AISTATS 2023
496 papers
A Blessing of Dimensionality in Membership Inference Through Regularization
Jasper Tan, Daniel LeJeune, Blake Mason, Hamid Javadi, Richard G. Baraniuk A Constant-Factor Approximation Algorithm for Reconciliation $k$-Median
Joachim Spoerhase, Kamyar Khodamoradi, Benedikt Riegel, Bruno Ordozgoiti, Aristides Gionis A New Modeling Framework for Continuous, Sequential Domains
Hailiang Dong, James Amato, Vibhav Gogate, Nicholas Ruozzi 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 A Statistical Analysis of Polyak-Ruppert Averaged Q-Learning
Xiang Li, Wenhao Yang, Jiadong Liang, Zhihua Zhang, Michael I. Jordan Actually Sparse Variational Gaussian Processes
Harry Jake Cunningham, Daniel Augusto Souza, So Takao, Mark Wilk, Marc Peter Deisenroth 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 Adaptive Cholesky Gaussian Processes
Simon Bartels, Kristoffer Stensbo-Smidt, Pablo Moreno-Munoz, Wouter Boomsma, Jes Frellsen, Soren Hauberg Adaptive Tuning for Metropolis Adjusted Langevin Trajectories
Lionel Riou-Durand, Pavel Sountsov, Jure Vogrinc, Charles Margossian, Sam Power Adversarial De-Confounding in Individualised Treatment Effects Estimation
Vinod K. Chauhan, Soheila Molaei, Marzia Hoque Tania, Anshul Thakur, Tingting Zhu, David A. Clifton Algorithm for Constrained Markov Decision Process with Linear Convergence
Egor Gladin, Maksim Lavrik-Karmazin, Karina Zainullina, Varvara Rudenko, Alexander Gasnikov, Martin Takac An Efficient and Continuous Voronoi Density Estimator
Giovanni Luca Marchetti, Vladislav Polianskii, Anastasiia Varava, Florian T. Pokorny, Danica Kragic An Unpooling Layer for Graph Generation
Yinglong Guo, Dongmian Zou, Gilad Lerman Approximating a RUM from Distributions on $k$-Slates
Flavio Chierichetti, Mirko Giacchini, Ravi Kumar, Alessandro Panconesi, Andrew Tomkins AUC-Based Selective Classification
Andrea Pugnana, Salvatore Ruggieri Average Case Analysis of Lasso Under Ultra Sparse Conditions
Koki Okajima, Xiangming Meng, Takashi Takahashi, Yoshiyuki Kabashima BaCaDI: Bayesian Causal Discovery with Unknown Interventions
Alexander Hägele, Jonas Rothfuss, Lars Lorch, Vignesh Ram Somnath, Bernhard Schölkopf, Andreas Krause Bayesian Optimization over High-Dimensional Combinatorial Spaces via Dictionary-Based Embeddings
Aryan Deshwal, Sebastian Ament, Maximilian Balandat, Eytan Bakshy, Janardhan Rao Doppa, David Eriksson Bayesian Optimization with Conformal Prediction Sets
Samuel Stanton, Wesley Maddox, Andrew Gordon Wilson Boosted Off-Policy Learning
Ben London, Levi Lu, Ted Sandler, Thorsten Joachims Bounding Evidence and Estimating Log-Likelihood in VAE
Łukasz Struski, Marcin Mazur, Paweł Batorski, Przemysław Spurek, Jacek Tabor But Are You Sure? an Uncertainty-Aware Perspective on Explainable AI
Charles Marx, Youngsuk Park, Hilaf Hasson, Yuyang Wang, Stefano Ermon, Luke Huan Byzantine-Robust Federated Learning with Optimal Statistical Rates
Banghua Zhu, Lun Wang, Qi Pang, Shuai Wang, Jiantao Jiao, Dawn Song, Michael I. Jordan Causal Entropy Optimization
