Multi-agent systems are characterized by environmental uncertainty, varying policies of agents, and partial observability, which result in significant risks.

However, PDs are point multi-sets which can not be used in machine learning algorithms for vector data.

However, these results lack reliable uncertainty estimates.

As prediction market platforms like Polymarket and Kalshi battle regulators in court, Senate Democrats are urging the CFTC to avoid weighing in, escalating a broader fight over the burgeoning industry. Senator Adam Schiff, a Democrat from California, is leading the group of lawmakers urging the CFTC to stay out of state prediction market lawsuits. A group of 23 Democratic US senators sent a letter Friday to the top federal regulator overseeing prediction markets, urging the agency to avoid weighing in on pending court cases over the legality of offerings on the platforms tied to "sports, war, and other prohibited events." Prediction markets, which sell contracts tied to the outcome of real-world developments, have exploded in popularity over the past year, attracting an increasingly mainstream fanbase eager to wager on everything from geopolitical conflicts to fashion choices to the Super Bowl. As they expanded, the platforms have become a magnet for ethical and legal controversies.

As electric vehicles have gone mainstream, buyers are facing a smorgasbord of options, and Tesla--once untouchable--is no longer the dominant force. Last year was a tough one for Elon Musk's auto brand: Sales efforts faltered, and the company lost its title of world's largest EV maker to China's BYD . Today, it feels like all automakers-- including luxury brands --are racing to release their own EVs. But at the same time, some companies are scaling back production plans . So where is the market headed?

Huber's contaminated heavy-tailed rewards and meanwhile needs to ensure differential privacy.

Huber's contaminated heavy-tailed rewards and meanwhile needs to ensure differential privacy.

Inferring the synaptic plasticity rules that govern learning in the brain is a key challenge in neuroscience. We present a novel computational method to infer these rules from experimental data, applicable to both neural and behavioral data. Our approach approximates plasticity rules using a parameterized function, employing either truncated Taylor series for theoretical interpretability or multilayer percep-trons. These plasticity parameters are optimized via gradient descent over entire trajectories to align closely with observed neural activity or behavioral learning dynamics. This method can uncover complex rules that induce long nonlinear time dependencies, particularly involving factors like postsynaptic activity and current synaptic weights. We validate our approach through simulations, successfully recovering established rules such as Oja's, as well as more intricate plasticity rules with reward-modulated terms. We assess the robustness of our technique to noise and apply it to behavioral data from Drosophila in a probabilistic reward-learning experiment. Notably, our findings reveal an active forgetting component in reward learning in flies, improving predictive accuracy over previous models. This modeling framework offers a promising new avenue for elucidating the computational principles of synaptic plasticity and learning in the brain.

Neural Information Processing Systems http://nips.cc/