Abstract is missing.
- Proximal Splitting Meets Variance ReductionFabian Pedregosa, Kilian Fatras, Mattia Casotto. 1-10 [doi]
- Optimal Noise-Adding Mechanism in Additive Differential PrivacyQuan Geng, Wei Ding, Ruiqi Guo, Sanjiv Kumar. 11-20 [doi]
- Tossing Coins Under MonotonicityMatey Neykov. 21-30 [doi]
- Gaussian Regression with Convex ConstraintsMatey Neykov. 31-38 [doi]
- Risk-Averse Stochastic Convex BanditAdrian Rivera Cardoso, Huan Xu. 39-47 [doi]
- Error bounds for sparse classifiers in high-dimensionsAntoine Dedieu. 48-56 [doi]
- Boosting Transfer Learning with Survival Data from Heterogeneous DomainsAlexis Bellot, Mihaela van der Schaar. 57-65 [doi]
- Resampled Priors for Variational AutoencodersMatthias Bauer, Andriy Mnih. 66-75 [doi]
- Scalable Bayesian Learning for State Space Models using Variational Inference with SMC SamplersMarcel Hirt, Petros Dellaportas. 76-86 [doi]
- Scalable Thompson Sampling via Optimal TransportRuiyi Zhang, Zheng Wen, Changyou Chen, Chen Fang, Tong Yu, Lawrence Carin. 87-96 [doi]
- Inferring Multidimensional Rates of Aging from Cross-Sectional DataEmma Pierson, Pang Wei Koh, Tatsunori B. Hashimoto, Daphne Koller, Jure Leskovec, Nick Eriksson, Percy Liang. 97-107 [doi]
- Interaction Detection with Bayesian Decision Tree EnsemblesJunliang Du, Antonio R. Linero. 108-117 [doi]
- On the Interaction Effects Between Prediction and ClusteringMatt Barnes, Artur Dubrawski. 118-126 [doi]
- Towards a Theoretical Understanding of Hashing-Based Neural NetsYibo Lin, Zhao Song, Lin F. Yang. 127-137 [doi]
- Faster First-Order Methods for Stochastic Non-Convex Optimization on Riemannian ManifoldsPan Zhou, Xiao-Tong Yuan, Jiashi Feng. 138-147 [doi]
- LF-PPL: A Low-Level First Order Probabilistic Programming Language for Non-Differentiable ModelsYuan Zhou, Bradley J. Gram-Hansen, Tobias Kohn, Tom Rainforth, Hongseok Yang, Frank Wood. 148-157 [doi]
- Identifiability of Generalized Hypergeometric Distribution (GHD) Directed Acyclic Graphical ModelsGunwoong Park, Hyewon Park. 158-166 [doi]
- Unbiased Implicit Variational InferenceMichalis K. Titsias, Francisco J. R. Ruiz. 167-176 [doi]
- Efficient Linear Bandits through Matrix SketchingIlja Kuzborskij, Leonardo Cella, Nicolò Cesa-Bianchi. 177-185 [doi]
- Orthogonal Estimation of Wasserstein DistancesMark Rowland, Jiri Hron, Yunhao Tang, Krzysztof Choromanski, Tamás Sarlós, Adrian Weller. 186-195 [doi]
- Linear Convergence of the Primal-Dual Gradient Method for Convex-Concave Saddle Point Problems without Strong ConvexitySimon S. Du, Wei Hu. 196-205 [doi]
- Greedy and IHT Algorithms for Non-convex Optimization with Monotone Costs of Non-zerosShinsaku Sakaue. 206-215 [doi]
- Block Stability for MAP InferenceHunter Lang, David Sontag, Aravindan Vijayaraghavan. 216-225 [doi]
- A Stein-Papangelou Goodness-of-Fit Test for Point ProcessesJiasen Yang, Vinayak Rao, Jennifer Neville. 226-235 [doi]
- KAMA-NNs: Low-dimensional Rotation Based Neural NetworksKrzysztof Choromanski, Aldo Pacchiano, Jeffrey Pennington, Yunhao Tang. 236-245 [doi]
- Statistical Windows in Testing for the Initial Distribution of a Reversible Markov ChainQuentin Berthet, Varun Kanade. 246-255 [doi]
- Sketching for Latent Dirichlet-Categorical ModelsJoseph Tassarotti, Jean-Baptiste Tristan, Michael Wick. 256-265 [doi]
- Adaptive Activity Monitoring with Uncertainty Quantification in Switching Gaussian Process ModelsRandy Ardywibowo, Guang Zhao, Zhangyang Wang, Bobak Mortazavi, Shuai Huang, Xiaoning Qian. 266-275 [doi]
- Near Optimal Algorithms for Hard Submodular Programs with Discounted Cooperative CostsRishabh K. Iyer, Jeffrey A. Bilmes. 276-285 [doi]