Nicola Branchini, Virginia Aglietti, Neil Dhir, Theodoros Damoulas Characterizing Internal Evasion Attacks in Federated Learning
Taejin Kim, Shubhranshu Singh, Nikhil Madaan, Carlee Joe-Wong Characterizing Polarization in Social Networks Using the Signed Relational Latent Distance Model
Nikolaos Nakis, Abdulkadir Celikkanat, Louis Boucherie, Christian Djurhuus, Felix Burmester, Daniel Mathias Holmelund, Monika Frolcová, Morten Mørup Coarse-Grained Smoothness for Reinforcement Learning in Metric Spaces
Omer Gottesman, Kavosh Asadi, Cameron S. Allen, Samuel Lobel, George Konidaris, Michael Littman Coherent Probabilistic Forecasting of Temporal Hierarchies
Syama Sundar Rangapuram, Shubham Kapoor, Rajbir Singh Nirwan, Pedro Mercado, Tim Januschowski, Yuyang Wang, Michael Bohlke-Schneider Computing Abductive Explanations for Boosted Trees
Gilles Audemard, Jean-Marie Lagniez, Pierre Marquis, Nicolas Szczepanski Conformal Off-Policy Prediction
Yingying Zhang, Chengchun Shi, Shikai Luo Contextual Linear Bandits Under Noisy Features: Towards Bayesian Oracles
Jung-Hun Kim, Se-Young Yun, Minchan Jeong, Junhyun Nam, Jinwoo Shin, Richard Combes Convolutional Persistence as a Remedy to Neural Model Analysis
Ekaterina Khramtsova, Guido Zuccon, Xi Wang, Mahsa Baktashmotlagh Coordinate Descent for SLOPE
Johan Larsson, Quentin Klopfenstein, Mathurin Massias, Jonas Wallin Data Augmentation for Imbalanced Regression
Samuel Stocksieker, Denys Pommeret, Arthur Charpentier Differentiable Change-Point Detection with Temporal Point Processes
Paramita Koley, Harshavardhan Alimi, Shrey Singla, Sourangshu Bhattacharya, Niloy Ganguly, Abir De Differentially Private Synthetic Control
Saeyoung Rho, Rachel Cummings, Vishal Misra Direct Inference of Effect of Treatment (DIET) for a Cookieless World
Shiv Shankar, Ritwik Sinha, Saayan Mitra, Moumita Sinha, Madalina Fiterau Discrete Langevin Samplers via Wasserstein Gradient Flow
Haoran Sun, Hanjun Dai, Bo Dai, Haomin Zhou, Dale Schuurmans Distill N’ Explain: Explaining Graph Neural Networks Using Simple Surrogates
Tamara Pereira, Erik Nascimento, Lucas E. Resck, Diego Mesquita, Amauri Souza Do Bayesian Neural Networks Need to Be Fully Stochastic?
Mrinank Sharma, Sebastian Farquhar, Eric Nalisnick, Tom Rainforth Domain Adaptation Under Missingness Shift
Helen Zhou, Sivaraman Balakrishnan, Zachary Lipton Doubly Fair Dynamic Pricing
Jianyu Xu, Dan Qiao, Yu-Xiang Wang Efficient and Light-Weight Federated Learning via Asynchronous Distributed Dropout
Chen Dun, Mirian Hipolito, Chris Jermaine, Dimitrios Dimitriadis, Anastasios Kyrillidis Efficient Fair PCA for Fair Representation Learning
Matthäus Kleindessner, Michele Donini, Chris Russell, Muhammad Bilal Zafar Entropic Risk Optimization in Discounted MDPs
Jia Lin Hau, Marek Petrik, Mohammad Ghavamzadeh Equivariant Representation Learning via Class-Pose Decomposition
Giovanni Luca Marchetti, Gustaf Tegnér, Anastasiia Varava, Danica Kragic Error Estimation for Random Fourier Features
