Convex Group Regularizations. Yasutoshi Ida, Sekitoshi Kanai, Atsutoshi Kumagai |
| 2023 | Fast Computation of Branching Process Transition Probabilities via ADMM. Achal Awasthi, Jason Xu |
| 2023 | Fast Distributed k-Means with a Small Number of Rounds. Tom Hess, Ron Visbord, Sivan Sabato |
| 2023 | Fast Feature Selection with Fairness Constraints. Francesco Quinzan, Rajiv Khanna, Moshik Hershcovitch, Sarel Cohen, Daniel G. Waddington, Tobias Friedrich, Michael W. Mahoney |
| 2023 | Fast Variational Estimation of Mutual Information for Implicit and Explicit Likelihood Models. Caleb Dahlke, Sue Zheng, Jason Pacheco |
| 2023 | Faster Projection-Free Augmented Lagrangian Methods via Weak Proximal Oracle. Dan Garber, Tsur Livney, Shoham Sabach |
| 2023 | Feasible Recourse Plan via Diverse Interpolation. Duy Nguyen, Ngoc Bui, Viet Anh Nguyen |
| 2023 | Federated Asymptotics: a model to compare federated learning algorithms. Gary Cheng, Karan N. Chadha, John C. Duchi |
| 2023 | Federated Averaging Langevin Dynamics: Toward a unified theory and new algorithms. Vincent Plassier, Eric Moulines, Alain Durmus |
| 2023 | Federated Learning for Data Streams. Othmane Marfoq, Giovanni Neglia, Laetitia Kameni, Richard Vidal |
| 2023 | Federated Learning under Distributed Concept Drift. Ellango Jothimurugesan, Kevin Hsieh, Jianyu Wang, Gauri Joshi, Phillip B. Gibbons |
| 2023 | Finding Regularized Competitive Equilibria of Heterogeneous Agent Macroeconomic Models via Reinforcement Learning. Ruitu Xu, Yifei Min, Tianhao Wang, Michael I. Jordan, Zhaoran Wang, Zhuoran Yang |
| 2023 | Finite time analysis of temporal difference learning with linear function approximation: Tail averaging and regularisation. Gandharv Patil, Prashanth L. A., Dheeraj Nagaraj, Doina Precup |
| 2023 | Fitting low-rank models on egocentrically sampled partial networks. Ga Ming Angus Chan, Tianxi Li |
| 2023 | Fix-A-Step: Semi-supervised Learning From Uncurated Unlabeled Data. Zhe Huang, Mary-Joy Sidhom, Benjamin Wessler, Michael C. Hughes |
| 2023 | Fixing by Mixing: A Recipe for Optimal Byzantine ML under Heterogeneity. Youssef Allouah, Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot, John Stephan |
| 2023 | Flexible and Efficient Contextual Bandits with Heterogeneous Treatment Effect Oracles. Aldo Gael Carranza, Sanath Kumar Krishnamurthy, Susan Athey |
| 2023 | Flexible risk design using bi-directional dispersion. Matthew J. Holland |
| 2023 | ForestPrune: Compact Depth-Pruned Tree Ensembles. Brian Liu, Rahul Mazumder |
| 2023 | Freeze then Train: Towards Provable Representation Learning under Spurious Correlations and Feature Noise. Haotian Ye, James Zou, Linjun Zhang |
| 2023 | Frequentist Uncertainty Quantification in Semi-Structured Neural Networks. Emilio Dorigatti, Benjamin Schubert, Bernd Bischl, David Rügamer |
| 2023 | From Shapley Values to Generalized Additive Models and back. Sebastian Bordt, Ulrike von Luxburg |
| 2023 | Further Adaptive Best-of-Both-Worlds Algorithm for Combinatorial Semi-Bandits. Taira Tsuchiya, Shinji Ito, Junya Honda |
| 2023 | Gaussian Processes on Distributions based on Regularized Optimal Transport. François Bachoc, Louis Béthune, Alberto González-Sanz, Jean-Michel Loubes |
| 2023 | Generalization in Graph Neural Networks: Improved PAC-Bayesian Bounds on Graph Diffusion. Haotian Ju, Dongyue Li, Aneesh Sharma, Hongyang R. Zhang |
| 2023 | Generalized PTR: User-Friendly Recipes for Data-Adaptive Algorithms with Differential Privacy. Rachel Redberg, Yuqing Zhu, Yu-Xiang Wang |
| 2023 | Generative Oversampling for Imbalanced Data via Majority-Guided VAE. Qingzhong Ai, Pengyun Wang, Lirong He, Liangjian Wen, Lujia Pan, Zenglin Xu |
| 2023 | Geometric Random Walk Graph Neural Networks via Implicit Layers. Giannis Nikolentzos, Michalis Vazirgiannis |
| 2023 | Global Convergence of Over-parameterized Deep Equilibrium Models. Zenan Ling, Xingyu Xie, Qiuhao Wang, Zongpeng Zhang, Zhouchen Lin |
| 2023 | Global-Local Regularization Via Distributional Robustness. Hoang Phan, Trung Le, Trung Phung, Anh Tuan Bui, Nhat Ho, Dinh Q. Phung |
| 2023 | Gradient-Informed Neural Network Statistical Robustness Estimation. Karim Tit, Teddy Furon, Mathias Rousset |
| 2023 | Graph Alignment Kernels using Weisfeiler and Leman Hierarchies. Giannis Nikolentzos, Michalis Vazirgiannis |
| 2023 | Graph Spectral Embedding using the Geodesic Betweenness Centrality. Shay Deutsch, Stefano Soatto |
| 2023 | Group Distributionally Robust Reinforcement Learning with Hierarchical Latent Variables. Mengdi Xu, Peide Huang, Yaru Niu, Visak Kumar, Jielin Qiu, Chao Fang, Kuan-Hui Lee, Xuewei Qi, Henry Lam, Bo Li, Ding Zhao |
| 2023 | Heavy Sets with Applications to Interpretable Machine Learning Diagnostics. Dmitry M. Malioutov, Sanjeeb Dash, Dennis Wei |
| 2023 | Hedging against Complexity: Distributionally Robust Optimization with Parametric Approximation. Garud Iyengar, Henry Lam, Tianyu Wang |
| 2023 | HeteRSGD: Tackling Heterogeneous Sampling Costs via Optimal Reweighted Stochastic Gradient Descent. Ziang Chen, Jianfeng Lu, Huajie Qian, Xinshang Wang, Wotao Yin |
| 2023 | Hierarchical-Hyperplane Kernels for Actively Learning Gaussian Process Models of Nonstationary Systems. Matthias Bitzer, Mona Meister, Christoph Zimmer |
| 2023 | High Probability Bounds for Stochastic Continuous Submodular Maximization. Evan Becker, Jingdong Gao, Ted Zadouri, Baharan Mirzasoleiman |
