Akash Srivastava

Publication Activity (10 Years)

Years Active: 2010-2024
Publications (10 Years): 65

Top Topics

Diffusion Models

Particle Swarm Optimizer

Variational Inference

Top Venues

Publications

Kaveh Alimohammadi, Hao Wang, Ojas Gulati, Akash Srivastava, Navid Azizan
Adapting Differentially Private Synthetic Data to Relational Databases. CoRR (2024)
Amin Heyrani Nobari, Akash Srivastava, Dan Gutfreund, Kai Xu, Faez Ahmed
LInK: Learning Joint Representations of Design and Performance Spaces through Contrastive Learning for Mechanism Synthesis. CoRR (2024)
Xinxi Zhang, Song Wen, Ligong Han, Felix Juefei-Xu, Akash Srivastava, Junzhou Huang, Hao Wang, Molei Tao, Dimitris N. Metaxas
Spectrum-Aware Parameter Efficient Fine-Tuning for Diffusion Models. CoRR (2024)
Zhang-Wei Hong, Idan Shenfeld, Tsun-Hsuan Wang, Yung-Sung Chuang, Aldo Pareja, James R. Glass, Akash Srivastava, Pulkit Agrawal
Curiosity-driven Red-teaming for Large Language Models. ICLR (2024)
Lazar Valkov, Akash Srivastava, Swarat Chaudhuri, Charles Sutton
A Probabilistic Framework for Modular Continual Learning. ICLR (2024)
Zhang-Wei Hong, Idan Shenfeld, Tsun-Hsuan Wang, Yung-Sung Chuang, Aldo Pareja, James R. Glass, Akash Srivastava, Pulkit Agrawal
Curiosity-driven Red-teaming for Large Language Models. CoRR (2024)
Seungwook Han, Idan Shenfeld, Akash Srivastava, Yoon Kim, Pulkit Agrawal
Value Augmented Sampling for Language Model Alignment and Personalization. CoRR (2024)
Shivchander Sudalairaj, Abhishek Bhandwaldar, Aldo Pareja, Kai Xu, David D. Cox, Akash Srivastava
LAB: Large-Scale Alignment for ChatBots. CoRR (2024)
Samuel J. K. Chin, Matthias Winkenbach, Akash Srivastava
Learning to Deliver: a Foundation Model for the Montreal Capacitated Vehicle Routing Problem. CoRR (2024)
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 N. Metaxas
ProxEdit: Improving Tuning-Free Real Image Editing with Proximal Guidance. WACV (2024)
Haoyuan Sun, Navid Azizan, Akash Srivastava, Hao Wang
Private Synthetic Data Meets Ensemble Learning. CoRR (2023)
Max Schrader, Navish Kumar, Nicolas Collignon, Esben Sørig, Soonmyeong Yoon, Akash Srivastava, Kai Xu, Maria Sinziana Astefanoaei
Modelling the performance of delivery vehicles across urban micro-regions to accelerate the transition to cargo-bike logistics. CoRR (2023)
Giorgio Giannone, Akash Srivastava, Ole Winther, Faez Ahmed
Aligning Optimization Trajectories with Diffusion Models for Constrained Design Generation. CoRR (2023)
Ligong Han, Seungwook Han, Shivchander Sudalairaj, Charlotte Loh, Rumen Dangovski, Fei Deng, Pulkit Agrawal, Dimitris N. Metaxas, Leonid Karlinsky, Tsui-Wei Weng, Akash Srivastava
Constructive Assimilation: Boosting Contrastive Learning Performance through View Generation Strategies. CoRR (2023)
Charlotte Loh, Seungwook Han, Shivchander Sudalairaj, Rumen Dangovski, Kai Xu, Florian Wenzel, Marin Soljacic, Akash Srivastava
Multi-Symmetry Ensembles: Improving Diversity and Generalization via Opposing Symmetries. ICML (2023)
Zhang-Wei Hong, Aviral Kumar, Sathwik Karnik, Abhishek Bhandwaldar, Akash Srivastava, Joni Pajarinen, Romain Laroche, Abhishek Gupta, Pulkit Agrawal