- Fast Stochastic Algorithms for Low-rank and Nonsmooth Matrix ProblemsDan Garber, Atara Kaplan. 286-294 [doi]
- Logarithmic Regret for Online Gradient Descent Beyond Strong ConvexityDan Garber. 295-303 [doi]
- Accelerated Coordinate Descent with Arbitrary Sampling and Best Rates for MinibatchesFilip Hanzely, Peter Richtárik. 304-312 [doi]
- Globally-convergent Iteratively Reweighted Least Squares for Robust Regression ProblemsBhaskar Mukhoty, Govind Gopakumar, Prateek Jain 0002, Purushottam Kar. 313-322 [doi]
- Modularity-based Sparse Soft Graph ClusteringAlexandre Hollocou, Thomas Bonald, Marc Lelarge. 323-332 [doi]
- Pathwise Derivatives for Multivariate DistributionsMartin Jankowiak, Theofanis Karaletsos. 333-342 [doi]
- Distributed Inexact Newton-type Pursuit for Non-convex Sparse LearningBo Liu 0005, Xiao-Tong Yuan, Lezi Wang, Qingshan Liu 0001, JunZhou Huang, Dimitris N. Metaxas. 343-352 [doi]
- Vine copula structure learning via Monte Carlo tree searchBo Chang, Shenyi Pan, Harry Joe. 353-361 [doi]
- Blind Demixing via Wirtinger Flow with Random InitializationJialin Dong, Yuanming Shi. 362-370 [doi]
- Performance Metric Elicitation from Pairwise Classifier ComparisonsGaurush Hiranandani, Shant Boodaghians, Ruta Mehta, Oluwasanmi Koyejo. 371-379 [doi]
- Analysis of Network Lasso for Semi-Supervised RegressionAlexander Jung, Natalia Vesselinova. 380-387 [doi]
- Learning Mixtures of Smooth Product Distributions: Identifiability and AlgorithmNikos Kargas, Nicholas D. Sidiropoulos. 388-396 [doi]
- Robust Matrix Completion from Quantized ObservationsJie Shen, Pranjal Awasthi, Ping Li. 397-407 [doi]
- Foundations of Sequence-to-Sequence Modeling for Time SeriesZelda Mariet, Vitaly Kuznetsov. 408-417 [doi]
- Nearly Optimal Adaptive Procedure with Change Detection for Piecewise-Stationary BanditYang Cao 0013, Zheng Wen, Branislav Kveton, Yao Xie 0002. 418-427 [doi]
- An Optimal Algorithm for Stochastic Three-Composite OptimizationRenbo Zhao, William B. Haskell, Vincent Y. F. Tan. 428-437 [doi]
- A Thompson Sampling Algorithm for Cascading BanditsWang Chi Cheung, Vincent Tan, Zixin Zhong. 438-447 [doi]
- Lifelong Optimization with Low RegretYi-Shan Wu, Po-An Wang, Chi-Jen Lu. 448-456 [doi]
- Sparse Multivariate Bernoulli Processes in High DimensionsParthe Pandit, Mojtaba Sahraee-Ardakan, Arash A. Amini, Sundeep Rangan, Alyson K. Fletcher. 457-466 [doi]
- An Optimal Algorithm for Stochastic and Adversarial BanditsJulian Zimmert, Yevgeny Seldin. 467-475 [doi]
- Efficient Bayesian Experimental Design for Implicit ModelsSteven Kleinegesse, Michael U. Gutmann. 476-485 [doi]
- Local Saddle Point Optimization: A Curvature Exploitation ApproachLeonard Adolphs, Hadi Daneshmand, Aurélien Lucchi, Thomas Hofmann. 486-495 [doi]
- Testing Conditional Independence on Discrete Data using Stochastic ComplexityAlexander Marx, Jilles Vreeken. 496-505 [doi]
- Distributionally Robust Submodular MaximizationMatthew Staib, Bryan Wilder, Stefanie Jegelka. 506-516 [doi]
- A Robust Zero-Sum Game Framework for Pool-based Active LearningDixian Zhu, Zhe Li 0008, Xiaoyu Wang, Boqing Gong, Tianbao Yang. 517-526 [doi]
- Support and Invertibility in Domain-Invariant RepresentationsFredrik D. Johansson, David Sontag, Rajesh Ranganath. 527-536 [doi]
- Efficient Inference in Multi-task Cox Process ModelsVirginia Aglietti, Theodoros Damoulas, Edwin V. Bonilla. 537-546 [doi]
- Optimization of Inf-Convolution Regularized Nonconvex Composite ProblemsEmanuel Laude, Tao Wu 0006, Daniel Cremers. 547-556 [doi]
