1: On Inapproximability of Reconfiguration Problems: PSPACE-Hardness and some Tight NP-Hardness Results
2: Rethinking Model-based, Policy-based, and Value-based Reinforcement Learning via the Lens of Representation Complexity
3: Competitive Search in the Line and the Star with Predictions
4: Dynamic $((1+\epsilon)\ln n)$-Approximation Algorithms for Minimum Set Cover and Dominating Set
5: Faster Fixed Parameter Tractable Algorithms for Counting Markov Equivalence Classes with Special Skeletons
6: Total Matching and Subdeterminants
7: Joint symbolic aggregate approximation of time series
8: Approximation algorithms for Job Scheduling with reconfigurable resources
9: Interpreting the Curse of Dimensionality from Distance Concentration and Manifold Effect