Srivastava, Akash

33 publications

NeurIPS 2025 Activation-Informed Merging of Large Language Models Amin Heyrani Nobari, Kaveh Alim, Ali ArjomandBigdeli, Akash Srivastava, Faez Ahmed, Navid Azizan

NeurIPS 2025 Rollout Roulette: A Probabilistic Inference Approach to Inference-Time Scaling of LLMs Using Particle-Based Monte Carlo Methods Isha Puri, Shivchander Sudalairaj, Guangxuan Xu, Abhishek Bhandwaldar, Kai Xu, Akash Srivastava

WACV 2025 SODA: Spectral Orthogonal Decomposition Adaptation for Diffusion Models Xinxi Zhang, Song Wen, Ligong Han, Felix Juefei-Xu, Akash Srivastava, Junzhou Huang, Vladimir Pavlovic, Hao Wang, Molei Tao, Dimitris Metaxas

ICLR 2025 Unveiling the Secret Recipe: A Guide for Supervised Fine-Tuning Small LLMs Aldo Pareja, Nikhil Shivakumar Nayak, Hao Wang, Krishnateja Killamsetty, Shivchander Sudalairaj, Wenlong Zhao, Seungwook Han, Abhishek Bhandwaldar, Guangxuan Xu, Kai Xu, Ligong Han, Luke Inglis, Akash Srivastava

ICLR 2024 A Probabilistic Framework for Modular Continual Learning Lazar Valkov, Akash Srivastava, Swarat Chaudhuri, Charles Sutton

TMLR 2024 Constraining Generative Models for Engineering Design with Negative Data Lyle Regenwetter, Giorgio Giannone, Akash Srivastava, Dan Gutfreund, Faez Ahmed

NeurIPSW 2024 Curiosity-Driven Red Teaming for Large Language Models Zhang-Wei Hong, Idan Shenfeld, Tsun-Hsuan Wang, Yung-Sung Chuang, Aldo Pareja, James R. Glass, Akash Srivastava, Pulkit Agrawal

ICLR 2024 Curiosity-Driven Red-Teaming for Large Language Models Zhang-Wei Hong, Idan Shenfeld, Tsun-Hsuan Wang, Yung-Sung Chuang, Aldo Pareja, James R. Glass, Akash Srivastava, Pulkit Agrawal

TMLR 2024 LInK: Learning Joint Representations of Design and Performance Spaces Through Contrastive Learning for Mechanism Synthesis Amin Heyrani Nobari, Akash Srivastava, Dan Gutfreund, Kai Xu, Faez Ahmed

WACV 2024 ProxEdit: Improving Tuning-Free Real Image Editing with Proximal Guidance Ligong Han, Song Wen, Qi Chen, Zhixing Zhang, Kunpeng Song, Mengwei Ren, Ruijiang Gao, Anastasis Stathopoulos, Xiaoxiao He, Yuxiao Chen, Di Liu, Qilong Zhangli, Jindong Jiang, Zhaoyang Xia, Akash Srivastava, Dimitris Metaxas

ICLRW 2024 Value Augmented Sampling: Predict Your Rewards to Align Language Models Seungwook Han, Idan Shenfeld, Akash Srivastava, Yoon Kim, Pulkit Agrawal

NeurIPS 2023 Aligning Optimization Trajectories with Diffusion Models for Constrained Design Generation Giorgio Giannone, Akash Srivastava, Ole Winther, Faez Ahmed

NeurIPS 2023 Analyzing Generalization of Neural Networks Through Loss Path Kernels Yilan Chen, Wei Huang, Hao Wang, Charlotte Loh, Akash Srivastava, Lam Nguyen, Lily Weng

NeurIPS 2023 Beyond Uniform Sampling: Offline Reinforcement Learning with Imbalanced Datasets Zhang-Wei Hong, Aviral Kumar, Sathwik Karnik, Abhishek Bhandwaldar, Akash Srivastava, Joni K. Pajarinen, Romain Laroche, Abhishek Gupta, Pulkit Agrawal

NeurIPS 2023 Compositional Foundation Models for Hierarchical Planning Anurag Ajay, Seungwook Han, Yilun Du, Shuang Li, Abhi Gupta, Tommi Jaakkola, Josh Tenenbaum, Leslie P. Kaelbling, Akash Srivastava, Pulkit Agrawal

