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Constructing Deep Neural Networks by Bayesian Network Structure Learning
Raanan Y. Rohekar, Shami Nisimov, Yaniv Gurwicz, Guy Koren, Gal Novik
We introduce a principled approach for unsupervised structure learning of deep neural networks.
A General Theory of Equivariant CNNs on Homogeneous Spaces
Taco S. Cohen, Mario Geiger, Maurice Weiler
The theory enables a systematicclassification of all existing G-CNNs in terms of their symmetry group, base space,andfieldtype.
RiskQ: Risk-sensitive Multi-Agent Reinforcement Learning Value Factorization
Multi-agent systems are characterized by environmental uncertainty, varying policies of agents, and partial observability, which result in significant risks.
Beyond Confidence Regions: Tight Bayesian Ambiguity Sets for Robust MDPs
Marek Petrik, Reazul Hasan Russel
Neural Information Processing Systems http://nips.cc/
Persistence Fisher Kernel: A Riemannian Manifold Kernel for Persistence Diagrams
However, PDs are point multi-sets which can not be used in machine learning algorithms for vector data.
Similarity-Navigated Conformal Prediction for Graph Neural Networks
However, these results lack reliable uncertainty estimates.
A loss framework for calibrated anomaly detection
Neural Information Processing Systems http://nips.cc/
Machine Teaching of Active Sequential Learners
Tomi Peltola, Mustafa Mert Çelikok, Pedram Daee, Samuel Kaski
On the other hand, for goal-oriented tasks, humans create mental models of the environment for planning their actions to achieve their goals [1,2]. In AI systems, recent research has shown that usersformmentalmodelsoftheAI'sstateandbehaviour[ 3,4].
Senators Urge Top Regulator to Stay Out of Prediction Market Lawsuits
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.