| 2023 | High-Dimensional Private Empirical Risk Minimization by Greedy Coordinate Descent. Paul Mangold, Aurélien Bellet, Joseph Salmon, Marc Tommasi |
| 2023 | How Does Pseudo-Labeling Affect the Generalization Error of the Semi-Supervised Gibbs Algorithm? Haiyun He, Gholamali Aminian, Yuheng Bu, Miguel R. D. Rodrigues, Vincent Y. F. Tan |
| 2023 | Huber-robust confidence sequences. Hongjian Wang, Aaditya Ramdas |
| 2023 | INO: Invariant Neural Operators for Learning Complex Physical Systems with Momentum Conservation. Ning Liu, Yue Yu, Huaiqian You, Neeraj Tatikola |
| 2023 | Ideal Abstractions for Decision-Focused Learning. Michael Poli, Stefano Massaroli, Stefano Ermon, Bryan Wilder, Eric Horvitz |
| 2023 | Identification of Blackwell Optimal Policies for Deterministic MDPs. Victor Boone, Bruno Gaujal |
| 2023 | Implications of sparsity and high triangle density for graph representation learning. Hannah Sansford, Alexander Modell, Nick Whiteley, Patrick Rubin-Delanchy |
| 2023 | Implicit Graphon Neural Representation. Xinyue Xia, Gal Mishne, Yusu Wang |
| 2023 | Improved Approximation for Fair Correlation Clustering. Sara Ahmadian, Maryam Negahbani |
| 2023 | Improved Bound on Generalization Error of Compressed KNN Estimator. Hang Zhang, Ping Li |
| 2023 | Improved Generalization Bound and Learning of Sparsity Patterns for Data-Driven Low-Rank Approximation. Shinsaku Sakaue, Taihei Oki |
| 2023 | Improved Rate of First Order Algorithms for Entropic Optimal Transport. Yiling Luo, Yiling Xie, Xiaoming Huo |
| 2023 | Improved Representation Learning Through Tensorized Autoencoders. Pascal Mattia Esser, Satyaki Mukherjee, Mahalakshmi Sabanayagam, Debarghya Ghoshdastidar |
| 2023 | Improved Robust Algorithms for Learning with Discriminative Feature Feedback. Sivan Sabato |
| 2023 | Improved Sample Complexity Bounds for Distributionally Robust Reinforcement Learning. Zaiyan Xu, Kishan Panaganti, Dileep Kalathil |
| 2023 | Improving Adaptive Conformal Prediction Using Self-Supervised Learning. Nabeel Seedat, Alan Jeffares, Fergus Imrie, Mihaela van der Schaar |
| 2023 | Improving Adversarial Robustness via Joint Classification and Multiple Explicit Detection Classes. Sina Baharlouei, Fatemeh Sheikholeslami, Meisam Razaviyayn, Zico Kolter |
| 2023 | Improving Dual-Encoder Training through Dynamic Indexes for Negative Mining. Nicholas Monath, Manzil Zaheer, Kelsey Allen, Andrew McCallum |
| 2023 | Incentive-aware Contextual Pricing with Non-parametric Market Noise. Negin Golrezaei, Patrick Jaillet, Jason Cheuk Nam Liang |
| 2023 | Incorporating functional summary information in Bayesian neural networks using a Dirichlet process likelihood approach. Vishnu Raj, Tianyu Cui, Markus Heinonen, Pekka Marttinen |
| 2023 | Incremental Aggregated Riemannian Gradient Method for Distributed PCA. Xiaolu Wang, Yuchen Jiao, Hoi-To Wai, Yuantao Gu |
| 2023 | Indeterminacy in Generative Models: Characterization and Strong Identifiability. Quanhan Xi, Benjamin Bloem-Reddy |
| 2023 | Inducing Neural Collapse in Deep Long-tailed Learning. Xuantong Liu, Jianfeng Zhang, Tianyang Hu, He Cao, Yuan Yao, Lujia Pan |
| 2023 | Inducing Point Allocation for Sparse Gaussian Processes in High-Throughput Bayesian Optimisation. Henry B. Moss, Sebastian W. Ober, Victor Picheny |
| 2023 | Influence Diagnostics under Self-concordance. Jillian Fisher, Lang Liu, Krishna Pillutla, Yejin Choi, Zaïd Harchaoui |
| 2023 | Instance-dependent Sample Complexity Bounds for Zero-sum Matrix Games. Arnab Maiti, Kevin Jamieson, Lillian J. Ratliff |
| 2023 | Interactive Learning with Pricing for Optimal and Stable Allocations in Markets. Yigit Efe Erginbas, Soham Phade, Kannan Ramchandran |
| 2023 | International Conference on Artificial Intelligence and Statistics, 25-27 April 2023, Palau de Congressos, Valencia, Spain. Francisco J. R. Ruiz, Jennifer G. Dy, Jan-Willem van de Meent |
| 2023 | Is interpolation benign for random forest regression? Ludovic Arnould, Claire Boyer, Erwan Scornet |
| 2023 | Isotropic Gaussian Processes on Finite Spaces of Graphs. Viacheslav Borovitskiy, Mohammad Reza Karimi, Vignesh Ram Somnath, Andreas Krause |
| 2023 | Iterative Teaching by Data Hallucination. Zeju Qiu, Weiyang Liu, Tim Z. Xiao, Zhen Liu, Umang Bhatt, Yucen Luo, Adrian Weller, Bernhard Schölkopf |
| 2023 | Kernel Conditional Moment Constraints for Confounding Robust Inference. Kei Ishikawa, Niao He |
| 2023 | Knowledge Acquisition for Human-In-The-Loop Image Captioning. Ervine Zheng, Qi Yu, Rui Li, Pengcheng Shi, Anne R. Haake |
| 2023 | Knowledge Sheaves: A Sheaf-Theoretic Framework for Knowledge Graph Embedding. Thomas Gebhart, Jakob Hansen, Paul Schrater |
| 2023 | Krylov-Bellman boosting: Super-linear policy evaluation in general state spaces. Eric Xia, Martin J. Wainwright |
| 2023 | LOFT: Finding Lottery Tickets through Filter-wise Training. Qihan Wang, Chen Dun, Fangshuo Liao, Chris Jermaine, Anastasios Kyrillidis |
| 2023 | Langevin Diffusion Variational Inference. Tomas Geffner, Justin Domke |
| 2023 | Large deviations rates for stochastic gradient descent with strongly convex functions. Dragana Bajovic, Dusan Jakovetic, Soummya Kar |
| 2023 | Last-Iterate Convergence with Full and Noisy Feedback in Two-Player Zero-Sum Games. Kenshi Abe, Kaito Ariu, Mitsuki Sakamoto, Kentaro Toyoshima, Atsushi Iwasaki |
| 2023 | Learning Constrained Structured Spaces with Application to Multi-Graph Matching. Hedda Cohen Indelman, Tamir Hazan |