NeurIPSW 2023 Compositional Foundation Models for Hierarchical Planning Anurag Ajay, Seungwook Han, Yilun Du, Shuang Li, Abhi Gupta, Tommi Jaakkola, Joshua Tenenbaum, Leslie Kaelbling, Akash Srivastava, Pulkit Agrawal

TMLR 2023 Estimating the Density Ratio Between Distributions with High Discrepancy Using Multinomial Logistic Regression Akash Srivastava, Seungwook Han, Kai Xu, Benjamin Rhodes, Michael U. Gutmann

NeurIPS 2023 Identifiability Guarantees for Causal Disentanglement from Soft Interventions Jiaqi Zhang, Kristjan Greenewald, Chandler Squires, Akash Srivastava, Karthikeyan Shanmugam, Caroline Uhler

TMLR 2023 Mitigating Confirmation Bias in Semi-Supervised Learning via Efficient Bayesian Model Averaging Charlotte Loh, Rumen Dangovski, Shivchander Sudalairaj, Seungwook Han, Ligong Han, Leonid Karlinsky, Marin Soljacic, Akash Srivastava

ICML 2023 Multi-Symmetry Ensembles: Improving Diversity and Generalization via Opposing Symmetries Charlotte Loh, Seungwook Han, Shivchander Sudalairaj, Rumen Dangovski, Kai Xu, Florian Wenzel, Marin Soljacic, Akash Srivastava

NeurIPS 2023 Post-Processing Private Synthetic Data for Improving Utility on Selected Measures Hao Wang, Shivchander Sudalairaj, John Henning, Kristjan Greenewald, Akash Srivastava

NeurIPS 2023 Towards Robust and Generalizable Representations of Extracellular Data Using Contrastive Learning Ankit Vishnubhotla, Charlotte Loh, Akash Srivastava, Liam Paninski, Cole Hurwitz

ICLR 2022 Equivariant Self-Supervised Learning: Encouraging Equivariance in Representations Rumen Dangovski, Li Jing, Charlotte Loh, Seungwook Han, Akash Srivastava, Brian Cheung, Pulkit Agrawal, Marin Soljacic

NeurIPS 2021 A Bayesian-Symbolic Approach to Reasoning and Learning in Intuitive Physics Kai Xu, Akash Srivastava, Dan Gutfreund, Felix Sosa, Tomer Ullman, Josh Tenenbaum, Charles A. Sutton

NeurIPS 2021 Targeted Neural Dynamical Modeling Cole Hurwitz, Akash Srivastava, Kai Xu, Justin Jude, Matthew Perich, Lee Miller, Matthias H. Hennig

ICLR 2020 Cz-Gem: A Framework for Disentangled Representation Learning Akash Srivastava, Yamini Bansal, Yukun Ding, Bernhard Egger, Prasanna Sattigeri, Josh Tenenbaum, David D. Cox, Dan Gutfreund

ICLR 2020 Generative Ratio Matching Networks Akash Srivastava, Kai Xu, Michael U. Gutmann, Charles Sutton

NeurIPS 2019 Scalable Spike Source Localization in Extracellular Recordings Using Amortized Variational Inference Cole Hurwitz, Kai Xu, Akash Srivastava, Alessio Buccino, Matthias Hennig

ICML 2019 Variational Russian Roulette for Deep Bayesian Nonparametrics Kai Xu, Akash Srivastava, Charles Sutton

ICML 2018 Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam Mohammad Khan, Didrik Nielsen, Voot Tangkaratt, Wu Lin, Yarin Gal, Akash Srivastava

NeurIPS 2018 HOUDINI: Lifelong Learning as Program Synthesis Lazar Valkov, Dipak Chaudhari, Akash Srivastava, Charles Sutton, Swarat Chaudhuri

ICLR 2017 Autoencoding Variational Inference for Topic Models Akash Srivastava, Charles Sutton

NeurIPS 2017 VEEGAN: Reducing Mode Collapse in GANs Using Implicit Variational Learning Akash Srivastava, Lazar Valkov, Chris Russell, Michael U. Gutmann, Charles Sutton