Risteski, Andrej

68 publications

ICLR 2025 On the Benefits of Memory for Modeling Time-Dependent PDEs Ricardo Buitrago, Tanya Marwah, Albert Gu, Andrej Risteski

ICML 2025 On the Query Complexity of Verifier-Assisted Language Generation Edoardo Botta, Yuchen Li, Aashay Mehta, Jordan T. Ash, Cyril Zhang, Andrej Risteski

ICLRW 2025 On the Query Complexity of Verifier-Assisted Language Generation Edoardo Botta, Yuchen Li, Aashay Mehta, Jordan T. Ash, Cyril Zhang, Andrej Risteski

ICLR 2025 Progressive Distillation Induces an Implicit Curriculum Abhishek Panigrahi, Bingbin Liu, Sadhika Malladi, Andrej Risteski, Surbhi Goel

ICML 2025 Towards Characterizing the Value of Edge Embeddings in Graph Neural Networks Dhruv Rohatgi, Tanya Marwah, Zachary Chase Lipton, Jianfeng Lu, Ankur Moitra, Andrej Risteski

ICLRW 2024 Augmentation Alone Leads to Generalization Runtian Zhai, Bingbin Liu, Andrej Risteski, J Zico Kolter, Pradeep Kumar Ravikumar

COLT 2024 Fit like You Sample: Sample-Efficient Generalized Score Matching from Fast Mixing Diffusions Yilong Qin, Andrej Risteski

ICLR 2024 Outliers with Opposing Signals Have an Outsized Effect on Neural Network Optimization Elan Rosenfeld, Andrej Risteski

ICLRW 2024 Outliers with Opposing Signals Have an Outsized Effect on Neural Network Optimization Elan Rosenfeld, Andrej Risteski

ICMLW 2024 Progressive Distillation Improves Feature Learning via Implicit Curriculum Abhishek Panigrahi, Bingbin Liu, Sadhika Malladi, Andrej Risteski, Surbhi Goel

ICMLW 2024 Progressive Distillation Improves Feature Learning via Implicit Curriculum Abhishek Panigrahi, Bingbin Liu, Sadhika Malladi, Andrej Risteski, Surbhi Goel

NeurIPSW 2024 Progressive Distillation Induces an Implicit Curriculum Abhishek Panigrahi, Bingbin Liu, Sadhika Malladi, Andrej Risteski, Surbhi Goel

ICML 2024 Promises and Pitfalls of Generative Masked Language Modeling: Theoretical Framework and Practical Guidelines Yuchen Li, Alexandre Kirchmeyer, Aashay Mehta, Yilong Qin, Boris Dadachev, Kishore Papineni, Sanjiv Kumar, Andrej Risteski

ICLRW 2024 Promises and Pitfalls of Generative Masked Language Modeling: Theoretical Framework and Practical Guidelines Yuchen Li, Alexandre Kirchmeyer, Aashay Mehta, Yilong Qin, Boris Dadachev, Kishore A Papineni, Sanjiv Kumar, Andrej Risteski

NeurIPSW 2024 Towards Characterizing the Value of Edge Embeddings in Graph Neural Networks Dhruv Rohatgi, Tanya Marwah, Zachary Chase Lipton, Jianfeng Lu, Ankur Moitra, Andrej Risteski

ICLR 2024 Understanding Augmentation-Based Self-Supervised Representation Learning via RKHS Approximation and Regression Runtian Zhai, Bingbin Liu, Andrej Risteski, J Zico Kolter, Pradeep Kumar Ravikumar

ICMLW 2023 (Un)interpretability of Transformers: A Case Study with Dyck Grammars Kaiyue Wen, Yuchen Li, Bingbin Liu, Andrej Risteski

NeurIPS 2023 Deep Equilibrium Based Neural Operators for Steady-State PDEs Tanya Marwah, Ashwini Pokle, J. Zico Kolter, Zachary Lipton, Jianfeng Lu, Andrej Risteski

ICMLW 2023 Deep Equilibrium Based Neural Operators for Steady-State PDEs Tanya Marwah, Ashwini Pokle, J Zico Kolter, Zachary Chase Lipton, Jianfeng Lu, Andrej Risteski