| 2023 | Learning Physics-Informed Neural Networks without Stacked Back-propagation. Di He, Shanda Li, Wenlei Shi, Xiaotian Gao, Jia Zhang, Jiang Bian, Liwei Wang, Tie-Yan Liu |
| 2023 | Learning Robust Graph Neural Networks with Limited Supervision. Abdullah Alchihabi, Yuhong Guo |
| 2023 | Learning Sparse Graphon Mean Field Games. Christian Fabian, Kai Cui, Heinz Koeppl |
| 2023 | Learning Treatment Effects from Observational and Experimental Data. Sofia Triantafillou, Fattaneh Jabbari, Gregory F. Cooper |
| 2023 | Learning While Scheduling in Multi-Server Systems With Unknown Statistics: MaxWeight with Discounted UCB. Zixian Yang, R. Srikant, Lei Ying |
| 2023 | Learning from Multiple Sources for Data-to-Text and Text-to-Data. Song Duong, Alberto Lumbreras, Mike Gartrell, Patrick Gallinari |
| 2023 | Learning in RKHM: a C*-Algebraic Twist for Kernel Machines. Yuka Hashimoto, Masahiro Ikeda, Hachem Kadri |
| 2023 | Learning k-qubit Quantum Operators via Pauli Decomposition. Mohsen Heidari, Wojciech Szpankowski |
| 2023 | Learning to Defer to Multiple Experts: Consistent Surrogate Losses, Confidence Calibration, and Conformal Ensembles. Rajeev Verma, Daniel Barrejón, Eric T. Nalisnick |
| 2023 | Learning to Generalize Provably in Learning to Optimize. Junjie Yang, Tianlong Chen, Mingkang Zhu, Fengxiang He, Dacheng Tao, Yingbin Liang, Zhangyang Wang |
| 2023 | Learning to Optimize with Stochastic Dominance Constraints. Hanjun Dai, Yuan Xue, Niao He, Yixin Wang, Na Li, Dale Schuurmans, Bo Dai |
| 2023 | Learning with Partial Forgetting in Modern Hopfield Networks. Toshihiro Ota, Ikuro Sato, Rei Kawakami, Masayuki Tanaka, Nakamasa Inoue |
| 2023 | Leveraging Instance Features for Label Aggregation in Programmatic Weak Supervision. Jieyu Zhang, Linxin Song, Alex Ratner |
| 2023 | Likelihood-Based Generative Radiance Field with Latent Space Energy-Based Model for 3D-Aware Disentangled Image Representation. Yaxuan Zhu, Jianwen Xie, Ping Li |
| 2023 | Linear Convergence of Gradient Descent For Finite Width Over-parametrized Linear Networks With General Initialization. Ziqing Xu, Hancheng Min, Salma Tarmoun, Enrique Mallada, René Vidal |
| 2023 | Loss-Curvature Matching for Dataset Selection and Condensation. Seungjae Shin, HeeSun Bae, DongHyeok Shin, Weonyoung Joo, Il-Chul Moon |
| 2023 | MARS: Masked Automatic Ranks Selection in Tensor Decompositions. Maxim Kodryan, Dmitry Kropotov, Dmitry P. Vetrov |
| 2023 | MMD-B-Fair: Learning Fair Representations with Statistical Testing. Namrata Deka, Danica J. Sutherland |
| 2023 | Manifold Restricted Interventional Shapley Values. Muhammad Faaiz Taufiq, Patrick Blöbaum, Lenon Minorics |
| 2023 | Matching Map Recovery with an Unknown Number of Outliers. Arshak Minasyan, Tigran Galstyan, Sona Hunanyan, Arnak S. Dalalyan |
| 2023 | Mean Parity Fair Regression in RKHS. Shaokui Wei, Jiayin Liu, Bing Li, Hongyuan Zha |
| 2023 | Mediated Uncoupled Learning and Validation with Bregman Divergences: Loss Family with Maximal Generality. Ikko Yamane, Yann Chevaleyre, Takashi Ishida, Florian Yger |
| 2023 | Membership Inference Attacks against Synthetic Data through Overfitting Detection. Boris van Breugel, Hao Sun, Zhaozhi Qian, Mihaela van der Schaar |
| 2023 | Meta-Learning with Adjoint Methods. Shibo Li, Zheng Wang, Akil Narayan, Robert M. Kirby, Shandian Zhe |
| 2023 | Meta-Uncertainty in Bayesian Model Comparison. Marvin Schmitt, Stefan T. Radev, Paul-Christian Bürkner |
| 2023 | Meta-learning for Robust Anomaly Detection. Atsutoshi Kumagai, Tomoharu Iwata, Hiroshi Takahashi, Yasuhiro Fujiwara |
| 2023 | Mind the (optimality) Gap: A Gap-Aware Learning Rate Scheduler for Adversarial Nets. Hussein Hazimeh, Natalia Ponomareva |
| 2023 | Minimax Nonparametric Two-Sample Test under Adversarial Losses. Rong Tang, Yun Yang |
| 2023 | Minimax-Bayes Reinforcement Learning. Thomas Kleine Buening, Christos Dimitrakakis, Hannes Eriksson, Divya Grover, Emilio Jorge |
| 2023 | Minimum-Entropy Coupling Approximation Guarantees Beyond the Majorization Barrier. Spencer Compton, Dmitriy Katz, Benjamin Qi, Kristjan H. Greenewald, Murat Kocaoglu |
| 2023 | Minority Oversampling for Imbalanced Data via Class-Preserving Regularized Auto-Encoders. Arnab Kumar Mondal, Lakshya Singhal, Piyush Tiwary, Parag Singla, Prathosh AP |
| 2023 | Mixed Linear Regression via Approximate Message Passing. Nelvin Tan, Ramji Venkataramanan |
| 2023 | Mixed-Effect Thompson Sampling. Imad Aouali, Branislav Kveton, Sumeet Katariya |
| 2023 | Mixtures of All Trees. Nikil Roashan Selvam, Honghua Zhang, Guy Van den Broeck |
| 2023 | Mode-Seeking Divergences: Theory and Applications to GANs. Cheuk Ting Li, Farzan Farnia |
| 2023 | Mode-constrained Model-based Reinforcement Learning via Gaussian Processes. Aidan Scannell, Carl Henrik Ek, Arthur Richards |
| 2023 | Model-Based Uncertainty in Value Functions. Carlos E. Luis, Alessandro G. Bottero, Julia Vinogradska, Felix Berkenkamp, Jan Peters |
| 2023 | Model-X Sequential Testing for Conditional Independence via Testing by Betting. Shalev Shaer, Gal Maman, Yaniv Romano |
| 2023 | Multi-Agent congestion cost minimization with linear function approximations. Prashant Trivedi, Nandyala Hemachandra |
| 2023 | Multi-Fidelity Bayesian Optimization with Unreliable Information Sources. Petrus Mikkola, Julien Martinelli, Louis Filstroff, Samuel Kaski |
| 2023 | Multi-armed Bandit Experimental Design: Online Decision-making and Adaptive Inference. David Simchi-Levi, Chonghuan Wang |
