Richard Baraniuk

CLEAR-3K: Assessing Causal Explanatory Capabilities in Language Models EACL 2026 MITIGATING OVER-EXPLORATION IN LATENT SPACE OPTIMIZATION USING LES ICML 2025 Deep Networks Always Grok and Here is Why ICML 2024 Implicit Neural Representations and the Algebra of Complex Wavelets ICLR 2024 MalAlgoQA: Pedagogical Evaluation of Counterfactual Reasoning in Large Language Models and Implications for AI in Education EMNLP 2024 Pedagogical Alignment of Large Language Models EMNLP 2024 PIDformer: Transformer Meets Control Theory ICML 2024 Self-Consuming Generative Models Go MAD ICLR 2024 Student Data Paradox and Curious Case of Single Student-Tutor Model: Regressive Side Effects of Training LLMs for Personalized Learning EMNLP 2024 A Primal-Dual Framework for Transformers and Neural Networks ICLR 2023 CLASS: A Design Framework for Building Intelligent Tutoring Systems Based on Learning Science principles EMNLP 2023 MANER: Mask Augmented Named Entity Recognition for Extreme Low-Resource Languages ACL 2023 Mitigating Over-smoothing in Transformers via Regularized Nonlocal Functionals NeurIPS 2023 MultiQG-TI: Towards Question Generation from Multi-modal Sources ACL 2023 Retrieval-based Controllable Molecule Generation ICLR 2023 Can Neural Nets Learn the Same Model Twice? Investigating Reproducibility and Double Descent From the Decision Boundary Perspective CVPR 2022 Improving Transformers with Probabilistic Attention Keys ICML 2022 MaGNET: Uniform Sampling from Deep Generative Network Manifolds Without Retraining ICLR 2022 Open-ended Knowledge Tracing for Computer Science Education EMNLP 2022 Parameters or Privacy: A Provable Tradeoff Between Overparameterization and Membership Inference NeurIPS 2022 Polarity Sampling: Quality and Diversity Control of Pre-Trained Generative Networks via Singular Values CVPR 2022 Math Word Problem Generation with Mathematical Consistency and Problem Context Constraints EMNLP 2021 The Flip Side of the Reweighted Coin: Duality of Adaptive Dropout and Regularization NeurIPS 2021 The Recurrent Neural Tangent Kernel ICLR 2021 Analytical Probability Distributions and Exact Expectation-Maximization for Deep Generative Networks NeurIPS 2020 Attention Word Embedding COLING 2020 MomentumRNN: Integrating Momentum into Recurrent Neural Networks NeurIPS 2020 Sub-linear Memory Sketches for Near Neighbor Search on Streaming Data ICML 2020 Subspace Fitting Meets Regression: The Effects of Supervision and Orthonormality Constraints on Double Descent of Generalization Errors ICML 2020 The Implicit Regularization of Ordinary Least Squares Ensembles AISTATS 2020 Thresholding Graph Bandits with GrAPL AISTATS 2020 Adaptive Estimation for Approximate k-Nearest-Neighbor Computations AISTATS 2019 A Max-Affine Spline Perspective of Recurrent Neural Networks ICLR 2019 From Hard to Soft: Understanding Deep Network Nonlinearities via Vector Quantization and Statistical Inference ICLR 2019 The Geometry of Deep Networks: Power Diagram Subdivision NeurIPS 2019 prDeep: Robust Phase Retrieval with a Flexible Deep Network ICML 2018 Spline Filters For End-to-End Deep Learning ICML 2018 Learned D-AMP: Principled Neural Network based Compressive Image Recovery NeurIPS 2017 RHash: Robust Hashing via L_infinity-norm Distortion IJCAI 2017 A Probabilistic Framework for Deep Learning NeurIPS 2016 Dealbreaker: A Nonlinear Latent Variable Model for Educational Data ICML 2016 Active Learning for Undirected Graphical Model Selection AISTATS 2014 Path Thresholding: Asymptotically Tuning-Free High-Dimensional Sparse Regression AISTATS 2014 When in Doubt, SWAP: High-Dimensional Sparse Recovery from Correlated Measurements NeurIPS 2013 SpaRCS: Recovering low-rank and sparse matrices from compressive measurements NeurIPS 2011 Sparse Signal Recovery Using Markov Random Fields NeurIPS 2008 Random Projections for Manifold Learning NeurIPS 2007