Junwen Yao, N. Benjamin Erichson, Miles E. Lopes Exploration in Reward Machines with Low Regret
Hippolyte Bourel, Anders Jonsson, Odalric-Ambrym Maillard, Mohammad Sadegh Talebi Fair Representation Learning with Unreliable Labels
Yixuan Zhang, Feng Zhou, Zhidong Li, Yang Wang, Fang Chen Faithful Heteroscedastic Regression with Neural Networks
Andrew Stirn, Harm Wessels, Megan Schertzer, Laura Pereira, Neville Sanjana, David Knowles Fast Feature Selection with Fairness Constraints
Francesco Quinzan, Rajiv Khanna, Moshik Hershcovitch, Sarel Cohen, Daniel Waddington, Tobias Friedrich, Michael W. Mahoney Federated Learning for Data Streams
Othmane Marfoq, Giovanni Neglia, Laetitia Kameni, Richard Vidal Federated Learning Under Distributed Concept Drift
Ellango Jothimurugesan, Kevin Hsieh, Jianyu Wang, Gauri Joshi, Phillip B. Gibbons Fixing by Mixing: A Recipe for Optimal Byzantine ML Under Heterogeneity
Youssef Allouah, Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot, John Stephan Gaussian Processes on Distributions Based on Regularized Optimal Transport
François Bachoc, Louis Béthune, Alberto Gonzalez-Sanz, Jean-Michel Loubes Generative Oversampling for Imbalanced Data via Majority-Guided VAE
Qingzhong Ai, Pengyun Wang, Lirong He, Liangjian Wen, Lujia Pan, Zenglin Xu Global Convergence of Over-Parameterized Deep Equilibrium Models
Zenan Ling, Xingyu Xie, Qiuhao Wang, Zongpeng Zhang, Zhouchen Lin Global-Local Regularization via Distributional Robustness
Hoang Phan, Trung Le, Trung Phung, Anh Tuan Bui, Nhat Ho, Dinh Phung 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 Ideal Abstractions for Decision-Focused Learning
Michael Poli, Stefano Massaroli, Stefano Ermon, Bryan Wilder, Eric Horvitz Improved Representation Learning Through Tensorized Autoencoders
Pascal Esser, Satyaki Mukherjee, Mahalakshmi Sabanayagam, Debarghya Ghoshdastidar Inducing Neural Collapse in Deep Long-Tailed Learning
Xuantong Liu, Jianfeng Zhang, Tianyang Hu, He Cao, Yuan Yao, Lujia Pan Influence Diagnostics Under Self-Concordance
Jillian Fisher, Lang Liu, Krishna Pillutla, Yejin Choi, Zaid Harchaoui Isotropic Gaussian Processes on Finite Spaces of Graphs
Viacheslav Borovitskiy, Mohammad Reza Karimi, Vignesh Ram Somnath, Andreas Krause Iterative Teaching by Data Hallucination
Zeju Qiu, Weiyang Liu, Tim Z. Xiao, Zhen Liu, Umang Bhatt, Yucen Luo, Adrian Weller, Bernhard Schölkopf Learning from Multiple Sources for Data-to-Text and Text-to-Data
Song Duong, Alberto Lumbreras, Mike Gartrell, Patrick Gallinari 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 Learning Sparse Graphon Mean Field Games
Christian Fabian, Kai Cui, Heinz Koeppl Learning to Generalize Provably in Learning to Optimize
Junjie Yang, Tianlong Chen, Mingkang Zhu, Fengxiang He, Dacheng Tao, Yingbin Liang, Zhangyang Wang Learning to Optimize with Stochastic Dominance Constraints
Hanjun Dai, Yuan Xue, Niao He, Yixin Wang, Na Li, Dale Schuurmans, Bo Dai Learning with Partial Forgetting in Modern Hopfield Networks
