From Understanding the Population Dynamics of the NSGA-II to the First Proven Lower Bounds

PAC Learning with Bandit Feedback: Sharp Sample Complexity in the Realizable Setting

Algorithms with Polynomially-Improved Approximation Factors for the $2 \rightarrow q$ Norm, and Applications

A computational phase transition for learning-to-sample from Ising models

Covering vertices by sequential stars

Fermi-Dirac machines as quantizations of neurons

A Comprehensive Evaluation of Vertex Elimination Algorithms for Algorithmic Differentiation

A Tight Bound on Localization of Electrical Flows

Optimal Dimension-Free Sampling for Regularized Classification

Reducing the Randomness in Partition Oracles for Bounded Degree Minor-Free Graphs

Beyond the Half-Approximation: Fair and Efficient Online Class Matching

Efficient Uniform Sampling of Surjections via their Profiles

Tractable Maximization of Budgeted Phylogenetic Diversity on Networks Utilizing Node Scanwidth

Fairness in Aggregation: Optimal Top-$k$ and Improved Full Ranking

Learning-Augmented Online Scheduling with Parsimonious Preemption

Entropy Equivalence Testing

Lumberjack: Better Differentially Private Random Forests through Heavy Hitter Detection in Trees

The Secretary Problem with a Stochastic Precursor

Polynomial-Time Robust Multiclass Linear Classification under Gaussian Marginals

Efficient Banzhaf-Based Data Valuation for $k$-Nearest Neighbors Classification

Block-Sphere Vector Quantization

An Approximation Algorithm for Graph Label Selection

Iterative Chow Filtering for Learning with Distribution Shift

Complexity of Non-Log-Concave Sampling in Fisher Information

Stochastic Matching via Local Sparsification

Finite Sample Bounds for Learning with Score Matching

What is Learnable in Valiant's Theory of the Learnable?

Provable Quantization with Randomized Hadamard Transform

Min-Max Optimization Requires Exponentially Many Queries

Fast and Compact Graph Cuts for the Boykov-Kolmogorov Algorithm

A proximal gradient algorithm for composite log-concave sampling

Adaptive Multi-Round Allocation with Stochastic Arrivals

The tractability landscape of diffusion alignment: regularization, rewards, and computational primitives

Mistake-Bounded Language Generation

Positional LSH: Binary Block Matrix Approximation for Attention with Linear Biases

Learning-Augmented Scalable Linear Assignment Problem Optimization via Neural Dual Warm-Starts

A Note on Non-Negative $L_1$-Approximating Polynomials

Curvature Beyond Positivity: Greedy Guarantees for Arbitrary Submodular Functions

Convex Optimization with Nested Evolving Feasible Sets

On the Complexity of the Matching Problem of Regular Expressions with Backreferences

Simple KNN-Based Outlier Detection Achieves Robust Clustering

Online Allocation with Unknown Shared Supply

Equivalence of Coarse and Fine-Grained Models for Learning with Distribution Shift

Accelerated Relax-and-Round for Concave Coverage Problems

Contrastive Identification and Generation in the Limit

Quantizing With Randomized Hadamard Transforms: Efficient Heuristic Now Proven

Nearly Optimal Attention Coresets

On Computing Total Variation Distance Between Mixtures of Product Distributions

Exact and Approximate Algorithms for Polytree Learning

Provable Accuracy Collapse in Embedding-Based Representations under Dimensionality Mismatch

New Bounds for Kernel Sums via Fast Spherical Embeddings

Unlearning Offline Stochastic Multi-Armed Bandits

Matroid Algorithms Under Size-Sensitive Independence Oracles

On the Learning Curves of Revenue Maximization

Asymptotically Robust Learning-Augmented Algorithms for Preemptive FIFO Buffer Management

Flashback: A Reversible Bilateral Run-Peeling Decomposition of Strings

Incremental Strongly Connected Components with Predictions

Characterizing Admissible Objective Functions for Hierarchical Clustering

Well-Conditioned Oblivious Perturbations in Linear Space

Mathematical Foundations for Peer-to-Peer Lattice Computation

Graph Neural Network-Informed Predictive Flows for Faster Ford-Fulkerson and PAC-Learnability

A weighted angle distance on strings

Towards Universal Convergence of Backward Error in Linear System Solvers

Constant-Factor Approximations for Doubly Constrained Fair k-Center, k-Median and k-Means

Tight Bounds for Learning Polyhedra with a Margin

Efficiency of Proportional Mechanisms in Online Auto-Bidding Advertising

Skyline-First Traversal as a Control Mechanism for Multi-Criteria Graph Search

Constant-Factor Approximation for the Uniform Decision Tree

Limited Perfect Monotonical Surrogates constructed using low-cost recursive linkage discovery with guaranteed output

Query Lower Bounds for Diffusion Sampling

Early Pruning for Public Transport Routing

Adapting Dijkstra for Buffers and Unlimited Transfers

Exploiting Low-Rank Structure in Max-K-Cut Problems

Partial Optimality in the Preordering Problem

High-accuracy log-concave sampling with stochastic queries

Learning to Approximate Uniform Facility Location via Graph Neural Networks

Linear Regression with Unknown Truncation Beyond Gaussian Features

Adaptive Power Iteration Method for Differentially Private PCA

Finite and Corruption-Robust Regret Bounds in Online Inverse Linear Optimization under M-Convex Action Sets

Learning Mixture Models via Efficient High-dimensional Sparse Fourier Transforms

Variance Computation for Weighted Model Counting with Knowledge Compilation Approach

Deterministic Coreset for Lp Subspace

Online Algorithms for Repeated Optimal Stopping: Balancing Baseline Guarantees and Regret

Learned Static Function Data Structures

Optimal hypersurface decision trees

A Perfectly Truthful Calibration Measure

The Geometry of LLM Quantization: GPTQ as Babai's Nearest Plane Algorithm

Best Agent Identification for General Game Playing

A Faster Generalized Two-Stage Approximate Top-K

Fast and Simple Densest Subgraph with Predictions

Smoothed Analysis of Learning from Positive Samples

Ineffectiveness for Search and Undecidability of PCSP Meta-Problems

Sample-Efficient Optimization over Generative Priors via Coarse Learnability

Efficient distributional regression trees learning algorithms for calibrated non-parametric probabilistic forecasts

Testing Noise Assumptions of Learning Algorithms

Testing Support Size More Efficiently Than Learning Histograms

Sharper Bounds for Chebyshev Moment Matching, with Applications

Expander Hierarchies for Normalized Cuts on Graphs

Multilayer Correlation Clustering

Efficient Parameter Estimation of Truncated Boolean Product Distributions

Faster Hamiltonian Monte Carlo by Learning Leapfrog Scale: a self-calibrated randomized solution

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