{"total":113,"rows":[{"id":"2207.08799","title":"Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit","created":"2022-07-18","cited_by_count":187,"area":null},{"id":"2302.11055","title":"SGD learning on neural networks: leap complexity and saddle-to-saddle dynamics","created":"2023-02-21","cited_by_count":143,"area":null},{"id":"2310.06110","title":"Grokking as the Transition from Lazy to Rich Training Dynamics","created":"2023-10-09","cited_by_count":108,"area":null},{"id":"2210.15651","title":"Learning Single-Index Models with Shallow Neural Networks","created":"2022-10-27","cited_by_count":92,"area":null},{"id":"2206.04030","title":"High-dimensional limit theorems for SGD: Effective dynamics and critical scaling","created":"2022-06-08","cited_by_count":70,"area":null},{"id":"2305.18270","title":"How Two-Layer Neural Networks Learn, One (Giant) Step at a Time","created":"2023-05-29","cited_by_count":68,"area":null},{"id":"2209.14863","title":"Neural Networks Efficiently Learn Low-Dimensional Representations with SGD","created":"2022-09-29","cited_by_count":63,"area":null},{"id":"2305.10633","title":"Smoothing the Landscape Boosts the Signal for SGD: Optimal Sample Complexity for Learning Single Index Models","created":"2023-05-18","cited_by_count":61,"area":null},{"id":"2303.00055","title":"Learning time-scales in two-layers neural networks","created":"2023-02-28","cited_by_count":57,"area":null},{"id":"2402.03220","title":"The Benefits of Reusing Batches for Gradient Descent in Two-Layer Networks: Breaking the Curse of Information and Leap Exponents","created":"2024-02-05","cited_by_count":52,"area":null},{"id":"2202.00293","title":"Phase diagram of Stochastic Gradient Descent in high-dimensional two-layer neural networks","created":"2022-02-01","cited_by_count":50,"area":null},{"id":"2310.03789","title":"Grokking as a First Order Phase Transition in Two Layer Networks","created":"2023-10-05","cited_by_count":48,"area":null},{"id":"2406.01581","title":"Neural network learns low-dimensional polynomials with SGD near the information-theoretic limit","created":"2024-06-03","cited_by_count":46,"area":null},{"id":"2302.05882","title":"From high-dimensional & mean-field dynamics to dimensionless ODEs: A unifying approach to SGD in two-layers networks","created":"2023-02-12","cited_by_count":43,"area":null},{"id":"2309.15111","title":"SGD Finds then Tunes Features in Two-Layer Neural Networks with near-Optimal Sample Complexity: A Case Study in the XOR problem","created":"2023-09-26","cited_by_count":39,"area":null},{"id":"2405.15459","title":"Repetita Iuvant: Data Repetition Allows SGD to Learn High-Dimensional Multi-Index Functions","created":"2024-05-24","cited_by_count":37,"area":null},{"id":"2406.06467","title":"How Far Can Transformers Reason? The Globality Barrier and Inductive Scratchpad","created":"2024-06-10","cited_by_count":36,"area":null},{"id":"2309.03843","title":"Gradient-Based Feature Learning under Structured Data","created":"2023-09-07","cited_by_count":32,"area":null},{"id":"2411.02544","title":"Pretrained transformer efficiently learns low-dimensional target functions in-context","created":"2024-11-04","cited_by_count":31,"area":null},{"id":"2410.19637","title":"A distributional simplicity bias in the learning dynamics of transformers","created":"2024-10-25","cited_by_count":24,"area":null},{"id":"2408.03733","title":"Bayes-optimal learning of an extensive-width neural network from quadratically many samples","created":"2024-08-07","cited_by_count":23,"area":null},{"id":"2306.16921","title":"Provable Advantage of Curriculum Learning on Parity Targets with Mixed Inputs","created":"2023-06-29","cited_by_count":22,"area":null},{"id":"2402.04084","title":"Provably learning a multi-head attention layer","created":"2024-02-06","cited_by_count":21,"area":null},{"id":"2106.10744","title":"On the Cryptographic Hardness of Learning Single Periodic Neurons","created":"2021-06-20","cited_by_count":20,"area":null},{"id":"2308.13431","title":"Six Lectures on Linearized Neural Networks","created":"2023-08-25","cited_by_count":20,"area":null},{"id":"2305.06986","title":"Provable Guarantees