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Surround-view panoramic imaging delivers extensive spatial coverage and is widely supported by professional and commodity capture devices. Research on inferring and exploring 3D indoor models from 360° images has recently flourished, resulting in highly ...

As repetition of activities can establish habits and exploration of new ones can provide a healthy variety, we investigate how a recommender system for physical activities can optimally balance these two approaches. We conducted an eight-week user study ...

A legacy of print is that much of our process and tooling is predicated on using text in paginated form, such as was required for (paper) printed media. Increasingly, digitally-created (‘born-digital’) documents will never be used non-digitally and yet ...

Exposure bias and its induced feedback loop effect are well-known problems in recommender systems. Exploration is believed to be the key to break such feedback loops. While classical contextual bandit algorithms such as Upper-Confidence-Bound and ...

Human curiosity compels us to explore and understand the unknown. The information retrieval and recommendation systems we relied upon daily for information acquisition and decision making however often fall prey to the "closed feedback loop". They ...

Evolutionary computation (EC) is often used to automatically discover solutions to optimization problems. It is valued because it allows the programmer to intuitively design a search space to fit a task, and because it is a relatively open-ended search ...

For real-world applications, autonomous mobile robotic platforms must be capable of navigating safely in a multitude of different and dynamic environments with accurate and robust localization being a key prerequisite. To support further research in this ...

When tackling high-dimensional, continuous simulation-based optimization (SO) problems, it is important to balance exploration and exploitation. Most past SO research focuses on the enhancement of exploitation techniques. The exploration technique of an ...

Exploration in reinforcement learning remains a difficult challenge. In order to drive exploration, ensembles with randomized prior functions have recently been popularized to quantify uncertainty in the value model. However these ensembles have no ...

Exploration in reinforcement learning remains a difficult challenge. In order to drive exploration, ensembles with randomized prior functions have recently been popularized to quantify uncertainty in the value model. There is no theoretical reason for ...

In reinforcement learning (RL), the class of deep deterministic off-policy algorithms is effectively applied to solve challenging continuous control problems. Current approaches commonly utilize random noise as an exploration method, which has several ...

Multiagent Reinforcement Learning (MARL) has been successfully applied to domains requiring close coordination among many agents. However, real-world tasks require safety specifications that are not generally considered by MARL algorithms. In this work, ...

We present a new computing model for intrinsic rewards in reinforcement learning that addresses the limitations of existing surprise-driven explorations. The reward is the novelty of the surprise rather than the surprise norm. We estimate the surprise ...

We present a coordinated autonomy pipeline for multi-sensor exploration of confined environments. We simultaneously address four broad challenges that are typically overlooked in prior work: (a) make effective use of both range and vision sensing ...

Exploration is often necessary in online learning to maximize long-term rewards, but it comes at the cost of short-term “regret.” We study how this cost of exploration is shared across multiple groups. For example, in a clinical trial setting, patients ...

Scripting environments support exploration from smaller programs to larger systems. From original Smalltalk workspaces to modern Python notebooks, such tool support is known to foster understanding. However, programmers struggle with unforeseen effects ...

Effective exploration is believed to positively influence the long-term user experience on recommendation platforms. Determining its exact benefits, however, has been challenging. Regular A/B tests on exploration often measure neutral or even negative ...

Meta-heuristic optimization algorithms are versatile and efficient techniques for solving complex optimization problems. When applied to clustering algorithms, these algorithms offer numerous advantages over traditional optimization methods, including ...

The fixed control structure of deterministic algorithms renders their behavior traceable but not amenable to interactive exploration.

In the accompanying poster, we illustrate how the runs of a transition system simulating a family of algorithms form the ...

Exploration of the behavior policy plays an important role in reinforcement learning as it helps learning algorithms escape local optima. Taking linear value function approximation as an example, exploration directly affects the sampling of states, ...