BAIR/CPAR/BDD Internal Weekly Seminar

The Berkeley Artificial Intelligence Research Lab co-hosts a weekly internal seminar series with the CITRIS People and Robots Initiative and Berkeley DeepDrive. The seminars are every Tuesday morning, from 11:10A-12P, and are open to BAIR faculty, students, and sponsors. Talks will take place in person on the 8th floor of Berkeley Way West (pending any new guidelines from the CDC and the University). There will also be a link to join via Zoom in case you are unable to attend in person. For any questions, please email bair-admin@berkeley.edu.

Fall 2021 Schedule

DateSpeaker 1: 11:10 AM -12:00 PM or 11:10-11:30 AMSpeaker 2: N/A or  11:30-11:50 AM
Aug 31Priya Moorjani: Big Data and Genomics: Insights into Human Evolutionary HistoryN/A
Sep 7Christian Borgs: Network Structure and EpidemicsN/A
Sep 14Wenshuo Guo: Towards Adaptive and Robust Learning in Data-Driven Mechanism DesignN/A
Sep 21Ashish Kumar: Rapid Motor Adaptation for Legged RobotsN/A
Sep 28Tijana Zrnic: Who leads and who follows in strategic classification?N/A
Oct 5Assaf Shocher: Deep Internal LearningN/A
Oct 12TBDTBD
Oct 19Kirthevasan Kandasamy: TBDTBD
Oct 26Inderjit Dhillon: TBDN/A
Nov 2Yixin Wang: TBDN/A
Nov 9Anil Aswani: Engineering Health Systems using Artificial IntelligenceN/A
Nov 16Natasha Jaques: Social Reinforcement LearningN/A


Spring 2021 Schedule

Date Speaker 1: 11:10 AM -12:00 PM or 11:10-11:30 AMSpeaker 2: N/A or  11:30-11:50 AM
Jan 26Hany Farid: Forensic Photographic IdentificationN/A
Feb 2Stephen Bates: Distribution-Free, Risk-Controlling Prediction SetsN/A
Feb 9Abhishek Gupta: Algorithms and Systems for Real World Robotic LearningN/A
Feb 16Glen Berseth: Developing Autonomous Agents to Learn and Plan in the Real WorldN/A
Feb 23Forrest Laine: Computing Feedback Nash Equilibria in Robotic GamesN/A
Mar 2Ellen Novoseller: Online Learning from Human Preference Feedback and Multi-Fidelity Models with Application to Assistive ExoskeletonsN/A
Mar 9Amir Gholami: Systematic Neural Network Pruning and QuantizationColorado Reed: Practical Self-Supervised Learning
Mar 16N/ATBD
Mar 23N/AN/A
Mar 30Adam Gleave: Understanding and Evaluating Learned Reward FunctionsN/A
Apr 6Jiachen Li: Relational Reasoning for Multi-Agent SystemsN/A
Apr 13Taesung Park: Machine Learning for Deep Image ManipulationN/A
Apr 20Samaneh Azadi: Towards Content-Creative AIN/A
Apr 27Daniel Seita: TBDN/A