| 2023 | Multi-task Representation Learning with Stochastic Linear Bandits. Leonardo Cella, Karim Lounici, Grégoire Pacreau, Massimiliano Pontil |
| 2023 | Multilevel Bayesian Quadrature. Kaiyu Li, Daniel Giles, Toni Karvonen, Serge Guillas, François-Xavier Briol |
| 2023 | Multiple-policy High-confidence Policy Evaluation. Christoph Dann, Mohammad Ghavamzadeh, Teodor V. Marinov |
| 2023 | NODAGS-Flow: Nonlinear Cyclic Causal Structure Learning. Muralikrishnna G. Sethuraman, Romain Lopez, Rahul Mohan, Faramarz Fekri, Tommaso Biancalani, Jan-Christian Hütter |
| 2023 | NTS-NOTEARS: Learning Nonparametric DBNs With Prior Knowledge. Xiangyu Sun, Oliver Schulte, Guiliang Liu, Pascal Poupart |
| 2023 | Nash Equilibria and Pitfalls of Adversarial Training in Adversarial Robustness Games. Maria-Florina Balcan, Rattana Pukdee, Pradeep Ravikumar, Hongyang Zhang |
| 2023 | Near-Optimal Differentially Private Reinforcement Learning. Dan Qiao, Yu-Xiang Wang |
| 2023 | Nearly Optimal Latent State Decoding in Block MDPs. Yassir Jedra, Junghyun Lee, Alexandre Proutière, Se-Young Yun |
| 2023 | Neural Discovery of Permutation Subgroups. Pavan Karjol, Rohan Kashyap, Prathosh AP |
| 2023 | Neural Laplace Control for Continuous-time Delayed Systems. Samuel Holt, Alihan Hüyük, Zhaozhi Qian, Hao Sun, Mihaela van der Schaar |
| 2023 | Neural Simulated Annealing. Alvaro H. C. Correia, Daniel E. Worrall, Roberto Bondesan |
| 2023 | No time to waste: practical statistical contact tracing with few low-bit messages. Rob Romijnders, Yuki M. Asano, Christos Louizos, Max Welling |
| 2023 | No-Regret Learning in Two-Echelon Supply Chain with Unknown Demand Distribution. Mengxiao Zhang, Shi Chen, Haipeng Luo, Yingfei Wang |
| 2023 | No-regret Sample-efficient Bayesian Optimization for Finding Nash Equilibria with Unknown Utilities. Sebastian Shenghong Tay, Quoc Phong Nguyen, Chuan Sheng Foo, Bryan Kian Hsiang Low |
| 2023 | Noise-Aware Statistical Inference with Differentially Private Synthetic Data. Ossi Räisä, Joonas Jälkö, Samuel Kaski, Antti Honkela |
| 2023 | Noisy Low-rank Matrix Optimization: Geometry of Local Minima and Convergence Rate. Ziye Ma, Somayeh Sojoudi |
| 2023 | Nonmyopic Multiclass Active Search with Diminishing Returns for Diverse Discovery. Quan Nguyen, Roman Garnett |
| 2023 | Nonparametric Gaussian Process Covariances via Multidimensional Convolutions. Thomas M. McDonald, Magnus Ross, Michael T. Smith, Mauricio A. Álvarez |
| 2023 | Nonparametric Indirect Active Learning. Shashank Singh |
| 2023 | Nonstationary Bandit Learning via Predictive Sampling. Yueyang Liu, Benjamin Van Roy, Kuang Xu |
| 2023 | Nonstochastic Contextual Combinatorial Bandits. Lukas Zierahn, Dirk van der Hoeven, Nicolò Cesa-Bianchi, Gergely Neu |
| 2023 | Nothing but Regrets - Privacy-Preserving Federated Causal Discovery. Osman Mian, David Kaltenpoth, Michael Kamp, Jilles Vreeken |
| 2023 | Nyström Method for Accurate and Scalable Implicit Differentiation. Ryuichiro Hataya, Makoto Yamada |
| 2023 | Oblivious near-optimal sampling for multidimensional signals with Fourier constraints. Xingyu Xu, Yuantao Gu |
| 2023 | On Generalization of Decentralized Learning with Separable Data. Hossein Taheri, Christos Thrampoulidis |
| 2023 | On Model Selection Consistency of Lasso for High-Dimensional Ising Models. Xiangming Meng, Tomoyuki Obuchi, Yoshiyuki Kabashima |
| 2023 | On The Convergence Of Policy Iteration-Based Reinforcement Learning With Monte Carlo Policy Evaluation. Anna Winnicki, R. Srikant |
| 2023 | On Universal Portfolios with Continuous Side Information. Alankrita Bhatt, J. Jon Ryu, Young-Han Kim |
| 2023 | On double-descent in uncertainty quantification in overparametrized models. Lucas Clarté, Bruno Loureiro, Florent Krzakala, Lenka Zdeborová |
| 2023 | On the Accelerated Noise-Tolerant Power Method. Zhiqiang Xu |
| 2023 | On the Calibration of Probabilistic Classifier Sets. Thomas Mortier, Viktor Bengs, Eyke Hüllermeier, Stijn Luca, Willem Waegeman |
| 2023 | On the Capacity Limits of Privileged ERM. Michal Sharoni, Sivan Sabato |
| 2023 | On the Complexity of Representation Learning in Contextual Linear Bandits. Andrea Tirinzoni, Matteo Pirotta, Alessandro Lazaric |
| 2023 | On the Consistency Rate of Decision Tree Learning Algorithms. Qin-Cheng Zheng, Shen-Huan Lyu, Shao-Qun Zhang, Yuan Jiang, Zhi-Hua Zhou |
| 2023 | On the Convergence of Distributed Stochastic Bilevel Optimization Algorithms over a Network. Hongchang Gao, Bin Gu, My T. Thai |
| 2023 | On the Implicit Geometry of Cross-Entropy Parameterizations for Label-Imbalanced Data. Tina Behnia, Ganesh Ramachandra Kini, Vala Vakilian, Christos Thrampoulidis |
| 2023 | On the Limitations of the Elo, Real-World Games are Transitive, not Additive. Quentin Bertrand, Wojciech Marian Czarnecki, Gauthier Gidel |
| 2023 | On the Neural Tangent Kernel Analysis of Randomly Pruned Neural Networks. Hongru Yang, Zhangyang Wang |
| 2023 | On the Privacy Risks of Algorithmic Recourse. Martin Pawelczyk, Himabindu Lakkaraju, Seth Neel |
| 2023 | On the Strategyproofness of the Geometric Median. El-Mahdi El-Mhamdi, Sadegh Farhadkhani, Rachid Guerraoui, Lê-Nguyên Hoang |
| 2023 | On the bias of K-fold cross validation with stable learners. Anass Aghbalou, Anne Sabourin, François Portier |
| 2023 | On-Demand Communication for Asynchronous Multi-Agent Bandits. Yu-Zhen Janice Chen, Lin Yang, Xuchuang Wang, Xutong Liu, Mohammad H. Hajiesmaili, John C. S. Lui, Don Towsley |