for Nonlinear Feature Learning in Three-Layer Neural Networks","created":"2023-05-11","cited_by_count":19,"area":null},{"id":"2405.15480","title":"Fundamental computational limits of weak learnability in high-dimensional multi-index models","created":"2024-05-24","cited_by_count":19,"area":null},{"id":"2307.15804","title":"On Single Index Models beyond Gaussian Data","created":"2023-07-28","cited_by_count":17,"area":null},{"id":"2410.18938","title":"A Random Matrix Theory Perspective on the Spectrum of Learned Features and Asymptotic Generalization Capabilities","created":"2024-10-24","cited_by_count":16,"area":null},{"id":"2305.18502","title":"Escaping mediocrity: how two-layer networks learn hard generalized linear models with SGD","created":"2023-05-29","cited_by_count":15,"area":null},{"id":"2407.05622","title":"On the Complexity of Learning Sparse Functions with Statistical and Gradient Queries","created":"2024-07-08","cited_by_count":15,"area":null},{"id":"2408.07254","title":"Learning Multi-Index Models with Neural Networks via Mean-Field Langevin Dynamics","created":"2024-08-14","cited_by_count":15,"area":null},{"id":"2504.05426","title":"Survey on Algorithms for multi-index models","created":"2025-04-07","cited_by_count":15,"area":null},{"id":"2507.02550","title":"Position: A Theory of Deep Learning Must Include Compositional Sparsity","created":"2025-07-03","cited_by_count":14,"area":null},{"id":"2309.03800","title":"Pareto Frontiers in Neural Feature Learning: Data, Compute, Width, and Luck","created":"2023-09-07","cited_by_count":13,"area":null},{"id":"2404.08602","title":"Sliding down the stairs: how correlated latent variables accelerate learning with neural networks","created":"2024-04-12","cited_by_count":12,"area":null},{"id":"2311.13774","title":"Learning Hierarchical Polynomials with Three-Layer Neural Networks","created":"2023-11-23","cited_by_count":9,"area":null},{"id":"2406.04657","title":"From Spikes to Heavy Tails: Unveiling the Spectral Evolution of Neural Networks","created":"2024-06-07","cited_by_count":9,"area":null},{"id":"2411.08798","title":"Learning Gaussian Multi-Index Models with Gradient Flow: Time Complexity and Directional Convergence","created":"2024-11-13","cited_by_count":9,"area":null},{"id":"2506.02651","title":"Asymptotics of SGD in Sequence-Single Index Models and Single-Layer Attention Networks","created":"2025-06-03","cited_by_count":9,"area":null},{"id":"2506.05500","title":"The Generative Leap: Sharp Sample Complexity for Efficiently Learning Gaussian Multi-Index Models","created":"2025-06-05","cited_by_count":9,"area":null},{"id":"2311.01644","title":"Should Under-parameterized Student Networks Copy or Average Teacher Weights?","created":"2023-11-03","cited_by_count":8,"area":null},{"id":"2502.01583","title":"Spectral Estimators for Multi-Index Models: Precise Asymptotics and Optimal Weak Recovery","created":"2025-02-03","cited_by_count":8,"area":null},{"id":"2502.06443","title":"Low-dimensional Functions are Efficiently Learnable under Randomly Biased Distributions","created":"2025-02-10","cited_by_count":8,"area":null},{"id":"2505.18346","title":"On the Mechanisms of Weak-to-Strong Generalization: A Theoretical Perspective","created":"2025-05-23","cited_by_count":8,"area":null},{"id":"2602.10680","title":"A solvable high-dimensional model where nonlinear autoencoders learn structure invisible to PCA while test loss misaligns with generalization","created":"2026-02-11","cited_by_count":8,"area":null},{"id":"2502.02545","title":"Optimal Spectral Transitions in High-Dimensional Multi-Index Models","created":"2025-02-04","cited_by_count":7,"area":null},{"id":"2503.23896","title":"Feature learning from non-Gaussian inputs: the case of Independent Component Analysis in high dimensions","created":"2025-03-31","cited_by_count":7,"area":null},{"id":"2509.21936","title":"Statistical Advantage of Softmax Attention: Insights from Single-Location Regression","created":"2025-09-26","cited_by_count":7,"area":null},{"id":"2209.02001","title":"On free energy barriers in Gaussian priors and failure of cold start MCMC for high-dimensional unimodal