ICMLW 2023 Fit like You Sample: Sample-Efficient Generalized Score Matching from Fast Mixing Markov Chains Yilong Qin, Andrej Risteski

ICMLW 2023 Fit like You Sample: Sample-Efficient Generalized Score Matching from Fast Mixing Markov Chains Yilong Qin, Andrej Risteski

NeurIPSW 2023 Fit like You Sample: Sample-Efficient Score Matching from Fast Mixing Diffusions Yilong Qin, Andrej Risteski

ICML 2023 How Do Transformers Learn Topic Structure: Towards a Mechanistic Understanding Yuchen Li, Yuanzhi Li, Andrej Risteski

ICML 2023 Neural Network Approximations of PDEs Beyond Linearity: A Representational Perspective Tanya Marwah, Zachary Chase Lipton, Jianfeng Lu, Andrej Risteski

NeurIPSW 2023 Outliers with Opposing Signals Have an Outsized Effect on Neural Network Optimization Elan Rosenfeld, Andrej Risteski

NeurIPSW 2023 Outliers with Opposing Signals Have an Outsized Effect on Neural Network Optimization Elan Rosenfeld, Andrej Risteski

ICLR 2023 Pitfalls of Gaussians as a Noise Distribution in NCE Holden Lee, Chirag Pabbaraju, Anish Prasad Sevekari, Andrej Risteski

NeurIPS 2023 Provable Benefits of Annealing for Estimating Normalizing Constants: Importance Sampling, Noise-Contrastive Estimation, and Beyond Omar Chehab, Aapo Hyvarinen, Andrej Risteski

NeurIPS 2023 Provable Benefits of Score Matching Chirag Pabbaraju, Dhruv Rohatgi, Anish Prasad Sevekari, Holden Lee, Ankur Moitra, Andrej Risteski

ICMLW 2023 Provable Benefits of Score Matching Chirag Pabbaraju, Dhruv Rohatgi, Anish Sevekari, Holden Lee, Ankur Moitra, Andrej Risteski

ICLR 2023 Statistical Efficiency of Score Matching: The View from Isoperimetry Frederic Koehler, Alexander Heckett, Andrej Risteski

NeurIPS 2023 Transformers Are Uninterpretable with Myopic Methods: A Case Study with Bounded Dyck Grammars Kaiyue Wen, Yuchen Li, Bingbin Liu, Andrej Risteski

AISTATS 2022 An Online Learning Approach to Interpolation and Extrapolation in Domain Generalization Elan Rosenfeld, Pradeep Ravikumar, Andrej Risteski

AISTATS 2022 Contrasting the Landscape of Contrastive and Non-Contrastive Learning Ashwini Pokle, Jinjin Tian, Yuchen Li, Andrej Risteski

ICLR 2022 Analyzing and Improving the Optimization Landscape of Noise-Contrastive Estimation Bingbin Liu, Elan Rosenfeld, Pradeep Kumar Ravikumar, Andrej Risteski

NeurIPS 2022 Continual Learning: A Feature Extraction Formalization, an Efficient Algorithm, and Fundamental Obstructions Binghui Peng, Andrej Risteski

NeurIPSW 2022 Domain-Adjusted Regression or: ERM May Already Learn Features Sufficient for Out-of-Distribution Generalization Elan Rosenfeld, Pradeep Kumar Ravikumar, Andrej Risteski

NeurIPS 2022 Iterative Feature Matching: Toward Provable Domain Generalization with Logarithmic Environments Yining Chen, Elan Rosenfeld, Mark Sellke, Tengyu Ma, Andrej Risteski

NeurIPS 2022 Masked Prediction: A Parameter Identifiability View Bingbin Liu, Daniel J. Hsu, Pradeep K. Ravikumar, Andrej Risteski

COLT 2022 Sampling Approximately Low-Rank Ising Models: MCMC Meets Variational Methods Frederic Koehler, Holden Lee, Andrej Risteski

NeurIPSW 2022 Statistical Efficiency of Score Matching: The View from Isoperimetry Frederic Koehler, Alexander Heckett, Andrej Risteski

ICLR 2022 The Effects of Invertibility on the Representational Complexity of Encoders in Variational Autoencoders Divyansh Pareek, Andrej Risteski

