Search Results for author:
Found 138 papers, 53 papers with code
PoisonedParrot: Subtle Data Poisoning Attacks to Elicit Copyright-Infringing Content from Large Language Models
In this paper, we introduce PoisonedParrot: the first stealthy data poisoning attack that induces an LLM to generate copyrighted content even when the model has not been directly trained on the specific copyrighted material.
Why Are Web AI Agents More Vulnerable Than Standalone LLMs? A Security Analysis
Recent advancements in Web AI agents have demonstrated remarkable capabilities in addressing complex web navigation tasks.
MAFE: Multi-Agent Fair Environments for Decision-Making Systems
Fairness constraints applied to machine learning (ML) models in static contexts have been shown to potentially produce adverse outcomes among demographic groups over time.
Model Tampering Attacks Enable More Rigorous Evaluations of LLM Capabilities
We pit state-of-the-art techniques for removing harmful LLM capabilities against a suite of 5 input-space and 6 model tampering attacks.
MergeME: Model Merging Techniques for Homogeneous and Heterogeneous MoEs
The recent success of specialized Large Language Models (LLMs) in domains such as mathematical reasoning and coding has led to growing interest in methods for merging these expert LLMs into a unified Mixture-of-Experts (MoE) model, with the goal of enhancing performance in each domain while retaining effectiveness on general tasks.
TraceVLA: Visual Trace Prompting Enhances Spatial-Temporal Awareness for Generalist Robotic Policies
Although large vision-language-action (VLA) models pretrained on extensive robot datasets offer promising generalist policies for robotic learning, they still struggle with spatial-temporal dynamics in interactive robotics, making them less effective in handling complex tasks, such as manipulation.
Ranked #6 on
Robot Manipulation
on SimplerEnv-Google Robot
(using extra training data)
HashEvict: A Pre-Attention KV Cache Eviction Strategy using Locality-Sensitive Hashing
This is achieved by computing the Hamming distance between binarized Gaussian projections of the current token query and cached token keys, with a projection length much smaller than the embedding dimension.
Political-LLM: Large Language Models in Political Science
In recent years, large language models (LLMs) have been widely adopted in political science tasks such as election prediction, sentiment analysis, policy impact assessment, and misinformation detection.
LIAR: Leveraging Alignment (Best-of-N) to Jailbreak LLMs in Seconds
To demonstrate the simplicity and effectiveness of our approach, we employ a best-of-N method to solve the alignment problem.
Scaling Inference-Time Search with Vision Value Model for Improved Visual Comprehension
In this paper, we present Vision Value Model (VisVM) that can guide VLM inference-time search to generate responses with better visual comprehension.
Immune: Improving Safety Against Jailbreaks in Multi-modal LLMs via Inference-Time Alignment
With the widespread deployment of Multimodal Large Language Models (MLLMs) for visual-reasoning tasks, improving their safety has become crucial.
Ensuring Safety and Trust: Analyzing the Risks of Large Language Models in Medicine
The remarkable capabilities of Large Language Models (LLMs) make them increasingly compelling for adoption in real-world healthcare applications.
Benchmarking Vision Language Model Unlearning via Fictitious Facial Identity Dataset
Specifically, we formulate the VLM unlearning task via constructing the Fictitious Facial Identity VQA dataset and apply a two-stage evaluation pipeline that is designed to precisely control the sources of information and their exposure levels.
Statistical Guarantees for Lifelong Reinforcement Learning using PAC-Bayesian Theory
Lifelong reinforcement learning (RL) has been developed as a paradigm for extending single-task RL to more realistic, dynamic settings.
AdvBDGen: Adversarially Fortified Prompt-Specific Fuzzy Backdoor Generator Against LLM Alignment
In this work, we explore the use of prompt-specific paraphrases as backdoor triggers, enhancing their stealth and resistance to removal during LLM alignment.
GenARM: Reward Guided Generation with Autoregressive Reward Model for Test-time Alignment
Traditional training-time methods finetune LLMs using human preference datasets but incur significant training costs and require repeated training to handle diverse user preferences.
