{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/learning-theory/papers/6","list_of":"/task/learning-theory","task":"Learning Theory","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":6,"pages_in_order":9,"rows_per_page":100,"rows":[501,600],"of":852,"counts":{"archive_papers_tagged":852,"with_a_code_link":140,"where_syntology_ran_a_sample":44,"not_listed_spam_title":0,"listed":852,"listed_where_code_ran":44,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":37,"every_run_a_failure_of_syntologys_instrument":7,"listed_with_a_run_with_no_instrument_failure":37,"listed_every_run_a_failure_of_syntologys_instrument":7,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/learning-theory","prev":"/task/learning-theory/papers/5","next":"/task/learning-theory/papers/7","papers":[{"url":null,"slug":"the-trade-offs-of-domain-adaptation-for","title":"The Trade-offs of Domain Adaptation for Neural Language Models","date":"2021-09-21","arxiv_id":"2109.10274","repositories_listed":0,"syntology":null},{"url":null,"slug":"generalized-optimization-a-first-step-towards","title":"Generalized Optimization: A First Step Towards Category Theoretic Learning Theory","date":"2021-09-20","arxiv_id":"2109.10262","repositories_listed":0,"syntology":null},{"url":null,"slug":"continuous-homeostatic-reinforcement-learning","title":"Continuous Homeostatic Reinforcement Learning for Self-Regulated Autonomous Agents","date":"2021-09-14","arxiv_id":"2109.06580","repositories_listed":0,"syntology":null},{"url":null,"slug":"cim-class-irrelevant-mapping-for-few-shot","title":"CIM: Class-Irrelevant Mapping for Few-Shot Classification","date":"2021-09-07","arxiv_id":"2109.02840","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-empirical-risk-minimization-with-dependent","title":"On Empirical Risk Minimization with Dependent and Heavy-Tailed Data","date":"2021-09-06","arxiv_id":"2109.02224","repositories_listed":0,"syntology":null},{"url":null,"slug":"visual-recognition-with-deep-learning-from","title":"Fighting Selection Bias in Statistical Learning: Application to Visual Recognition from Biased Image Databases","date":"2021-09-06","arxiv_id":"2109.02357","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-bayesian-approach-to-online-transfer","title":"A Bayesian Approach to (Online) Transfer Learning: Theory and Algorithms","date":"2021-09-03","arxiv_id":"2109.01377","repositories_listed":0,"syntology":null},{"url":null,"slug":"lower-bounds-on-the-total-variation-distance","title":"Lower Bounds on the Total Variation Distance Between Mixtures of Two Gaussians","date":"2021-09-02","arxiv_id":"2109.01064","repositories_listed":0,"syntology":null},{"url":null,"slug":"under-bagging-nearest-neighbors-for","title":"Under-bagging Nearest Neighbors for Imbalanced Classification","date":"2021-09-01","arxiv_id":"2109.00531","repositories_listed":0,"syntology":null},{"url":null,"slug":"convergence-rates-for-learning-linear","title":"Convergence Rates for Learning Linear Operators from Noisy Data","date":"2021-08-27","arxiv_id":"2108.12515","repositories_listed":0,"syntology":null},{"url":null,"slug":"adversarial-robustness-of-deep-learning","title":"Adversarial Robustness of Deep Learning: Theory, Algorithms, and Applications","date":"2021-08-24","arxiv_id":"2108.10451","repositories_listed":0,"syntology":null},{"url":null,"slug":"primal-and-dual-combinatorial-dimensions","title":"Primal and Dual Combinatorial Dimensions","date":"2021-08-23","arxiv_id":"2108.10037","repositories_listed":0,"syntology":null},{"url":null,"slug":"introduction-to-quantum-reinforcement","title":"Introduction to Quantum Reinforcement Learning: Theory and PennyLane-based Implementation","date":"2021-08-16","arxiv_id":"2108.06849","repositories_listed":0,"syntology":null},{"url":null,"slug":"empirical-risk-minimization-for-time-series","title":"Empirical Risk Minimization for Time Series: Nonparametric Performance Bounds for Prediction","date":"2021-08-11","arxiv_id":"2108.05184","repositories_listed":0,"syntology":null},{"url":null,"slug":"unified-regularity-measures-for-sample-wise","title":"Unified Regularity Measures for Sample-wise Learning and Generalization","date":"2021-08-09","arxiv_id":"2108.03913","repositories_listed":0,"syntology":null},{"url":null,"slug":"path-classification-by-stochastic-linear","title":"Path classification by stochastic linear recurrent neural networks","date":"2021-08-06","arxiv_id":"2108.03090","repositories_listed":0,"syntology":null},{"url":null,"slug":"characterizing-the-generalization-error-of","title":"Characterizing the Generalization Error of Gibbs Algorithm with Symmetrized