Toshihiro Ota, Ikuro Sato, Rei Kawakami, Masayuki Tanaka, Nakamasa Inoue LOFT: Finding Lottery Tickets Through Filter-Wise Training
Qihan Wang, Chen Dun, Fangshuo Liao, Chris Jermaine, Anastasios Kyrillidis Loss-Curvature Matching for Dataset Selection and Condensation
Seungjae Shin, Heesun Bae, Donghyeok Shin, Weonyoung Joo, Il-Chul Moon Manifold Restricted Interventional Shapley Values
Muhammad Faaiz Taufiq, Patrick Blöbaum, Lenon Minorics Matching mAP Recovery with an Unknown Number of Outliers
Arshak Minasyan, Tigran Galstyan, Sona Hunanyan, Arnak Dalalyan Mean Parity Fair Regression in RKHS
Shaokui Wei, Jiayin Liu, Bing Li, Hongyuan Zha Meta-Learning for Robust Anomaly Detection
Atsutoshi Kumagai, Tomoharu Iwata, Hiroshi Takahashi, Yasuhiro Fujiwara Meta-Learning with Adjoint Methods
Shibo Li, Zheng Wang, Akil Narayan, Robert Kirby, Shandian Zhe Meta-Uncertainty in Bayesian Model Comparison
Marvin Schmitt, Stefan T. Radev, Paul-Christian Bürkner Minimax-Bayes Reinforcement Learning
Thomas Kleine Buening, Christos Dimitrakakis, Hannes Eriksson, Divya Grover, Emilio Jorge Mixed-Effect Thompson Sampling
Imad Aouali, Branislav Kveton, Sumeet Katariya Mixtures of All Trees
Nikil Roashan Selvam, Honghua Zhang, Guy Broeck Model-Based Uncertainty in Value Functions
Carlos E. Luis, Alessandro G. Bottero, Julia Vinogradska, Felix Berkenkamp, Jan Peters Multi-Task Representation Learning with Stochastic Linear Bandits
Leonardo Cella, Karim Lounici, Grégoire Pacreau, Massimiliano Pontil Multilevel Bayesian Quadrature
Kaiyu Li, Daniel Giles, Toni Karvonen, Serge Guillas, Francois-Xavier Briol Multiple-Policy High-Confidence Policy Evaluation
Chris Dann, Mohammad Ghavamzadeh, Teodor V. Marinov Nearly Optimal Latent State Decoding in Block MDPs
Yassir Jedra, Junghyun Lee, Alexandre Proutiere, Se-Young Yun Neural Discovery of Permutation Subgroups
Pavan Karjol, Rohan Kashyap, Prathosh Ap Neural Laplace Control for Continuous-Time Delayed Systems
Samuel Holt, Alihan Hüyük, Zhaozhi Qian, Hao Sun, Mihaela Schaar Neural Simulated Annealing
Alvaro H.C. Correia, Daniel E. Worrall, Roberto Bondesan NODAGS-Flow: Nonlinear Cyclic Causal Structure Learning
Muralikrishnna G Sethuraman, Romain Lopez, Rahul Mohan, Faramarz Fekri, Tommaso Biancalani, Jan-Christian Huetter Nonstochastic Contextual Combinatorial Bandits
Lukas Zierahn, Dirk Hoeven, Nicolò Cesa-Bianchi, Gergely Neu On the Calibration of Probabilistic Classifier Sets
Thomas Mortier, Viktor Bengs, Eyke Hüllermeier, Stijn Luca, Willem Waegeman On the Consistency Rate of Decision Tree Learning Algorithms
Qin-Cheng Zheng, Shen-Huan Lyu, Shao-Qun Zhang, Yuan Jiang, Zhi-Hua Zhou On the Privacy Risks of Algorithmic Recourse
Martin Pawelczyk, Himabindu Lakkaraju, Seth Neel On the Strategyproofness of the Geometric Median
El-Mahdi El-Mhamdi, Sadegh Farhadkhani, Rachid Guerraoui, Lê-Nguyên Hoang On-Demand Communication for Asynchronous Multi-Agent Bandits
Yu-Zhen Janice Chen, Lin Yang, Xuchuang Wang, Xutong Liu, Mohammad Hajiesmaili, John C. S. Lui, Don Towsley Online Algorithms with Costly Predictions