Fall 2020 Schedule

DateSpeaker 1: 11:10 AM -12:00 PM or 11:10-11:30 AMSpeaker 2: N/A or  11:30-11:50 AM
Sep 1Alvin Wan: What Explainable AI Fails to Explain (and how we fix that)Amir GholamiZhewei Yao: ADAHESSIAN: An Adaptive Second Order Optimizer for Machine Learning
Sep 8Nikita Kitaev: Is Unstructured Computation All You Need for Natural Language Processing?Lucy Li: Content Analysis of Textbooks via Natural Language Processing: Findings on Gender, Race, and Ethnicity in Texas U.S. History Textbooks
Sep 15Georgios Pavlakos: Learning to Reconstruct 3D HumansN/A
Sep 22Lerrel Pinto: Rich Robotic Supervision through Cheap Human DemonstrationsN/A
Sep 29Philippe Laban: Text Summarization Without The SummariesKatie Stasaski: More Diverse Dialogue Datasets via Diversity-Informed Data Collection
Oct 6Aldo Pacchiano:  Learning to Score Behaviors for Guided Policy OptimizationN/A
Oct 13Paria Rashidinejad: Learning to Predict in Unknown Dynamical Systems with Long-Term MemoryZhen Dong: Efficient Neural Networks Through Systematic Quantization
Oct 20Eugene Vinitsky: Optimizing Energy Efficiency of Traffic at Scale via Multi-agent Deep RL
Anastasios Angelopoulos: Uncertainty for Black Box Models
Oct 27Misha Laskin: Escaping the Simulator: Accelerating Real Robot Learning through Unsupervised LearningN/A
Nov 3Wei Zhan: Close-loop Evaluation of Prediction Algorithms with Complex Scenarios from Naturalistic Driving DataLiting Sun: Modeling Human Behaviors in Human-robot Interactions
Nov 10
Daniel Brown
: Safe and Efficient Imitation Learning
N/A
Nov 17Edward Kim: Scenic: Dynamic Scenario Description Language for Autonomous SystemsKimin Lee: Ensemble methods in reinforcement learning
Nov 24Xin Wang: Last-Mile Delivery of Computer Vision with Test-Time AdaptationN/A
Dec 1Huijuan Xu: Temporal Action Detection in Videos with Multi-Level SupervisionN/A

Spring 2020 Schedule

DateSpeaker 1: 3:10-4:00 or 3:10-3:30Speaker 2: N/A or 3:30-3:50
Jan. 24Dylan Hadfield-Menell: The Principal-Agent Value Alignment ProblemN/A
Jan. 31Somil Bansal: Safe and Data-efficient Learning for Physical SystemsN/A
Feb. 7Nick Antipa: Lensless Computational Imaging: Seeing More with LessN/A
Feb. 14Ruoxi Jia: Towards a Responsible Data Economy: Fairness, Privacy, and SecurityN/A
Feb. 21Chandan Singh: Interpreting and Improving Neural Networks via Disentangled AttributionsN/A
Feb. 28Alexei (Alyosha) Efros: Image Manipulation… And Ways to Detect ItN/A
Mar. 6Brijen Thananjeyan and Ashwin Balakrishna:  Safety Augmented Value Estimation from Demonstrations (SAVED): Safe Deep Model-Based RL for Sparse Cost Robotic TasksJeff Ichnowski: Fog-Robotics Serverless and Deep Grasp-Optimized Robot Motion Planning
Mar. 13Aravind Srinivas: Self-Supervised Visual Representation LearningN/A
Mar. 20Ronghang Hu: Structured Models for Vision-and-Language ReasoningN/A
Mar. 27No SeminarN/A
Apr. 3Misha Laskin: Improving Reinforcement Learning with Unsupervised and Self-Supervised LearningN/A
Apr. 10Dequan Wang: Object-Centric Representation for Perception, Prediction, and PlanningN/A
Apr. 17Coline Devin:Learning with Modularity and Compositionality for Robotics
Apr. 24Cecilia Zhang: Bringing Cinema Quality Rendering into Casual Photos and Videos.N/A
May 1Sylvia Herbert: Safe Real-World Autonomy in Uncertain and Unstructured EnvironmentsN/A
May 8Sasha Sax: Robust Learning Through Cross-Task ConsistencyN/A
May 15Carlos Florensa: What Supervision Scales? Practical Learning Through InteractionN/A

