Andrej Risteski

Publication Activity (10 Years)

Years Active: 2012-2024
Publications (10 Years): 93

Top Venues

CoRR

NeurIPS

ICLR

ICML

Publications

Yilong Qin, Andrej Risteski
Fit Like You Sample: Sample-Efficient Generalized Score Matching from Fast Mixing Diffusions. COLT (2024)
Runtian Zhai, Bingbin Liu, Andrej Risteski, J. Zico Kolter, Pradeep Kumar Ravikumar
Understanding Augmentation-based Self-Supervised Representation Learning via RKHS Approximation and Regression. ICLR (2024)
Elan Rosenfeld, Andrej Risteski
Outliers with Opposing Signals Have an Outsized Effect on Neural Network Optimization. ICLR (2024)
Yuchen Li, Alexandre Kirchmeyer, Aashay Mehta, Yilong Qin, Boris Dadachev, Kishore Papineni, Sanjiv Kumar, Andrej Risteski
Promises and Pitfalls of Generative Masked Language Modeling: Theoretical Framework and Practical Guidelines. CoRR (2024)
Omar Chehab, Aapo Hyvärinen, Andrej Risteski
Provable benefits of annealing for estimating normalizing constants: Importance Sampling, Noise-Contrastive Estimation, and beyond. CoRR (2023)
Yilong Qin, Andrej Risteski
Fit Like You Sample: Sample-Efficient Generalized Score Matching from Fast Mixing Markov Chains. CoRR (2023)
Chirag Pabbaraju, Dhruv Rohatgi, Anish Prasad Sevekari, Holden Lee, Ankur Moitra, Andrej Risteski
Provable benefits of score matching. NeurIPS (2023)
Kaiyue Wen, Yuchen Li, Bingbin Liu, Andrej Risteski
Transformers are uninterpretable with myopic methods: a case study with bounded Dyck grammars. NeurIPS (2023)
Tanya Marwah, Ashwini Pokle, J. Zico Kolter, Zachary C. Lipton, Jianfeng Lu, Andrej Risteski
Deep Equilibrium Based Neural Operators for Steady-State PDEs. CoRR (2023)
Chirag Pabbaraju, Dhruv Rohatgi, Anish Prasad Sevekari, Holden Lee, Ankur Moitra, Andrej Risteski
Provable benefits of score matching. CoRR (2023)
Yuchen Li, Yuanzhi Li, Andrej Risteski
How Do Transformers Learn Topic Structure: Towards a Mechanistic Understanding. ICML (2023)
Tanya Marwah, Ashwini Pokle, J. Zico Kolter, Zachary C. Lipton, Jianfeng Lu, Andrej Risteski
Deep Equilibrium Based Neural Operators for Steady-State PDEs. NeurIPS (2023)
Frederic Koehler, Alexander Heckett, Andrej Risteski
Statistical Efficiency of Score Matching: The View from Isoperimetry. ICLR (2023)
Elan Rosenfeld, Andrej Risteski
Outliers with Opposing Signals Have an Outsized Effect on Neural Network Optimization. CoRR (2023)
Omar Chehab, Aapo Hyvärinen, Andrej Risteski
Provable benefits of annealing for estimating normalizing constants: Importance Sampling, Noise-Contrastive Estimation, and beyond. NeurIPS (2023)
Tanya Marwah, Zachary Chase Lipton, Jianfeng Lu, Andrej Risteski
Neural Network Approximations of PDEs Beyond Linearity: A Representational Perspective. ICML (2023)
Yuchen Li, Yuanzhi Li, Andrej Risteski
How Do Transformers Learn Topic Structure: Towards a Mechanistic Understanding. CoRR (2023)
Holden Lee, Chirag Pabbaraju, Anish Prasad Sevekari, Andrej Risteski
Pitfalls of Gaussians as a noise distribution in NCE. ICLR (2023)
Runtian Zhai, Bingbin Liu, Andrej Risteski, Zico Kolter, Pradeep Ravikumar
Understanding Augmentation-based Self-Supervised Representation Learning via RKHS Approximation. CoRR (2023)
Kaiyue Wen, Yuchen Li, Bingbin Liu, Andrej Risteski
Transformers are uninterpretable with myopic methods: a case study with bounded Dyck grammars. CoRR (2023)
Frederic Koehler, Holden Lee, Andrej Risteski
Sampling Approximately Low-Rank Ising Models: MCMC meets Variational Methods. CoRR (2022)
Frederic Koehler, Viraj Mehta, Chenghui Zhou, Andrej Risteski
Variational autoencoders in the presence of low-dimensional data: landscape and implicit bias. ICLR (2022)
Binghui Peng, Andrej Risteski