Towards Self-Improvement of LLMs via MCTS: Leveraging Stepwise Knowledge with Curriculum Preference Learning
Monte Carlo Tree Search (MCTS) has recently emerged as a powerful technique for enhancing the reasoning capabilities of LLMs.
EnsemW2S: Can an Ensemble of LLMs be Leveraged to Obtain a Stronger LLM?
To achieve this, we develop a novel AdaBoost-inspired ensemble method, demonstrating that an ensemble of weak supervisors can enhance the performance of stronger LLMs across classification and generative tasks on difficult QA datasets.
Boosting Sample Efficiency and Generalization in Multi-agent Reinforcement Learning via Equivariance
In this paper, we demonstrate that EGNNs improve the sample efficiency and generalization in MARL.
SAFLEX: Self-Adaptive Augmentation via Feature Label Extrapolation
Data augmentation, a cornerstone technique in deep learning, is crucial in enhancing model performance, especially with scarce labeled data.
Auction-Based Regulation for Artificial Intelligence
In an era of "moving fast and breaking things", regulators have moved slowly to pick up the safety, bias, and legal debris left in the wake of broken Artificial Intelligence (AI) deployment.
Easy2Hard-Bench: Standardized Difficulty Labels for Profiling LLM Performance and Generalization
While generalization over tasks from easy to hard is crucial to profile language models (LLMs), the datasets with fine-grained difficulty annotations for each problem across a broad range of complexity are still blank.
Automatic Pseudo-Harmful Prompt Generation for Evaluating False Refusals in Large Language Models
Our method and dataset can help developers evaluate and fine-tune safer and more usable LLMs.
CSRec: Rethinking Sequential Recommendation from A Causal Perspective
The essence of sequential recommender systems (RecSys) lies in understanding how users make decisions.
Can Watermarking Large Language Models Prevent Copyrighted Text Generation and Hide Training Data?
Surprisingly, we find that watermarking adversely affects the success rate of MIAs, complicating the task of detecting copyrighted text in the pretraining dataset.
Make-An-Agent: A Generalizable Policy Network Generator with Behavior-Prompted Diffusion
Can we generate a control policy for an agent using just one demonstration of desired behaviors as a prompt, as effortlessly as creating an image from a textual description?
Sketch-GNN: Scalable Graph Neural Networks with Sublinear Training Complexity
Graph Neural Networks (GNNs) are widely applied to graph learning problems such as node classification.
SAIL: Self-Improving Efficient Online Alignment of Large Language Models
Reinforcement Learning from Human Feedback (RLHF) is a key method for aligning large language models (LLMs) with human preferences.
Multi-Stage Balanced Distillation: Addressing Long-Tail Challenges in Sequence-Level Knowledge Distillation
Large language models (LLMs) have significantly advanced various natural language processing tasks, but deploying them remains computationally expensive.
Adversarial Attacks on Large Language Models in Medicine
The integration of Large Language Models (LLMs) into healthcare applications offers promising advancements in medical diagnostics, treatment recommendations, and patient care.
Is poisoning a real threat to LLM alignment? Maybe more so than you think
Recent advancements in Reinforcement Learning with Human Feedback (RLHF) have significantly impacted the alignment of Large Language Models (LLMs).
AutoHallusion: Automatic Generation of Hallucination Benchmarks for Vision-Language Models
This motivates the development of AutoHallusion, the first automated benchmark generation approach that employs several key strategies to create a diverse range of hallucination examples.
Ranked #1 on
Visual Question Answering (VQA)
on AutoHallusion
World Models with Hints of Large Language Models for Goal Achieving
By assigning higher intrinsic rewards to samples that align with the hints outlined by the language model during model rollouts, DLLM guides the agent toward meaningful and efficient exploration.
Transfer Q Star: Principled Decoding for LLM Alignment
Hence, prior SoTA methods either approximate this $Q^*$ using $Q^{\pi_{\texttt{sft}}}$ (derived from the reference $\texttt{SFT}$ model) or rely on short-term rewards, resulting in sub-optimal decoding performance.