- On Connecting Stochastic Gradient MCMC and Differential PrivacyBai Li, Changyou Chen, Hao Liu 0015, Lawrence Carin. 557-566 [doi]
- What made you do this? Understanding black-box decisions with sufficient input subsetsBrandon Carter, Jonas Mueller, Siddhartha Jain, David K. Gifford. 567-576 [doi]
- Computation Efficient Coded Linear TransformSinong Wang, Jiashang Liu, Ness B. Shroff, Pengyu Yang. 577-585 [doi]
- Mixing of Hamiltonian Monte Carlo on strongly log-concave distributions 2: Numerical integratorsOren Mangoubi, Aaron Smith. 586-595 [doi]
- Temporal Quilting for Survival AnalysisChangHee Lee, William R. Zame, Ahmed M. Alaa, Mihaela van der Schaar. 596-605 [doi]
- Learning Classifiers with Fenchel-Young Losses: Generalized Entropies, Margins, and AlgorithmsMathieu Blondel, André F. T. Martins, Vlad Niculae. 606-615 [doi]
- On Target Shift in Adversarial Domain AdaptationYitong Li, michael Murias, samantha Major, Geraldine Dawson, David E. Carlson. 616-625 [doi]
- Optimal Testing in the Experiment-rich RegimeSven Schmit, Virag Shah, Ramesh Johari. 626-633 [doi]
- Reversible Jump Probabilistic ProgrammingDavid A. Roberts, Marcus Gallagher, Thomas Taimre. 634-643 [doi]
- Graph Embedding with Shifted Inner Product Similarity and Its Improved Approximation CapabilityAkifumi Okuno, Geewook Kim, Hidetoshi Shimodaira. 644-653 [doi]
- High-dimensional Mixed Graphical Model with Ordinal Data: Parameter Estimation and Statistical InferenceHuijie Feng, Yang Ning. 654-663 [doi]
- Robust Graph Embedding with Noisy Link WeightsAkifumi Okuno, Hidetoshi Shimodaira. 664-673 [doi]
- Exploring Fast and Communication-Efficient Algorithms in Large-Scale Distributed NetworksYue Yu, Jiaxiang Wu, JunZhou Huang. 674-683 [doi]
- Defending against Whitebox Adversarial Attacks via Randomized DiscretizationYuchen Zhang, Percy Liang. 684-693 [doi]
- Fisher Information and Natural Gradient Learning in Random Deep NetworksShun-ichi Amari, Ryo Karakida, Masafumi Oizumi. 694-702 [doi]
- Robust descent using smoothed multiplicative noiseMatthew J. Holland. 703-711 [doi]
- Classification using margin pursuitMatthew J. Holland. 712-720 [doi]
- Linear Queries Estimation with Local Differential PrivacyRaef Bassily. 721-729 [doi]
- Bayesian Learning of Neural Network ArchitecturesGeorgi Dikov, Justin Bayer. 730-738 [doi]
- Nonlinear Acceleration of Primal-Dual AlgorithmsRaghu Bollapragada, Damien Scieur, Alexandre d'Aspremont. 739-747 [doi]
- Gaussian Process Latent Variable Alignment LearningIeva Kazlauskaite, Carl Henrik Ek, Neill D. F. Campbell. 748-757 [doi]
- A Bayesian model for sparse graphs with flexible degree distribution and overlapping community structureJuho Lee, Lancelot F. James, Seungjin Choi, Francois Caron. 758-767 [doi]
- Pseudo-Bayesian Learning with Kernel Fourier Transform as PriorGaël Letarte, Emilie Morvant, Pascal Germain. 768-776 [doi]
- Forward Amortized Inference for Likelihood-Free Variational MarginalizationLuca Ambrogioni, Umut Güçlü, Julia Berezutskaya, Eva W. P. van den Borne, Yagmur Güçlütürk, Max Hinne, Eric Maris, Marcel van Gerven. 777-786 [doi]
- SpikeCaKe: Semi-Analytic Nonparametric Bayesian Inference for Spike-Spike Neuronal ConnectivityLuca Ambrogioni, Patrick Ebel, Max Hinne, Umut Güçlü, Marcel van Gerven, Eric Maris. 787-795 [doi]
- Scalable Gaussian Process Inference with Finite-data Mean and Variance GuaranteesJonathan H. Huggins, Trevor Campbell, Mikolaj Kasprzak, Tamara Broderick. 796-805 [doi]
- Exponential convergence rates for Batch Normalization: The power of length-direction decoupling in non-convex optimizationJonas Moritz Kohler, Hadi Daneshmand, Aurélien Lucchi, Thomas Hofmann, Ming Zhou, Klaus Neymeyr. 806-815 [doi]