Spring 2019 Schedule

DateSpeaker 1: 3:10-4:00 or 3:10-3:30Speaker 2: N/A or 3:30-3:50
Jan. 18Eric Jonas: Structured Prediction via Machine Learning for Inverse ProblemsN/A
Jan. 25Dinesh Jayaraman: Towards Embodied Visual IntelligenceN/A
Feb. 1Ke Li: Advances in Machine Learning: Learning to Optimize, Generative Modelling and Nearest Neighbour SearchN/A
Feb. 8Deepak Pathak: Self-Directed LearningN/A
Feb. 15Samaneh Azadi: Rectify the GAN Generator Distribution by Rejecting Bad SamplesSasha Sax: On Perception for Robotics: Mid-level Visual Representations Improve Generalization and Sample Complexity for Learning Active Tasks
Feb. 22Jaime Fisac: Resilient Safety Assurance for Robotic Systems: Staying Safe Even When Models Are WrongN/A
Mar. 1Fisher Yu: Towards Human-Level Recognition via Contextual, Dynamic, and Predictive RepresentationsN/A
Mar. 8Andrew Owens: Sight and SoundN/A
Mar. 15Daniel Fried: Pragmatic Models for Generating and Following Grounded InstructionsNikita Kitaev: Syntactic Parsing with Self-Attention
Mar. 22**Oriol Vinyals: AlphaStar: Mastering the Real-Time Strategy Game StarCraft II (**This will be held 11am-12pm in 250 SDH**)N/A
Mar. 22Jacob Steinhardt: What Makes Neural Networks (Non-)Robust?N/A
Mar. 29HolidayHoliday
Apr. 5Alex Lee: Visual Dynamics Models for Robotic Planning and ControlN/A
Apr. 12Lisa Anne Hendricks: Visual Understanding through Natural LanguageN/A
Apr. 19Evan Shelhamer: Blurring the Line between Structure and Learning for Adaptive Local RecognitionN/A
Apr. 26Yian Ma: Bridging MCMC and OptimizationN/A
May 3Sandy Huang: Optimizing for Robot TransparencyN/A
May 10Jeffrey Regier: Statistical Inference for Cataloging the Visible UniverseN/A

Fall 2018 Schedule

DateSpeaker 1: 3:10-4:00 or 3:10-3:30Speaker 2: N/A or 3:30-3:50
Aug. 24Jaime FisacAndrea BajcsySylvia Herbert: Probabilistically Safe Robot Planning with Confidence-Based Human PredictionsN/A
Aug. 31Aviv Tamar: Learning Representations for PlanningN/A
Sept. 7Chi Jin: Is Q-learning Provably Efficient?N/A
Sept. 14Wojciech Zaremba: Learning dexterityN/A
Sept. 21Tuomas Haarnoja: Acquiring Diverse Robot Skills via Maximum Entropy Reinforcement LearningN/A
Sept. 28Professor Ruzena Bajcsy: Data Driven vs. Model Driven Analysis of Human Ability for Human Robot InteractionN/A
Oct. 5Ke Li: Implicit Maximum Likelihood EstimationN/A
Oct. 12Roy Fox: Multi-Task Hierarchical Imitation Learning of Robot SkillsN/A
Oct. 19Ankush Desai: DRONA: A Framework for Programming Safe Robotics SystemsN/A
Oct. 26Ajay Tanwani: A Fog Robotics Approach to Large Scale Robot LearningLaura Hallock: Human Muscle Force Modeling for Enhanced Assistive Device Control
Nov. 2Abhishek Gupta: Unsupervised (Meta) RLAnja Rohrbach: Diagnosing and correcting bias in captioning models
Nov. 9HolidayHoliday
Nov. 16Daniel Fried: Pragmatic Models for Generating and Following Grounded InstructionsNikita Kitaev: Syntactic Parsing with Self-Attention
Nov. 23HolidayHoliday
Dec. 7Jiantao Jiao: Deconstructing Generative Adversarial NetworksN/A

Spring 2018 Schedule

DateSpeaker 1: 3:10-4:00 or 3:10-3:30Speaker 2: N/A or 3:30-3:50
Jan. 19Pulkit Agrawal: Continually Evolving Machines: Learning by ExperimentingN/A
Jan. 26Sanjay Krishnan: Dirty Data, Robotics, and Artificial IntelligenceN/A
Feb. 2David Fouhey: Towards a 3D World of InteractionN/A
Feb. 9Saurabh Gupta: Visual Perception and Navigation in 3D ScenesN/A
Feb. 16Jacob Andreas: Learning from LanguageN/A
Feb. 23Chelsea Finn: Generalization and Self-Supervision in Deep Robotic LearningN/A
Mar. 2Jennifer Listgarten: Where genetics and biology meet machine learningN/A
Mar. 9Yi Ma: Low-dimensional Structures and Deep Models for High-dimensional DataTBD
Mar. 16Cathy Wu: Mixed-autonomy mobility: scalable learning and optimizationN/A
Mar. 23Xiang Cheng: Langevin MCMC as gradient flow over the probability spaceEric Tzeng and Andreea Bobu: Domain Adaptation for Fixed and Continuously Varying Domains
Apr. 6Shubham Tulsiani: Learning Single-view 3D Reconstruction of Objects and ScenesTBD
Apr. 13Tinghui Zhou: Beyond Direct Supervision: Visual Learning via Data-driven Consistency Consistency (Location: SWARM Lab 490 Cory)N/A
Apr. 20Richard Zhang: Image Synthesis for Self-Supervised Representation LearningN/A
Apr. 27Reza Abbasi-Asl: Structural compression of Convolutional Neural NetworksFereshteh Sadeghi: Sim2Real Viewpoint Invariant Visual Servoing by Recurrent Control