KL information","date":"2021-07-28","arxiv_id":"2107.13656","repositories_listed":0,"syntology":null},{"url":null,"slug":"accelerating-federated-edge-learning-via","title":"Accelerating Federated Edge Learning via Optimized Probabilistic Device Scheduling","date":"2021-07-24","arxiv_id":"2107.11588","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-explaining-adversarial-examples","title":"Towards Explaining Adversarial Examples Phenomenon in Artificial Neural Networks","date":"2021-07-22","arxiv_id":"2107.10599","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-induction-proof-of-the-backpropagation","title":"An induction proof of the backpropagation algorithm in matrix notation","date":"2021-07-20","arxiv_id":"2107.09384","repositories_listed":0,"syntology":null},{"url":null,"slug":"improved-learning-rates-for-stochastic","title":"Improved Learning Rates for Stochastic Optimization: Two Theoretical Viewpoints","date":"2021-07-19","arxiv_id":"2107.08686","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-variance-of-the-fisher-information-for","title":"On the Variance of the Fisher Information for Deep Learning","date":"2021-07-09","arxiv_id":"2107.04205","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-last-iterate-convergence-rate-of","title":"The Last-Iterate Convergence Rate of Optimistic Mirror Descent in Stochastic Variational Inequalities","date":"2021-07-05","arxiv_id":"2107.01906","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-systems-theory-of-transfer-learning","title":"A Systems Theory of Transfer Learning","date":"2021-07-02","arxiv_id":"2107.01196","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-principles-of-deep-learning-theory","title":"The Principles of Deep Learning Theory","date":"2021-06-18","arxiv_id":"2106.10165","repositories_listed":0,"syntology":null},{"url":null,"slug":"gbht-gradient-boosting-histogram-transform","title":"GBHT: Gradient Boosting Histogram Transform for Density Estimation","date":"2021-06-10","arxiv_id":"2106.05738","repositories_listed":0,"syntology":null},{"url":null,"slug":"fractal-structure-and-generalization","title":"Fractal Structure and Generalization Properties of Stochastic Optimization Algorithms","date":"2021-06-09","arxiv_id":"2106.04881","repositories_listed":0,"syntology":null},{"url":null,"slug":"encoding-dependent-generalization-bounds-for","title":"Encoding-dependent generalization bounds for parametrized quantum circuits","date":"2021-06-07","arxiv_id":"2106.03880","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-an-understanding-of-benign","title":"Towards an Understanding of Benign Overfitting in Neural Networks","date":"2021-06-06","arxiv_id":"2106.03212","repositories_listed":0,"syntology":null},{"url":null,"slug":"sparsification-for-sums-of-exponentials-and","title":"Robust Model Selection and Nearly-Proper Learning for GMMs","date":"2021-06-05","arxiv_id":"2106.02774","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-on-optimal-transport-for-machine","title":"A Survey on Optimal Transport for Machine Learning: Theory and Applications","date":"2021-06-03","arxiv_id":"2106.01963","repositories_listed":0,"syntology":null},{"url":null,"slug":"post-mortem-on-a-deep-learning-contest-a","title":"Post-mortem on a deep learning contest: a Simpson's paradox and the complementary roles of scale metrics versus shape metrics","date":"2021-06-01","arxiv_id":"2106.00734","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-simple-and-general-debiased-machine","title":"A Simple and General Debiased Machine Learning Theorem with Finite Sample Guarantees","date":"2021-05-31","arxiv_id":"2105.15197","repositories_listed":0,"syntology":null},{"url":null,"slug":"tesseract-tensorised-actors-for-multi-agent","title":"Tesseract: Tensorised Actors for Multi-Agent Reinforcement Learning","date":"2021-05-31","arxiv_id":"2106.00136","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-union-of-integer-hypercubes-with","title":"Learning Union of Integer Hypercubes with Queries (Technical Report)","date":"2021-05-27","arxiv_id":"2105.13071","repositories_listed":0,"syntology":null},{"url":null,"slug":"yes-we-care-certification-for-machine","title":"Yes We Care! -- Certification for Machine Learning Methods through the Care Label Framework","date":"2021-05-21","arxiv_id":"2105.10197","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-modern-mathematics-of-deep-learning","title":"The Modern Mathematics of Deep Learning","date":"2021-05-09","arxiv_id":"2105.04026","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantum-machine-learning-for-classical-data","title":"Quantum