Calibrated Dataset Condensation for Faster Hyperparameter Search
We aim to generate a synthetic validation dataset so that the validation-performance rankings of the models, with different hyperparameters, on the condensed and original datasets are comparable.
Spectral Greedy Coresets for Graph Neural Networks
However, owing to the interdependence of graph nodes, coreset selection, which selects subsets of the data examples, has not been successfully applied to speed up GNN training on large graphs, warranting special treatment.
Enhancing Visual-Language Modality Alignment in Large Vision Language Models via Self-Improvement
In this paper, we propose SIMA, a framework that enhances visual and language modality alignment through self-improvement, eliminating the needs for external models or data.
Ranked #191 on
Visual Question Answering
on MM-Vet
FACT or Fiction: Can Truthful Mechanisms Eliminate Federated Free Riding?
Standard federated learning (FL) approaches are vulnerable to the free-rider dilemma: participating agents can contribute little to nothing yet receive a well-trained aggregated model.
A Linear Time and Space Local Point Cloud Geometry Encoder via Vectorized Kernel Mixture (VecKM)
We propose VecKM, a local point cloud geometry encoder that is descriptive and efficient to compute.
Large Language Models and Causal Inference in Collaboration: A Comprehensive Survey
Causal inference has shown potential in enhancing the predictive accuracy, fairness, robustness, and explainability of Natural Language Processing (NLP) models by capturing causal relationships among variables.
ACE : Off-Policy Actor-Critic with Causality-Aware Entropy Regularization
The varying significance of distinct primitive behaviors during the policy learning process has been overlooked by prior model-free RL algorithms.
Beyond Worst-case Attacks: Robust RL with Adaptive Defense via Non-dominated Policies
In light of the burgeoning success of reinforcement learning (RL) in diverse real-world applications, considerable focus has been directed towards ensuring RL policies are robust to adversarial attacks during test time.
PRISE: LLM-Style Sequence Compression for Learning Temporal Action Abstractions in Control
To do so, we bring a subtle but critical component of LLM training pipelines -- input tokenization via byte pair encoding (BPE) -- to the seemingly distant task of learning skills of variable time span in continuous control domains.
A survey of recent methods for addressing AI fairness and bias in biomedicine
Such biases can occur before, during, or after the development of AI models, making it critical to understand and address potential biases to enable the accurate and reliable application of AI models in clinical settings.
Premier-TACO is a Few-Shot Policy Learner: Pretraining Multitask Representation via Temporal Action-Driven Contrastive Loss
We present Premier-TACO, a multitask feature representation learning approach designed to improve few-shot policy learning efficiency in sequential decision-making tasks.
Shadowcast: Stealthy Data Poisoning Attacks Against Vision-Language Models
Vision-Language Models (VLMs) excel in generating textual responses from visual inputs, but their versatility raises security concerns.
Unmasking and Quantifying Racial Bias of Large Language Models in Medical Report Generation
Large language models like GPT-3. 5-turbo and GPT-4 hold promise for healthcare professionals, but they may inadvertently inherit biases during their training, potentially affecting their utility in medical applications.
Mementos: A Comprehensive Benchmark for Multimodal Large Language Model Reasoning over Image Sequences
However, current MLLM benchmarks are predominantly designed to evaluate reasoning based on static information about a single image, and the ability of modern MLLMs to extrapolate from image sequences, which is essential for understanding our ever-changing world, has been less investigated.
WAVES: Benchmarking the Robustness of Image Watermarks
Our evaluation examines two pivotal dimensions: the degree of image quality degradation and the efficacy of watermark detection after attacks.
TrustLLM: Trustworthiness in Large Language Models
This paper introduces TrustLLM, a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions.
conv_einsum: A Framework for Representation and Fast Evaluation of Multilinear Operations in Convolutional Tensorial Neural Networks
Modern ConvNets continue to achieve state-of-the-art results over a vast array of vision and image classification tasks, but at the cost of increasing parameters.