- A new evaluation framework for topic modeling algorithms based on synthetic corporaHanyu Shi, Martin Gerlach, Isabel Diersen, Doug Downey, Luis A. N. Amaral. 816-826 [doi]
- On Kernel Derivative Approximation with Random Fourier FeaturesZoltán Szabó 0001, Bharath K. Sriperumbudur. 827-836 [doi]
- Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive FlowsGeorge Papamakarios, David C. Sterratt, Iain Murray 0001. 837-848 [doi]
- Optimal Transport for Multi-source Domain Adaptation under Target ShiftIevgen Redko, Nicolas Courty, Rémi Flamary, Devis Tuia. 849-858 [doi]
- Nonlinear ICA Using Auxiliary Variables and Generalized Contrastive LearningAapo Hyvärinen, Hiroaki Sasaki, Richard E. Turner. 859-868 [doi]
- Deep Neural Networks Learn Non-Smooth Functions EffectivelyMasaaki Imaizumi, Kenji Fukumizu. 869-878 [doi]
- Attenuating Bias in Word vectorsSunipa Dev, Jeff M. Phillips. 879-887 [doi]
- Fisher-Rao Metric, Geometry, and Complexity of Neural NetworksTengyuan Liang, Tomaso A. Poggio, Alexander Rakhlin, James Stokes. 888-896 [doi]
- Accelerated Decentralized Optimization with Local Updates for Smooth and Strongly Convex ObjectivesHadrien Hendrikx, Francis Bach, Laurent Massoulié. 897-906 [doi]
- Interaction Matters: A Note on Non-asymptotic Local Convergence of Generative Adversarial NetworksTengyuan Liang, James Stokes. 907-915 [doi]
- On Constrained Nonconvex Stochastic Optimization: A Case Study for Generalized Eigenvalue DecompositionZhehui Chen, Xingguo Li, Lin Yang, Jarvis D. Haupt, Tuo Zhao. 916-925 [doi]
- Generalized Boltzmann Machine with Deep Neural StructureYingru Liu, Dongliang Xie, Xin Wang. 926-934 [doi]
- Extreme Stochastic Variational Inference: Distributed Inference for Large Scale Mixture ModelsJiong Zhang, Parameswaran Raman, Shihao Ji, Hsiang-Fu Yu, S. V. N. Vishwanathan, Inderjit S. Dhillon. 935-943 [doi]
- Correcting the bias in least squares regression with volume-rescaled samplingMichal Derezinski, Manfred K. Warmuth, Daniel Hsu 0001. 944-953 [doi]
- Conservative Exploration using InterleavingSumeet Katariya, Branislav Kveton, Zheng Wen, Vamsi K. Potluru. 954-963 [doi]
- Conditionally Independent Multiresolution Gaussian ProcessesJalil Taghia, Thomas B. Schön. 964-973 [doi]
- Active Exploration in Markov Decision ProcessesJean Tarbouriech, Alessandro Lazaric. 974-982 [doi]
- On the Convergence of Stochastic Gradient Descent with Adaptive StepsizesXiaoyu Li, Francesco Orabona. 983-992 [doi]
- Bandit Online Learning with Unknown DelaysBingcong Li, Tianyi Chen, Georgios B. Giannakis. 993-1002 [doi]
- Learning Invariant Representations with Kernel WarpingYingyi Ma, Vignesh Ganapathiraman, Xinhua Zhang. 1003-1012 [doi]
- $β^3$-IRT: A New Item Response Model and its ApplicationsYu Chen, Telmo de Menezes e Silva Filho, Ricardo B. C. Prudêncio, Tom Diethe, Peter A. Flach. 1013-1021 [doi]
- Can You Trust This Prediction? Auditing Pointwise Reliability After LearningPeter Schulam, Suchi Saria. 1022-1031 [doi]
- Universal Statistics of Fisher Information in Deep Neural Networks: Mean Field ApproachRyo Karakida, Shotaro Akaho, Shun-ichi Amari. 1032-1041 [doi]
- Conditional Sparse $L_p$-norm Regression With Optimal ProbabilityJohn Hainline, Brendan Juba, Hai S. Le, David P. Woodruff. 1042-1050 [doi]
- On the Connection Between Learning Two-Layer Neural Networks and Tensor DecompositionMarco Mondelli, Andrea Montanari. 1051-1060 [doi]
- Autoencoding any Data through Kernel AutoencodersPierre Laforgue, Stéphan Clémençon, Florence d'Alché-Buc. 1061-1069 [doi]