Fall 2017 Schedule

DateSpeaker 1: 3:10-3:40Speaker 2: 3:40-4:10
Aug. 25Jitendra Malik: What have we learned from datasets in computer vision?Angjoo Kanazawa: Single-View 3D Reconstruction of Deformable Objects like Animals and People
Sept. 1Chang Liu: Neural Network-based Graph Embedding for Cross-Platform Binary Code Similarity DetectionClaire Tomlin: Safe Learning
Sept. 8Andrew Critch: Open-source game theory is weirdAnna Rohrbach: Generation and grounding of natural language descriptions for visual data
Sept. 15Bo Li: Secure learning in adversarial environmentsChing-Yao Chan: Safety of Automated Driving Systems (ADS) and AI/ML – A Dialogue
Sept. 22Alex Anderson: The High-Dimensional Geometry of Binary Neural NetworksJeff Mahler: Learning Deep Policies for Robot Bin Picking using Discrete-Event Simulation of Robust Grasping Sequences
Sept. 29Fisher Yu: Towards Universal Representation for Image RecognitionMax Rabinovich: Abstract Syntax Networks for Code Generation and Semantic Parsing
Oct. 6Michael Laskey: Learning Home Robotics Manipulation Tasks from Remote SupervisionPulkit Agrawal: Continually Evolving Agents: Curiosity & Experimentation
Oct. 13Ron Fearing: Dextrous LocomotionSylvia Herbert and David Fridovich: Planning, Fast and Slow with FaSTrack: A Framework for Adaptive Real-Time Safe Trajectory Planning
Oct. 20Emrah Bostan: Learning Convex Regularizers for Optimal Bayesian DenoisingSomil Bansal: Overcoming Model Bias in Model-based Learning
Oct. 27Roberto CalandraAaditya Ramdas: Sequential testing and online false discovery rate control
Nov. 3Roy Fox: Discovery of Hierarchical Structures for Robot Learning and Neural ProgrammingLeila Wehbe: Modeling brain responses to natural language stimuli
Nov. 10HolidayHoliday
Nov. 17Jacob Andreas: Learning with Latent LanguageNiladri Chatterji: Alternating minimization for dictionary learning with random initialization
Nov. 24HolidayHoliday
Dec. 1Eric Jonas: Could a neuroscientist understand a microprocessorTuomas Haarnoja: Soft Q-Learning

