We thank all reviewers for their detailed and constructive comments. Reviewer 1 and 3 commented that it would be better if the algorithm can handle many (e.g., 90% or 99%) irrelevant features. We certainly agree with this. However, we want to point out that the subspace clustering problem with corrupted feature is an open problem, even when the fraction of irrelevant features are mild. Indeed, as shown in the paper, SSC and LASSO-SSC break down even with few irrelevant features.

The paper investigates the problem of inferring an agent's belief of the system dynamics of an MDP, given demonstrations of its behavior and the reward function it was optimizing. Knowledge of this internal belief can be used for Inverse Reinforcement Learning of an unknown task in the same environment. Furthermore, given the action provided by the agent, its intended action on the true dynamics can be inferred. This allows for assistive tele-operation, by applying the intended actions to the system instead of the provided ones. The proposed method models the agent using the model derived in maximum causal entropy inverse reinforcement learning.

Earlier this week, the Toyota Research Institute opened the doors of its Bay Area offices to members of the media for the first time. It was a day full of demos, ranging from driving simulators and drifting instructors to conversations around machine learning and sustainability. Robotics, a longtime focus of Toyota's research division, were on display, as well. First was something more along the lines of what one would expect from Toyota: an industrial arm with a modified gripper designed for the surprisingly complex task of moving boxes from the back of a truck to nearby conveyor belts -- something most factories are hoping to automate in the future. The other is a bit more surprising -- at least for those who haven't followed the division's work that closely.

In this paper, we study the \underline{R}obust \underline{o}ptimization for \underline{se}quence \underline{Net}worked \underline{s}ubmodular maximization (RoseNets) problem. We interweave the robust optimization with the sequence networked submodular maximization. The elements are connected by a directed acyclic graph and the objective function is not submodular on the elements but on the edges in the graph. Under such networked submodular scenario, the impact of removing an element from a sequence depends both on its position in the sequence and in the network. This makes the existing robust algorithms inapplicable. In this paper, we take the first step to study the RoseNets problem. We design a robust greedy algorithm, which is robust against the removal of an arbitrary subset of the selected elements. The approximation ratio of the algorithm depends both on the number of the removed elements and the network topology. We further conduct experiments on real applications of recommendation and link prediction. The experimental results demonstrate the effectiveness of the proposed algorithm.

Recently, high-quality video conferencing with fewer transmission bits has become a very hot and challenging problem. We propose FAIVConf, a specially designed video compression framework for video conferencing, based on the effective neural human face generation techniques. FAIVConf brings together several designs to improve the system robustness in real video conference scenarios: face-swapping to avoid artifacts in background animation; facial blurring to decrease transmission bit-rate and maintain the quality of extracted facial landmarks; and dynamic source update for face view interpolation to accommodate a large range of head poses. Our method achieves a significant bit-rate reduction in the video conference and gives much better visual quality under the same bit-rate compared with H.264 and H.265 coding schemes.

The U.S. and almost all countries today identify AI as a critical strategic area in the future of computing. Companies are more invested than ever in discovering how AI can provide advantages in their competitive markets. According to a report released earlier this year by Appen Limited, AI budgets increased 55% year over year, ranging from $500,000 to $5 million, with more attention placed on internal processes, a better understanding of data and efficiency gains. Fueling this interest are super-accelerated digital transformations driven by a "digital or die" theme mitigating the limitations imposed by the Covid-19 pandemic. With digitization, the volume, variety and velocity of data have increased exponentially for many years. Capturing, managing and exploiting the data proved challenging.

The world of UI/UX is changing every month. What if you could use machine learning to help you keep up with all of the changes? Machine learning can help developers make more user-friendly web applications. Learn some background on machine learning and algorithms and see examples of where Brain.js The world of UI/UX is changing every month.