Writing and sketching in the air, recognizing and controlling on the fly. - Sharad Vikram; Lei Li 005; Stuart Russell 001 (CHI Extended Abstracts, 2013)

Capturing Meaning in Product Reviews with Character-Level Generative Text Models. - Zachary Chase Lipton; Sharad Vikram; Julian J. McAuley (CoRR, 2015)

Interactive Bayesian Hierarchical Clustering. - Sharad Vikram; Sanjoy Dasgupta (ICML, 2016)

Estimating Reactions and Recommending Products with Generative Models of Reviews. - Jianmo Ni; Zachary C. Lipton; Sharad Vikram; Julian J. McAuley (IJCNLP(1), 2017)

SOLAR: Deep Structured Latent Representations for Model-Based Reinforcement Learning. - Marvin Zhang; Sharad Vikram; Laura M. Smith; Pieter Abbeel; Matthew J. Johnson 002; Sergey Levine (CoRR, 2018)

The LORACs Prior for VAEs: Letting the Trees Speak for the Data. - Sharad Vikram; Matthew D. Hoffman; Matthew J. Johnson 002 (AISTATS, 2019)

SOLAR: Deep Structured Representations for Model-Based Reinforcement Learning. - Marvin Zhang; Sharad Vikram; Laura M. Smith; Pieter Abbeel; Matthew J. Johnson 002; Sergey Levine (ICML, 2019)

How to pick the domain randomization parameters for sim-to-real transfer of reinforcement learning policies? - Quan Vuong; Sharad Vikram; Hao Su 001; Sicun Gao; Henrik I. Christensen (CoRR, 2019)

Automatic structured variational inference. - Luca Ambrogioni; Kate Lin; Emily Fertig; Sharad Vikram; Max Hinne; Dave Moore; Marcel van Gerven (AISTATS, 2021)

What Are Bayesian Neural Network Posteriors Really Like? - Pavel Izmailov; Sharad Vikram; Matthew D. Hoffman; Andrew Gordon Wilson (ICML, 2021)

Evaluating Approximate Inference in Bayesian Deep Learning. - Andrew Gordon Wilson; Pavel Izmailov; Matthew D. Hoffman; Yarin Gal; Yingzhen Li; Melanie F. Pradier; Sharad Vikram; Andrew Y. K. Foong; Sanae Lotfi; Sebastian Farquhar (NeurIPS (Competition and Demos), 2021)

Training Chain-of-Thought via Latent-Variable Inference. - Matthew Douglas Hoffman; Du Phan; David Dohan; Sholto Douglas; Tuan Anh Le; Aaron Parisi; Pavel Sountsov; Charles Sutton; Sharad Vikram; Rif A. Saurous (NeurIPS, 2023)

RecurrentGemma: Moving Past Transformers for Efficient Open Language Models. - Aleksandar Botev; Soham De; Samuel L. Smith; Anushan Fernando; George-Cristian Muraru; Ruba Haroun; Leonard Berrada; Razvan Pascanu; Pier Giuseppe Sessa; Robert Dadashi; Léonard Hussenot; Johan Ferret; et al. (CoRR, 2024)

Training Language Models on the Knowledge Graph: Insights on Hallucinations and Their Detectability. - Jiri Hron; Laura Culp; Gamaleldin F. Elsayed; Rosanne Liu; Ben Adlam; Maxwell L. Bileschi; Bernd Bohnet; JD Co-Reyes; Noah Fiedel; C. Daniel Freeman; Izzeddin Gur; Kathleen Kenealy; et al. (CoRR, 2024)