Marina Drygala, Sai Ganesh Nagarajan, Ola Svensson Online Learning for Traffic Routing Under Unknown Preferences
Devansh Jalota, Karthik Gopalakrishnan, Navid Azizan, Ramesh Johari, Marco Pavone Online Linearized LASSO
Shuoguang Yang, Yuhao Yan, Xiuneng Zhu, Qiang Sun Optimal Algorithms for Latent Bandits with Cluster Structure
Soumyabrata Pal, Arun Sai Suggala, Karthikeyan Shanmugam, Prateek Jain Optimal Sketching Bounds for Sparse Linear Regression
Tung Mai, Alexander Munteanu, Cameron Musco, Anup Rao, Chris Schwiegelshohn, David Woodruff Overcoming Prior Misspecification in Online Learning to Rank
Javad Azizi, Ofer Meshi, Masrour Zoghi, Maryam Karimzadehgan Performative Prediction with Neural Networks
Mehrnaz Mofakhami, Ioannis Mitliagkas, Gauthier Gidel Pointwise Sampling Uncertainties on the Precision-Recall Curve
Ralph E.Q. Urlus, Max Baak, Stéphane Collot, Ilan Fridman Rojas Posterior Tracking Algorithm for Classification Bandits
Koji Tabata, Junpei Komiyama, Atsuyoshi Nakamura, Tamiki Komatsuzaki Precision/Recall on Imbalanced Test Data
Hongwei Shang, Jean-Marc Langlois, Kostas Tsioutsiouliklis, Changsung Kang Prediction-Oriented Bayesian Active Learning
Freddie Bickford Smith, Andreas Kirsch, Sebastian Farquhar, Yarin Gal, Adam Foster, Tom Rainforth Pricing Against a Budget and ROI Constrained Buyer
Negin Golrezaei, Patrick Jaillet, Jason Cheuk Nam Liang, Vahab Mirrokni Probabilistic Conformal Prediction Using Conditional Random Samples
Zhendong Wang, Ruijiang Gao, Mingzhang Yin, Mingyuan Zhou, David Blei Probing Graph Representations
Mohammad Sadegh Akhondzadeh, Vijay Lingam, Aleksandar Bojchevski 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 Randomized Geometric Tools for Anomaly Detection in Stock Markets
Cyril Bachelard, Apostolos Chalkis, Vissarion Fisikopoulos, Elias Tsigaridas Randomized Primal-Dual Methods with Adaptive Step Sizes
Erfan Yazdandoost Hamedani, Afrooz Jalilzadeh, Necdet S. Aybat Reinforcement Learning for Adaptive Mesh Refinement
Jiachen Yang, Tarik Dzanic, Brenden Petersen, Jun Kudo, Ketan Mittal, Vladimir Tomov, Jean-Sylvain Camier, Tuo Zhao, Hongyuan Zha, Tzanio Kolev, Robert Anderson, Daniel Faissol Reinforcement Learning with Stepwise Fairness Constraints
Zhun Deng, He Sun, Steven Wu, Linjun Zhang, David Parkes 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 Risk Bounds on Aleatoric Uncertainty Recovery
Yikai Zhang, Jiahe Lin, Fengpei Li, Yeshaya Adler, Kashif Rasul, Anderson Schneider, Yuriy Nevmyvaka Sample Complexity of Distinguishing Cause from Effect
Jayadev Acharya, Sourbh Bhadane, Arnab Bhattacharyya, Saravanan Kandasamy, Ziteng Sun Sample Complexity of Kernel-Based Q-Learning
Sing-Yuan Yeh, Fu-Chieh Chang, Chang-Wei Yueh, Pei-Yuan Wu, Alberto Bernacchia, Sattar Vakili Sample Efficiency of Data Augmentation Consistency Regularization
Shuo Yang, Yijun Dong, Rachel Ward, Inderjit S. Dhillon, Sujay Sanghavi, Qi Lei Score-Based Quickest Change Detection for Unnormalized Models
