Paper proposes GANs for generating business process suffixes and remaining times.
problem Generating accurate suffixes and remaining times for business process models.
method Encoder-decoder GAN architecture with Gumbel-Softmax distribution for categorical sequences.
result The approach outperforms baselines in suffix and remaining time prediction accuracy.
Improved PSTs learn from approximate string matching.
problem Exact matching in PSTs limits predictive performance.
method Approximate suffix matching and self-bounded PST.
result Approximate matching PSTs outperform other variants.
Improved sentence modeling using Suffix Bidirectional LSTM.
problem Sequential bias in BiLSTMs limits long-range dependencies.
method Encodes each suffix and prefix of a sequence in both forward and reverse directions.
result SuBiLSTM improves performance in various NLP tasks.
Shorter adversarial prompts help protect LLMs from jailbreak attacks.
problem Protecting large language models from jailbreak attacks with long adversarial suffixes.
method Adversarial training on shorter adversarial suffixes to defend against longer adversarial suffixes.
result Aligning LLMs on shorter adversarial suffixes can effectively defend against jailbreak attacks with longer suffixes.
Observer learns optimal policy from learner's actions without rewards.
problem Learning optimal policy from non-rewarded actions of a non-stationary learner.
method Two-Phase Suffix Imitation framework.
result Observer achieves convergence rate of O ~ ( 1 / N ) \tilde O(1/\sqrt{N}) O ~ ( 1/ N ) . Optimal Completion Distillation improves speech recognition models efficiently.
problem Improving sequence to sequence models for speech recognition.
method Optimal suffix selection using dynamic programming and target distribution.
result Achieves state-of-the-art performance on speech datasets.
Graph neural network predicts natural paths in graphs.
problem Predicting natural paths in graphs.
method Graph Neural Network (Gretel) for path extrapolation.
result Gretel efficiently predicts and samples from future path distributions.
CROP verifies clean prefixes in reasoning traces, improving downstream repair accuracy.
problem Uncertainty in reasoning traces prevents full certification of entire responses.
method CROP selects a calibrated threshold to certify the longest prefix with low risk proxies.
result CROP improves downstream repair accuracy by preserving valid reasoning and discarding misleading suffixes.
Ultra-fast search algorithm for trillion-scale corpora with semantic flexibility.
problem Efficiently searching over large natural language corpora with semantic variations.
method String matching based on suffix arrays, vector representation of words, dynamic corpus-aware pruning, fast exact lookup.
result Substantially lower search latency compared to existing methods on FineWeb-Edu corpus.
Improved stochastic gradient descent analysis for non-smooth convex functions.
problem Minimizing non-smooth, non-differentiable convex functions.
method Stochastic gradient descent with suffix averaging method analysis.
result Error rate of final iterate is O ( log ( T ) / T ) O(\log(T)/T) O ( log ( T ) / T ) with high probability. Part-of-speech (POS) tagging is a fundamental component for performing natural language tasks such as parsing, information extraction, and question answering. When POS taggers are trained in one domain and applied in significantly different domains, their performance can degrade dramatically. We present a methodology f…
New averaging strategy achieves optimal convergence rate with high probability.
problem Optimizing convergence rate for strongly-convex functions.
method Simple non-uniform averaging strategy combined with Freedman's inequality.
result Achieves optimal O ( 1 / T ) O(1/T) O ( 1/ T ) convergence rate with high probability. SGLBO optimizes quantum circuits with fewer measurements, improving accuracy and noise resilience.
problem Efficiently optimizing parameterized quantum circuits with reduced measurement shots and noise.
method Developed SGLBO combining SGD and BO, with adaptive measurement-shot strategy and suffix averaging.
result Significantly reduces measurement-shot cost while improving accuracy and noise resilience.
DKMD is a fast signed statistic for comparing univariate distributions.
problem Comparing univariate distributions, especially preserving directionality.
method DKMD integrates kernel mean embeddings against an odd weighting function.
result DKMD preserves directionality and is robust to outliers.
Stochastic Gradient Descent (SGD) is one of the simplest and most popular stochastic optimization methods. While it has already been theoretically studied for decades, the classical analysis usually required non-trivial smoothness assumptions, which do not apply to many modern applications of SGD with non-smooth object…
Improved shuffling gradient methods converge faster for nonsmooth convex optimization.
problem Improving convergence rates for nonsmooth convex optimization problems.
method Analysis of shuffling gradient methods, focusing on Random Reshuffle and Single Shuffle strategies.
result Shuffling gradient methods, particularly Random Reshuffle and Single Shuffle, converge faster than Proximal Gradient Descent for nonsmooth convex optimization.
New HMC method handles features in POS tagging, outperforming MEMM.
problem HMC struggles with arbitrary features in POS tagging.
method Introduced Entropic Forward-Backward (EFB) probabilities to compute HMC restorations.
result EFB-based HMC outperforms MEMM in POS tagging.
Online monitoring system for safety classifiers with shift detection and conformal adaptation
problem Detecting and adapting to distributional shifts in deployed safety classifiers
method Calibrated sequential statistics for online monitoring, conformal abstention for adaptation
result 86.6% valid detection with mean latency of 39.5 steps
Algorithm learns to bid in auctions with shilling, masking real bids.
problem Learning to bid in auctions manipulated by shilling.
method Combines interval-elimination and optimistic branches, debiases losing-side reports.
result Achieves dynamic-pricing rate i l d e O ( T 2 / 3 ) ilde{\mathcal{O}}(T^{2/3}) i l d e O ( T 2/3 ) and first-price auctions rate i l d e O ( T ) ilde{\mathcal{O}}(\sqrt{T}) i l d e O ( T ) . Improved reasoning model by sampling from power distribution without additional training.
problem Efficiently sampling from a sharpened distribution to improve reasoning models.
method Entropy-Cut Metropolis-Hastings algorithm that identifies key decision points for resampling.
result The method consistently improves reasoning models across various datasets.
Simple adaptive attacks can jailbreak state-of-the-art safety-aligned language models.
problem Vulnerability of safety-aligned language models to simple adaptive attacks.
method Designing adversarial prompt templates, random search on suffixes, and transfer/prefilling attacks.
result 100% attack success rate on various models including GPT-4, Vicuna, Mistral, and Claude.