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ORCIDAkash Srivastava
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2020 – today
- 2024
[c23]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- [c22]Lazar Valkov, Akash Srivastava, Swarat Chaudhuri, Charles Sutton:
A Probabilistic Framework for Modular Continual Learning. ICLR 2024 - [c21]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: 4279-4289 - [i38]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 abs/2402.19464 (2024) - [i37]Samuel J. K. Chin, Matthias Winkenbach, Akash Srivastava:
Learning to Deliver: a Foundation Model for the Montreal Capacitated Vehicle Routing Problem. CoRR abs/2403.00026 (2024) - [i36]Shivchander Sudalairaj, Abhishek Bhandwaldar, Aldo Pareja, Kai Xu, David D. Cox, Akash Srivastava:
LAB: Large-Scale Alignment for ChatBots. CoRR abs/2403.01081 (2024) - [i35]Seungwook Han, Idan Shenfeld, Akash Srivastava, Yoon Kim, Pulkit Agrawal:
Value Augmented Sampling for Language Model Alignment and Personalization. CoRR abs/2405.06639 (2024) - [i34]Kaveh Alimohammadi, Hao Wang, Ojas Gulati, Akash Srivastava, Navid Azizan:
Adapting Differentially Private Synthetic Data to Relational Databases. CoRR abs/2405.18670 (2024) - [i33]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 abs/2405.20592 (2024) - [i32]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 abs/2405.21050 (2024) - [i31]Maxwell Schrader, Navish Kumar, Esben Sørig, Soonmyeong Yoon, Akash Srivastava, Kai Xu, Maria Sinziana Astefanoaei, Nicolas Collignon:
Urban context and delivery performance: Modelling service time for cargo bikes and vans across diverse urban environments. CoRR abs/2409.06730 (2024) - 2023
- [j7]Lyle Regenwetter
, Akash Srivastava, Dan Gutfreund, Faez Ahmed:
Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design. Comput. Aided Des. 165: 103609 (2023) - [j6]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) - [j5]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) - [c20]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: 22614-22630 - [c19]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 - [c18]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 - [c17]Giorgio Giannone, Akash Srivastava, Ole Winther, Faez Ahmed:
Aligning Optimization Trajectories with Diffusion Models for Constrained Design Generation. NeurIPS 2023 - [c16]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 - [c15]Ankit Vishnubhotla, Charlotte Loh, Akash Srivastava, Liam Paninski, Cole L. Hurwitz:
Towards robust and generalizable representations of extracellular data using contrastive learning. NeurIPS 2023 - [c14]Hao Wang, Shivchander Sudalairaj, John Henning, Kristjan H. Greenewald, Akash Srivastava:
Post-processing Private Synthetic Data for Improving Utility on Selected Measures. NeurIPS 2023 - [c13]Jiaqi Zhang, Kristjan H. Greenewald, Chandler Squires, Akash Srivastava, Karthikeyan Shanmugam, Caroline Uhler:
Identifiability Guarantees for Causal Disentanglement from Soft Interventions. NeurIPS 2023 - [i30]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 abs/2301.12887 (2023) - [i29]Lyle Regenwetter, Akash Srivastava, Dan Gutfreund, Faez Ahmed:
Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design. CoRR abs/2302.02913 (2023) - [i28]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 abs/2303.02484 (2023) - [i27]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 abs/2304.00601 (2023) - [i26]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 abs/2305.00869 (2023) - [i25]Hao Wang, Shivchander Sudalairaj, John Henning, Kristjan H. Greenewald, Akash Srivastava:
Post-processing Private Synthetic Data for Improving Utility on Selected Measures. CoRR abs/2305.15538 (2023) - [i24]Giorgio Giannone, Akash Srivastava, Ole Winther, Faez Ahmed:
Aligning Optimization Trajectories with Diffusion Models for Constrained Design Generation. CoRR abs/2305.18470 (2023) - [i23]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 Tuning-Free Real Image Editing with Proximal Guidance. CoRR abs/2306.05414 (2023) - [i22]Lazar Valkov, Akash Srivastava, Swarat Chaudhuri, Charles Sutton:
A Probabilistic Framework for Modular Continual Learning. CoRR abs/2306.06545 (2023) - [i21]Giorgio Giannone, Lyle Regenwetter, Akash Srivastava, Dan Gutfreund, Faez Ahmed:
Learning from Invalid Data: On Constraint Satisfaction in Generative Models. CoRR abs/2306.15166 (2023) - [i20]Jiaqi Zhang, Chandler Squires, Kristjan H. Greenewald, Akash Srivastava, Karthikeyan Shanmugam, Caroline Uhler:
Identifiability Guarantees for Causal Disentanglement from Soft Interventions. CoRR abs/2307.06250 (2023) - [i19]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 abs/2309.08587 (2023) - [i18]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 abs/2310.04413 (2023) - [i17]Haoyuan Sun, Navid Azizan, Akash Srivastava, Hao Wang:
Private Synthetic Data Meets Ensemble Learning. CoRR abs/2310.09729 (2023) - 2022
- [c12]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 - [i16]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 abs/2208.14567 (2022) - [i15]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 abs/2210.04783 (2022) - 2021
- [c11]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: 1-10 - [c10]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: 2478-2490 - [c9]Cole L. Hurwitz, Akash Srivastava, Kai Xu, Justin Jude, Matthew G. Perich, Lee E. Miller, Matthias H. Hennig:
Targeted Neural Dynamical Modeling. NeurIPS 2021: 29379-29392 - [i14]Hardik Manocha, Akash Srivastava, Chetan Verma, Ratan Gupta, Bhavya Bansal:
Security Assessment Rating Framework for Enterprises using MITRE ATT&CK Matrix. CoRR abs/2108.06559 (2021) - [i13]Rumen Dangovski, Li Jing, Charlotte Loh, Seungwook Han, Akash Srivastava, Brian Cheung, Pulkit Agrawal, Marin Soljacic:
Equivariant Contrastive Learning. CoRR abs/2111.00899 (2021) - 2020
- [j4]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): 430-442 (2020) - [c8]Akash Srivastava, Kai Xu, Michael U. Gutmann, Charles Sutton:
Generative Ratio Matching Networks. ICLR 2020 - [i12]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 abs/2003.13070 (2020) - [i11]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 abs/2009.04433 (2020) - [i10]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 abs/2010.13187 (2020)
2010 – 2019
- 2019
- [b1]Akash Srivastava:
Deep generative modelling for amortised variational inference. University of Edinburgh, UK, 2019 - [j3]Anshu S. Anand, Akash Srivastava, R. K. Shyamasundar:
A deadlock-free lock-based synchronization for GPUs. Concurr. Comput. Pract. Exp. 31(7) (2019) - [c7]Kai Xu, Akash Srivastava, Charles Sutton:
Variational Russian Roulette for Deep Bayesian Nonparametrics. ICML 2019: 6963-6972 - [c6]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: 4726-4738 - [i9]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 abs/1905.12375 (2019) - [i8]Akash Srivastava, Kristjan H. Greenewald, Farzaneh Mirzazadeh:
BreGMN: scaled-Bregman Generative Modeling Networks. CoRR abs/1906.00313 (2019) - [i7]Akash Srivastava, Jessie C. Rosenberg, Dan Gutfreund, David D. Cox:
SimVAE: Simulator-Assisted Training forInterpretable Generative Models. CoRR abs/1911.08051 (2019) - 2018
- [c5]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: 2616-2625 - [c4]Lazar Valkov, Dipak Chaudhari, Akash Srivastava, Charles Sutton, Swarat Chaudhuri:
HOUDINI: Lifelong Learning as Program Synthesis. NeurIPS 2018: 8701-8712 - [i6]Lazar Valkov, Dipak Chaudhari, Akash Srivastava, Charles Sutton, Swarat Chaudhuri:
Synthesis of Differentiable Functional Programs for Lifelong Learning. CoRR abs/1804.00218 (2018) - [i5]Akash Srivastava, Charles Sutton:
Variational Inference In Pachinko Allocation Machines. CoRR abs/1804.07944 (2018) - [i4]Akash Srivastava, Kai Xu, Michael U. Gutmann, Charles Sutton:
Ratio Matching MMD Nets: Low dimensional projections for effective deep generative models. CoRR abs/1806.00101 (2018) - [i3]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 abs/1806.04854 (2018) - 2017
- [j2]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): 1451-1468 (2017) - [c3]Akash Srivastava, Charles Sutton:
Autoencoding Variational Inference For Topic Models. ICLR (Poster) 2017 - [c2]Akash Srivastava, Lazar Valkov, Chris Russell, Michael U. Gutmann, Charles Sutton:
VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning. NIPS 2017: 3308-3318 - 2016
- [i2]Akash Srivastava, James Y. Zou, Ryan P. Adams, Charles Sutton:
Clustering with a Reject Option: Interactive Clustering as Bayesian Prior Elicitation. CoRR abs/1602.06886 (2016) - [i1]Akash Srivastava, James Y. Zou, Ryan P. Adams, Charles Sutton:
Clustering with a Reject Option: Interactive Clustering as Bayesian Prior Elicitation. CoRR abs/1606.05896 (2016) - 2015
- [c1]Akash Srivastava:
Performance Comparison of Various Particle Swarm Optimizers in DWT-SVD watermarking for RGB Images. ICCCT 2015: 244-250 - 2010
- [j1]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): 531-541 (2010)
Coauthor Index
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