Explore Spurious Correlations at the Concept Level in Language Models for Text Classification
We find that LMs, when encountering spurious correlations between a concept and a label in training or prompts, resort to shortcuts for predictions.
Decodable and Sample Invariant Continuous Object Encoder
In addition, the encoding is decodable, which enables neural networks to regress continuous objects by regressing their encodings.
DrM: Mastering Visual Reinforcement Learning through Dormant Ratio Minimization
To quantify this inactivity, we adopt dormant ratio as a metric to measure inactivity in the RL agent's network.
C-Disentanglement: Discovering Causally-Independent Generative Factors under an Inductive Bias of Confounder
Representation learning assumes that real-world data is generated by a few semantically meaningful generative factors (i. e., sources of variation) and aims to discover them in the latent space.
AutoDAN: Interpretable Gradient-Based Adversarial Attacks on Large Language Models
Safety alignment of Large Language Models (LLMs) can be compromised with manual jailbreak attacks and (automatic) adversarial attacks.
HallusionBench: An Advanced Diagnostic Suite for Entangled Language Hallucination and Visual Illusion in Large Vision-Language Models
Our comprehensive case studies within HallusionBench shed light on the challenges of hallucination and illusion in LVLMs.
Ranked #1 on
Visual Question Answering (VQA)
on HallusionBench
Towards Possibilities & Impossibilities of AI-generated Text Detection: A Survey
But in parallel to the development of detection frameworks, researchers have also concentrated on designing strategies to elude detection, i. e., focusing on the impossibilities of AI-generated text detection.
Towards Realistic Mechanisms That Incentivize Federated Participation and Contribution
On real-world data, RealFM improves device and server utility, as well as data contribution, by over 3 and 4 magnitudes respectively compared to baselines.
Progressively Efficient Learning
By equipping a learning agent with an abstract, dynamic language and an intrinsic motivation to learn with minimal communication effort, CEIL leads to emergence of a human-like pattern where the learner and the teacher communicate progressively efficiently by exchanging increasingly more abstract intentions.
Robustness to Multi-Modal Environment Uncertainty in MARL using Curriculum Learning
This work is the first to formulate the generalised problem of robustness to multi-modal environment uncertainty in MARL.
COPlanner: Plan to Roll Out Conservatively but to Explore Optimistically for Model-Based RL
In this paper, we propose $\texttt{COPlanner}$, a planning-driven framework for model-based methods to address the inaccurately learned dynamics model problem with conservative model rollouts and optimistic environment exploration.
Adapting Static Fairness to Sequential Decision-Making: Bias Mitigation Strategies towards Equal Long-term Benefit Rate
To address biases in sequential decision-making, we introduce a long-term fairness concept named Equal Long-term Benefit Rate (ELBERT).
PARL: A Unified Framework for Policy Alignment in Reinforcement Learning from Human Feedback
We present a novel unified bilevel optimization-based framework, \textsf{PARL}, formulated to address the recently highlighted critical issue of policy alignment in reinforcement learning using utility or preference-based feedback.
PerceptionCLIP: Visual Classification by Inferring and Conditioning on Contexts
Inspired by it, we observe that providing CLIP with contextual attributes improves zero-shot image classification and mitigates reliance on spurious features.
Game-Theoretic Robust Reinforcement Learning Handles Temporally-Coupled Perturbations
To tackle this challenge, we propose GRAD, a novel game-theoretic approach that treats the temporally-coupled robust RL problem as a partially observable two-player zero-sum game.
TACO: Temporal Latent Action-Driven Contrastive Loss for Visual Reinforcement Learning
Despite recent progress in reinforcement learning (RL) from raw pixel data, sample inefficiency continues to present a substantial obstacle.
Reviving Shift Equivariance in Vision Transformers
While the self-attention operator in vision transformers (ViT) is permutation-equivariant and thus shift-equivariant, patch embedding, positional encoding, and subsampled attention in ViT variants can disrupt this property, resulting in inconsistent predictions even under small shift perturbations.