| 2023 | One Arrow, Two Kills: A Unified Framework for Achieving Optimal Regret Guarantees in Sleeping Bandits. Pierre Gaillard, Aadirupa Saha, Soham Dan |
| 2023 | One Policy is Enough: Parallel Exploration with a Single Policy is Near-Optimal for Reward-Free Reinforcement Learning. Pedro Cisneros-Velarde, Boxiang Lyu, Sanmi Koyejo, Mladen Kolar |
| 2023 | Online Algorithms with Costly Predictions. Marina Drygala, Sai Ganesh Nagarajan, Ola Svensson |
| 2023 | Online Defense Strategies for Reinforcement Learning Against Adaptive Reward Poisoning. Andi Nika, Adish Singla, Goran Radanovic |
| 2023 | Online Learning for Non-monotone DR-Submodular Maximization: From Full Information to Bandit Feedback. Qixin Zhang, Zengde Deng, Zaiyi Chen, Kuangqi Zhou, Haoyuan Hu, Yu Yang |
| 2023 | Online Learning for Traffic Routing under Unknown Preferences. Devansh Jalota, Karthik Gopalakrishnan, Navid Azizan, Ramesh Johari, Marco Pavone |
| 2023 | Online Linearized LASSO. Shuoguang Yang, Yuhao Yan, Xiuneng Zhu, Qiang Sun |
| 2023 | Optimal Algorithms for Latent Bandits with Cluster Structure. Soumyabrata Pal, Arun Sai Suggala, Karthikeyan Shanmugam, Prateek Jain |
| 2023 | Optimal Contextual Bandits with Knapsacks under Realizability via Regression Oracles. Yuxuan Han, Jialin Zeng, Yang Wang, Yang Xiang, Jiheng Zhang |
| 2023 | Optimal Sample Complexity Bounds for Non-convex Optimization under Kurdyka-Lojasiewicz Condition. Qian Yu, Yining Wang, Baihe Huang, Qi Lei, Jason D. Lee |
| 2023 | Optimal Sketching Bounds for Sparse Linear Regression. Tung Mai, Alexander Munteanu, Cameron Musco, Anup Rao, Chris Schwiegelshohn, David P. Woodruff |
| 2023 | Optimal and Private Learning from Human Response Data. Duc Nguyen, Anderson Ye Zhang |
| 2023 | Optimal robustness-consistency tradeoffs for learning-augmented metrical task systems. Nicolas Christianson, Junxuan Shen, Adam Wierman |
| 2023 | Optimism and Delays in Episodic Reinforcement Learning. Benjamin Howson, Ciara Pike-Burke, Sarah Filippi |
| 2023 | Optimizing Pessimism in Dynamic Treatment Regimes: A Bayesian Learning Approach. Yunzhe Zhou, Zhengling Qi, Chengchun Shi, Lexin Li |
| 2023 | Oracle-free Reinforcement Learning in Mean-Field Games along a Single Sample Path. Muhammad Aneeq uz Zaman, Alec Koppel, Sujay Bhatt, Tamer Basar |
| 2023 | Origins of Low-Dimensional Adversarial Perturbations. Elvis Dohmatob, Chuan Guo, Morgane Goibert |
| 2023 | Overcoming Prior Misspecification in Online Learning to Rank. Javad Azizi, Ofer Meshi, Masrour Zoghi, Maryam Karimzadehgan |
| 2023 | Overparameterized Random Feature Regression with Nearly Orthogonal Data. Zhichao Wang, Yizhe Zhu |
| 2023 | PAC Learning of Halfspaces with Malicious Noise in Nearly Linear Time. Jie Shen |
| 2023 | PAC-Bayesian Learning of Optimization Algorithms. Michael Sucker, Peter Ochs |
| 2023 | PF Jixiang Qing, Henry B. Moss, Tom Dhaene, Ivo Couckuyt |
| 2023 | Particle algorithms for maximum likelihood training of latent variable models. Juan Kuntz, Jen Ning Lim, Adam M. Johansen |
| 2023 | Performative Prediction with Neural Networks. Mehrnaz Mofakhami, Ioannis Mitliagkas, Gauthier Gidel |
| 2023 | Piecewise Stationary Bandits under Risk Criteria. Sujay Bhatt, Guanhua Fang, Ping Li |
| 2023 | Pointwise sampling uncertainties on the Precision-Recall curve. Ralph E. Q. Urlus, Max Baak, Stéphane Collot, Ilan Fridman Rojas |
| 2023 | Positional Encoder Graph Neural Networks for Geographic Data. Konstantin Klemmer, Nathan S. Safir, Daniel B. Neill |
| 2023 | Posterior Tracking Algorithm for Classification Bandits. Koji Tabata, Junpei Komiyama, Atsuyoshi Nakamura, Tamiki Komatsuzaki |
| 2023 | Precision Recall Cover: A Method For Assessing Generative Models. Fasil Cheema, Ruth Urner |
| 2023 | Precision/Recall on Imbalanced Test Data. Hongwei Shang, Jean-Marc Langlois, Kostas Tsioutsiouliklis, Changsung Kang |
| 2023 | Prediction-Oriented Bayesian Active Learning. Freddie Bickford Smith, Andreas Kirsch, Sebastian Farquhar, Yarin Gal, Adam Foster, Tom Rainforth |
| 2023 | Preferential Subsampling for Stochastic Gradient Langevin Dynamics. Srshti Putcha, Christopher Nemeth, Paul Fearnhead |
| 2023 | Pricing against a Budget and ROI Constrained Buyer. Negin Golrezaei, Patrick Jaillet, Jason Cheuk Nam Liang, Vahab Mirrokni |
| 2023 | Principled Approaches for Private Adaptation from a Public Source. Raef Bassily, Mehryar Mohri, Ananda Theertha Suresh |
| 2023 | Privacy-preserving Sparse Generalized Eigenvalue Problem. Lijie Hu, Zihang Xiang, Jiabin Liu, Di Wang |
| 2023 | Private Non-Convex Federated Learning Without a Trusted Server. Andrew Lowy, Ali Ghafelebashi, Meisam Razaviyayn |
| 2023 | ProbNeRF: Uncertainty-Aware Inference of 3D Shapes from 2D Images. Matthew D. Hoffman, Tuan Anh Le, Pavel Sountsov, Christopher Suter, Ben Lee, Vikash K. Mansinghka, Rif A. Saurous |
| 2023 | Probabilistic Conformal Prediction Using Conditional Random Samples. Zhendong Wang, Ruijiang Gao, Mingzhang Yin, Mingyuan Zhou, David M. Blei |
| 2023 | Probabilistic Querying of Continuous-Time Event Sequences. Alex Boyd, Yuxin Chang, Stephan Mandt, Padhraic Smyth |
| 2023 | Probabilities of Causation: Role of Observational Data. Ang Li, Judea Pearl |
| 2023 | Probing Graph Representations. Mohammad Sadegh Akhondzadeh, Vijay Lingam, Aleksandar Bojchevski |
| 2023 | Protecting Global Properties of Datasets with Distribution Privacy Mechanisms. Michelle Chen, Olga Ohrimenko |
| 2023 | Provable Hierarchy-Based Meta-Reinforcement Learning. Kurtland Chua, Qi Lei, Jason D. Lee |