- Towards Understanding the Generalization Bias of Two Layer Convolutional Linear Classifiers with Gradient DescentYifan Wu, Barnabás Póczos, Aarti Singh. 1070-1078 [doi]
- Learning to Optimize under Non-StationarityWang Chi Cheung, David Simchi-Levi, Ruihao Zhu. 1079-1087 [doi]
- SPONGE: A generalized eigenproblem for clustering signed networksMihai Cucuringu, Peter Davies, Aldo Glielmo, Hemant Tyagi. 1088-1098 [doi]
- Deep Neural Networks with Multi-Branch Architectures Are Intrinsically Less Non-ConvexHongyang Zhang, Junru Shao, Ruslan Salakhutdinov. 1099-1109 [doi]
- Are we there yet? Manifold identification of gradient-related proximal methodsYifan Sun, Halyun Jeong, Julie Nutini, Mark W. Schmidt. 1110-1119 [doi]
- Hadamard Response: Estimating Distributions Privately, Efficiently, and with Little CommunicationJayadev Acharya, Ziteng Sun, Huanyu Zhang. 1120-1129 [doi]
- XBART: Accelerated Bayesian Additive Regression TreesJingyu He, Saar Yalov, P. Richard Hahn. 1130-1138 [doi]
- A Swiss Army Infinitesimal JackknifeRyan Giordano, William T. Stephenson, Runjing Liu, Michael I. Jordan, Tamara Broderick. 1139-1147 [doi]
- Online Multiclass Boosting with Bandit FeedbackDaniel T. Zhang, Young-Hun Jung, Ambuj Tewari. 1148-1156 [doi]
- Auto-Encoding Total Correlation ExplanationShuyang Gao, Rob Brekelmans, Greg Ver Steeg, Aram Galstyan. 1157-1166 [doi]
- Towards Efficient Data Valuation Based on the Shapley ValueRuoxi Jia, David Dao, Boxin Wang, Frances Ann Hubis, Nick Hynes, Nezihe Merve Gürel, Bo Li, Ce Zhang, Dawn Song, Costas J. Spanos. 1167-1176 [doi]
- Bayesian optimisation under uncertain inputsRafael Oliveira, Lionel Ott, Fabio Ramos. 1177-1184 [doi]
- Optimal Minimization of the Sum of Three Convex Functions with a Linear OperatorSeyoon Ko, Joong-Ho Won. 1185-1194 [doi]
- Fast and Faster Convergence of SGD for Over-Parameterized Models and an Accelerated PerceptronSharan Vaswani, Francis Bach, Mark Schmidt. 1195-1204 [doi]
- No-regret algorithms for online $k$-submodular maximizationTasuku Soma. 1205-1214 [doi]
- Lagrange Coded Computing: Optimal Design for Resiliency, Security, and PrivacyQian Yu, Songze Li, Netanel Raviv, Seyed Mohammadreza Mousavi Kalan, Mahdi Soltanolkotabi, Amir Salman Avestimehr. 1215-1225 [doi]
- Subsampled Renyi Differential Privacy and Analytical Moments AccountantYu-Xiang Wang, Borja Balle, Shiva Prasad Kasiviswanathan. 1226-1235 [doi]
- Model Consistency for Learning with Mirror-Stratifiable RegularizersJalal Fadili, Guillaume Garrigos, Jérôme Malick, Gabriel Peyré. 1236-1244 [doi]
- From Cost-Sensitive to Tight F-measure BoundsKevin Bascol, Rémi Emonet, Élisa Fromont, Amaury Habrard, Guillaume Metzler, Marc Sebban. 1245-1253 [doi]
- Feature subset selection for the multinomial logit model via mixed-integer optimizationShunsuke Kamiya, Ryuhei Miyashiro, Yuichi Takano. 1254-1263 [doi]
- Low-Precision Random Fourier Features for Memory-constrained Kernel ApproximationJian Zhang, Avner May, Tri Dao, Christopher Ré. 1264-1274 [doi]
- Restarting Frank-WolfeThomas Kerdreux, Alexandre d'Aspremont, Sebastian Pokutta. 1275-1283 [doi]
- Adaptive Ensemble Prediction for Deep Neural Networks based on Confidence LevelHiroshi Inoue. 1284-1293 [doi]
- Infinite Task Learning in RKHSsRomain Brault, Alex Lambert, Zoltán Szabó, Maxime Sangnier, Florence d'Alché-Buc. 1294-1302 [doi]
- Detection of Planted Solutions for Flat Satisfiability ProblemsQuentin Berthet, Jordan S. Ellenberg. 1303-1312 [doi]
- Markov Properties of Discrete Determinantal Point ProcessesKayvan Sadeghi, Alessandro Rinaldo. 1313-1321 [doi]