Large-Scale Distributed Learning via Private On-Device Locality-Sensitive Hashing
This allows devices to avoid maintaining (i) the fully-sized model and (ii) large amounts of hash tables in local memory for LSH analysis.
Rethinking Adversarial Policies: A Generalized Attack Formulation and Provable Defense in RL
To address these limitations, we introduce a generalized attack framework that has the flexibility to model to what extent the adversary is able to control the agent, and allows the attacker to regulate the state distribution shift and produce stealthier adversarial policies.
GFairHint: Improving Individual Fairness for Graph Neural Networks via Fairness Hint
Given the growing concerns about fairness in machine learning and the impressive performance of Graph Neural Networks (GNNs) on graph data learning, algorithmic fairness in GNNs has attracted significant attention.
On the Possibilities of AI-Generated Text Detection
Our work addresses the critical issue of distinguishing text generated by Large Language Models (LLMs) from human-produced text, a task essential for numerous applications.
Exploring and Exploiting Decision Boundary Dynamics for Adversarial Robustness
However, it is unclear whether existing robust training methods effectively increase the margin for each vulnerable point during training.
Is Model Ensemble Necessary? Model-based RL via a Single Model with Lipschitz Regularized Value Function
To test this hypothesis, we devise two practical robust training mechanisms through computing the adversarial noise and regularizing the value network's spectral norm to directly regularize the Lipschitz condition of the value functions.
STEERING: Stein Information Directed Exploration for Model-Based Reinforcement Learning
Directed Exploration is a crucial challenge in reinforcement learning (RL), especially when rewards are sparse.
Model-based Reinforcement Learning
reinforcement-learning
+2
SMART: Self-supervised Multi-task pretrAining with contRol Transformers
Self-supervised pretraining has been extensively studied in language and vision domains, where a unified model can be easily adapted to various downstream tasks by pretraining representations without explicit labels.
Adversarial Auto-Augment with Label Preservation: A Representation Learning Principle Guided Approach
In experiments, we show that our method consistently brings non-trivial improvements to the three aforementioned learning tasks from both efficiency and final performance, either or not combined with strong pre-defined augmentations, e. g., on medical images when domain knowledge is unavailable and the existing augmentation techniques perform poorly.
SWIFT: Rapid Decentralized Federated Learning via Wait-Free Model Communication
Furthermore, we provide theoretical results for IID and non-IID settings without any bounded-delay assumption for slow clients which is required by other asynchronous decentralized FL algorithms.
Efficient Adversarial Training without Attacking: Worst-Case-Aware Robust Reinforcement Learning
Recent studies reveal that a well-trained deep reinforcement learning (RL) policy can be particularly vulnerable to adversarial perturbations on input observations.
An Energy Optimized Specializing DAG Federated Learning based on Event Triggered Communication
Specializing Directed Acyclic Graph Federated Learning(SDAGFL) is a new federated learning framework which updates model from the devices with similar data distribution through Directed Acyclic Graph Distributed Ledger Technology (DAG-DLT).
Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise
We observe that the generative behavior of diffusion models is not strongly dependent on the choice of image degradation, and in fact an entire family of generative models can be constructed by varying this choice.
Live in the Moment: Learning Dynamics Model Adapted to Evolving Policy
Model-based reinforcement learning (RL) often achieves higher sample efficiency in practice than model-free RL by learning a dynamics model to generate samples for policy learning.
Transferring Fairness under Distribution Shifts via Fair Consistency Regularization
In many real-world applications, however, such an assumption is often violated as previously trained fair models are often deployed in a different environment, and the fairness of such models has been observed to collapse.
FedBC: Calibrating Global and Local Models via Federated Learning Beyond Consensus
In this work, we quantitatively calibrate the performance of global and local models in federated learning through a multi-criterion optimization-based framework, which we cast as a constrained program.
