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Andrej Risteski

  1. Linear Algebraic Structure of Word Senses, with Applications to Polysemy
    2016/01/14 by Sanjeev Arora, Arora, Sanjeev, Yuanzhi Li +7 · 46 citations
    Computer Science · Mathematics · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #cs.CL #cs.LG #stat.ML
  2. The Risks of Invariant Risk Minimization
    2020/10/12 by Elan Rosenfeld, Pradeep Ravikumar, Rosenfeld, Elan +3 · 20 citations
    Computer Science · Mathematics · #Adversarial Robustness in Machine Learning #Artificial Intelligence (cs.AI) #Artificial intelligence #Computer science #Domain Adaptation and Few-Shot Learning #Econometrics #Empirical risk minimization #FOS: Computer and information sciences #Generalization #Invariant (physics) #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Data Classification #Mathematical optimization #Mathematics #Minification #Training set #cs.AI #cs.LG #stat.ML
  3. On the Benefits of Memory for Modeling Time-Dependent PDEs
    2024/09/03 by Ricardo Buitrago Ruiz, Tanya Marwah, Ruiz, Ricardo Buitrago +5 · 20 citations
    Decision Sciences · #Simulation Techniques and Applications
  4. How Do Transformers Learn Topic Structure: Towards a Mechanistic Understanding
    2023/03/07 by Yuchen Li, Li, Yuchen, Yuanzhi Li +3 · 11 citations
    Computer Science · #Topic Modeling #Natural Language Processing Techniques #Text and Document Classification Technologies
  5. Beyond Log-concavity: Provable Guarantees for Sampling Multi-modal Distributions using Simulated Tempering Langevin Monte Carlo
    2017/10/07 by Rong Ge, Ge, Rong, Holden Lee +3 · 7 citations
    Computer Science · Mathematics · #Bayesian Methods and Mixture Models #Data Structures and Algorithms (cs.DS) #FOS: Computer and information sciences #FOS: Mathematics #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Markov Chains and Monte Carlo Methods #Probability (math.PR) #cs.DS #cs.LG #math.PR #stat.ML
  6. Statistical Efficiency of Score Matching: The View from Isoperimetry
    2022/10/03 by Frederic Koehler, Alexander Heckett, Koehler, Frederic +3 · 8 citations
    Computer Science · Mathematics · #FOS: Computer and information sciences #FOS: Mathematics #Gaussian Processes and Bayesian Inference #Generative Adversarial Networks and Image Synthesis #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Markov Chains and Monte Carlo Methods #Statistics Theory (math.ST)
  7. Sampling Approximately Low-Rank Ising Models: MCMC meets Variational Methods
    2022/02/17 by Frederic Koehler, Holden Lee, Koehler, Frederic +3 · 6 citations
    Computer Science · Mathematics · #Data Structures and Algorithms (cs.DS) #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Probability (math.PR) #cs.DS #cs.LG #math.PR #stat.ML
  8. Mean-field approximation, convex hierarchies, and the optimality of correlation rounding: a unified perspective
    2018/08/22 by Vishesh Jain, Frederic Koehler, Jain, Vishesh +3 · 4 citations
    Computer Science · Mathematics · Physics and Astronomy · #Markov Chains and Monte Carlo Methods #Stochastic processes and statistical mechanics #Theoretical and Computational Physics #cs.DS #cs.LG #math.PR #stat.ML
  9. Recovery guarantee of weighted low-rank approximation via alternating minimization
    2016/02/06 by Yuanzhi Li, Li, Yuanzhi, Yingyu Liang +3 · 5 citations
    Computer Science · Engineering · Mathematics · #Data Structures and Algorithms (cs.DS) #FOS: Computer and information sciences #Image and Signal Denoising Methods #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Medical Image Segmentation Techniques #Sparse and Compressive Sensing Techniques #Tensor decomposition and applications #cs.DS #cs.LG #stat.ML
  10. Representational aspects of depth and conditioning in normalizing flows
    2020/10/02 by Frederic Koehler, Koehler, Frederic, Viraj Mehta +3 · 5 citations
    Computer Science · Mathematics · Medicine · #Advanced Neuroimaging Techniques and Applications #Computer Graphics and Visualization Techniques #Generative Adversarial Networks and Image Synthesis #cs.LG #stat.ML