Suya Wu, Enmao Diao, Taposh Banerjee, Jie Ding, Vahid Tarokh 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 Smoothly Giving up: Robustness for Simple Models
Tyler Sypherd, Nathaniel Stromberg, Richard Nock, Visar Berisha, Lalitha Sankar Sparse Bayesian Optimization
Sulin Liu, Qing Feng, David Eriksson, Benjamin Letham, Eytan Bakshy Sparsity-Inducing Categorical Prior Improves Robustness of the Information Bottleneck
Anirban Samaddar, Sandeep Madireddy, Prasanna Balaprakash, Taps Maiti, Gustavo Campos, Ian Fischer Spread Flows for Manifold Modelling
Mingtian Zhang, Yitong Sun, Chen Zhang, Steven Mcdonagh Stochastic Mirror Descent for Large-Scale Sparse Recovery
Sasila Ilandarideva, Yannis Bekri, Anatoli Iouditski, Vianney Perchet Stochastic Optimization for Spectral Risk Measures
Ronak Mehta, Vincent Roulet, Krishna Pillutla, Lang Liu, Zaid Harchaoui SurvivalGAN: Generating Time-to-Event Data for Survival Analysis
Alexander Norcliffe, Bogdan Cebere, Fergus Imrie, Pietro Lió, Mihaela Schaar TabLLM: Few-Shot Classification of Tabular Data with Large Language Models
Stefan Hegselmann, Alejandro Buendia, Hunter Lang, Monica Agrawal, Xiaoyi Jiang, David Sontag Testing of Horn Samplers
Ansuman Banerjee, Shayak Chakraborty, Sourav Chakraborty, Kuldeep S. Meel, Uddalok Sarkar, Sayantan Sen The ELBO of Variational Autoencoders Converges to a Sum of Entropies
Simon Damm, Dennis Forster, Dmytro Velychko, Zhenwen Dai, Asja Fischer, Jörg Lücke The Lie-Group Bayesian Learning Rule
Eren Mehmet Kiral, Thomas Moellenhoff, Mohammad Emtiyaz Khan The Ordered Matrix Dirichlet for State-Space Models
Niklas Stoehr, Benjamin J. Radford, Ryan Cotterell, Aaron Schein Thresholded Linear Bandits
Nishant A. Mehta, Junpei Komiyama, Vamsi K. Potluru, Andrea Nguyen, Mica Grant-Hagen To Impute or Not to Impute? Missing Data in Treatment Effect Estimation
Jeroen Berrevoets, Fergus Imrie, Trent Kyono, James Jordon, Mihaela Schaar 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 Transport Elliptical Slice Sampling
Alberto Cabezas, Christopher Nemeth Transport Reversible Jump Proposals
Laurence Davies, Robert Salomone, Matthew Sutton, Chris Drovandi Uncertainty-Aware Unsupervised Video Hashing
Yucheng Wang, Mingyuan Zhou, Yu Sun, Xiaoning Qian Understanding Multimodal Contrastive Learning and Incorporating Unpaired Data
Ryumei Nakada, Halil Ibrahim Gulluk, Zhun Deng, Wenlong Ji, James Zou, Linjun Zhang Uni6Dv2: Noise Elimination for 6d Pose Estimation
Mingshan Sun, Ye Zheng, Tianpeng Bao, Jianqiu Chen, Guoqiang Jin, Liwei Wu, Rui Zhao, Xiaoke Jiang Uniformly Conservative Exploration in Reinforcement Learning
Wanqiao Xu, Yecheng Ma, Kan Xu, Hamsa Bastani, Osbert Bastani Universal Agent Mixtures and the Geometry of Intelligence
Samuel Allen Alexander, David Quarel, Len Du, Marcus Hutter Variational Boosted Soft Trees
Tristan Cinquin, Tammo Rukat, Philipp Schmidt, Martin Wistuba, Artur Bekasov Who Should Predict? Exact Algorithms for Learning to Defer to Humans
Hussein Mozannar, Hunter Lang, Dennis Wei, Prasanna Sattigeri, Subhro Das, David Sontag