Certifiably Robust Policy Learning against Adversarial Communication in Multi-agent Systems
Communication is important in many multi-agent reinforcement learning (MARL) problems for agents to share information and make good decisions.
Posterior Coreset Construction with Kernelized Stein Discrepancy for Model-Based Reinforcement Learning
Model-based approaches to reinforcement learning (MBRL) exhibit favorable performance in practice, but their theoretical guarantees in large spaces are mostly restricted to the setting when transition model is Gaussian or Lipschitz, and demands a posterior estimate whose representational complexity grows unbounded with time.
End-to-end Algorithm Synthesis with Recurrent Networks: Logical Extrapolation Without Overthinking
Algorithmic extrapolation can be achieved through recurrent systems, which can be iterated many times to solve difficult reasoning problems.
Transfer RL across Observation Feature Spaces via Model-Based Regularization
In many reinforcement learning (RL) applications, the observation space is specified by human developers and restricted by physical realizations, and may thus be subject to dramatic changes over time (e. g. increased number of observable features).
Understanding the Generalization Benefit of Model Invariance from a Data Perspective
Based on this notion, we refine the generalization bound for invariant models and characterize the suitability of a set of data transformations by the sample covering number induced by transformations, i. e., the smallest size of its induced sample covers.
VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization
Our framework avoids the "neighbor explosion" problem of GNNs using quantized representations combined with a low-rank version of the graph convolution matrix.
Ranked #12 on
Node Classification
on Reddit
A Closer Look at Distribution Shifts and Out-of-Distribution Generalization on Graphs
We observe that in most cases, we need both a suitable domain generalization algorithm and a strong GNN backbone model to optimize out-of-distribution test performance.
DP-InstaHide: Data Augmentations Provably Enhance Guarantees Against Dataset Manipulations
Data poisoning and backdoor attacks manipulate training data to induce security breaches in a victim model.
Thinking Deeper With Recurrent Networks: Logical Extrapolation Without Overthinking
Classical machine learning systems perform best when they are trained and tested on the same distribution, and they lack a mechanism to increase model power after training is complete.
Tuformer: Data-Driven Design of Expressive Transformer by Tucker Tensor Representation
Guided by tensor diagram representations, we formulate a design space where we can analyze the expressive power of the network structure, providing new directions and possibilities for enhanced performance.
Scaling-up Diverse Orthogonal Convolutional Networks by a Paraunitary Framework
Some of these designs are not exactly orthogonal, while others only consider standard convolutional layers and propose specific classes of their realizations.
Reinforcement Learning under a Multi-agent Predictive State Representation Model: Method and Theory
This paper proposes a new algorithm for learning the optimal policies under a novel multi-agent predictive state representation reinforcement learning model.
Comfetch: Federated Learning of Large Networks on Constrained Clients via Sketching
Federated learning (FL) is a popular paradigm for private and collaborative model training on the edge.
Practical and Fast Momentum-Based Power Methods
The power method is a classical algorithm with broad applications in machine learning tasks, including streaming PCA, spectral clustering, and low-rank matrix approximation.
Datasets for Studying Generalization from Easy to Hard Examples
We describe new datasets for studying generalization from easy to hard examples.
Where do Models go Wrong? Parameter-Space Saliency Maps for Explainability
We find that samples which cause similar parameters to malfunction are semantically similar.
Certified Defense via Latent Space Randomized Smoothing with Orthogonal Encoders
Randomized Smoothing (RS), being one of few provable defenses, has been showing great effectiveness and scalability in terms of defending against $\ell_2$-norm adversarial perturbations.
Scaling-up Diverse Orthogonal Convolutional Networks with a Paraunitary Framework
To address this problem, we propose a theoretical framework for orthogonal convolutional layers, which establishes the equivalence between various orthogonal convolutional layers in the spatial domain and the paraunitary systems in the spectral domain.
Who Is the Strongest Enemy? Towards Optimal and Efficient Evasion Attacks in Deep RL
Existing works on adversarial RL either use heuristics-based methods that may not find the strongest adversary, or directly train an RL-based adversary by treating the agent as a part of the environment, which can find the optimal adversary but may become intractable in a large state space.