Continual learning: a feature extraction formalization, an efficient algorithm, and fundamental obstructions. NeurIPS (2022)
Elan Rosenfeld, Pradeep Ravikumar, Andrej Risteski
Domain-Adjusted Regression or: ERM May Already Learn Features Sufficient for Out-of-Distribution Generalization. CoRR (2022)
Bingbin Liu, Elan Rosenfeld, Pradeep Kumar Ravikumar, Andrej Risteski
Analyzing and Improving the Optimization Landscape of Noise-Contrastive Estimation. ICLR (2022)
Frederic Koehler, Holden Lee, Andrej Risteski
Sampling Approximately Low-Rank Ising Models: MCMC meets Variational Methods. COLT (2022)
Yining Chen, Elan Rosenfeld, Mark Sellke, Tengyu Ma, Andrej Risteski
Iterative Feature Matching: Toward Provable Domain Generalization with Logarithmic Environments. NeurIPS (2022)
Ashwini Pokle, Jinjin Tian, Yuchen Li, Andrej Risteski
Contrasting the landscape of contrastive and non-contrastive learning. AISTATS (2022)
Divyansh Pareek, Andrej Risteski
The Effects of Invertibility on the Representational Complexity of Encoders in Variational Autoencoders. ICLR (2022)
Holden Lee, Chirag Pabbaraju, Anish Prasad Sevekari, Andrej Risteski
Pitfalls of Gaussians as a noise distribution in NCE. CoRR (2022)
Frederic Koehler, Alexander Heckett, Andrej Risteski
Statistical Efficiency of Score Matching: The View from Isoperimetry. CoRR (2022)
Elan Rosenfeld, Pradeep Ravikumar, Andrej Risteski
An Online Learning Approach to Interpolation and Extrapolation in Domain Generalization. AISTATS (2022)
Ashwini Pokle, Jinjin Tian, Yuchen Li, Andrej Risteski
Contrasting the landscape of contrastive and non-contrastive learning. CoRR (2022)
Binghui Peng, Andrej Risteski
Continual learning: a feature extraction formalization, an efficient algorithm, and fundamental obstructions. CoRR (2022)
Bingbin Liu, Daniel Hsu, Pradeep Ravikumar, Andrej Risteski
Masked prediction tasks: a parameter identifiability view. CoRR (2022)
Bingbin Liu, Daniel J. Hsu, Pradeep Ravikumar, Andrej Risteski
Masked Prediction: A Parameter Identifiability View. NeurIPS (2022)
Tanya Marwah, Zachary C. Lipton, Jianfeng Lu, Andrej Risteski
Neural Network Approximations of PDEs Beyond Linearity: Representational Perspective. CoRR (2022)
Divyansh Pareek, Andrej Risteski
The Effects of Invertibility on the Representational Complexity of Encoders in Variational Autoencoders. CoRR (2021)
Yining Chen, Elan Rosenfeld, Mark Sellke, Tengyu Ma, Andrej Risteski
Iterative Feature Matching: Toward Provable Domain Generalization with Logarithmic Environments. CoRR (2021)
Tanya Marwah, Zachary C. Lipton, Andrej Risteski
Parametric Complexity Bounds for Approximating PDEs with Neural Networks. CoRR (2021)
Rong Ge, Holden Lee, Jianfeng Lu, Andrej Risteski
Efficient sampling from the Bingham distribution. ALT (2021)
Elan Rosenfeld, Pradeep Ravikumar, Andrej Risteski
An Online Learning Approach to Interpolation and Extrapolation in Domain Generalization. CoRR (2021)
Yuchen Li, Andrej Risteski
The Limitations of Limited Context for Constituency Parsing. ACL/IJCNLP (1) (2021)
Holden Lee, Chirag Pabbaraju, Anish Prasad Sevekari, Andrej Risteski
Universal Approximation Using Well-Conditioned Normalizing Flows. NeurIPS (2021)
Frederic Koehler, Viraj Mehta, Andrej Risteski
Representational aspects of depth and conditioning in normalizing flows. ICML (2021)
Holden Lee, Chirag Pabbaraju, Anish Sevekari, Andrej Risteski
Universal Approximation for Log-concave Distributions using Well-conditioned Normalizing Flows. CoRR (2021)
Tanya Marwah, Zachary C. Lipton, Andrej Risteski
Parametric Complexity Bounds for Approximating PDEs with Neural Networks. NeurIPS (2021)
Bingbin Liu, Pradeep Ravikumar, Andrej Risteski