Spring 2017 Schedule

DateSpeaker 1: 3:10-3:40Speaker 2: 3:40-4:10
Jan. 20Jack Gallant: Melding neuroscience and computer scienceJavad Lavaei: On optimization theory, numerical algorithms and machine learning for nationwide energy systems
Jan. 27Trevor Darrell: Adaptive Learning of Driving Models from Large-scale Video DatasetsColine Devin: Transfer learning for robotics
Feb. 3Phillip Isola: Image-to-Image Translation with Conditional Adversarial NetworksAnil Aswani: Human Modeling Using Inverse Optimization
Feb. 10Zeynep Akata: Generating Fine-Grained Visual Explanations and Realistic ImagesDeirdre Mulligan: Hand-offs in AI: Functional Fidelity, Values, and Governance
Feb. 17(3:10- 3:25) Jaime Fernandez Fisac: Generating plans that predict themselves/ (3:25-3:40) Aaron Bestick: Implicitly Assisting Humans to Choose Good Grasps in Robot to Human HandoversWilliam Guss: WaveLayers: Neural Topology Inspired by Topology
Feb. 24Laurent El Ghaoui: Safe feature elimination in sparse learningLaura Waller: Computational Microscopy with sparsity
Mar. 3Chi Jin: How to Escape Saddle Points EfficientlyHoria Mania: Universality of Mallows’ and degeneracy of Kendall’s kernels for rankings
Mar. 10Matthew Matl: Automated Grasp Transfer with Mesh Segmentation and Point Cloud RegistrationPaul Grigas: An Extended Frank-Wolfe Method with “In-Face” Directions, and its Application to Low-Rank Matrix Completion
Mar. 17Roy Fox: Multi-Level Discovery of Deep OptionsParvez Ahammad: Beyond navigation metrics: Teaching computers how users perceive web application performance
Mar. 24Ke Li: Learning to Optimize and Fast k-Nearest Neighbour SearchJessica Hamrick: Metacontrol for Imagination-Based Optimization
Apr. 7Deepak Pathak: Exploring Four Axes of Self-SupervisionSaurabh Gupta: Cognitive Mapping and Planning for Visual Navigation
Apr. 14Jeff Mahler: Dex-Net: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp MetricsJudy Hoffman: A General Framework for Domain Adversarial Learning
Apr. 21Fereshteh Sadeghi: Sim2Real Collision Avoidance for Indoor Navigation of Mobile Robots via Deep Reinforcement LearningEvan Shelhamer: Loss is its own Reward: Self-Supervision for Reinforcement Learning
Apr. 28Roberto Calandra: Robustness in Multi-objective Bayesian optimizationRichard Zhang: Cross-Channel Visual Prediction
May 5Abhishek Gupta: Imitation Learning for Dexterous Manipulation and Transfer in Reinforcement LearningTinghui Zhou: 3D Visual Synthesis and Understanding from 2D Views
May 12Karl Zipser: New Trajectories in the Autonomous Model Car ProjectDaniel Drew: Silent Swarms: Flying Microrobots Using Atmospheric Ion Thrusters

Fall 2016 Schedule

DateSpeaker 1: 3:10-3:40Speaker 2: 3:40-4:10
Aug. 26Ken Goldberg: Deep DexteritySanjay Krishnan: Inverse Reinforcement Learning For Sequential Robotic Tasks
Sep 2Anca Dragan: How Robots Influence Our ActionsJeff Donahue: Adversarial Feature Learning
Sep 9Robert Nishihara & Philipp Moritz: Distributed Machine Learning with RayJohn DeNero: Interactive machine translation
Sep 16Joseph Gonzalez: Prediction Serving and RISEKevin Jamieson: Bayesian Optimization and other bad ideas for hyperparameter tuning
Sep 23Stuart Russell: Human-Compatible AIMarcus Rohrbach: Explain and Answer: Intelligent systems which can communicate about what they see.
Sep 30Richard Zhang: Colorful Image ColorizationVirginia Smith: A General Framework for Communication-Efficient Distributed Optimization
Oct 7Gregory Kahn: Learning Control Policies for Partially Observable Safety-Critical SystemsJohn Canny: Accountable Deep Networks
Oct 14Ruzena Bajcsy: Individualized Human Models for Cyberphysical InteractionJacob Andreas: Neural module networks
Oct 21Dave Moore: Bayesian seismic monitoring from raw waveformsGreg Durrett: Data-Driven Text Analysis with Joint Models
Oct 28Jun-Yan Zhu: Visual Manipulation and Synthesis on the Natural Image ManifoldDylan Hadfield-Menell: The Off-Switch
Nov 4Bin Yu: Artificial neurons meet real neurons: building stable interpretations of V4 neurons from CNN+regression modelsPulkit Agrawal: Forecasting from Pixels: Intuitive Physics and Intuitive Behavior
Dec 02Chelsea Finn: Adversarial Inverse Reinforcement LearningAviv Tamar: Deep Policy Representations based on a Planning Computation
Dec 09Michael Laskey: A Human-Centric Approach to Deep Robotic Learning from Demonstrations.Michael Oliver: Using artificial neural networks to model visual neurons
Dec 16Lisa Anne Hendricks: Localizing Moments in Video with Natural LanguageAndrew Owens: Learning visual models from paired audio-visual examples