Machine Learning For Classical Data","date":"2021-05-08","arxiv_id":"2105.03684","repositories_listed":0,"syntology":null},{"url":null,"slug":"regret-optimal-full-information-control","title":"Regret-Optimal LQR Control","date":"2021-05-04","arxiv_id":"2105.01244","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-kernel-based-distribution-regression","title":"Robust Kernel-based Distribution Regression","date":"2021-04-21","arxiv_id":"2104.10637","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-description-logic-ontologies-five","title":"Learning Description Logic Ontologies. Five Approaches. Where Do They Stand?","date":"2021-04-02","arxiv_id":"2104.01193","repositories_listed":0,"syntology":null},{"url":null,"slug":"why-is-ai-hard-and-physics-simple","title":"Why is AI hard and Physics simple?","date":"2021-03-31","arxiv_id":"2104.00008","repositories_listed":0,"syntology":null},{"url":null,"slug":"understanding-the-role-of-importance-1","title":"Understanding the role of importance weighting for deep learning","date":"2021-03-28","arxiv_id":"2103.15209","repositories_listed":0,"syntology":null},{"url":null,"slug":"leaky-nets-recovering-embedded-neural-network","title":"Leaky Nets: Recovering Embedded Neural Network Models and Inputs through Simple Power and Timing Side-Channels -- Attacks and Defenses","date":"2021-03-26","arxiv_id":"2103.14739","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-complexity-of-learning-description","title":"On the Complexity of Learning Description Logic Ontologies","date":"2021-03-25","arxiv_id":"2103.13694","repositories_listed":0,"syntology":null},{"url":null,"slug":"risk-bounds-and-rademacher-complexity-in","title":"Risk Bounds and Rademacher Complexity in Batch Reinforcement Learning","date":"2021-03-25","arxiv_id":"2103.13883","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-deep-learning-theory-for-neural-networks","title":"A deep learning theory for neural networks grounded in physics","date":"2021-03-18","arxiv_id":"2103.09985","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-impact-of-applying-machine-learning-in","title":"On the Impact of Applying Machine Learning in the Decision-Making of Self-Adaptive Systems","date":"2021-03-18","arxiv_id":"2103.10194","repositories_listed":0,"syntology":null},{"url":null,"slug":"constrained-learning-with-non-convex-losses","title":"Constrained Learning with Non-Convex Losses","date":"2021-03-08","arxiv_id":"2103.05134","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-prediction-intervals-for-regression","title":"Learning Prediction Intervals for Regression: Generalization and Calibration","date":"2021-02-26","arxiv_id":"2102.13625","repositories_listed":0,"syntology":null},{"url":null,"slug":"theoretical-understandings-of-product","title":"Theoretical Understandings of Product Embedding for E-commerce Machine Learning","date":"2021-02-24","arxiv_id":"2102.12029","repositories_listed":0,"syntology":null},{"url":null,"slug":"debiased-kernel-methods","title":"Kernel Ridge Riesz Representers: Generalization, Mis-specification, and the Counterfactual Effective Dimension","date":"2021-02-22","arxiv_id":"2102.11076","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-robustness-of-randomized-classifiers","title":"On the robustness of randomized classifiers to adversarial examples","date":"2021-02-22","arxiv_id":"2102.10875","repositories_listed":0,"syntology":null},{"url":null,"slug":"universal-approximation-properties-of-neural","title":"KAM Theory Meets Statistical Learning Theory: Hamiltonian Neural Networks with Non-Zero Training Loss","date":"2021-02-22","arxiv_id":"2102.11923","repositories_listed":0,"syntology":null},{"url":null,"slug":"double-descent-curves-in-neural-networks-a","title":"Double-descent curves in neural networks: a new perspective using Gaussian processes","date":"2021-02-14","arxiv_id":"2102.07238","repositories_listed":0,"syntology":null},{"url":null,"slug":"private-learning-implies-quantum-stability","title":"Private learning implies quantum stability","date":"2021-02-14","arxiv_id":"2102.07171","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-predictive-normalized-maximum-likelihood","title":"Distribution Free Uncertainty for the Minimum Norm Solution of Over-parameterized Linear Regression","date":"2021-02-14","arxiv_id":"2102.07181","repositories_listed":0,"syntology":null},{"url":null,"slug":"local-and-global-uniform-convexity-conditions","title":"Local and Global Uniform Convexity Conditions","date":"2021-02-09","arxiv_id":"2102.05134","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-hardness-of-pac-learning-stabilizer","title":"On