Beyond Uniform Sampling: Offline Reinforcement Learning with Imbalanced Datasets. NeurIPS (2023)
Yilan Chen, Wei Huang, Hao Wang, Charlotte Loh, Akash Srivastava, Lam M. Nguyen, Lily Weng
Analyzing Generalization of Neural Networks through Loss Path Kernels. NeurIPS (2023)
Zhang-Wei Hong, Aviral Kumar, Sathwik Karnik, Abhishek Bhandwaldar, Akash Srivastava, Joni Pajarinen, Romain Laroche, Abhishek Gupta, Pulkit Agrawal
Beyond Uniform Sampling: Offline Reinforcement Learning with Imbalanced Datasets. CoRR (2023)
Charlotte Loh, Rumen Dangovski, Shivchander Sudalairaj, Seungwook Han, Ligong Han, Leonid Karlinsky, Marin Soljacic, Akash Srivastava
Mitigating Confirmation Bias in Semi-supervised Learning via Efficient Bayesian Model Averaging. Trans. Mach. Learn. Res. 2023 (2023)
Lyle Regenwetter, Akash Srivastava, Dan Gutfreund, Faez Ahmed
Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design. Comput. Aided Des. 165 (2023)
Akash Srivastava, Seungwook Han, Kai Xu, Benjamin Rhodes, Michael U. Gutmann
Estimating the Density Ratio between Distributions with High Discrepancy using Multinomial Logistic Regression. CoRR (2023)
Jiaqi Zhang, Kristjan H. Greenewald, Chandler Squires, Akash Srivastava, Karthikeyan Shanmugam, Caroline Uhler
Identifiability Guarantees for Causal Disentanglement from Soft Interventions. NeurIPS (2023)
Giorgio Giannone, Akash Srivastava, Ole Winther, Faez Ahmed
Aligning Optimization Trajectories with Diffusion Models for Constrained Design Generation. NeurIPS (2023)
Ligong Han, Song Wen, Qi Chen, Zhixing Zhang, Kunpeng Song, Mengwei Ren, Ruijiang Gao, Yuxiao Chen, Di Liu, Qilong Zhangli, Anastasis Stathopoulos, Jindong Jiang, Zhaoyang Xia, Akash Srivastava, Dimitris N. Metaxas
Improving Negative-Prompt Inversion via Proximal Guidance. CoRR (2023)
Anurag Ajay, Seungwook Han, Yilun Du, Shuang Li, Abhi Gupta, Tommi S. Jaakkola, Josh Tenenbaum, Leslie Pack Kaelbling, Akash Srivastava, Pulkit Agrawal
Compositional Foundation Models for Hierarchical Planning. CoRR (2023)
Lyle Regenwetter, Akash Srivastava, Dan Gutfreund, Faez Ahmed
Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design. CoRR (2023)
Hao Wang, Shivchander Sudalairaj, John Henning, Kristjan H. Greenewald, Akash Srivastava
Post-processing Private Synthetic Data for Improving Utility on Selected Measures. CoRR (2023)
Lazar Valkov, Akash Srivastava, Swarat Chaudhuri, Charles Sutton
A Probabilistic Framework for Modular Continual Learning. CoRR (2023)
Akash Srivastava, Seungwook Han, Kai Xu, Benjamin Rhodes, Michael U. Gutmann
Estimating the Density Ratio between Distributions with High Discrepancy using Multinomial Logistic Regression. Trans. Mach. Learn. Res. 2023 (2023)
Giorgio Giannone, Lyle Regenwetter, Akash Srivastava, Dan Gutfreund, Faez Ahmed
Learning from Invalid Data: On Constraint Satisfaction in Generative Models. CoRR (2023)
Hao Wang, Shivchander Sudalairaj, John Henning, Kristjan H. Greenewald, Akash Srivastava
Post-processing Private Synthetic Data for Improving Utility on Selected Measures. NeurIPS (2023)
Ankit Vishnubhotla, Charlotte Loh, Akash Srivastava, Liam Paninski, Cole L. Hurwitz