| 2023 | Provable Safe Reinforcement Learning with Binary Feedback. Andrew Bennett, Dipendra Misra, Nathan Kallus |
| 2023 | Provably Efficient Model-Free Algorithms for Non-stationary CMDPs. Honghao Wei, Arnob Ghosh, Ness B. Shroff, Lei Ying, Xingyu Zhou |
| 2023 | Provably Efficient Reinforcement Learning via Surprise Bound. Hanlin Zhu, Ruosong Wang, Jason D. Lee |
| 2023 | Random Features Model with General Convex Regularization: A Fine Grained Analysis with Precise Asymptotic Learning Curves. David Bosch, Ashkan Panahi, Ayça Özçelikkale, Devdatt P. Dubhashi |
| 2023 | Randomized Greedy Learning for Non-monotone Stochastic Submodular Maximization Under Full-bandit Feedback. Fares Fourati, Vaneet Aggarwal, Christopher J. Quinn, Mohamed-Slim Alouini |
| 2023 | Randomized Primal-Dual Methods with Adaptive Step Sizes. Erfan Yazdandoost Hamedani, Afrooz Jalilzadeh, Necdet S. Aybat |
| 2023 | Randomized geometric tools for anomaly detection in stock markets. Cyril Bachelard, Apostolos Chalkis, Vissarion Fisikopoulos, Elias P. Tsigaridas |
| 2023 | Rank-Based Causal Discovery for Post-Nonlinear Models. Grigor Keropyan, David Strieder, Mathias Drton |
| 2023 | Reconstructing Training Data from Model Gradient, Provably. Zihan Wang, Jason Lee, Qi Lei |
| 2023 | Recurrent Neural Networks and Universal Approximation of Bayesian Filters. Adrian N. Bishop, Edwin V. Bonilla |
| 2023 | Reducing Discretization Error in the Frank-Wolfe Method. Zhaoyue Chen, Yifan Sun |
| 2023 | Refined Convergence and Topology Learning for Decentralized SGD with Heterogeneous Data. Batiste Le Bars, Aurélien Bellet, Marc Tommasi, Erick Lavoie, Anne-Marie Kermarrec |
| 2023 | Regression as Classification: Influence of Task Formulation on Neural Network Features. Lawrence Stewart, Francis R. Bach, Quentin Berthet, Jean-Philippe Vert |
| 2023 | Regularization for Shuffled Data Problems via Exponential Family Priors on the Permutation Group. Zhenbang Wang, Emanuel Ben-David, Martin Slawski |
| 2023 | Reinforcement Learning for Adaptive Mesh Refinement. Jiachen Yang, Tarik Dzanic, Brenden K. Petersen, Jun Kudo, Ketan Mittal, Vladimir Z. Tomov, Jean-Sylvain Camier, Tuo Zhao, Hongyuan Zha, Tzanio V. Kolev, Robert W. Anderson, Daniel M. Faissol |
| 2023 | Reinforcement Learning with Stepwise Fairness Constraints. Zhun Deng, He Sun, Steven Wu, Linjun Zhang, David C. Parkes |
| 2023 | Representation Learning in Deep RL via Discrete Information Bottleneck. Riashat Islam, Hongyu Zang, Manan Tomar, Aniket Didolkar, Md Mofijul Islam, Samin Yeasar Arnob, Tariq Iqbal, Xin Li, Anirudh Goyal, Nicolas Heess, Alex Lamb |
| 2023 | Resolving the Approximability of Offline and Online Non-monotone DR-Submodular Maximization over General Convex Sets. Loay Mualem, Moran Feldman |
| 2023 | Rethinking Initialization of the Sinkhorn Algorithm. James Thornton, Marco Cuturi |
| 2023 | Retrospective Uncertainties for Deep Models using Vine Copulas. Natasa Tagasovska, Firat Ozdemir, Axel Brando |
| 2023 | Revisiting Fair-PAC Learning and the Axioms of Cardinal Welfare. Cyrus Cousins |
| 2023 | Revisiting Weighted Strategy for Non-stationary Parametric Bandits. Jing Wang, Peng Zhao, Zhi-Hua Zhou |
| 2023 | Reward Learning as Doubly Nonparametric Bandits: Optimal Design and Scaling Laws. Kush Bhatia, Wenshuo Guo, Jacob Steinhardt |
| 2023 | Riemannian Accelerated Gradient Methods via Extrapolation. Andi Han, Bamdev Mishra, Pratik Jawanpuria, Junbin Gao |
| 2023 | Risk Bounds on Aleatoric Uncertainty Recovery. Yikai Zhang, Jiahe Lin, Fengpei Li, Yeshaya Adler, Kashif Rasul, Anderson Schneider, Yuriy Nevmyvaka |
| 2023 | Risk-aware linear bandits with convex loss. Patrick Saux, Odalric Maillard |
| 2023 | Robust Linear Regression for General Feature Distribution. Tom Norman, Nir Weinberger, Kfir Y. Levy |
| 2023 | Robust Linear Regression: Gradient-descent, Early-stopping, and Beyond. Meyer Scetbon, Elvis Dohmatob |
| 2023 | Robust Variational Autoencoding with Wasserstein Penalty for Novelty Detection. Chieh-Hsin Lai, Dongmian Zou, Gilad Lerman |
| 2023 | Robust and Agnostic Learning of Conditional Distributional Treatment Effects. Nathan Kallus, Miruna Oprescu |
| 2023 | Root Cause Identification for Collective Anomalies in Time Series given an Acyclic Summary Causal Graph with Loops. Charles K. Assaad, Imad Ez-zejjari, Lei Zan |
| 2023 | SMCP3: Sequential Monte Carlo with Probabilistic Program Proposals. Alexander K. Lew, George Matheos, Tan Zhi-Xuan, Matin Ghavamizadeh, Nishad Gothoskar, Stuart Russell, Vikash K. Mansinghka |
| 2023 | Safe Sequential Testing and Effect Estimation in Stratified Count Data. Rosanne Turner, Peter Grunwald |
| 2023 | Sample Complexity of Distinguishing Cause from Effect. Jayadev Acharya, Sourbh Bhadane, Arnab Bhattacharyya, Saravanan Kandasamy, Ziteng Sun |
| 2023 | Sample Complexity of Kernel-Based Q-Learning. Sing-Yuan Yeh, Fu-Chieh Chang, Chang-Wei Yueh, Pei-Yuan Wu, Alberto Bernacchia, Sattar Vakili |
| 2023 | Sample Efficiency of Data Augmentation Consistency Regularization. Shuo Yang, Yijun Dong, Rachel A. Ward, Inderjit S. Dhillon, Sujay Sanghavi, Qi Lei |
| 2023 | Sampling From a Schrödinger Bridge. Austin J. Stromme |
| 2023 | Scalable Bayesian Optimization Using Vecchia Approximations of Gaussian Processes. Felix Jimenez, Matthias Katzfuss |
| 2023 | Scalable Bicriteria Algorithms for Non-Monotone Submodular Cover. Victoria G. Crawford |
| 2023 | Scalable Spectral Clustering with Group Fairness Constraints. Ji Wang, Ding Lu, Ian Davidson, Zhaojun Bai |