Can You Learn an Algorithm? Generalizing from Easy to Hard Problems with Recurrent Networks
In this work, we show that recurrent networks trained to solve simple problems with few recurrent steps can indeed solve much more complex problems simply by performing additional recurrences during inference.
Guided Hyperparameter Tuning Through Visualization and Inference
Moreover, an analysis on the variance in a selected performance metric in the context of the model hyperparameters shows the impact that certain hyperparameters have on the performance metric.
Insta-RS: Instance-wise Randomized Smoothing for Improved Robustness and Accuracy
Randomized smoothing (RS) is an effective and scalable technique for constructing neural network classifiers that are certifiably robust to adversarial perturbations.
DP-InstaHide: Provably Defusing Poisoning and Backdoor Attacks with Differentially Private Data Augmentations
The InstaHide method has recently been proposed as an alternative to DP training that leverages supposed privacy properties of the mixup augmentation, although without rigorous guarantees.
Are Adversarial Examples Created Equal? A Learnable Weighted Minimax Risk for Robustness under Non-uniform Attacks
Adversarial Training is proved to be an efficient method to defend against adversarial examples, being one of the few defenses that withstand strong attacks.
Vulnerability-Aware Poisoning Mechanism for Online RL with Unknown Dynamics
Poisoning attacks on Reinforcement Learning (RL) systems could take advantage of RL algorithm's vulnerabilities and cause failure of the learning.
MaxVA: Fast Adaptation of Step Sizes by Maximizing Observed Variance of Gradients
Adaptive gradient methods such as RMSProp and Adam use exponential moving estimate of the squared gradient to compute adaptive step sizes, achieving better convergence than SGD in face of noisy objectives.
Using Wavelets and Spectral Methods to Study Patterns in Image-Classification Datasets
We show that each image can be written as the summation of a finite number of rank-1 patterns in the wavelet space, providing a low rank approximation that captures the structures and patterns essential for learning.
Improving the Tightness of Convex Relaxation Bounds for Training Certifiably Robust Classifiers
Convex relaxations are effective for training and certifying neural networks against norm-bounded adversarial attacks, but they leave a large gap between certifiable and empirical robustness.
Convolutional Tensor-Train LSTM for Spatio-temporal Learning
Learning from spatio-temporal data has numerous applications such as human-behavior analysis, object tracking, video compression, and physics simulation. However, existing methods still perform poorly on challenging video tasks such as long-term forecasting.
Ranked #1 on
Video Prediction
on KTH
(Cond metric)
TempLe: Learning Template of Transitions for Sample Efficient Multi-task RL
Transferring knowledge among various environments is important to efficiently learn multiple tasks online.
ARMA Nets: Expanding Receptive Field for Dense Prediction
In this work, we propose to replace any traditional convolutional layer with an autoregressive moving-average (ARMA) layer, a novel module with an adjustable receptive field controlled by the learnable autoregressive coefficients.
Understanding Generalization in Deep Learning via Tensor Methods
Recently proposed complexity measures have provided insights to understanding the generalizability in neural networks from perspectives of PAC-Bayes, robustness, overparametrization, compression and so on.
Can Agents Learn by Analogy? An Inferable Model for PAC Reinforcement Learning
We propose a new model-based method called Greedy Inference Model (GIM) that infers the unknown dynamics from known dynamics based on the internal spectral properties of the environment.
Model-based Reinforcement Learning
reinforcement-learning
+2
Sampling-Free Learning of Bayesian Quantized Neural Networks
Bayesian learning of model parameters in neural networks is important in scenarios where estimates with well-calibrated uncertainty are important.
Label Smoothing and Logit Squeezing: A Replacement for Adversarial Training?
Adversarial training is one of the strongest defenses against adversarial attacks, but it requires adversarial examples to be generated for every mini-batch during optimization.