  11. Deep Equilibrium Based Neural Operators for Steady-State PDEs
    2023/11/30 by Tanya Marwah, Ashwini Pokle, Marwah, Tanya +9 · 6 citations
    Engineering · Physics and Astronomy · #FOS: Computer and information sciences #FOS: Mathematics #Fluid Dynamics and Turbulent Flows #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Model Reduction and Neural Networks #Numerical Analysis (math.NA)
  12. Domain-Adjusted Regression or: ERM May Already Learn Features Sufficient for Out-of-Distribution Generalization
    2022/02/14 by Elan Rosenfeld, Pradeep Ravikumar, Rosenfeld, Elan +3 · 4 citations
    Computer Science · #Domain Adaptation and Few-Shot Learning #Machine Learning and Data Classification
  13. Understanding Augmentation-based Self-Supervised Representation Learning via RKHS Approximation and Regression
    2023/06/01 by Runtian Zhai, Zhai, Runtian, Bingbin Liu +7 · 5 citations
    Computer Science · #Advanced Graph Neural Networks #Topic Modeling #Domain Adaptation and Few-Shot Learning
  14. Neural Network Approximations of PDEs Beyond Linearity: A Representational Perspective
    2022/10/21 by Tanya Marwah, Marwah, Tanya, Zachary C. Lipton +5 · 4 citations
    Engineering · Physics and Astronomy · #Advanced Numerical Analysis Techniques #Advanced Numerical Methods in Computational Mathematics #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Model Reduction and Neural Networks #Numerical Analysis (math.NA)
  15. On the ability of neural nets to express distributions
    2017/02/22 by Holden Lee, Rong Ge, Lee, Holden +7 · 2 citations
    Computer Science · #FOS: Computer and information sciences #Machine Learning (cs.LG) #cs.LG
  16. Provable benefits of score matching
    2023/06/03 by Chirag Pabbaraju, Pabbaraju, Chirag, Dhruv Rohatgi +9 · 3 citations
    Computer Science · Engineering · Mathematics · #Data Structures and Algorithms (cs.DS) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Sparse and Compressive Sensing Techniques #Statistical Methods and Inference #Stochastic Gradient Optimization Techniques
  17. An Online Learning Approach to Interpolation and Extrapolation in Domain Generalization
    2021/02/25 by Elan Rosenfeld, Pradeep Ravikumar, Rosenfeld, Elan +3 · 3 citations
    Computer Science · Mathematics · #Domain Adaptation and Few-Shot Learning #Machine Learning and Algorithms #Machine Learning and Data Classification #cs.AI #cs.GT #cs.LG #stat.ML
  18. Iterative Feature Matching: Toward Provable Domain Generalization with Logarithmic Environments
    2021/06/18 by Yining Chen, Elan Rosenfeld, Chen, Yining +7 · 2 citations
    Computer Science · Mathematics · #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Algorithms #Multimodal Machine Learning Applications #cs.LG #stat.ML
  19. Approximability of Discriminators Implies Diversity in GANs
    2018/06/27 by Yu Bai, Bai, Yu, Tengyu Ma +3 · 4 citations
    Computer Science · Mathematics · Physics and Astronomy · #Anomaly Detection Techniques and Applications #Data Structures and Algorithms (cs.DS) #FOS: Computer and information sciences #Generative Adversarial Networks and Image Synthesis #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Model Reduction and Neural Networks #cs.DS #cs.LG #stat.ML
  20. Outliers with Opposing Signals Have an Outsized Effect on Neural Network Optimization
    2023/11/07 by Elan Rosenfeld, Andrej Risteski, Rosenfeld, Elan +1 · 1 voice · 3 citations
    Computer Science · Mathematics · Physics and Astronomy · #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Data Classification #Model Reduction and Neural Networks #Neural Networks and Applications #cs.AI #cs.CV #cs.LG #stat.ML
  21. On Routing Disjoint Paths in Bounded Treewidth Graphs
    2015/12/06 by Alina Ene, Matthias Mnich, Ene, Alina +5 · 1 citation
    Computer Science · #Data Structures and Algorithms (cs.DS) #FOS: Computer and information sciences #cs.DS
