Research
Optimization methods are the engine of machine learning algorithms. Examples abound, such as training neural networks with stochastic gradient descent, segmenting images with submodular optimization, or efficiently searching a game tree with bandit algorithms. We…
Many real-world problems, such as network packet routing and the coordination of autonomous vehicles, are naturally modelled as cooperative multi-agent systems. In this talk, I overview some of the key challenges in developing reinforcement learning…
In this tutorial we discuss several recent advances in deep reinforcement learning involving policy gradient methods. These methods have shown significant success in a wide range of domains, including continuous-action domains such as manipulation, locomotion,…
Effective coordination of post-disaster relief operations depends critically on the availability of reliable situational information, as well as on citizen participation in the operations. The advent of online social media (e.g., Twitter, Facebook) and the…
As the interaction over the web has increased, incidents of aggression and related events like trolling, cyberbullying, flaming, hate speech, etc. too have increased manifold across the globe. While most of these behaviours like bullying…
Voice-driven automated agents such as personal assistants are becoming increasingly popular. However, in a multilingual and multi-cultural country like India, deploying such agents to engage with large sections of the population is highly challenging. A…
Most of the AI systems today are driven by three key components (i) data (ii) common sense knowledge and (iii) powerful learning algorithms which can harness this data and knowledge to learn task specific meaningful…
Support for under-represented Ph.D. students in computer sciences By Lynn Parker, Writer, Microsoft The Microsoft Research Dissertation Grant program offers financial support to selected doctoral students from groups that are under-represented in the field of…