- Analysis of Thompson Sampling for Combinatorial Multi-armed Bandit with Probabilistically Triggered ArmsAlihan Hüyük, Cem Tekin. 1322-1330 [doi]
- Distilling Policy DistillationWojciech M. Czarnecki, Razvan Pascanu, Simon Osindero, Siddhant M. Jayakumar, Grzegorz Swirszcz, Max Jaderberg. 1331-1340 [doi]
- Support Localization and the Fisher Metric for off-the-grid Sparse RegularizationClarice Poon, Nicolas Keriven, Gabriel Peyré. 1341-1350 [doi]
- Fast Gaussian process based gradient matching for parameter identification in systems of nonlinear ODEsPhilippe Wenk, Alkis Gotovos, Stefan Bauer, Nico S. Gorbach, Andreas Krause 0001, Joachim M. Buhmann. 1351-1360 [doi]
- Semi-Generative Modelling: Covariate-Shift Adaptation with Cause and Effect FeaturesJulius von Kügelgen, Alexander Mey, Marco Loog. 1361-1369 [doi]
- A Continuous-Time View of Early Stopping for Least Squares RegressionAlnur Ali, J. Zico Kolter, Ryan J. Tibshirani. 1370-1378 [doi]
- Towards Clustering High-dimensional Gaussian Mixture Clouds in Linear Running TimeDan Kushnir, Shirin Jalali, Iraj Saniee. 1379-1387 [doi]
- Classifying Signals on Irregular Domains via Convolutional Cluster PoolingAngelo Porrello, Davide Abati, Simone Calderara, Rita Cucchiara. 1388-1397 [doi]
- Learning Rules-First ClassifiersDeborah Cohen, Amit Daniely, Amir Globerson, Gal Elidan. 1398-1406 [doi]
- Wasserstein regularization for sparse multi-task regressionHicham Janati, Marco Cuturi, Alexandre Gramfort. 1407-1416 [doi]
- Stochastic Gradient Descent with Exponential Convergence Rates of Expected Classification ErrorsAtsushi Nitanda, Taiji Suzuki. 1417-1426 [doi]
- Black Box Quantiles for Kernel LearningAnthony Tompkins, Ransalu Senanayake, Philippe Morere, Fabio Ramos. 1427-1437 [doi]
- Adversarial Variational Optimization of Non-Differentiable SimulatorsGilles Louppe, Joeri Hermans, Kyle Cranmer. 1438-1447 [doi]
- Active Probabilistic Inference on Matrices for Pre-Conditioning in Stochastic OptimizationFilip de Roos, Philipp Hennig. 1448-1457 [doi]
- Projection Free Online Learning over Smooth SetsKfir Levy, Andreas Krause. 1458-1466 [doi]
- Confidence Scoring Using Whitebox Meta-models with Linear Classifier ProbesTongfei Chen, Jirí Navrátil 0001, Vijay Iyengar, Karthikeyan Shanmugam. 1467-1475 [doi]
- Learning Influence-Receptivity Network Structure with GuaranteeMing Yu, Varun Gupta, Mladen Kolar. 1476-1485 [doi]
- Iterative Bayesian Learning for Crowdsourced RegressionJungseul Ok, Sewoong Oh, Yunhun Jang, Jinwoo Shin, Yung Yi. 1486-1495 [doi]
- Nonconvex Matrix Factorization from Rank-One MeasurementsYuanxin Li, Cong Ma, Yuxin Chen 0002, Yuejie Chi. 1496-1505 [doi]
- Fast and Robust Shortest Paths on Manifolds Learned from DataGeorgios Arvanitidis, Søren Hauberg, Philipp Hennig, Michael Schober. 1506-1515 [doi]
- Training a Spiking Neural Network with Equilibrium PropagationPeter O'Connor, Efstratios Gavves, Max Welling. 1516-1523 [doi]
- Learning One-hidden-layer ReLU Networks via Gradient DescentXiao Zhang, Yaodong Yu, Lingxiao Wang, Quanquan Gu. 1524-1534 [doi]
- Gain estimation of linear dynamical systems using Thompson SamplingMatias I. Müller, Cristian R. Rojas. 1535-1543 [doi]
- Universal Hypothesis Testing with Kernels: Asymptotically Optimal Tests for Goodness of FitShengyu Zhu, Biao Chen, Pengfei Yang, Zhitang Chen. 1544-1553 [doi]
- Calibrating Deep Convolutional Gaussian ProcessesGia-Lac Tran, Edwin V. Bonilla, John P. Cunningham, Pietro Michiardi, Maurizio Filippone. 1554-1563 [doi]
- Stochastic algorithms with descent guarantees for ICAPierre Ablin, Alexandre Gramfort, Jean-François Cardoso, Francis Bach. 1564-1573 [doi]
