Tensor decomposition is a fundamental tool for analyzing multi-dimensional data by learning low-rank factors to represent high-order interactions. While recent works on temporal tensor decomposition have made significant progress by incorporating continuous timestamps in latent factors, they still struggle with general tensor data with continuous…
To establish last-iterate convergence for Counterfactual Regret Minimization (CFR) algorithms in learning a Nash equilibrium (NE) of extensive-form games (EFGs), recent studies reformulate learning an NE of the original EFG as learning the NEs of a sequence of (perturbed) regularized EFGs. Hence, proving last-iterate convergence…
Transformer models have become the dominant backbone for sequence modeling, leveraging self-attention to produce contextualized token representations. These are typically aggregated into fixed-size vectors via pooling operations for downstream tasks. While much of the literature has focused on attention mechanisms, the role of pooling remains…
Given a noisy linear measurement [equation] of a distribution [equation], and a good approximation to the prior [equation], when can we sample from the posterior [equation]? Posterior sampling provides an accurate and fair framework for tasks such as inpainting, deblurring, and MRI reconstruction, and several…
Update 2025-10-15: Our artifact evaluated by the ACM CCS 2025 AEC, companion to our paper, has received the ACM CCS 2025 Distinguished Artifact Award! Incorrect handling of security-critical data formats, particularly in low-level languages, are the root cause of many security vulnerabilities. Provably correct parsing…
Knowledge distillation from Large Language Models (LLMs) to locally hosted Small Language Models (SLMs) provides advantages for Data Analysis Code Generation (DACG) such as privacy protection. However, achieving effective distillation without resource-intensive training is challenging. This paper investigates whether LLMs can distill knowledge to SLMs…
This report presents a review of recent empirical evidence of generative AI (GenAI) impact on learning outcomes in formal education. Its purpose is to provide educators with an overview of top concerns for ensuring students’ learning gains when using LLM-based learning tools and concludes with…
Designing empathic interactions for helpful, human-centered AI As AI agents become woven into everyday life, from education and healthcare to customer support and productivity tools, we face a basic question. How should these systems understand and respond to us. We see digital empathy as an…
Watch the founder, former and current leaders, and researchers at the Microsoft Research India lab talk about its unique culture. They reflect on their journeys, growth, and what makes the lab such a great place to be in.
Learn about Microsoft Research India’s journey from its inception to becoming a leading research center in computer science in India.
Large language models (LLMs) demonstrate impressive performance on a wide variety of tasks, but they often struggle with tasks that require multi-step reasoning or goal-directed planning. To address this, we take inspiration from the human brain, in which planning is accomplished via component processes that…
Empathy is increasingly recognized as a key factor in human–AI communication, yet conventional approaches to “digital empathy” often focus on simulating internal, human like emotional states while overlooking the inherently subjective, contextual, and relational facets of empathy as perceived by users. In this work, we…