the Hardness of PAC-learning Stabilizer States with Noise","date":"2021-02-09","arxiv_id":"2102.05174","repositories_listed":0,"syntology":null},{"url":null,"slug":"effects-of-quantum-resources-on-the","title":"Effects of quantum resources on the statistical complexity of quantum circuits","date":"2021-02-05","arxiv_id":"2102.03282","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-while-dissipating-information","title":"Generalization Bounds for Noisy Iterative Algorithms Using Properties of Additive Noise Channels","date":"2021-02-05","arxiv_id":"2102.02976","repositories_listed":0,"syntology":null},{"url":null,"slug":"query-complexity-of-least-absolute-deviation","title":"Query Complexity of Least Absolute Deviation Regression via Robust Uniform Convergence","date":"2021-02-03","arxiv_id":"2102.02322","repositories_listed":0,"syntology":null},{"url":null,"slug":"device-sampling-for-heterogeneous-federated","title":"Device Sampling for Heterogeneous Federated Learning: Theory, Algorithms, and Implementation","date":"2021-01-04","arxiv_id":"2101.00787","repositories_listed":0,"syntology":null},{"url":null,"slug":"f-domain-adversarial-learning-theory-and","title":"f-Domain-Adversarial Learning: Theory and Algorithms for Unsupervised Domain Adaptation with Neural Networks","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"illuminating-dark-knowledge-via-random-matrix","title":"Illuminating Dark Knowledge via Random Matrix Ensembles","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"mirror-sample-based-distribution-alignment","title":"Mirror Sample Based Distribution Alignment for Unsupervised Domain Adaption","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"an-online-algorithm-for-maximum-likelihood","title":"Maximum-Likelihood Quantum State Tomography by Soft-Bayes","date":"2020-12-31","arxiv_id":"2012.15498","repositories_listed":0,"syntology":null},{"url":null,"slug":"continuity-of-generalized-entropy-and","title":"Continuity of Generalized Entropy and Statistical Learning","date":"2020-12-31","arxiv_id":"2012.15829","repositories_listed":0,"syntology":null},{"url":null,"slug":"risk-guarantees-for-end-to-end-prediction-and","title":"Risk Guarantees for End-to-End Prediction and Optimization Processes","date":"2020-12-30","arxiv_id":"2012.15046","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-vip-gallery-for-video-processing","title":"The VIP Gallery for Video Processing Education","date":"2020-12-29","arxiv_id":"2012.14625","repositories_listed":0,"syntology":null},{"url":null,"slug":"blackwell-online-learning-for-markov-decision","title":"Blackwell Online Learning for Markov Decision Processes","date":"2020-12-28","arxiv_id":"2012.14043","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-tight-lower-bound-for-uniformly-stable","title":"A Tight Lower Bound for Uniformly Stable Algorithms","date":"2020-12-24","arxiv_id":"2012.13326","repositories_listed":0,"syntology":null},{"url":null,"slug":"recent-advances-in-deep-learning-theory","title":"Recent advances in deep learning theory","date":"2020-12-20","arxiv_id":"2012.10931","repositories_listed":0,"syntology":null},{"url":null,"slug":"hardness-of-learning-halfspaces-with-massart","title":"Near-Optimal Statistical Query Hardness of Learning Halfspaces with Massart Noise","date":"2020-12-17","arxiv_id":"2012.09720","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-understanding-ensemble-knowledge","title":"Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning","date":"2020-12-17","arxiv_id":"2012.09816","repositories_listed":0,"syntology":null},{"url":null,"slug":"testing-and-reconstruction-via-decision-trees","title":"Reconstructing decision trees","date":"2020-12-16","arxiv_id":"2012.08735","repositories_listed":0,"syntology":null},{"url":null,"slug":"notes-on-deep-learning-theory","title":"Notes on Deep Learning Theory","date":"2020-12-10","arxiv_id":"2012.05760","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantum-learning-algorithms-imply-circuit","title":"Quantum learning algorithms imply circuit lower bounds","date":"2020-12-03","arxiv_id":"2012.01920","repositories_listed":0,"syntology":null},{"url":null,"slug":"margin-based-transfer-bounds-for-meta","title":"Margin-Based Transfer Bounds for Meta Learning with Deep Feature Embedding","date":"2020-12-02","arxiv_id":"2012.01602","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-a-chatbot-navigating-a-user-through-a","title":"On a Chatbot Navigating a User through a Concept-Based Knowledge Model","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-convenient-infinite-dimensional-framework","title":"A