distributions","created":"2022-09-05","cited_by_count":6,"area":null},{"id":"2304.00707","title":"High-dimensional scaling limits and fluctuations of online least-squares SGD with smooth covariance","created":"2023-04-03","cited_by_count":6,"area":null},{"id":"2502.16849","title":"Provable Benefits of Unsupervised Pre-training and Transfer Learning via Single-Index Models","created":"2025-02-24","cited_by_count":6,"area":null},{"id":"2509.24914","title":"Single-Head Attention in High Dimensions: A Theory of Generalization, Weights Spectra, and Scaling Laws","created":"2025-09-29","cited_by_count":6,"area":null},{"id":"2601.19756","title":"Provable Learning of Random Hierarchy Models and Hierarchical Shallow-to-Deep Chaining","created":"2026-01-27","cited_by_count":6,"area":null},{"id":"2310.02117","title":"Symmetric Single Index Learning","created":"2023-10-03","cited_by_count":5,"area":null},{"id":"2410.16449","title":"Robust Feature Learning for Multi-Index Models in High Dimensions","created":"2024-10-21","cited_by_count":5,"area":null},{"id":"2411.15385","title":"Gradient dynamics for low-rank fine-tuning beyond kernels","created":"2024-11-23","cited_by_count":5,"area":null},{"id":"2505.09167","title":"Online Learning of Neural Networks","created":"2025-05-14","cited_by_count":5,"area":null},{"id":"2509.24510","title":"Specialization after Generalization: Towards Understanding Test-Time Training in Foundation Models","created":"2025-09-29","cited_by_count":5,"area":null},{"id":"2602.10867","title":"Deep Learning of Compositional Targets with Hierarchical Spectral Methods","created":"2026-02-11","cited_by_count":5,"area":null},{"id":"2310.01959","title":"Beyond Labeling Oracles: What does it mean to steal ML models?","created":"2023-10-03","cited_by_count":4,"area":null},{"id":"2401.07085","title":"Three Mechanisms of Feature Learning in a Linear Network","created":"2024-01-13","cited_by_count":4,"area":null},{"id":"2406.01766","title":"How Does Gradient Descent Learn Features -- A Local Analysis for Regularized Two-Layer Neural Networks","created":"2024-06-03","cited_by_count":4,"area":null},{"id":"2411.05708","title":"Sample and Computationally Efficient Robust Learning of Gaussian Single-Index Models","created":"2024-11-08","cited_by_count":4,"area":null},{"id":"2503.23642","title":"Learning a Single Index Model from Anisotropic Data with vanilla Stochastic Gradient Descent","created":"2025-03-31","cited_by_count":4,"area":null},{"id":"2309.04788","title":"Stochastic Gradient Descent outperforms Gradient Descent in recovering a high-dimensional signal in a glassy energy landscape","created":"2023-09-09","cited_by_count":3,"area":null},{"id":"2406.02157","title":"Online Learning and Information Exponents: On The Importance of Batch size, and Time/Complexity Tradeoffs","created":"2024-06-04","cited_by_count":3,"area":null},{"id":"2506.01582","title":"Bayes optimal learning of attention-indexed models","created":"2025-06-02","cited_by_count":3,"area":null},{"id":"2509.25741","title":"Test time training enhances in-context learning of nonlinear functions","created":"2025-09-30","cited_by_count":3,"area":null},{"id":"2510.02532","title":"Learning Multi-Index Models with Hyper-Kernel Ridge Regression","created":"2025-10-02","cited_by_count":3,"area":null},{"id":"2512.13634","title":"Universality of high-dimensional scaling limits of stochastic gradient descent","created":"2025-12-15","cited_by_count":3,"area":null},{"id":"2602.08907","title":"Positive Distribution Shift as a Framework for Understanding Tractable Learning","created":"2026-02-09","cited_by_count":3,"area":null},{"id":"2605.22010","title":"Uniform-in-Time Weak Propagation-of-Chaos in Shallow Neural Networks","created":"2026-05-21","cited_by_count":3,"area":null},{"id":"2606.05899","title":"High-Dimensional Theory of LoRA Fine-Tuning in a Solvable Attention Model","created":"2026-06-04","cited_by_count":3,"area":null},{"id":"2204.04476","title":"High-dimensional Asymptotics of Langevin Dynamics in Spiked Matrix Models","created":"2022-04-09","cited_by_count":2,"area":null},{"id":"2302.09815","title":"On the Stability and Generalization of