← Authors

Richard Baraniuk

47 papers · 2007–2026 · 10 conferences · across top CS/AI conferences

Achievements

🧭 Keyword Pioneer 🌍 Conference Polyglot (9) πŸƒ Academic Marathon (18) 🌈 Renaissance Researcher (10) πŸŒ‰ Interdisciplinary Bridge 🐝 Cross-Pollinator (13) πŸ—ΊοΈ Taxonomy Completionist (11) 🐣 Hot Topic Early Bird 🌟 Keyword Trendsetter Combo (6) 🀝 Dynamic Duo (10) πŸ‘‘ Triple Crown πŸ† Keyword Champion 🧬 Topic Evolution πŸ’Ž Century Club (46) πŸ“ˆ Trend Setter ⚑ Prolific Year (6) ❓ The Questioner πŸ—ƒοΈ Keyword Collector (188) πŸ”₯ Unstoppable (10)

Conferences

NIPS (12) ICML (9) ICLR (8) EMNLP (6) AISTATS (5) ACL (2) CVPR (2) COLING (1) EACL (1) IJCAI (1)

Top co-authors

Randall Balestriero (10) Shashank Sonkar (7) Zichao Wang (7) Naiming Liu (5) Ahmed Imtiaz Humayun (5) Daniel LeJeune (5) Andrew Lan (4) Divyanshu Vats (3) Tan Minh Nguyen (3) Stanley Osher (3)

Research topics

Education (4) Generation (1)

Keywords

large language model (4) compressive sensing (4) compressed sensing (3) nearest neighbor search (3) intelligent tutoring system (2) neural network (2) decision boundary (2) greedy algorithm (2) convolutional neural network (2) double descent (2) probabilistic modeling (2) neural network optimization (2) graphical model (2) transformer architecture (2) language model (2) language modeling (2) model selection (1) self-attention mechanism (1) neural network pruning (1) image generation (1)

Papers

CLEAR-3K: Assessing Causal Explanatory Capabilities in Language Models EACL 2026
MITIGATING OVER-EXPLORATION IN LATENT SPACE OPTIMIZATION USING LES ICML 2025
Student Data Paradox and Curious Case of Single Student-Tutor Model: Regressive Side Effects of Training LLMs for Personalized Learning EMNLP 2024
MalAlgoQA: Pedagogical Evaluation of Counterfactual Reasoning in Large Language Models and Implications for AI in Education EMNLP 2024
Implicit Neural Representations and the Algebra of Complex Wavelets ICLR 2024
PIDformer: Transformer Meets Control Theory ICML 2024
Self-Consuming Generative Models Go MAD ICLR 2024
Deep Networks Always Grok and Here is Why ICML 2024
Pedagogical Alignment of Large Language Models EMNLP 2024
CLASS: A Design Framework for Building Intelligent Tutoring Systems Based on Learning Science principles EMNLP 2023
Mitigating Over-smoothing in Transformers via Regularized Nonlocal Functionals NIPS 2023
MultiQG-TI: Towards Question Generation from Multi-modal Sources ACL 2023
MANER: Mask Augmented Named Entity Recognition for Extreme Low-Resource Languages ACL 2023
Retrieval-based Controllable Molecule Generation ICLR 2023
A Primal-Dual Framework for Transformers and Neural Networks ICLR 2023
Polarity Sampling: Quality and Diversity Control of Pre-Trained Generative Networks via Singular Values CVPR 2022
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
Parameters or Privacy: A Provable Tradeoff Between Overparameterization and Membership Inference NIPS 2022
Open-ended Knowledge Tracing for Computer Science Education EMNLP 2022
The Flip Side of the Reweighted Coin: Duality of Adaptive Dropout and Regularization NIPS 2021
The Recurrent Neural Tangent Kernel ICLR 2021
Math Word Problem Generation with Mathematical Consistency and Problem Context Constraints EMNLP 2021
The Implicit Regularization of Ordinary Least Squares Ensembles AISTATS 2020
MomentumRNN: Integrating Momentum into Recurrent Neural Networks NIPS 2020
Analytical Probability Distributions and Exact Expectation-Maximization for Deep Generative Networks NIPS 2020
Thresholding Graph Bandits with GrAPL AISTATS 2020
Attention Word Embedding COLING 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
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 NIPS 2019
Spline Filters For End-to-End Deep Learning ICML 2018
prDeep: Robust Phase Retrieval with a Flexible Deep Network ICML 2018
RHash: Robust Hashing via L_infinity-norm Distortion IJCAI 2017
Learned D-AMP: Principled Neural Network based Compressive Image Recovery NIPS 2017
Dealbreaker: A Nonlinear Latent Variable Model for Educational Data ICML 2016
A Probabilistic Framework for Deep Learning NIPS 2016
Path Thresholding: Asymptotically Tuning-Free High-Dimensional Sparse Regression AISTATS 2014
Active Learning for Undirected Graphical Model Selection AISTATS 2014
When in Doubt, SWAP: High-Dimensional Sparse Recovery from Correlated Measurements NIPS 2013
SpaRCS: Recovering low-rank and sparse matrices from compressive measurements NIPS 2011
Sparse Signal Recovery Using Markov Random Fields NIPS 2008
Random Projections for Manifold Learning NIPS 2007