Convenient Infinite Dimensional Framework for Generative Adversarial Learning","date":"2020-11-24","arxiv_id":"2011.12087","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-general-framework-for-distributed-inference","title":"A General Framework for Distributed Inference with Uncertain Models","date":"2020-11-20","arxiv_id":"2011.10669","repositories_listed":0,"syntology":null},{"url":null,"slug":"chaos-and-complexity-from-quantum-neural","title":"Chaos and Complexity from Quantum Neural Network: A study with Diffusion Metric in Machine Learning","date":"2020-11-16","arxiv_id":"2011.07145","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-optimal-problem-dependent","title":"Towards Optimal Problem Dependent Generalization Error Bounds in Statistical Learning Theory","date":"2020-11-12","arxiv_id":"2011.06186","repositories_listed":0,"syntology":null},{"url":null,"slug":"direction-matters-on-the-implicit-1","title":"Direction Matters: On the Implicit Bias of Stochastic Gradient Descent with Moderate Learning Rate","date":"2020-11-04","arxiv_id":"2011.02538","repositories_listed":0,"syntology":null},{"url":null,"slug":"understanding-double-descent-requires-a-fine","title":"Understanding Double Descent Requires a Fine-Grained Bias-Variance Decomposition","date":"2020-11-04","arxiv_id":"2011.03321","repositories_listed":0,"syntology":null},{"url":null,"slug":"geometry-perspective-of-estimating-learning","title":"Geometry Perspective Of Estimating Learning Capability Of Neural Networks","date":"2020-11-03","arxiv_id":"2011.04588","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-learning-theoretic-perspective-on-local-1","title":"A Learning Theoretic Perspective on Local Explainability","date":"2020-11-02","arxiv_id":"2011.01205","repositories_listed":0,"syntology":null},{"url":null,"slug":"toward-better-generalization-bounds-with","title":"Toward Better Generalization Bounds with Locally Elastic Stability","date":"2020-10-27","arxiv_id":"2010.13988","repositories_listed":0,"syntology":null},{"url":null,"slug":"enforcing-interpretability-and-its","title":"Enforcing Interpretability and its Statistical Impacts: Trade-offs between Accuracy and Interpretability","date":"2020-10-26","arxiv_id":"2010.13764","repositories_listed":0,"syntology":null},{"url":null,"slug":"regularised-least-squares-regression-with","title":"Regularised Least-Squares Regression with Infinite-Dimensional Output Space","date":"2020-10-21","arxiv_id":"2010.10973","repositories_listed":0,"syntology":null},{"url":null,"slug":"failures-of-model-dependent-generalization","title":"Failures of model-dependent generalization bounds for least-norm interpolation","date":"2020-10-16","arxiv_id":"2010.08479","repositories_listed":0,"syntology":null},{"url":null,"slug":"depth-width-trade-offs-for-neural-networks","title":"Depth-Width Trade-offs for Neural Networks via Topological Entropy","date":"2020-10-15","arxiv_id":"2010.07587","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-theory-for-inferring-interaction","title":"Learning Theory for Inferring Interaction Kernels in Second-Order Interacting Agent Systems","date":"2020-10-08","arxiv_id":"2010.03729","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-note-on-high-probability-versus-in","title":"A Note on High-Probability versus In-Expectation Guarantees of Generalization Bounds in Machine Learning","date":"2020-10-06","arxiv_id":"2010.02576","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-framework-of-learning-through-empirical","title":"A Framework of Learning Through Empirical Gain Maximization","date":"2020-09-29","arxiv_id":"2009.14250","repositories_listed":0,"syntology":null},{"url":null,"slug":"benign-overfitting-in-ridge-regression","title":"Benign overfitting in ridge regression","date":"2020-09-29","arxiv_id":"2009.14286","repositories_listed":0,"syntology":null},{"url":null,"slug":"do-deeper-convolutional-networks-perform","title":"Do Deeper Convolutional Networks Perform Better?","date":"2020-09-28","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"putting-theory-to-work-from-learning-bounds","title":"Putting Theory to Work: From Learning Bounds to Meta-Learning Algorithms","date":"2020-09-28","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"generalized-leverage-score-sampling-for","title":"Generalized Leverage Score Sampling for Neural Networks","date":"2020-09-21","arxiv_id":"2009.09829","repositories_listed":0,"syntology":null}],"record_sha256":"8ce3b332073881c4c50b4f827682688c871405e748876d335c00d60093117180","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}