Towards robust and generalizable representations of extracellular data using contrastive learning. NeurIPS (2023)
Charlotte Loh, Seungwook Han, Shivchander Sudalairaj, Rumen Dangovski, Kai Xu, Florian Wenzel, Marin Soljacic, Akash Srivastava
Multi-Symmetry Ensembles: Improving Diversity and Generalization via Opposing Symmetries. CoRR (2023)
Anurag Ajay, Seungwook Han, Yilun Du, Shuang Li, Abhi Gupta, Tommi S. Jaakkola, Joshua B. Tenenbaum, Leslie Pack Kaelbling, Akash Srivastava, Pulkit Agrawal
Compositional Foundation Models for Hierarchical Planning. NeurIPS (2023)
Jiaqi Zhang, Chandler Squires, Kristjan H. Greenewald, Akash Srivastava, Karthikeyan Shanmugam, Caroline Uhler
Identifiability Guarantees for Causal Disentanglement from Soft Interventions. CoRR (2023)
Rumen Dangovski, Li Jing, Charlotte Loh, Seungwook Han, Akash Srivastava, Brian Cheung, Pulkit Agrawal, Marin Soljacic
Equivariant Self-Supervised Learning: Encouraging Equivariance in Representations. ICLR (2022)
Amin Heyrani Nobari, Akash Srivastava, Dan Gutfreund, Faez Ahmed
LINKS: A dataset of a hundred million planar linkage mechanisms for data-driven kinematic design. CoRR (2022)
Charlotte Loh, Rumen Dangovski, Shivchander Sudalairaj, Seungwook Han, Ligong Han, Leonid Karlinsky, Marin Soljacic, Akash Srivastava
On the Importance of Calibration in Semi-supervised Learning. CoRR (2022)
Rumen Dangovski, Li Jing, Charlotte Loh, Seungwook Han, Akash Srivastava, Brian Cheung, Pulkit Agrawal, Marin Soljacic
Equivariant Contrastive Learning. CoRR (2021)
Hardik Manocha, Akash Srivastava, Chetan Verma, Ratan Gupta, Bhavya Bansal
Security Assessment Rating Framework for Enterprises using MITRE ATT&CK Matrix. CoRR (2021)
Robin Hirt, Akash Srivastava, Carlos Berg, Niklas Kühl
Sequential Transfer Machine Learning in Networks: Measuring the Impact of Data and Neural Net Similarity on Transferability. HICSS (2021)
Kai Xu, Akash Srivastava, Dan Gutfreund, Felix Sosa, Tomer D. Ullman, Josh Tenenbaum, Charles Sutton
A Bayesian-Symbolic Approach to Reasoning and Learning in Intuitive Physics. NeurIPS (2021)
Cole L. Hurwitz, Akash Srivastava, Kai Xu, Justin Jude, Matthew G. Perich, Lee E. Miller, Matthias H. Hennig
Targeted Neural Dynamical Modeling. NeurIPS (2021)
Robin Hirt, Akash Srivastava, Carlos Berg, Niklas Kühl
Sequential Transfer Machine Learning in Networks: Measuring the Impact of Data and Neural Net Similarity on Transferability. CoRR (2020)
Akash Srivastava, Yamini Bansal, Yukun Ding, Cole L. Hurwitz, Kai Xu, Bernhard Egger, Prasanna Sattigeri, Josh Tenenbaum, David D. Cox, Dan Gutfreund
Improving the Reconstruction of Disentangled Representation Learners via Multi-Stage Modelling. CoRR (2020)
Akash Srivastava, Emanuel Barth, Maria A. Ermolaeva, Madlen Guenther, Christiane Frahm, Manja Marz, Otto W. Witte
Tissue-specific Gene Expression Changes Are Associated with Aging in Mice. Genom. Proteom. Bioinform. 18 (4) (2020)
Akash Srivastava, Kai Xu, Michael U. Gutmann, Charles Sutton
Generative Ratio Matching Networks. ICLR (2020)
Seungwook Han, Akash Srivastava, Cole L. Hurwitz, Prasanna Sattigeri, David D. Cox