Triplet Learning","created":"2023-02-20","cited_by_count":2,"area":null},{"id":"2406.01481","title":"Learning from Streaming Data when Users Choose","created":"2024-06-03","cited_by_count":2,"area":null},{"id":"2507.05644","title":"The Features at Convergence Theorem: a first-principles alternative to the Neural Feature Ansatz for how networks learn representations","created":"2025-07-08","cited_by_count":2,"area":null},{"id":"2510.01930","title":"Precise Dynamics of Diagonal Linear Networks: A Unifying Analysis by Dynamical Mean-Field Theory","created":"2025-10-02","cited_by_count":2,"area":null},{"id":"2510.21020","title":"From Information to Generative Exponent: Learning Rate Induces Phase Transitions in SGD","created":"2025-10-23","cited_by_count":2,"area":null},{"id":"2602.02855","title":"When pre-training hurts LoRA fine-tuning: a dynamical analysis via single-index models","created":"2026-02-02","cited_by_count":2,"area":null},{"id":"2603.03993","title":"Specialization of softmax attention heads: insights from the high-dimensional single-location model","created":"2026-03-04","cited_by_count":2,"area":null},{"id":"2603.17896","title":"A Noise Sensitivity Exponent Controls Large Statistical-to-Computational Gaps in Single- and Multi-Index Models","created":"2026-03-18","cited_by_count":2,"area":null},{"id":"2302.09727","title":"Statistical Inference for Linear Functionals of Online SGD in High-dimensional Linear Regression","created":"2023-02-20","cited_by_count":1,"area":null},{"id":"2311.15221","title":"The Local Landscape of Phase Retrieval Under Limited Samples","created":"2023-11-26","cited_by_count":1,"area":null},{"id":"2505.21336","title":"Joint Learning in the Gaussian Single Index Model","created":"2025-05-27","cited_by_count":1,"area":null},{"id":"2509.22764","title":"In-Context Learning can Perform Continual Learning Like Humans","created":"2025-09-26","cited_by_count":1,"area":null},{"id":"2510.19734","title":"Statistical Inference for Linear Functionals of Online Least-squares SGD when $t \\gtrsim d^{1+\\delta}$","created":"2025-10-22","cited_by_count":1,"area":null},{"id":"2511.02258","title":"Limit Theorems for Stochastic Gradient Descent in High-Dimensional Single-Layer Networks","created":"2025-11-04","cited_by_count":1,"area":null},{"id":"2512.01231","title":"Implicitly Normalized Online PCA: A Regularized Algorithm with Exact High-Dimensional Dynamics","created":"2025-12-01","cited_by_count":1,"area":null},{"id":"2602.17876","title":"Interactive Learning of Single-Index Models via Stochastic Gradient Descent","created":"2026-02-19","cited_by_count":1,"area":null},{"id":"2603.06028","title":"Improved high-dimensional estimation with Langevin dynamics and stochastic weight averaging","created":"2026-03-06","cited_by_count":1,"area":null},{"id":"2604.03068","title":"Escape dynamics and implicit bias of one-pass SGD in overparameterized quadratic networks","created":"2026-04-03","cited_by_count":1,"area":null},{"id":"2605.05710","title":"On the Blessing of Pre-training in Weak-to-Strong Generalization","created":"2026-05-07","cited_by_count":1,"area":null},{"id":"2605.10237","title":"The Benefits of Temporal Correlations: SGD Learns k-Juntas from Random Walks Efficiently","created":"2026-05-11","cited_by_count":1,"area":null},{"id":"2605.12908","title":"The Mechanism of Weak-to-Strong Generalization: Feature Elicitation from Latent Knowledge","created":"2026-05-13","cited_by_count":1,"area":null},{"id":"2605.14927","title":"Learning with Shallow Neural Networks on Cluster-Structured Features","created":"2026-05-14","cited_by_count":1,"area":null},{"id":"2312.12323","title":"Topological complexity of spiked random polynomials and finite-rank spherical integrals","created":"2023-12-19","cited_by_count":0,"area":null},{"id":"2401.09376","title":"Online Performance Estimation with Unlabeled Data: A Bayesian Application of the Hui-Walter Paradigm","created":"2024-01-17","cited_by_count":0,"area":null},{"id":"2412.14650","title":"Permutation recovery of spikes in noisy high-dimensional tensor estimation","created":"2024-12-19","cited_by_count":0,"area":null}],"next_cursor":"100"}