Selfie Detection by Synergy-Constraint Based Convolutional Neural Network. - Yashas Annadani; Vijayakrishna Naganoor; Akshay Kumar Jagadish; Krishnan Chemmangat (SITIS, 2016)

Sliding Dictionary Based Sparse Representation For Action Recognition. - Yashas Annadani; D. L. Rakshith; Soma Biswas (CoRR, 2016)

GrowBit: Incremental Hashing for Cross-Modal Retrieval. - Devraj Mandal; Yashas Annadani; Soma Biswas (ACCV (4), 2018)

Preserving Semantic Relations for Zero-Shot Learning. - Yashas Annadani; Soma Biswas (CVPR, 2018)

Augment and Adapt: A Simple Approach to Image Tampering Detection. - Yashas Annadani; C. V. Jawahar (ICPR, 2018)

Structural Autoencoders Improve Representations for Generation and Transfer. - Felix Leeb; Yashas Annadani; Stefan Bauer; Bernhard Schölkopf (CoRR, 2020)

Variational Causal Networks: Approximate Bayesian Inference over Causal Structures. - Yashas Annadani; Jonas Rothfuss; Alexandre Lacoste; Nino Scherrer; Anirudh Goyal; Yoshua Bengio; Stefan Bauer (CoRR, 2021)

Learning Neural Causal Models with Active Interventions. - Nino Scherrer; Olexa Bilaniuk; Yashas Annadani; Anirudh Goyal; Patrick Schwab; Bernhard Schölkopf; Michael C. Mozer; Yoshua Bengio; Stefan Bauer; Nan Rosemary Ke (CoRR, 2021)

Interventions, Where and How? Experimental Design for Causal Models at Scale. - Panagiotis Tigas; Yashas Annadani; Andrew Jesson; Bernhard Schölkopf; Yarin Gal; Stefan Bauer (NeurIPS, 2022)

Latent Variable Models for Bayesian Causal Discovery. - Jithendaraa Subramanian; Yashas Annadani; Ivaxi Sheth; Stefan Bauer; Derek Nowrouzezahrai; Samira Ebrahimi Kahou (CoRR, 2022)

Learning Latent Structural Causal Models. - Jithendaraa Subramanian; Yashas Annadani; Ivaxi Sheth; Nan Rosemary Ke; Tristan Deleu; Stefan Bauer; Derek Nowrouzezahrai; Samira Ebrahimi Kahou (CoRR, 2022)

Structure by Architecture: Structured Representations without Regularization. - Felix Leeb; Giulia Lanzillotta; Yashas Annadani; Michel Besserve; Stefan Bauer; Bernhard Schölkopf (ICLR, 2023)

Differentiable Multi-Target Causal Bayesian Experimental Design. - Panagiotis Tigas; Yashas Annadani; Desi R. Ivanova; Andrew Jesson; Yarin Gal; Adam Foster 001; Stefan Bauer (ICML, 2023)

BayesDAG: Gradient-Based Posterior Inference for Causal Discovery. - Yashas Annadani; Nick Pawlowski; Joel Jennings; Stefan Bauer; Cheng Zhang 005; Wenbo Gong 001 (NeurIPS, 2023)

Trust Your 𝛁: Gradient-based Intervention Targeting for Causal Discovery. - Mateusz Olko; Michal Zajac 005; Aleksandra Nowak 001; Nino Scherrer; Yashas Annadani; Stefan Bauer; Lukasz Kucinski; Piotr Milos (NeurIPS, 2023)

BayesDAG: Gradient-Based Posterior Sampling for Causal Discovery. - Yashas Annadani; Nick Pawlowski; Joel Jennings; Stefan Bauer; Cheng Zhang 005; Wenbo Gong 001 (CoRR, 2023)

Challenges and Considerations in the Evaluation of Bayesian Causal Discovery. - Amir Mohammad Karimi-Mamaghan; Panagiotis Tigas; Karl Henrik Johansson; Yarin Gal; Yashas Annadani; Stefan Bauer (ICML, 2024)

Amortized Active Causal Induction with Deep Reinforcement Learning. - Yashas Annadani; Panagiotis Tigas; Stefan Bauer; Adam Foster 001 (NeurIPS, 2024)

Preference-Guided Diffusion for Multi-Objective Offline Optimization. - Yashas Annadani; Syrine Belakaria; Stefano Ermon; Stefan Bauer; Barbara E. Engelhardt (CoRR, 2025)

Causal Bayesian Optimization with Unknown Graphs. - Jean Durand; Yashas Annadani; Stefan Bauer; Sonali Parbhoo (CoRR, 2025)