- Sample Complexity of Sinkhorn DivergencesAude Genevay, Lénaïc Chizat, Francis Bach, Marco Cuturi, Gabriel Peyré. 1574-1583 [doi]
- Adaptive Gaussian Copula ABCYanzhi Chen, Michael U. Gutmann. 1584-1592 [doi]
- Top Feasible Arm IdentificationJulian Katz-Samuels, Clayton Scott. 1593-1601 [doi]
- Direct Acceleration of SAGA using Sampled Negative MomentumKaiwen Zhou, Qinghua Ding, Fanhua Shang, James Cheng, Danli Li, Zhi-Quan Luo. 1602-1610 [doi]
- Does data interpolation contradict statistical optimality?Mikhail Belkin, Alexander Rakhlin, Alexandre B. Tsybakov. 1611-1619 [doi]
- Inverting Supervised Representations with Autoregressive Neural Density ModelsCharlie Nash, Nate Kushman, Christopher K. I. Williams. 1620-1629 [doi]
- Connecting Weighted Automata and Recurrent Neural Networks through Spectral LearningGuillaume Rabusseau, Tianyu Li, Doina Precup. 1630-1639 [doi]
- A Family of Exact Goodness-of-Fit Tests for High-Dimensional Discrete DistributionsFeras A. Saad, Cameron E. Freer, Nathanael L. Ackerman, Vikash K. Mansinghka. 1640-1649 [doi]
- Differentially Private Online Submodular MinimizationAdrian Rivera Cardoso, Rachel Cummings. 1650-1658 [doi]
- Semi-supervised clustering for de-duplicationShrinu Kushagra, Shai Ben-David, Ihab F. Ilyas. 1659-1667 [doi]
- Finding the bandit in a graph: Sequential search-and-stopPierre Perrault, Vianney Perchet, Michal Valko. 1668-1677 [doi]
- Statistical Learning under Nonstationary Mixing ProcessesSteve Hanneke, Liu Yang 0001. 1678-1686 [doi]
- On Structure Priors for Learning Bayesian NetworksRalf Eggeling, Jussi Viinikka, Aleksis Vuoksenmaa, Mikko Koivisto. 1687-1695 [doi]
- Partial Optimality of Dual Decomposition for MAP Inference in Pairwise MRFsAlexander Bauer 0001, Shinichi Nakajima, Nico Görnitz, Klaus-Robert Müller. 1696-1703 [doi]
- Sparse Feature Selection in Kernel Discriminant Analysis via Optimal ScoringAlexander F. Lapanowski, Irina Gaynanova. 1704-1713 [doi]
- Learning Natural Programs from a Few Examples in Real-TimeNagarajan Natarajan, Danny Simmons, Naren Datha, Prateek Jain 0002, Sumit Gulwani. 1714-1722 [doi]
- Truncated Back-propagation for Bilevel OptimizationAmirreza Shaban, Ching-An Cheng, Nathan Hatch, Byron Boots. 1723-1732 [doi]
- Empirical Risk Minimization and Stochastic Gradient Descent for Relational DataVictor Veitch, Morgane Austern, Wenda Zhou, David M. Blei, Peter Orbanz. 1733-1742 [doi]
- Variable selection for Gaussian processes via sensitivity analysis of the posterior predictive distributionTopi Paananen, Juho Piironen, Michael Riis Andersen, Aki Vehtari. 1743-1752 [doi]
- Lifted Weight Learning of Markov Logic Networks RevisitedOndrej Kuzelka, Vyacheslav Kungurtsev. 1753-1761 [doi]
- Causal Discovery in the Presence of Missing DataRuibo Tu, Cheng Zhang 0005, Paul Ackermann, Karthika Mohan, Hedvig Kjellström, Kun Zhang. 1762-1770 [doi]
- Learning Tree Structures from Noisy DataKonstantinos E. Nikolakakis, Dionysios S. Kalogerias, Anand D. Sarwate. 1771-1782 [doi]
- Active multiple matrix completion with adaptive confidence setsAndrea Locatelli, Alexandra Carpentier, Michal Valko. 1783-1791 [doi]
- Confidence-based Graph Convolutional Networks for Semi-Supervised LearningShikhar Vashishth, Prateek Yadav, Manik Bhandari, Partha Talukdar. 1792-1801 [doi]
- Negative Momentum for Improved Game DynamicsGauthier Gidel, Reyhane Askari Hemmat, Mohammad Pezeshki, Rémi Le Priol, Gabriel Huang, Simon Lacoste-Julien, Ioannis Mitliagkas. 1802-1811 [doi]
- Deep learning with differential Gaussian process flowsPashupati Hegde, Markus Heinonen, Harri Lähdesmäki, Samuel Kaski. 1812-1821 [doi]
