A Modern Take on the Bias-Variance Tradeoff in Neural Networks. - Brady Neal; Sarthak Mittal; Aristide Baratin; Vinayak Tantia; Matthew Scicluna; Simon Lacoste-Julien; Ioannis Mitliagkas (CoRR, 2018)

Prediction of employee performance using machine learning techniques. - Anu Singh Lather; Ruchika Malhotra; Priya Saloni; Prabhjot Singh; Sarthak Mittal (AISS, 2019)

Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention over Modules. - Sarthak Mittal; Alex Lamb; Anirudh Goyal; Vikram Voleti; Murray Shanahan; Guillaume Lajoie; Michael Mozer; Yoshua Bengio (ICML, 2020)

Congestion Control utilizing Software Defined Control Architecture at the Traffic Light Intersection. - Vaibhav Garg; Anuj Sachan; Neetesh Kumar; Sarthak Mittal (MASS, 2021)

Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning. - Nan Rosemary Ke; Aniket Didolkar; Sarthak Mittal; Anirudh Goyal; Guillaume Lajoie; Stefan Bauer; Danilo Jimenez Rezende; Michael Mozer; Yoshua Bengio; Chris Pal (NeurIPS Datasets and Benchmarks, 2021)

Compositional Attention: Disentangling Search and Retrieval. - Sarthak Mittal; Sharath Chandra Raparthy; Irina Rish; Yoshua Bengio; Guillaume Lajoie (ICLR, 2022)

Is a Modular Architecture Enough? - Sarthak Mittal; Yoshua Bengio; Guillaume Lajoie (NeurIPS, 2022)

On Neural Architecture Inductive Biases for Relational Tasks. - Giancarlo Kerg; Sarthak Mittal; David Rolnick; Yoshua Bengio; Blake A. Richards; Guillaume Lajoie (CoRR, 2022)

From Points to Functions: Infinite-dimensional Representations in Diffusion Models. - Sarthak Mittal; Guillaume Lajoie; Stefan Bauer; Arash Mehrjou (CoRR, 2022)

MixupE: Understanding and Improving Mixup from Directional Derivative Perspective. - Vikas Verma; Sarthak Mittal; Wai Hoh Tang; Hieu Pham 001; Juho Kannala; Yoshua Bengio; Arno Solin; Kenji Kawaguchi (CoRR, 2022)

Deep Reinforcement Learning-Based Traffic Light Scheduling Framework for SDN-Enabled Smart Transportation System. - Neetesh Kumar; Sarthak Mittal; Vaibhav Garg; Neeraj Kumar 001 (IEEE Trans. Intell. Transp. Syst., 2022)

Leveraging Synthetic Targets for Machine Translation. - Sarthak Mittal; Oleksii Hrinchuk; Oleksii Kuchaiev (ACL (Findings), 2023)

Diffusion Based Representation Learning. - Sarthak Mittal; Korbinian Abstreiter; Stefan Bauer; Bernhard Schölkopf; Arash Mehrjou (ICML, 2023)

MixupE: Understanding and improving Mixup from directional derivative perspective. - Yingtian Zou; Vikas Verma; Sarthak Mittal; Wai Hoh Tang; Hieu Pham 001; Juho Kannala; Yoshua Bengio; Arno Solin; Kenji Kawaguchi (UAI, 2023)

Iterated Denoising Energy Matching for Sampling from Boltzmann Densities. - Tara Akhound-Sadegh; Jarrid Rector-Brooks; Avishek Joey Bose; Sarthak Mittal; Pablo Lemos; Cheng-Hao Liu; Marcin Sendera; Siamak Ravanbakhsh; Gauthier Gidel; Yoshua Bengio; Nikolay Malkin; Alexander Tong 001 (ICML, 2024)

Improved off-policy training of diffusion samplers. - Marcin Sendera; Minsu Kim 004; Sarthak Mittal; Pablo Lemos; Luca Scimeca; Jarrid Rector-Brooks; Alexandre Adam; Yoshua Bengio; Nikolay Malkin (NeurIPS, 2024)

Amortizing intractable inference in diffusion models for vision, language, and control. - Siddarth Venkatraman; Moksh Jain; Luca Scimeca; Minsu Kim 004; Marcin Sendera; Mohsin Hasan; Luke Rowe; Sarthak Mittal; Pablo Lemos; Emmanuel Bengio; Alexandre Adam; Jarrid Rector-Brooks; et al. (NeurIPS, 2024)

On diffusion models for amortized inference: Benchmarking and improving stochastic control and sampling. - Marcin Sendera; Minsu Kim 004; Sarthak Mittal; Pablo Lemos; Luca Scimeca; Jarrid Rector-Brooks; Alexandre Adam; Yoshua Bengio; Nikolay Malkin (CoRR, 2024)

Does learning the right latent variables necessarily improve in-context learning? - Sarthak Mittal; Eric Elmoznino; Léo Gagnon; Sangnie Bhardwaj; Dhanya Sridhar; Guillaume Lajoie (CoRR, 2024)

Recurrent Interpolants for Probabilistic Time Series Prediction. - Yu Chen; Marin Bilos; Sarthak Mittal; Wei Deng 002; Kashif Rasul; Anderson Schneider (CoRR, 2024)

Steering Masked Discrete Diffusion Models via Discrete Denoising Posterior Prediction. - Jarrid Rector-Brooks; Mohsin Hasan; Zhangzhi Peng; Zachary Quinn; Cheng-Hao Liu; Sarthak Mittal; Nouha Dziri; Michael M. Bronstein; Yoshua Bengio; Pranam Chatterjee; Alexander Tong 001; Avishek Joey Bose (CoRR, 2024)

In-context learning and Occam's razor. - Eric Elmoznino; Tom Marty; Tejas Kasetty; Léo Gagnon; Sarthak Mittal; Mahan Fathi; Dhanya Sridhar; Guillaume Lajoie (CoRR, 2024)

Steering Masked Discrete Diffusion Models via Discrete Denoising Posterior Prediction. - Jarrid Rector-Brooks; Mohsin Hasan; Zhangzhi Peng; Cheng-Hao Liu; Sarthak Mittal; Nouha Dziri; Michael M. Bronstein; Pranam Chatterjee; Alexander Tong 001; Joey Bose (ICLR, 2025)

Amortized In-Context Bayesian Posterior Estimation. - Sarthak Mittal; Niels Bracher; Guillaume Lajoie; Priyank Jaini; Marcus A. Brubaker (CoRR, 2025)

In-Context Parametric Inference: Point or Distribution Estimators? - Sarthak Mittal; Yoshua Bengio; Nikolay Malkin; Guillaume Lajoie (CoRR, 2025)

Solving Bayesian inverse problems with diffusion priors and off-policy RL. - Luca Scimeca; Siddarth Venkatraman; Moksh Jain; Minsu Kim 004; Marcin Sendera; Mohsin Hasan; Luke Rowe; Sarthak Mittal; Pablo Lemos; Emmanuel Bengio; Alexandre Adam; Jarrid Rector-Brooks; et al. (CoRR, 2025)

Next-Token Prediction Should be Ambiguity-Sensitive: A Meta-Learning Perspective. - Léo Gagnon; Eric Elmoznino; Sarthak Mittal; Tom Marty; Tejas Kasetty; Dhanya Sridhar; Guillaume Lajoie (CoRR, 2025)