| 2023 | Scalable Unbalanced Sobolev Transport for Measures on a Graph. Tam Le, Truyen Nguyen, Kenji Fukumizu |
| 2023 | Scalable marked point processes for exchangeable and non-exchangeable event sequences. Aristeidis Panos, Ioannis Kosmidis, Petros Dellaportas |
| 2023 | Score-based Quickest Change Detection for Unnormalized Models. Suya Wu, Enmao Diao, Taposh Banerjee, Jie Ding, Vahid Tarokh |
| 2023 | Second Order Path Variationals in Non-Stationary Online Learning. Dheeraj Baby, Yu-Xiang Wang |
| 2023 | Select and Optimize: Learning to aolve large-scale TSP instances. Hanni Cheng, Haosi Zheng, Ya Cong, Weihao Jiang, Shiliang Pu |
| 2023 | Semantic Strengthening of Neuro-Symbolic Learning. Kareem Ahmed, Kai-Wei Chang, Guy Van den Broeck |
| 2023 | Semi-Verified PAC Learning from the Crowd. Shiwei Zeng, Jie Shen |
| 2023 | Sequential Gradient Descent and Quasi-Newton's Method for Change-Point Analysis. Xianyang Zhang, Trisha Dawn |
| 2023 | Simulator-Based Inference with WALDO: Confidence Regions by Leveraging Prediction Algorithms and Posterior Estimators for Inverse Problems. Luca Masserano, Tommaso Dorigo, Rafael Izbicki, Mikael Kuusela, Ann B. Lee |
| 2023 | Singular Value Representation: A New Graph Perspective On Neural Networks. Dan Meller, Nicolas Berkouk |
| 2023 | Smoothly Giving up: Robustness for Simple Models. Tyler Sypherd, Nathaniel Stromberg, Richard Nock, Visar Berisha, Lalitha Sankar |
| 2023 | SoundSynp: Sound Source Detection from Raw Waveforms with Multi-Scale Synperiodic Filterbanks. Yuhang He, Andrew Markham |
| 2023 | Sparse Bayesian optimization. Sulin Liu, Qing Feng, David Eriksson, Benjamin Letham, Eytan Bakshy |
| 2023 | Sparse Spectral Bayesian Permanental Process with Generalized Kernel. Jeremy Sellier, Petros Dellaportas |
| 2023 | Sparsity-Inducing Categorical Prior Improves Robustness of the Information Bottleneck. Anirban Samaddar, Sandeep Madireddy, Prasanna Balaprakash, Taps Maiti, Gustavo de los Campos, Ian Fischer |
| 2023 | Spectral Augmentations for Graph Contrastive Learning. Amur Ghose, Yingxue Zhang, Jianye Hao, Mark Coates |
| 2023 | Spread Flows for Manifold Modelling. Mingtian Zhang, Yitong Sun, Chen Zhang, Steven McDonagh |
| 2023 | Squeeze All: Novel Estimator and Self-Normalized Bound for Linear Contextual Bandits. Wonyoung Kim, Myunghee Cho Paik, Min-hwan Oh |
| 2023 | Statistical Analysis of Karcher Means for Random Restricted PSD Matrices. Hengchao Chen, Xiang Li, Qiang Sun |
| 2023 | Stochastic Gradient Descent-Ascent: Unified Theory and New Efficient Methods. Aleksandr Beznosikov, Eduard Gorbunov, Hugo Berard, Nicolas Loizou |
| 2023 | Stochastic Methods for AUC Optimization subject to AUC-based Fairness Constraints. Yao Yao, Qihang Lin, Tianbao Yang |
| 2023 | Stochastic Mirror Descent for Large-Scale Sparse Recovery. Sasila Ilandarideva, Yannis Bekri, Anatoli B. Juditsky, Vianney Perchet |
| 2023 | Stochastic Optimization for Spectral Risk Measures. Ronak Mehta, Vincent Roulet, Krishna Pillutla, Lang Liu, Zaïd Harchaoui |
| 2023 | Stochastic Tree Ensembles for Estimating Heterogeneous Effects. Nikolay Krantsevich, Jingyu He, P. Richard Hahn |
| 2023 | Strong Lottery Ticket Hypothesis with ε-perturbation. Zheyang Xiong, Fangshuo Liao, Anastasios Kyrillidis |
| 2023 | Structure of Nonlinear Node Embeddings in Stochastic Block Models. Christopher Harker, Aditya Bhaskara |
| 2023 | Subset verification and search algorithms for causal DAGs. Davin Choo, Kirankumar Shiragur |
| 2023 | Surveillance Evasion Through Bayesian Reinforcement Learning. Dongping Qi, David Bindel, Alexander Vladimirsky |
| 2023 | SurvivalGAN: Generating Time-to-Event Data for Survival Analysis. Alexander Norcliffe, Bogdan Cebere, Fergus Imrie, Pietro Liò, Mihaela van der Schaar |
| 2023 | SwAMP: Swapped Assignment of Multi-Modal Pairs for Cross-Modal Retrieval. Minyoung Kim |
| 2023 | Symmetric (Optimistic) Natural Policy Gradient for Multi-Agent Learning with Parameter Convergence. Sarath Pattathil, Kaiqing Zhang, Asuman E. Ozdaglar |
| 2023 | T-Phenotype: Discovering Phenotypes of Predictive Temporal Patterns in Disease Progression. Yuchao Qin, Mihaela van der Schaar, Changhee Lee |
| 2023 | TS-UCB: Improving on Thompson Sampling With Little to No Additional Computation. Jackie Baek, Vivek F. Farias |
| 2023 | TabLLM: Few-shot Classification of Tabular Data with Large Language Models. Stefan Hegselmann, Alejandro Buendia, Hunter Lang, Monica Agrawal, Xiaoyi Jiang, David A. Sontag |
| 2023 | Temporal Graph Neural Networks for Irregular Data. Joel Oskarsson, Per Sidén, Fredrik Lindsten |
| 2023 | Tensor-based Kernel Machines with Structured Inducing Points for Large and High-Dimensional Data. Frederiek Wesel, Kim Batselier |
| 2023 | Testing of Horn Samplers. Ansuman Banerjee, Shayak Chakraborty, Sourav Chakraborty, Kuldeep S. Meel, Uddalok Sarkar, Sayantan Sen |
| 2023 | The ELBO of Variational Autoencoders Converges to a Sum of Entropies. Simon Damm, Dennis Forster, Dmytro Velychko, Zhenwen Dai, Asja Fischer, Jörg Lücke |
| 2023 | The Lauritzen-Chen Likelihood For Graphical Models. Ilya Shpitser |
| 2023 | The Lie-Group Bayesian Learning Rule. Eren Mehmet Kiral, Thomas Möllenhoff, Mohammad Emtiyaz Khan |
| 2023 | The Ordered Matrix Dirichlet for State-Space Models. Niklas Stoehr, Benjamin J. Radford, Ryan Cotterell, Aaron Schein |
| 2023 | The Power of Recursion in Graph Neural Networks for Counting Substructures. Behrooz Tahmasebi, Derek Lim, Stefanie Jegelka |