Contrastive learning of strong-mixing continuous-time stochastic processes. CoRR (2021)
Frederic Koehler, Viraj Mehta, Andrej Risteski, Chenghui Zhou
Variational autoencoders in the presence of low-dimensional data: landscape and implicit bias. CoRR (2021)
Yuchen Li, Andrej Risteski
The Limitations of Limited Context for Constituency Parsing. CoRR (2021)
Elan Rosenfeld, Pradeep Kumar Ravikumar, Andrej Risteski
The Risks of Invariant Risk Minimization. ICLR (2021)
Bingbin Liu, Elan Rosenfeld, Pradeep Ravikumar, Andrej Risteski
Analyzing and Improving the Optimization Landscape of Noise-Contrastive Estimation. CoRR (2021)
Bingbin Liu, Pradeep Ravikumar, Andrej Risteski
Contrastive learning of strong-mixing continuous-time stochastic processes. AISTATS (2021)
Han Zhao, Junjie Hu, Andrej Risteski
On Learning Language-Invariant Representations for Universal Machine Translation. CoRR (2020)
Han Zhao, Junjie Hu, Andrej Risteski
On Learning Language-Invariant Representations for Universal Machine Translation. ICML (2020)
Frederic Koehler, Viraj Mehta, Andrej Risteski
Representational aspects of depth and conditioning in normalizing flows. CoRR (2020)
Elan Rosenfeld, Pradeep Ravikumar, Andrej Risteski
The Risks of Invariant Risk Minimization. CoRR (2020)
Rong Ge, Holden Lee, Jianfeng Lu, Andrej Risteski
Efficient sampling from the Bingham distribution. CoRR (2020)
Ankur Moitra, Andrej Risteski
Fast Convergence for Langevin Diffusion with Matrix Manifold Structure. CoRR (2020)
Rares-Darius Buhai, Yoni Halpern, Yoon Kim, Andrej Risteski, David A. Sontag
Empirical Study of the Benefits of Overparameterization in Learning Latent Variable Models. ICML (2020)
Vishesh Jain, Frederic Koehler, Andrej Risteski
Mean-field approximation, convex hierarchies, and the optimality of correlation rounding: a unified perspective. STOC (2019)
Dylan J. Foster, Andrej Risteski
Sum-of-squares meets square loss: Fast rates for agnostic tensor completion. COLT (2019)
Dylan J. Foster, Andrej Risteski
Sum-of-squares meets square loss: Fast rates for agnostic tensor completion. CoRR (2019)
Frederic Koehler, Andrej Risteski
The Comparative Power of ReLU Networks and Polynomial Kernels in the Presence of Sparse Latent Structure. ICLR (Poster) (2019)
Yu Bai, Tengyu Ma, Andrej Risteski
Approximability of Discriminators Implies Diversity in GANs. ICLR (Poster) (2019)
Rares-Darius Buhai, Andrej Risteski, Yoni Halpern, David A. Sontag
Benefits of Overparameterization in Single-Layer Latent Variable Generative Models. CoRR (2019)
Sanjeev Arora, Yuanzhi Li, Yingyu Liang, Tengyu Ma, Andrej Risteski
Linear Algebraic Structure of Word Senses, with Applications to Polysemy. Trans. Assoc. Comput. Linguistics 6 (2018)
Holden Lee, Andrej Risteski, Rong Ge
Beyond Log-concavity: Provable Guarantees for Sampling Multi-modal Distributions using Simulated Tempering Langevin Monte Carlo. NeurIPS (2018)
Vishesh Jain, Frederic Koehler, Andrej Risteski
Mean-field approximation, convex hierarchies, and the optimality of correlation rounding: a unified perspective. CoRR (2018)
Sanjeev Arora, Andrej Risteski, Yi Zhang
Do GANs learn the distribution? Some Theory and Empirics. ICLR (Poster) (2018)
Frederic Koehler, Andrej Risteski
Representational Power of ReLU Networks and Polynomial Kernels: Beyond Worst-Case Analysis. CoRR (2018)
Yu Bai, Tengyu Ma, Andrej Risteski
Approximability of Discriminators Implies Diversity in GANs. CoRR (2018)
Rong Ge, Holden Lee, Andrej Risteski
Simulated Tempering Langevin Monte Carlo II: An Improved Proof using Soft Markov Chain Decomposition. CoRR (2018)
Rong Ge, Holden Lee, Andrej Risteski