not-so-BigGAN: Generating High-Fidelity Images on a Small Compute Budget. CoRR (2020)
Akash Srivastava, Jessie C. Rosenberg, Dan Gutfreund, David D. Cox
SimVAE: Simulator-Assisted Training forInterpretable Generative Models. CoRR (2019)
Akash Srivastava, Kristjan H. Greenewald, Farzaneh Mirzazadeh
BreGMN: scaled-Bregman Generative Modeling Networks. CoRR (2019)
Anshu S. Anand, Akash Srivastava, R. K. Shyamasundar
A deadlock-free lock-based synchronization for GPUs. Concurr. Comput. Pract. Exp. 31 (7) (2019)
Cole L. Hurwitz, Kai Xu, Akash Srivastava, Alessio Paolo Buccino, Matthias H. Hennig
Scalable Spike Source Localization in Extracellular Recordings using Amortized Variational Inference. NeurIPS (2019)
Cole L. Hurwitz, Kai Xu, Akash Srivastava, Alessio Paolo Buccino, Matthias H. Hennig
Scalable Spike Source Localization in Extracellular Recordings using Amortized Variational Inference. CoRR (2019)
Kai Xu, Akash Srivastava, Charles Sutton
Variational Russian Roulette for Deep Bayesian Nonparametrics. ICML (2019)
Mohammad Emtiyaz Khan, Didrik Nielsen, Voot Tangkaratt, Wu Lin, Yarin Gal, Akash Srivastava
Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam. CoRR (2018)
Lazar Valkov, Dipak Chaudhari, Akash Srivastava, Charles Sutton, Swarat Chaudhuri
HOUDINI: Lifelong Learning as Program Synthesis. NeurIPS (2018)
Akash Srivastava, Charles Sutton
Variational Inference In Pachinko Allocation Machines. CoRR (2018)
Mohammad Emtiyaz Khan, Didrik Nielsen, Voot Tangkaratt, Wu Lin, Yarin Gal, Akash Srivastava
Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam. ICML (2018)
Lazar Valkov, Dipak Chaudhari, Akash Srivastava, Charles Sutton, Swarat Chaudhuri
Synthesis of Differentiable Functional Programs for Lifelong Learning. CoRR (2018)
Akash Srivastava, Kai Xu, Michael U. Gutmann, Charles Sutton
Ratio Matching MMD Nets: Low dimensional projections for effective deep generative models. CoRR (2018)
Sanjeev Kumar Raghuwanshi, Nimish Kumar Srivastava, Akash Srivastava, Bidhanshel S. Athokpam
Effect of Laser Modulation on Dispersion Induced Chirp Microwave Signal Generation by Using Temporal Pulse Shaping Technique. Wirel. Pers. Commun. 95 (2) (2017)
Akash Srivastava, Charles Sutton
Autoencoding Variational Inference For Topic Models. ICLR (Poster) (2017)
Akash Srivastava, Lazar Valkov, Chris Russell, Michael U. Gutmann, Charles Sutton
VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning. NIPS (2017)
Akash Srivastava, James Y. Zou, Ryan P. Adams, Charles Sutton
Clustering with a Reject Option: Interactive Clustering as Bayesian Prior Elicitation. CoRR (2016)
Akash Srivastava, James Y. Zou, Ryan P. Adams, Charles Sutton
Clustering with a Reject Option: Interactive Clustering as Bayesian Prior Elicitation. CoRR (2016)
Akash Srivastava
Performance Comparison of Various Particle Swarm Optimizers in DWT-SVD watermarking for RGB Images. ICCCT (2015)
Jayant Mishra, Amit Kumar, Amita Sinha, Silpa Das, Akash Srivastava
Ingenuity in pattern recognition: a novel bioinformatics approach towards lung cancer identification. Int. J. Bioinform. Res. Appl. 6 (6) (2010)