ICLR 2022 Variational Autoencoders in the Presence of Low-Dimensional Data: Landscape and Implicit Bias Frederic Koehler, Viraj Mehta, Chenghui Zhou, Andrej Risteski

AISTATS 2021 Contrastive Learning of Strong-Mixing Continuous-Time Stochastic Processes Bingbin Liu, Pradeep Ravikumar, Andrej Risteski

ALT 2021 Efficient Sampling from the Bingham Distribution Rong Ge, Holden Lee, Jianfeng Lu, Andrej Risteski

NeurIPS 2021 Parametric Complexity Bounds for Approximating PDEs with Neural Networks Tanya Marwah, Zachary Lipton, Andrej Risteski

ICML 2021 Representational Aspects of Depth and Conditioning in Normalizing Flows Frederic Koehler, Viraj Mehta, Andrej Risteski

ICMLW 2021 Representational Aspects of Depth and Conditioning in Normalizing Flows Frederic Koehler, Viraj Mehta, Andrej Risteski

ICMLW 2021 The Effects of Invertibility on the Representational Complexity of Encoders in Variational Autoencoders Andrej Risteski, Divyansh Pareek

ICLR 2021 The Risks of Invariant Risk Minimization Elan Rosenfeld, Pradeep Kumar Ravikumar, Andrej Risteski

NeurIPS 2021 Universal Approximation Using Well-Conditioned Normalizing Flows Holden Lee, Chirag Pabbaraju, Anish Prasad Sevekari, Andrej Risteski

ICMLW 2021 Universal Approximation for Log-Concave Distributions Using Well-Conditioned Normalizing Flows Holden Lee, Chirag Pabbaraju, Anish Prasad Sevekari, Andrej Risteski

ICML 2020 Empirical Study of the Benefits of Overparameterization in Learning Latent Variable Models Rares-Darius Buhai, Yoni Halpern, Yoon Kim, Andrej Risteski, David Sontag

ICLRW 2020 Fast Convergence for Langevin with Matrix Manifold Structure Ankur Moitra, Andrej Risteski

ICML 2020 On Learning Language-Invariant Representations for Universal Machine Translation Han Zhao, Junjie Hu, Andrej Risteski

ICLR 2019 Approximability of Discriminators Implies Diversity in GANs Yu Bai, Tengyu Ma, Andrej Risteski

COLT 2019 Sum-of-Squares Meets Square Loss: Fast Rates for Agnostic Tensor Completion Dylan J. Foster, Andrej Risteski

ICLR 2019 The Comparative Power of ReLU Networks and Polynomial Kernels in the Presence of Sparse Latent Structure Frederic Koehler, Andrej Risteski

NeurIPS 2018 Beyond Log-Concavity: Provable Guarantees for Sampling Multi-Modal Distributions Using Simulated Tempering Langevin Monte Carlo Holden Lee, Andrej Risteski, Rong Ge

ICLR 2018 Do GANs Learn the Distribution? Some Theory and Empirics Sanjeev Arora, Andrej Risteski, Yi Zhang

COLT 2017 On the Ability of Neural Nets to Express Distributions Holden Lee, Rong Ge, Tengyu Ma, Andrej Risteski, Sanjeev Arora

NeurIPS 2016 Algorithms and Matching Lower Bounds for Approximately-Convex Optimization Andrej Risteski, Yuanzhi Li

NeurIPS 2016 Approximate Maximum Entropy Principles via Goemans-Williamson with Applications to Provable Variational Methods Andrej Risteski, Yuanzhi Li

COLT 2016 How to Calculate Partition Functions Using Convex Programming Hierarchies: Provable Bounds for Variational Methods Andrej Risteski

NeurIPS 2016 Recovery Guarantee of Non-Negative Matrix Factorization via Alternating Updates Yuanzhi Li, Yingyu Liang, Andrej Risteski

ICML 2016 Recovery Guarantee of Weighted Low-Rank Approximation via Alternating Minimization Yuanzhi Li, Yingyu Liang, Andrej Risteski

COLT 2015 Label Optimal Regret Bounds for Online Local Learning Pranjal Awasthi, Moses Charikar, Kevin A. Lai, Andrej Risteski

NeurIPS 2015 On Some Provably Correct Cases of Variational Inference for Topic Models Pranjal Awasthi, Andrej Risteski