  22. Promises and Pitfalls of Generative Masked Language Modeling: Theoretical Framework and Practical Guidelines
    2024/07/22 by Yuchen Li, Li, Yuchen, Alexandre Kirchmeyer +13 · 3 citations
    Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Natural Language Processing Techniques #Speech and dialogue systems
  23. On some provably correct cases of variational inference for topic models
    2015/03/23 by Pranjal Awasthi, Awasthi, Pranjal, Andrej Risteski +1 · 2 citations
    Computer Science · Mathematics · #Bayesian Methods and Mixture Models #Data Structures and Algorithms (cs.DS) #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Algorithms #cs.DS #cs.LG #stat.ML
  24. Transformers are uninterpretable with myopic methods: a case study with bounded Dyck grammars
    2023/12/03 by Kaiyue Wen, Wen, Kaiyue, Yuchen Li +5 · 2 citations
    Computer Science · #Computation and Language (cs.CL) #Explainable Artificial Intelligence (XAI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Algorithms #Topic Modeling
  25. Parametric Complexity Bounds for Approximating PDEs with Neural Networks
    2021/03/03 by Tanya Marwah, Marwah, Tanya, Zachary C. Lipton +3 · 1 citation
    Computer Science · Mathematics · Physics and Astronomy · #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Model Reduction and Neural Networks #Neural Networks and Applications #Numerical Analysis (math.NA) #Numerical Methods and Algorithms #cs.LG #cs.NA #math.NA #stat.ML
  26. Analyzing and Improving the Optimization Landscape of Noise-Contrastive Estimation
    2021/10/21 by Bingbin Liu, Liu, Bingbin, Elan Rosenfeld +5 · 1 citation
    Computer Science · Mathematics · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #cs.LG #stat.ML
  27. Empirical Study of the Benefits of Overparameterization in Learning Latent Variable Models
    2019/06/28 by Rares-Darius Buhai, Buhai, Rares-Darius, Yoni Halpern +7 · 2 citations
    Computer Science · Mathematics · #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Data Classification #Topic Modeling #cs.LG #stat.ML
  28. Continual learning: a feature extraction formalization, an efficient algorithm, and fundamental obstructions
    2022/03/27 by Binghui Peng, Andrej Risteski, Peng, Binghui +1 · 1 citation
    Computer Science · #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Multimodal Machine Learning Applications
  29. On the Query Complexity of Verifier-Assisted Language Generation
    2025/02/17 by Botta, Edoardo, Yuchen Li, Aashay Mehta +7 · 3 citations
    Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Natural Language Processing Techniques #Speech and dialogue systems #Topic Modeling
  30. Fast Convergence for Langevin Diffusion with Manifold Structure
    2020/02/13 by Ankur Moitra, Andrej Risteski, Moitra, Ankur +1 · 2 citations
    Computer Science · Mathematics · #Bayesian Methods and Mixture Models #Data Structures and Algorithms (cs.DS) #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Markov Chains and Monte Carlo Methods #Probability (math.PR) #Statistical Methods and Inference #cs.DS #cs.LG #math.PR #stat.ML
  31. Pitfalls of Gaussians as a noise distribution in NCE
    2022/10/01 by Holden Lee, Lee, Holden, Chirag Pabbaraju +5 · 1 citation
    Computer Science · #Anomaly Detection Techniques and Applications #Bayesian Methods and Mixture Models #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML)
  32. Provable benefits of representation learning
    2017/06/14 by Sanjeev Arora, Andrej Risteski, Arora, Sanjeev +1 · 1 citation
    Computer Science · Mathematics · #Bayesian Methods and Mixture Models #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Algorithms #cs.LG #stat.ML
  33. Taming Imperfect Process Verifiers: A Sampling Perspective on Backtracking
    2025/10/03 by Dhruv Rohatgi, Rohatgi, Dhruv, Abhishek Shetty +11 · 1 voice · 2 citations
    Computer Science · #Formal Methods in Verification #Machine Learning and Algorithms #Natural Language Processing Techniques #cs.DS #cs.LG