- Data-dependent compression of random features for large-scale kernel approximationRaj Agrawal, Trevor Campbell, Jonathan H. Huggins, Tamara Broderick. 1822-1831 [doi]
- Large-Margin Classification in Hyperbolic SpaceHyunghoon Cho, Benjamin Demeo, Jian Peng 0001, Bonnie Berger. 1832-1840 [doi]
- Generalizing the theory of cooperative inferencePei Wang, Pushpi Paranamana, Patrick Shafto. 1841-1850 [doi]
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- Matroids, Matchings, and FairnessFlavio Chierichetti, Ravi Kumar 0001, Silvio Lattanzi, Sergei Vassilvitskii. 2212-2220 [doi]
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- Least Squares Estimation of Weakly Convex FunctionsSun Sun, Yaoliang Yu. 2271-2280 [doi]
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- Online Decentralized Leverage Score Sampling for Streaming Multidimensional Time SeriesRui Xie, Zengyan Wang, Shuyang Bai, Ping Ma, Wenxuan Zhong. 2301-2311 [doi]
- Interpretable Cascade Classifiers with AbstentionMatthieu Clertant, Nataliya Sokolovska, Yann Chevaleyre, Blaise Hanczar. 2312-2320 [doi]
- Kernel Exponential Family Estimation via Doubly Dual EmbeddingBo Dai, Hanjun Dai, Arthur Gretton, Le Song, Dale Schuurmans, Niao He. 2321-2330 [doi]
- Revisiting Adversarial RiskArun Sai Suggala, Adarsh Prasad, Vaishnavh Nagarajan, Pradeep Ravikumar. 2331-2339 [doi]
- A Memoization Framework for Scaling Submodular Optimization to Large Scale ProblemsRishabh K. Iyer, Jeffrey A. Bilmes. 2340-2349 [doi]
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- Augmented Ensemble MCMC sampling in Factorial Hidden Markov ModelsKaspar Märtens, Michalis K. Titsias, Christopher Yau. 2359-2367 [doi]
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- Efficient Nonconvex Empirical Risk Minimization via Adaptive Sample Size MethodsAryan Mokhtari, Asuman E. Ozdaglar, Ali Jadbabaie. 2485-2494 [doi]
- An Optimal Control Approach to Sequential Machine TeachingLaurent Lessard, Xuezhou Zhang, Xiaojin Zhu 0001. 2495-2503 [doi]
- An Online Algorithm for Smoothed Regression and LQR ControlGautam Goel, Adam Wierman. 2504-2513 [doi]
- Uncertainty Autoencoders: Learning Compressed Representations via Variational Information MaximizationAditya Grover, Stefano Ermon. 2514-2524 [doi]
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- Estimating Network Structure from Incomplete Event DataBenjamin Mark, Garvesh Raskutti, Rebecca Willett. 2535-2544 [doi]
- Locally Private Mean Estimation: $Z$-test and Tight Confidence IntervalsMarco Gaboardi, Ryan Rogers 0002, Or Sheffet. 2545-2554 [doi]
- Estimation of Non-Normalized Mixture ModelsTakeru Matsuda, Aapo Hyvärinen. 2555-2563 [doi]
- Rotting bandits are no harder than stochastic onesJulien Seznec, Andrea Locatelli, Alexandra Carpentier, Alessandro Lazaric, Michal Valko. 2564-2572 [doi]
- A Topological Regularizer for Classifiers via Persistent HomologyChao Chen, Xiuyan Ni, Qinxun Bai, Yusu Wang. 2573-2582 [doi]
- Overcomplete Independent Component Analysis via SDPAnastasia Podosinnikova, Amelia Perry, Alexander S. Wein, Francis Bach, Alexandre d'Aspremont, David Sontag. 2583-2592 [doi]
- Doubly Semi-Implicit Variational InferenceDmitry Molchanov, Valery Kharitonov, Artem Sobolev, Dmitry P. Vetrov. 2593-2602 [doi]
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- Correspondence Analysis Using Neural NetworksHsiang Hsu, Salman Salamatian, Flávio du Pin Calmon. 2671-2680 [doi]
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- Variational Noise-Contrastive EstimationBenjamin Rhodes, Michael U. Gutmann. 2741-2750 [doi]
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