Beyond Log-concavity: Provable Guarantees for Sampling Multi-modal Distributions using Simulated Tempering Langevin Monte Carlo. CoRR (2017)
Holden Lee, Rong Ge, Andrej Risteski, Tengyu Ma, Sanjeev Arora
On the ability of neural nets to express distributions. CoRR (2017)
Sanjeev Arora, Andrej Risteski
Provable benefits of representation learning. CoRR (2017)
Sanjeev Arora, Rong Ge, Tengyu Ma, Andrej Risteski
Provable learning of noisy-OR networks. STOC (2017)
Holden Lee, Rong Ge, Tengyu Ma, Andrej Risteski, Sanjeev Arora
On the Ability of Neural Nets to Express Distributions. COLT (2017)
Mikhail Khodak, Andrej Risteski, Christiane Fellbaum, Sanjeev Arora
Extending and Improving Wordnet via Unsupervised Word Embeddings. CoRR (2017)
Sanjeev Arora, Andrej Risteski, Yi Zhang
Theoretical limitations of Encoder-Decoder GAN architectures. CoRR (2017)
Yuanzhi Li, Andrej Risteski
Approximate maximum entropy principles via Goemans-Williamson with applications to provable variational methods. CoRR (2016)
Yuanzhi Li, Yingyu Liang, Andrej Risteski
Recovery Guarantee of Non-negative Matrix Factorization via Alternating Updates. CoRR (2016)
Yuanzhi Li, Yingyu Liang, Andrej Risteski
Recovery Guarantee of Non-negative Matrix Factorization via Alternating Updates. NIPS (2016)
Andrej Risteski
How to calculate partition functions using convex programming hierarchies: provable bounds for variational methods. COLT (2016)
Sanjeev Arora, Yuanzhi Li, Yingyu Liang, Tengyu Ma, Andrej Risteski
A Latent Variable Model Approach to PMI-based Word Embeddings. Trans. Assoc. Comput. Linguistics 4 (2016)
Andrej Risteski, Yuanzhi Li
Algorithms and matching lower bounds for approximately-convex optimization. NIPS (2016)
Sanjeev Arora, Rong Ge, Tengyu Ma, Andrej Risteski
Provable learning of Noisy-or Networks. CoRR (2016)
Andrej Risteski
How to calculate partition functions using convex programming hierarchies: provable bounds for variational methods. CoRR (2016)
Sanjeev Arora, Yuanzhi Li, Yingyu Liang, Tengyu Ma, Andrej Risteski
Linear Algebraic Structure of Word Senses, with Applications to Polysemy. CoRR (2016)
Andrej Risteski, Yuanzhi Li
Approximate maximum entropy principles via Goemans-Williamson with applications to provable variational methods. NIPS (2016)
Alina Ene, Matthias Mnich, Marcin Pilipczuk, Andrej Risteski
On Routing Disjoint Paths in Bounded Treewidth Graphs. SWAT (2016)
Yuanzhi Li, Yingyu Liang, Andrej Risteski
Recovery guarantee of weighted low-rank approximation via alternating minimization. ICML (2016)
Yuanzhi Li, Yingyu Liang, Andrej Risteski
Recovery guarantee of weighted low-rank approximation via alternating minimization. CoRR (2016)
Pranjal Awasthi, Andrej Risteski
On some provably correct cases of variational inference for topic models. CoRR (2015)
Pranjal Awasthi, Moses Charikar, Kevin A. Lai, Andrej Risteski
Label optimal regret bounds for online local learning. CoRR (2015)
Pranjal Awasthi, Moses Charikar, Kevin A. Lai, Andrej Risteski
Label optimal regret bounds for online local learning. COLT (2015)
Alina Ene, Matthias Mnich, Marcin Pilipczuk, Andrej Risteski
On Routing Disjoint Paths in Bounded Treewidth Graphs. CoRR (2015)
Pranjal Awasthi, Andrej Risteski
On some provably correct cases of variational inference for topic models. NIPS (2015)
Sanjeev Arora, Yuanzhi Li, Yingyu Liang, Tengyu Ma, Andrej Risteski
Random Walks on Context Spaces: Towards an Explanation of the Mysteries of Semantic Word Embeddings. CoRR (2015)
Howard Cheng, Satyan L. Devadoss, Brian Li, Andrej Risteski
Skeletal configurations of ribbon trees. Discret. Appl. Math. 170 (2014)

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Publication Activity (10 Years)

Top Topics

Top Venues

Andrej Risteski