| 2023 | The Role of Codeword-to-Class Assignments in Error-Correcting Codes: An Empirical Study. Itay Evron, Ophir Onn, Tamar Weiss Orzech, Hai Azeroual, Daniel Soudry |
| 2023 | The Schrödinger Bridge between Gaussian Measures has a Closed Form. Charlotte Bunne, Ya-Ping Hsieh, Marco Cuturi, Andreas Krause |
| 2023 | The communication cost of security and privacy in federated frequency estimation. Wei-Ning Chen, Ayfer Özgür, Graham Cormode, Akash Bharadwaj |
| 2023 | Theoretically Grounded Loss Functions and Algorithms for Adversarial Robustness. Pranjal Awasthi, Anqi Mao, Mehryar Mohri, Yutao Zhong |
| 2023 | Theory and Algorithm for Batch Distribution Drift Problems. Pranjal Awasthi, Corinna Cortes, Christopher Mohri |
| 2023 | Thresholded linear bandits. Nishant A. Mehta, Junpei Komiyama, Vamsi K. Potluru, Andrea Nguyen, Mica Grant-Hagen |
| 2023 | Tight Regret and Complexity Bounds for Thompson Sampling via Langevin Monte Carlo. Tom Huix, Matthew Zhang, Alain Durmus |
| 2023 | Tighter PAC-Bayes Generalisation Bounds by Leveraging Example Difficulty. Felix Biggs, Benjamin Guedj |
| 2023 | To Impute or not to Impute? Missing Data in Treatment Effect Estimation. Jeroen Berrevoets, Fergus Imrie, Trent Kyono, James Jordon, Mihaela van der Schaar |
| 2023 | Toward Fairness in Text Generation via Mutual Information Minimization based on Importance Sampling. Rui Wang, Pengyu Cheng, Ricardo Henao |
| 2023 | Towards Balanced Representation Learning for Credit Policy Evaluation. Yiyan Huang, Cheuk Hang Leung, Shumin Ma, Zhiri Yuan, Qi Wu, Siyi Wang, Dongdong Wang, Zhixiang Huang |
| 2023 | Towards Scalable and Robust Structured Bandits: A Meta-Learning Framework. Runzhe Wan, Lin Ge, Rui Song |
| 2023 | Transport Elliptical Slice Sampling. Alberto Cabezas, Christopher Nemeth |
| 2023 | Transport Reversible Jump Proposals. Laurence Davies, Robert Salomone, Matthew Sutton, Chris Drovandi |
| 2023 | Two-Sample Tests for Inhomogeneous Random Graphs in L Sayak Chatterjee, Dibyendu Saha, Soham Dan, Bhaswar B. Bhattacharya |
| 2023 | USIM Gate: UpSampling Module for Segmenting Precise Boundaries concerning Entropy. Kyungsu Lee, Haeyun Lee, Jae Youn Hwang |
| 2023 | Ultra-marginal Feature Importance: Learning from Data with Causal Guarantees. Joseph Janssen, Vincent Guan, Elina Robeva |
| 2023 | Uncertainty Estimates of Predictions via a General Bias-Variance Decomposition. Sebastian Gruber, Florian Buettner |
| 2023 | Uncertainty-aware Unsupervised Video Hashing. Yucheng Wang, Mingyuan Zhou, Yu Sun, Xiaoning Qian |
| 2023 | Understanding Multimodal Contrastive Learning and Incorporating Unpaired Data. Ryumei Nakada, Halil Ibrahim Gulluk, Zhun Deng, Wenlong Ji, James Zou, Linjun Zhang |
| 2023 | Understanding the Impact of Competing Events on Heterogeneous Treatment Effect Estimation from Time-to-Event Data. Alicia Curth, Mihaela van der Schaar |
| 2023 | Uni6Dv2: Noise Elimination for 6D Pose Estimation. Mingshan Sun, Ye Zheng, Tianpeng Bao, Jianqiu Chen, Guoqiang Jin, Liwei Wu, Rui Zhao, Xiaoke Jiang |
| 2023 | Unified Perspective on Probability Divergence via the Density-Ratio Likelihood: Bridging KL-Divergence and Integral Probability Metrics. Masahiro Kato, Masaaki Imaizumi, Kentaro Minami |
| 2023 | Uniformly Conservative Exploration in Reinforcement Learning. Wanqiao Xu, Yecheng Jason Ma, Kan Xu, Hamsa Bastani, Osbert Bastani |
| 2023 | Unifying local and global model explanations by functional decomposition of low dimensional structures. Munir Hiabu, Joseph T. Meyer, Marvin N. Wright |
| 2023 | Universal Agent Mixtures and the Geometry of Intelligence. Samuel Allen Alexander, David Quarel, Len Du, Marcus Hutter |
| 2023 | Unsupervised representation learning with recognition-parametrised probabilistic models. William I. Walker, Hugo Soulat, Changmin Yu, Maneesh Sahani |
| 2023 | Using Sliced Mutual Information to Study Memorization and Generalization in Deep Neural Networks. Shelvia Wongso, Rohan Ghosh, Mehul Motani |
| 2023 | Variational Boosted Soft Trees. Tristan Cinquin, Tammo Rukat, Philipp Schmidt, Martin Wistuba, Artur Bekasov |
| 2023 | Variational Inference for Neyman-Scott Processes. Chengkuan Hong, Christian R. Shelton |
| 2023 | Vector Optimization with Stochastic Bandit Feedback. Çagin Ararat, Cem Tekin |
| 2023 | Vector Quantized Time Series Generation with a Bidirectional Prior Model. Daesoo Lee, Sara Malacarne, Erlend Aune |
| 2023 | Wasserstein Distributional Learning via Majorization-Minimization. Chengliang Tang, Nathan Lenssen, Ying Wei, Tian Zheng |
| 2023 | Wasserstein Distributionally Robust Linear-Quadratic Estimation under Martingale Constraints. Kyriakos Lotidis, Nicholas Bambos, Jose H. Blanchet, Jiajin Li |
| 2023 | Weather2K: A Multivariate Spatio-Temporal Benchmark Dataset for Meteorological Forecasting Based on Real-Time Observation Data from Ground Weather Stations. Xun Zhu, Yutong Xiong, Ming Wu, Gaozhen Nie, Bin Zhang, Ziheng Yang |
| 2023 | Weisfeiler and Leman go Hyperbolic: Learning Distance Preserving Node Representations. Giannis Nikolentzos, Michail Chatzianastasis, Michalis Vazirgiannis |
| 2023 | Who Should Predict? Exact Algorithms For Learning to Defer to Humans. Hussein Mozannar, Hunter Lang, Dennis Wei, Prasanna Sattigeri, Subhro Das, David A. Sontag |
| 2023 | qEUBO: A Decision-Theoretic Acquisition Function for Preferential Bayesian Optimization. Raul Astudillo, Zhiyuan (Jerry) Lin, Eytan Bakshy, Peter I. Frazier |