Improved Training of Certifiably Robust Models
Convex relaxations are effective for training and certifying neural networks against norm-bounded adversarial attacks, but they leave a large gap between certifiable and empirical (PGD) robustness.
Convolutional Tensor-Train LSTM for Long-Term Video Prediction
Long-term video prediction is highly challenging since it entails simultaneously capturing spatial and temporal information across a long range of image frames. Standard recurrent models are ineffective since they are prone to error propagation and cannot effectively capture higher-order correlations.
Understanding Generalization through Visualizations
The power of neural networks lies in their ability to generalize to unseen data, yet the underlying reasons for this phenomenon remain elusive.
MLSys: The New Frontier of Machine Learning Systems
Machine learning (ML) techniques are enjoying rapidly increasing adoption.
An end-to-end Differentially Private Latent Dirichlet Allocation Using a Spectral Algorithm
Overall, by combining the sensitivity and utility characterization, we obtain an end-to-end differentially private spectral algorithm for LDA and identify the corresponding configuration that outperforms others in any specific regime.
Guaranteed Simultaneous Asymmetric Tensor Decomposition via Orthogonalized Alternating Least Squares
We propose a Slicing Initialized Alternating Subspace Iteration (s-ASI) method that is guaranteed to recover top $r$ components ($\epsilon$-close) simultaneously for (a)symmetric tensors almost surely under the noiseless case (with high probability for a bounded noise) using $O(\log(\log \frac{1}{\epsilon}))$ steps of tensor subspace iterations.
Tensorial Neural Networks: Generalization of Neural Networks and Application to Model Compression
We propose tensorial neural networks (TNNs), a generalization of existing neural networks by extending tensor operations on low order operands to those on high order ones.
Learning Deep ResNet Blocks Sequentially using Boosting Theory
We prove that the training error decays exponentially with the depth $T$ if the \emph{weak module classifiers} that we train perform slightly better than some weak baseline.
Non-negative Factorization of the Occurrence Tensor from Financial Contracts
We propose an algorithm for the non-negative factorization of an occurrence tensor built from heterogeneous networks.
Unsupervised learning of transcriptional regulatory networks via latent tree graphical models
We use a latent tree graphical model to analyze gene expression without relying on transcription factor expression as a proxy for regulator activity.
Discovery of Latent Factors in High-dimensional Data Using Tensor Methods
This thesis presents theoretical results on convergence to globally optimal solution of tensor decomposition using the stochastic gradient descent, despite non-convexity of the objective.
Unsupervised Learning of Word-Sequence Representations from Scratch via Convolutional Tensor Decomposition
More importantly, it is challenging for pre-trained models to obtain word-sequence embeddings that are universally good for all downstream tasks or for any new datasets.
Discovering Neuronal Cell Types and Their Gene Expression Profiles Using a Spatial Point Process Mixture Model
Single-cell RNA sequencing can now be used to measure the gene expression profiles of individual neurons and to categorize neurons based on their gene expression profiles.
Convolutional Dictionary Learning through Tensor Factorization
Tensor methods have emerged as a powerful paradigm for consistent learning of many latent variable models such as topic models, independent component analysis and dictionary learning.
Escaping From Saddle Points --- Online Stochastic Gradient for Tensor Decomposition
To the best of our knowledge this is the first work that gives global convergence guarantees for stochastic gradient descent on non-convex functions with exponentially many local minima and saddle points.
Guaranteed Scalable Learning of Latent Tree Models
We present an integrated approach for structure and parameter estimation in latent tree graphical models.
Online Tensor Methods for Learning Latent Variable Models
We introduce an online tensor decomposition based approach for two latent variable modeling problems namely, (1) community detection, in which we learn the latent communities that the social actors in social networks belong to, and (2) topic modeling, in which we infer hidden topics of text articles.
Learning Mixtures of Tree Graphical Models
We consider unsupervised estimation of mixtures of discrete graphical models, where the class variable is hidden and each mixture component can have a potentially different Markov graph structure and parameters over the observed variables.
